Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

 The ever growing video data over the internet has raised the challenge of efficient storage and retrieval of multimedia data. Video Summarization is one of the solutions to the problem which extracts interesting parts of a video. These summaries capture the essential content of the original video and enable the viewers to pick out interesting videos from huge video repositories by viewing its compact representation. To address the challenge of managing the video data, a new method for static video summarization is proposed here which is capable of describing input videos using a precise yet meaningful subset of frames. The method utilizes HOG descriptors of Gabor maps of input frames. A high-level representation of HOG descriptors is created using sparse auto-encoders (SAE). The final summary of the video is formed using candidate frames obtained after K-means clustering of these feature vectors. The summarization step is preceded by redundancy elimination to overcome computational burden. The method provides better results compared to the other state-of-the-art video summarization methods.

 Deep Rectified System for High-speed Tracking in Images (DRSHTI) is a unified open-source web portal developed for object detection in images. It aims to be a platform for the end user, where he/she can perform object detection on images without going through the hassles of debugging countless lines of code or setting up the right environment to perform computer vision tasks. By making the platform open-source, this work targets beginners in computer vision to form a basic understanding of object detection as an artificial intelligence task. This is made possible by releasing source codes, tools and tutorials on its usage via GitHub. This open-source portal offers two detection pipelines based on Faster-RCNN – a model to detect ground vehicles in aerial images and a model to detect everyday objects in 37 different classes in normal images. The former model is trained on VEDAI dataset, which gave 98.6% accuracy during testing and is offered as proof-of-concept that showcases the models ability to perform small target detections, but the latter model is trained on the PASCAL VOC dataset. Making the project open-source also aims at bringing in more development and tweaking to the existing vehicle detection module. The web portal can be accessed via https://drshti.github.io, where user can upload images and get annotations on objects present in it. Tutorials and source codes can be found at https://github.com/vyzboy92/Object-Detection-Net.

 Anomaly detection from crowd is a widely addressed problem in the field of computer vision. It is an essential part of video surveillance and security. In surveillance videos, very little information about anomalous behaviors is available, so it becomes difficult to identify such activities. In this work, transfer learning technique is used to train the network. A convolutional neural network (CNN) based VGG16 pre-trained model is used to learn spatial level appearance features for anomalous and normal patterns. Two approaches are explored to detect anomalies, i) homogeneous approach and ii) hybrid approach. In homogeneous approach, pre-trained network is used to fine-tune CNN for each dataset, while testing, single dataset is considered. Whereas, in hybrid approach, pre-trained network is used to fine-tune CNN on one dataset and it is further used to fine-tune another dataset. The performance of proposed system is verified on standard benchmark datasets such as UCSD and UMN available for anomaly detection, also the results of proposed system are compared with existing deep learning approaches.

 One of the major issues of road accidents all over the world is drowsiness state of the driver. It is a complex phenomenon to measure a driver’s consciousness in a direct manner. This work proposes with three deep neural architecture for learning facial features which consists of 68 attributes from the RGB video input of a driver. The experimentation is conducted by three different CNN models such as ResNet50, VGG16 and InceptionV3. These three networks are combined for representation learning which then put together the features to form a feature fused architecture(FFA). The trained features as well as facial movements such as eye blinking, yawning and head swaying are again trained with a softmax classifier to classify the drowsiness state of driver. Out of the three networks and FFA, InceptionV3 shows 78% accuracy.

Deep neural networks have dramatically gained immense potential in almost every field like computer vision, natural language processing, biomedical informatics etc. Among these networks, autoencoders are popular in performing dimensionality reduction task, while learning a representation for an unlabeled dataset. A usual way of dealing with such networks is to pre-train them in a layer-wise fashion, and consequently fine-tune the whole stack in a supervised manner. In this paper, a pair-wise training strategy is proposed to determine optimum model parameters by reducing training time as well as the complexity of training a convolutional autoencoder without compromising its accuracy. The proposed approach works in a fully unsupervised manner and has been tested on datasets like MNIST, CIFAR10, and CIFAR100 and it shows that the training time has improved by an average of 25% on these three datasets.

Plant species recognition from images or videos is challenging due to a large diversity of plants, variation in orientation, viewpoint, background clutter, etc. In this paper, plant species recognition is carried out using two approaches, namely, traditional method and deep learning approach. In traditional method, feature extraction is carried out using Hu moments (shape features), Haralick texture, local binary pattern (LBP) (texture features) and color channel statistics (color features). The extracted features are classified using different classifiers (linear discriminant analysis, logistic regression, classification and regression tree, naïve Bayes, k-nearest neighbor, random forest and bagging classifier). Also, different deep learning architectures are tested in the context of plant species recognition. Three standard datasets (Folio, Swedish leaf and Flavia) and one real-time dataset (Leaf12) is used. It is observed that, in traditional method, feature vector obtained by the combination of color channel statistics+LBP+Hu+Haralick with Random Forest classifier for Leaf12 dataset resulted in a plant recognition accuracy (rank-1) of 82.38%. VGG 16 Convolutional Neural Network (CNN) architecture with logistic regression resulted in an accuracy of 97.14% for Leaf12 dataset. An accuracy of 96.53%, 96.25% and 99.41% is obtained for Folio, Flavia and Swedish leaf datasets using VGG 19 CNN architecture with logistic regression as a classifier. It is also observed that the VGG (Very large Convolutional Neural Network) CNN models provided a higher accuracy rate compared to traditional methods.

Elephant play a major role in maintaining the ecosystem. Elephants move out of their corridor in search of food and water, result in rise of human-elephant conflicts. Human-Elephant conflict arises in different form such as destruction of field by elephants, elephants are runover by train, elephants getting electrocuted etc. Prevention of elephants entering into human living areas, will reduce the human-elephant conflict to a greater extent. In this paper, performance of conventional image processing techniques, custom Convolutional Neural Network (CNN) architecture and transfer learning based CNN architectures is investigated in the context of human-elephant conflict management system. On identification of elephant from the acquired video feed, the sounds (humming of Bee, Tiger growls) are generated to mitigate the progress of elephant into human living areas. It is observed that the convolutional neural networks produced a higher accuracy or prediction rate compared to conventional image processing technique. It is also observed that, VGG16 CNN model produced an accuracy of 94% with an average computation time varying between 1.5 to 2.1 s. For real time implementation, SqueezeNet CNN model is used because of its lower computation time (varying between 0.02 to 0.05 s) and moderate accuracy of 92.67%.

 Grading of prostate cancer is usually done using Transrectal Ultrasound (TRUS) biopsy followed by microscopic examination of histological images by the pathologist. TRUS is a painful procedure which leads to infections of severe nature. In the recent past, Magnetic Resonance Imaging (MRI) has emerged as a modality which can be used for the diagnosis of prostate cancer without subjecting patients to biopsies. A novel method for grading of prostate cancer based on MRI utilizing Convolutional Neural Networks (CNN) and LADTree classifier is explored in this paper. T2 weighted (T2W), high B-value Diffusion Weighted (BVALDW) and Apparent Diffusion Coefficient (ADC) MRI images obtained from the training dataset of PROSTATEx-2 2017 challenge are used for this study. A quadratic weighted Cohen’s kappa score of 0.3772 is attained in predicting different grade groups of cancer and a positive predictive value of 81.58% in predicting high-grade cancer. The method also attained an unweighted kappa score of 0.3993, and weighted Area Under Receiver Operating Characteristic Curve (AUC), accuracy and F-score of 0.74, 58.04 and 0.56, respectively. The above-mentioned results are better than that obtained by the winning method of PROSTATEx-2 2017 challenge.

Stroke is a cerebrovascular disease which is one of the significant causes of adult impairment. Research shows that retinal fundus images carry vital information for the prediction of various cardiovascular diseases like Stroke. This work investigates a multi-texture description for the computer aided diagnosis of Stroke from retinal fundus images. Texture of the retinal background is analyzed, thereby eliminating the need for segmentation. Gabor Filter (GF), Local Binary Pattern (LBP) and Histogram of Oriented gradients (HOG) are the texture descriptors implemented in this work. The texture descriptors are applied to the second Eigen channel obtained by Principal Component Analysis (PCA). Extracted features are concatenated to form a multi-texture representation and dimensionality reduction is done by ReliefF feature selection method. The compact feature vector is given to Naïve Bayes classifier and performance metrics are evaluated. We have evaluated the performance of individual feature descriptors and multiple feature descriptors in retinal fundus images for stroke diagnosis. Multi-texture description outperforms individual texture descriptors by an accuracy of 95.1 %.

Autism Spectrum Disorder (ASD) is increasing rapidly at higher rate which has a greater impact in many organizations to contribute to ASD coaching and training. The highest complexity lies in the factor of early identification of ASD to support training. Many researchers have proved that early identification of autism and appropriate coaching to children with high ASD can result in a quality improvement in child’s lifestyle. This early identification of autism can be screened through stages involving evaluation of eye gaze, emotion, expression, linguistic ability, responsiveness. Objective of this paper mainly focuses on analyzing the facial expression of children with autism in a contact less environment as the children could even respond to the target object they face. The paper identifies the various facial expressions of autism children excluding the time and event occurrence. These expressions are used as an early screening method to identify children who may fall under autistic characteristic in the near future. Moreover the facial expressions could be analyzed in a live video environment as stimuli emotional sequence that further leads to next level of screening. The paper also analyses the major facial expression perceived by the children along with the variation in facial expression dynamics that a normal Toddler (TD) possess.

Local Binary Pattern (LBP) is considered as an effective image descriptor as it is based on joint distribution of gray level differences. The main attributes of LBP are discriminatory power, robustness to brilliance change, simplicity and computational efficiency. In contrary LBP is highly sensitive to noise, rotation, non-rigid deformation, view point variations and scaling. Therefore, in the present work an improved version of LBP i.e. ILBP is proposed to overcome the limitations of basic LBP. ILBP replaces the fixed-weighted matrix of basic LBP by a pixel difference matrix. The proposed method is assessed on synthetic as well as real-time images. The results obtained are compared with LBP and other state-of-the-art edge detection techniques like HLBP, Canny and Sobel methods. The results reveal that performance of ILBP is superior to other edge detection methods under consideration. Further the proposed technique is highly efficient for noisy, blurred and low pixel valued images.

 Accurate detection and localization of eye features under spectacles - is quite a relevant but challenging problem in the application field of Human-Computer Interactive (HCI) systems. The ill-effects caused by the usage of spectacles like occlusion, glare and secondary reflection formation are termed as “The Spectacle Problem.” In this paper, the authors alleviate the spectacle problem by employing a two-image based data fusion approach. Detail-preserving filters like Joint Bilateral Filter (JBF) and Guided Image Filter (GIF) are compared individually, to find out the suitability and consistency of the filters in the proposed data fusion approach. Experimentation on CASIA NIR-VIS 2.0 and self-generated facial database demonstrate that GIF based filtering approach has higher local and global eye feature preservation capability while mitigating the spectacle problem.

 Hyperspectral Image (HSI) store the reflectance values of a single scene or object in several continuous bands of electromagnetic spectrum. When the image is recorded, the information in some of the spectral bands gets mixed with noise. The classification accuracy of hyperspectral image varies inversely with the quantity and nature of noise present in the cluster of spectral bands. Thus, denoising is a fundamental prerequisite in image processing applications like classification, unmixing, etc. In this paper, we compare the effect of denoising via classification using Vectorized Convolutional Neural Network (VCNN), kernel based Support Vector Machine (SVM) and Grand Unified Regularized Least Squares (GURLS) classifiers. The classifiers are provided with raw data (without denoising) and denoised data using spectral and spatial Least Square (LS) techniques. The data given to the network are in the form of pixels, so we call the convolutional neural network (CNN) as VCNN. The experiments are performed on three standard HSI datasets. The performance of the classifiers are evaluated based on overall and class-wise accuracy.

 The need for understanding the terrain or conditions of large areas aerially has gained prominence as the aerial images provide a near clear coverage of the area under study. Individual image provides just a portion of the area, thus to understand the whole area, mosaicking or stitching of these images is needed. Image mosaicking aids in providing with a ”Big Picture” as an outcome by joining the images taken during the flight. In this paper we propose a method which aims at generating a seamless aerial mosaick using only the images captured by the UAV as input. This involves identifying candidate images from the images captured by the UAV periodically during its flight and stitching the images together. This method evaluates various feature descriptors and feature matching techniques that can be integrated into the mosaicking system. The proposed work is a hybrid approach that uses the Scale Invariant Feature Transform (SIFT) for feature extraction and the key features are matched using the Fast Library for Approximate Nearest Neighbors (FLANN). RANdom Sample Consensus (RANSAC), is used for the removal of features that are redundant or act as outliner, providing candidates for Homography estimation. This is followed by image stitching that involves the use of Multi-Band Blending to produce a visually seamless mosaick. The results obtained were evaluated for quality using Universal Quality index Measure (QIM) and is found to be perfect.

 Real-time occupant posture tracking information has significant value in ADAS equipped vehicles to enable better safety and mitigate risks of injuries to occupants within vehicular cabin during sudden deceleration due to abrupt braking maneuvers or crash scenarios. This information will be helpful for timely activation of airbags and various other conventional safety restraints that provide safety and mitigate injuries. Here, a proximity sensing system is proposed that uses capacitive electrodes to acquire the occupant posture and motion data. These electrodes are deployed as an array placed along three orthogonal sensing axes such as in the seats, along the roof and dashboard, and along the door panel. Motion detection cameras and other sensors like ultrasound or infrared sensors have line of sight issue, while the accumulation of dirt would become a problem for sensing the data accurately. A prototype hardware has been implemented and the proximity capacitance data was acquired for discrete distances from 0.1 to 0.8 m, in the three electrode orientations. Applying optimization and curve fitting techniques on this data, derived data sets were then obtained, that mimic a typical crash-test dummy behavior during impact. The resultant algorithm can offer a precise localization estimate of the occupant with respect to an electrode layout along the roof, seat and door orientations, and hence classify the occupant posture inside the vehicular cabin.

 The state-of-the-art video compression standard, High Efficiency Video Coding (HEVC) has standardised to contemplate a better compression of high and ultra high resolution videos. HEVC introduced a lot of new coding tools to meet its improved coding efficiency, but at a bit increase in the computational cost. The intra prediction mode decision strategy is one among them and the use of a large number of intra prediction modes at different PU sizes is the reason for an improved mode prediction as well as the high computational complexity. Besides the number of prediction modes, HEVC adopts a Rough Mode Decision (RMD) process as well as Rate Distortion Optimization (RDO) process in the intra prediction stage, which takes substantial amount of execution time. Hence a method to reduce this time complexity by incorporating machine learning technique in RMD process is proposed in this work. In this method, we use Support Vector Machine (SVM) to find out the best mode in the RMD stage. Experimental results indicate that the proposed method considerably reduces the computational cost of the HEVC reference software while retaining the visual quality of videos. Since HEVC supports real-time video processing, the proposed method sounds to be applicable.

Mathematics is the cradle of all creations, without which the world cannot move an inch. Mathematical functions are ‘extensively’ used in physics, ‘structurally’ used in graphics, ‘practically’ used in civil engineering, ‘potentially’ used in mechanical engineering and in many other fields as well. One fairly common difficulty faced by the engineers and scientists is to find the right function that solves their problem. This involves a lot of time consuming tasks. The idea proposed here is an android application that captures the mathematical expression using a built-in camera which produces a java code that can be utilized for solving the inputs of their needs. Along with the code, it also displays a basic plot of the function, which will be more helpful for selecting the apt solution for any problem. There are many applications in the market which can compute the result of a mathematical equation. But, this application will give an executable java code which can be used for further problem solving. So, the application is unique in its way and this is a new dimension of using image processing for code generation. This application supports polynomial, logarithmic, trigonometric and exponential functions up to four variables.

Human activity recognition emerges as one of the prominent research areas in the recent past. However, the activity recognition still encounters many challenges like reliability of sensor data and accuracy of prediction that severely affects the aspect of decision making. In this paper, a futuristic framework has been proposed and experimented to build a precision-centric activity recognition method by analyzing the data obtained from Environment Monitoring System (EMS) and Personalized Positions Detection System (PPDS) using machine learning methods such as AdaBoost, Support Vector Machine (SVM) and Probabilistic Neural Networks (PNN). Further, the proposed approach utilizes the Dempster-Shafer Theory (DST)-based complete sensor data fusion thereby improving the global activity recognition performance. Finally, the proposed approach is validated using a real-world dataset obtained from UCI machine learning repository. The results conclude that the proposed activity recognition framework outperforms its existing context/situation-awareness approaches in terms of reliability, efficiency, and accuracy.

A myoelectric prosthetic limb can be directed by sEMG signals from amputee’s residual muscles. The capability of such prosthetic hand may be enhanced by classifying additional hand motion commands. As the amputee’s residual muscles are limited and it is essential to come up with the ways to identify as many hand motion directions as possible with sEMG signals recognized by few sensors. Recent algorithms for pattern recognition in sEMG signals are tested with limited recognition patterns and inconsistent classification accuracy. The proper choice of denoising algorithm has intense effect on classification rates. Therefore FIR-median hybrid (FMH) filter, and discrete wavelet transform (DWT) denoising methods are used in this work for filtering sEMG signals. Five time domain features are used for classification of motions and four different physical activities are classified using ANN. It is observed from the results that FMH filter removes noise more effectively as compared to DWT which improves the classification accuracy.

This article presents a novel information criterion based optimal model parameter selection algorithm for behavioral modeling of Radio Frequency Power Amplifiers (RF PAs). The proposed approach uses Particle Swarm Optimization (PSO) along with the Information Criterion (IC) based cost functions for determining the most parsimonious model from all the available combinatorial models. The proposed technique thereby helps in deriving complexity reduced models without compromising modeling accuracy. The validation of the proposed approach was carried out by modeling a GaAs based PA driven by a 20-MHz generic random input signal. It was shown that, the model performance was maintained while its complexity in terms of number of coefficients was reduced by around 35% in the considered cases. In addition, the proposed PSO based approach helps in deriving the most parsimonious PA model in a very short amount of time compared to the conventional sweep technique.

This paper presents a technique to detect the six affective states of individual using audio cues. Bi-spectral features extracted from entire speech signal and voiced part of speech are used to create feature vectors. For classification K-Nearest Neighbor (KNN) and Simple Logistic Classifiers (SL) are used. eNTERFACE audio-visual emotional speech corpus that consists of six archetypal affective states: Fear, Anger, Disgust, Sad, Happy, and Surprise is considered. The performance of the system is analyzed based on features obtained from voiced part of speech and features obtained from the entire speech signal. The work proposed is first of its kind in affect computation, where a compact 13-dimensional Bi-spectral features extracted from the voiced speech segments is able to yield promising performance. A considerable improvement of 8.46% – 27.6% recognition rate is achieved with the proposed methodology compared to the existing approaches using emotion samples from the same speech corpus adding novelty to the proposed work.

In recent times, Dynamic Time Warping (DTW) based template matching systems have again come to the forefront in the field of text-dependent speaker verification. Its integration with the latest technology, like i-vector/Probabilistic Linear Discriminant Analysis (PLDA) and Deep Neural Network (DNN), has resulted in significant improvement in the performance of the systems. DTW algorithm time-aligns two templates and gives a similarity score based on the optimal warping path. It however weighs all the local distances equally, along the optimal path. In this paper, we propose complementing the DTW based text-dependent speaker verification systems with local scores derived from the vicinity of speaker-identity-rich regions. The vowel regions are used to determine portions along the warping path that are more important in terms of speaker discriminating information content. Two systems, namely the DTW/ Mel-frequency Cepstral Coefficients (MFCC) system and the online i-vector/PLDA/DTW system have been extended to incorporate the knowledge of specific regions of interest. The results have been evaluated on Part 1 of RSR2015 database. Relative improvements of upto 11.85% and 49.41% are observed for the extended systems based on MFCC and i-vector respectively.

 Small perturbations quite often lead to power system instability. Power system stabilizer damp the electro mechanical oscillations in generator. Design of power system stabilizer has changed over past few decades. Artificial intelligence based controllers are found to be more effective to handle complex control situations during perturbations in power system. Fuzzy logic supported power system stabilizers were proposed to control such perturbations in more efficient way than conventional one. Fractional order controllers perform better than their counterparts in various engineering applications. This paper suggests use of such state of art fractional order controller in conjunction with existing power system stabilizer to enhance performance in terms of damping of electromechanical oscillations. From the results obtained from simulation models, it is observed that judicious design of fractional order controller in power system gives better handling of control operations. Comparison of conventional, fuzzy based and Fractional Order based power system stabilizer reveals usefulness of proposed controller.

 This paper proposes Multi verse optimization (MVO) based MPPT controller to mitigate the possibility of losing tracking direction in solar photovoltaic system under variable irradiance. Generally conventional Perturb & Observe and hill climbing MPPT techniques are used due to effortless implementation. However, these techniques are not capable of handling oscillations in power at MPP and exhibit drift under variable irradiance conditions which leads to power loss. Therefore a hybrid of standard MVO and direct duty cycle control is proposed to minimize the inadequacies occurring in conventional controllers. Three cases i.e. constant irradiance, rapid and step changes in irradiance are considered for the analysis. The supremacy of proposed method is justified by comparing it with traditional P&O, Particle swarm optimization (PSO) based MPPT and Grey wolf optimization (GWO) based MPPT techniques. It is observed from the results that MVO based MPPT controller is capable of avoiding drift and offers fast convergence. Therefore proposed controller outperforms in terms of tracking efficiency, settling time, peak overshoot, and integral absolute error.

Combination of conventional brakes with Eddy Current Brake is the current trend in many applications where superior braking performance at high speed is desired. Eddy Current Brakes being frictionless and contactless offer numerous advantages over conventional brakes. This paper gives detailed insight into the hardware model development, analysis and control of a multi disc Eddy Current Braking System using different intelligent controllers. Firstly, Fuzzy Logic Controller has been developed which can give the feasible value of the electromagnet current required which leads to improved braking performance. Further, Artificial Neural Network Controller has been designed for existing hardware system which gives better, reliable, efficient results in comparison to the Fuzzy Logic Controller and hardware reference model for the sample period of time.

 Stability of a parallel manipulator is a very important issue due to its high nonlinearity and vague dynamics. This problem may be overcome by a controller, based on the combination of Lyapunov theory and fuzzy logic. In this paper a novel linguistic Lyapunov based fuzzy controller (LLFC) is proposed in which fuzzy logic controller improves trajectory tracking performance of parallel manipulator and application of Lyapunov theory provides stable control action. The subsequent part of rule base in fuzzy controller is constrained by Lyapunov criteria so as to generate a control action which stabilizes the system. Non dominated sorting genetic algorithm–II (NSGA-II) optimization technique is used to evaluate the optimal values of controller parameters. The effectiveness of proposed LLFC controller is tested on Maryland manipulator and compared with PID, Fractional order PID (FOPID) and Fractional order fuzzy pre-compensated fractional order PID (FOFP FOPID) controllers. Simulation results reveal that the proposed controller shows stable, robust and better tracking performance for Maryland manipulator in comparison to PID, FOPID and FOFP FOPID controllers.

 This paper proposes a pre-classification based language identification (LID) system for Indian languages. In this system, firstly, languages are pre-classified into tonal and non-tonal categories and then individual languages are identified from the languages of the respective category. In this work, language discriminating ability of various acoustic features like, pitch Chroma, mel-frequency Cepstral coefficients (MFCCs) and their combination has been investigated. The system performance has been analyzed for features extracted using different analysis units, like, syllables and utterances. The effectiveness of deep residual networks (ResNets) model in identification of Indian languages has been studied. Also, the system performance has been compared with the performances of other deep neural network architectures like, Convolutional Neural network (CNN) model, cascade CNN-long short-term memory (LSTM) model and shallow architecture like, ANN. Experiments have been carried out on NIT Silchar language database (NITS-LD) and OGI-Multilingual database (OGI-MLTS). Experimental analysis suggests that proposed ResNets model, based on syllable-level features, outperforms the other models. The pre-classification module provides accuracies of 96.6%, 93.2% and 90.6% for NITS-LD, and 92.1%, 89.3% and 85.4% for OGI-MLTS database, with 30s, 10s and 3s test data respectively. The pre-classification module helps to improve the system performance by 3.8%, 4.1% and 4.3% for 30s, 10s and 3s test data respectively. For OGI-MLTS database, the respective improvements are 6.8%, 6.5% and 5.4%.

 Cross-Lingual Information Retrieval (CLIR) enables a user to query in a language which is different than the target documents language. CLIR incorporates a machine translation technique, like, Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) which use either a dictionary or a parallel corpus for the training. A Hindi language word may have multiple variations due to the morphological richness of the language, these morphological variants may or may not be present in the dictionary or parallel corpus. The morphological variants which are not present in the dictionary or parallel corpus, are not translated by the state-of-art SMT or NMT translation techniques. Conventional Information Retrieval (IR) technique eliminates the stop-words to improve the IR effectiveness, but there are some significant stop-words whose presence may improve the IR effectiveness. In this paper, a translation induction algorithm, incorporates the refined stop-words list, morphological variants solutions, and translates the words based on the contextual words, is proposed. The proposed algorithm is compared to the manual dictionary, probabilistic dictionary, SMT and NMT based translation techniques for the experimental analysis of Hindi-English CLIR, where it outperforms the other CLIR approaches.

Finding coherent topics in Twitter data is difficult task because of the sparseness and informal language. Tweets also provide rich contextual and auxiliary metadata which can be used to supervise the topic modeling to get more coherent topics. In this paper, a novel topic model is proposed which extends Author Topic Model for twitter. Standard Author Topic Model cannot be used on Twitter data as every tweet has exactly one author. The proposed User Graph Topic Model (UGTM) considers the semantic relationships among tweet users based on the contextual information like hashtags, user mentions and replies to make a user graph. Related users of author of a tweet are found and used in tweet generation process. Related user information from the user graph is used to obtain the dirichlet prior for user generation. Empirical results show that the proposed UGTM outperforms standard Author Topic Model (ATM) on experimental data.

Research has proved that DNA Microarray data containing gene expression profiles are potentially excellent diagnostic tools in the medical industry. A persistent problem with regard to accessible microarray datasets is that the number of samples are much lesser than the number of features that are present. Thus, in order to extract accurate information from the dataset, one must use a robust technique. Feature selection (FS) has proved to be an effective way by which irrelevant and noisy data can be discarded. In FS, relevant features are picked, and result in commendable classification accuracy. This paper proposes a model that employs a compounded hybrid feature selection technique (Filter + Wrapper) to classify microarray cancer data. Initially, a filter method called Information Gain (IG) to eliminate redundant features that will not contribute significantly to the final classification is used. Following to that, an evolutionary computing technique (micro Genetic Algorithm (mGA)) to find the best minimal subset of required features is employed. Then the features are classified using a traditional Support Vector Classifier and also cross validated to obtain high classification accuracy, using a minimal number of features. The complexity of the model is reduced significantly by adding mGA, as opposed to already existing models that use various other feature selection algorithms.

In the context of optimal subset selection, hybrid feature selection approaches play a significant role that has been the topic of a substantial number of studies because of the growing need for data mining applications. In feature subset selection (FSS) problem; there are two significant shortcomings that need to be addressed: At first, finding a suitable filter method that can be reasonably fast and energetically computed for large volume of data, and second, an efficient wrapper strategy that can discriminate the samples over the entire search space in a considerable amount of time. After a study of the shortcomings of individual feature selection methods (filter or wrapper), this paper investigated a new hybrid feature selection approach with conjunction of filter and wrapper method that can take benefit of both ways for a classification problem. The proposed hybrid uses the filter method as conditional mutual information maximization and wrapper method as genetic algorithm to enhance the overall classification performance and speed up the search process to identify the essential features. The proposed method is known as FWFSS. To get rid of meaningless features and determine the biomarkers, wrapper method as genetic algorithm uses the naïve Bayes (NB) classifier as a fitness function. The proposed method is verified on the University of California, Irvine (UCI) repository, and microarray datasets. From experimental study, it is observed that our approach outperforms convenient methods regarding classification accuracy, the number of optimal features reported in the literature.

Breakthrough classification performances have been achieved by utilizing ensemble techniques in machine learning and data mining. Bagging is one such ensemble technique that has outperformed single models in obtaining higher predictive performances. This paper proposes an ensemble technique by utilizing the basic bootstrap aggregating technique on hybridization of two base learners namely Naïve Bayes (NB) and Decision Tree (DT). Before induction of the DT, NB algorithm is employed for eliminating mislabeled or contradictory instances from the training set. Consequently, bagging approach is applied on hybrid NBDT as the base learner. The resultant Bagged Naïve Bayes-Decision Tree (BNBDT) algorithm is then used for improving the classification accuracy of various multi-class problems. This algorithm iteratively trains the base learner from random samples of the training set, and then performs majority voting of their predictions. The proposed algorithm is compared with both ensemble and single classification techniques such as Random Forest, Bagged NB, Bagged DT, NB, and DT. Experimental results over 52 UCI data sets with bag size 100 demonstrate that the proposed algorithm significantly outperforms the existing algorithms.

 This article presents optimal drug scheduling in chemotherapeutic treatment for eradication of cancerous cells while maintaining tolerable toxicity for the complete period of treatment. For this purpose a cascade control technique is designed wherein individual 2DOF FOPID controllers are employed to regulate drug concentration and toxicity. Conventional schemes fail to address the needs of divergent objectives of cancer chemotherapy which motivates the authors to employ a multi-objective optimization technique, NSGA-II to optimally tune the controller parameters. 2DOF FOPID, its integer order counterpart and PID control schemes are tested on cancer patient model for comparative analysis. The performance of proposed controller is evaluated on the basis of number of cancer cells and normal cells remaining at the end of treatment. Further robustness of the controller is analysed for parametric uncertainty in patient model and disturbance in infusion pump which affects the input drug dosages. The results reveal that proposed control scheme provides optimal drug scheduling and is significantly robust in the presence of uncertainty and disturbances.

This paper presents a deterministic algorithm for approximating the solution of the Symmetric Traveling Salesman Problem (STSP) using a multi perfect matching and partitioning technique. Initially, we find the minimum cost collection of sub-tours that cover all cities, such that each sub-tour consists of at least four edges. The obtained solution is then partitioned into k branches, where k is the length of the smallest sub-tour in the resulting solution. The algorithm solves the sub-problems in parallel and selects the sub-problem with the minimum resulting cost to be partitioned further. The algorithm converges when a complete cycle without sub-tours is found. The performance of the proposed algorithm is evaluated and compared with the optimal values obtained by some well-known algorithms for solving STSP using 24 instances from the TSPLIB online library. The results of the experiments carried out in this study show that our approach yields optimum or near-optimum solutions in polynomial execution time.

 Residuated lattices are algebraic frameworks with crucial bond to mathematical logic. A multiset is a collection that bearing repetition of objects in it. In this paper, the notion of multisets is applied to filters of residuated lattices and introduced the new concept of multiset filters. The relation between multiset filters and their n-level sets is showed and some principal characterizations of multiset filter are discussed. Furthermore, as an application of the proposed concept, a decision making problem is presented.

 In 2018, we presented the structure of lattice on one of the efficient hybrid models interval-valued hesitant fuzzy soft set. As a result of this intention, the new idealogy of lattice on IVHFSS was introduced with vital properties and its real life application was examined. In this current work, we instigated how the idea of homomorphism and isomorphism on L - IVHFSS is working and few concomitant theorems are proved.

 Future cloud computing creates a new trend of opting service over the internet through some intelligent third-party broker. In cloud market, both consumer and provider compete with each other against the conflicting requirements, and the competition among cloud providers to trade their services to potential consumers of cloud market. There is an increasing need for automated negotiation framework to quickly reach agreement in competitive cloud market which can provide maximum utility value and success rate among the negotiating parties. Researchers develop various behavioral learning negotiation strategies (such as market driven) in the existing negotiation frameworks for maximizing either the choice of utility value or success rate of parties. Moreover these strategies can be applicable to the environment, where the opponent’s behaviors are predictable or precisely known. It may be daunting to apply in the dynamically varying competitive cloud market. So, the proposed Adaptive Neuro-Fuzzy Behavioral Learning (ANFBL) strategy can be applicable, where the opponent’s behavior is partially and imprecisely known. Therefore, the proposed strategy can maximize both utility value and success rate without compromising either choice. An extensive simulation is conducted to evaluate the efficiency of strategies which shows that proposed strategy achieve higher utility and higher success rate than existing learning approach, without any negotiation conflict among the parties.

 Molodtsov instigated the concept of soft set theory as a generic mathematical tool for dealing with uncertainty. Yong Yang et.al propounded the idea of multi-fuzzy soft set and investigated its application in decision making problems. The main objective of this paper is to derive the notion of lattice approach on multi-fuzzy soft set and analyse its application using forecasting process.

In this paper, a hybrid fuzzy clustering techniques using Salp Swarm Algorithm (SSA) is proposed. The proposed fuzzy clustering method is used to optimize the cluster centroids obtained as an under sampling method. The performance of the proposed fuzzy clustering method is compared with some well-known clustering algorithms to shows the superiority of the proposed clustering algorithm. In addition, a novel hybrid Automobile Insurance Fraud Detection System is proposed in which undersampling of the majority class is performed by using the proposed fuzzy clustering algorithm which eliminates the outliers from the majority class samples. The balanced dataset for automobile fraud detection obtained after undersampling undergoes classification. Different classifiers used for this purpose are Random Forest Classifier, Logistic Regression Classifier and XGBoost Classifier. The performance of each of the three classifiers is evaluated by considering different performance metrics such as sensitivity, accuracy and specificity. The proposed fuzzy clustering method along with XGBoost outperforms the other methods presented.

Automatic identification systems represent a wide classification of devices used primarily in commercial settings for inventory/logistics control. Familiar examples of such devices are bar codes, magnetic strips, smart cards, RFID (Radio-frequency identification) and biometric and voice recognition. Security is especially lax in low powered radio frequency systems communicating through an unsecured radio wave channel. Security represents a critical component for enabling the large scale adoption of automatic identification systems. Providing an effective security solution for low powered systems is a major area of concern; it directs research towards ‘power consumption aware’ computations in security solutions. This paper proposes a lightweight inter-zonal authentication Protocol for moving objects in low powered radio frequency systems. Formal validation and a thorough analysis of the protocol in SPAN security tool reveal its effectiveness and resiliency to attacks – eavesdropping, reader and tag impersonation, replay and desynchronization.

Data Integrity attack is a major hindrance to the evolution of Industrial Internet of Things (IIoT) as it leads to immense financial loss or even human fatality. The existing security features in Software Defined Networking (SDN), which is emphatically superior to the traditional networks mitigate the integrity attacks to some extent. However, a generic, robust, secure and resilient Intrusion Detection System (IDS) for IIoT is still lacking in the literature. Towards this goal, a generic IDS is already proposed in our earlier research work which combines both anomaly as well as rule-based intrusion detection techniques and successfully tested against the real-time dataset obtained from the water purification process in a test bed at the Singapore University of Technology and Design (SUTD). This research work proposes a supervised learning approach that utilizes Roll-forward technique for validation and Classification and Regression Trees (CART) with invariants for categorization to find anomalousness in the water treatment process. The proposed work incorporates the capability to substantiate time-series data through Roll-forward validation which is then succeeded by utilization of the CART with invariants for classification. The proposed work is simulated using Mininet tool and the train and test accuracies are 99.9% and 98.1% respectively.

 An underwater acoustic sensor network (UASN) offers a promising solution for the exploration of underwater resources remotely. As the UASN acoustic channel is open and the environment is hostile, the risk of malicious activities is very high, particularly in time-critical military applications. In this paper, we propose an unsupervised anomaly detection system by learning the social behavioral correlation among nodes. The location data retrieved from sensors are learned using long short term memory (LSTM) networks to capture the anomalous nature. The network is simulated by modeling anomalies and analyzed the performance. The analysis of results indicates that the anomaly detection system offers an acceptable accuracy with high true positive rate and F-Score by showing consistency in multiple mobility behavior.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

 Today’s e-health system is centralized. This system obliges all users (patients and healthcare staff) to have a high level of trust in the intermediary who stores the data. Moreover, healthcare employees must trust that their patients are providing authentic medical records and are not altering them in order to obtain drugs for illegal purposes. The solution proposed in this paper is a new blockchain-based architecture for the creation of an e-health system. In this architecture, wireless sensor networks (WSN) will be used in association with a WSN controller v.2 to supervise patients and their testing by healthcare staff. Transactions between blockchain and WSN are made through smart contracts, removing the need for intermediaries and preventing human error. This new model facilitates a distributed ledger, creating an e-health system that is much more optimized than the current one.

VANET has been an area of great interest and exploration for researchers to solve several challenging issues regarding communication, topology, security etc. in the last few years. Currently, a lot of work has been done and is looked after to establish effective communication and message passing among the vehicles (V2V and V2I routing) with a number of algorithmic models developed. The paper presents a survey of the routing algorithms proposed to have communication inside a VANET among the nodes. Since V2V and V2I interactions is a complex combinatorial problem which falls under the class of NP-Complete set of problems. The paper here presents a modified mobicast routing version using genetic algorithm with certain considerations for mutation and crossover operator for the algorithm in order to achieve more accuracy for the results. The method shows a great enhancement in the execution timing for a considerable number of vehicles where the traditional algorithms may hang up to produce a route. But still the serial version stucks up for heavy density vehicle scenarios for message passing in real time. So, the efficiency of the proposed method is enhanced using parallel processing power of multi-core and many-core processors using OpenMP and Computationally Unified Device Architecture (CUDA) API. The enhanced results show a great improvement in the performance in terms of execution time when compared with the serial algorithm, especially for the cases where the solutions cannot be obtained in real time. The results over GPU based architecture suggests that the proposed method has a huge potential to scale up with the vehicles on the road, thus, reducing the road side units for providing a larger range of coverage.

 The cognitive radio network provides a pioneered solution to the spectrum scarcity problem and represents a new paradigm for designing intelligent wireless networks. Energy efficient cognitive radio system maintaining reliability holds great importance in the present scenario of wireless communications. In a cognitive radio network, relays are used to enhance energy efficiency as well as to maintain the sensing reliability. Most of the works in the area of cognitive radio network focused on optimization of energy consumed during data transmission only, while neglecting the energy consumed during spectrum sensing. In this paper, an energy efficient multi-relay cognitive radio network is designed, in which both sensing energy and data transmission energy are jointly optimized. Also, optimal values of system parameters like sensing time and amplifying gain of the relays are determined for the energy efficient system. The minimization of the energy consumed under constraints of target throughput and sensing requirements of cognitive radio network is considered as an optimization problem. Swarm intelligence based optimization techniques like particle swarm optimization (PSO), Particle Swarm Optimization with Aging Leader and Challengers (ALCPSO), Human behavior based Particle Swarm Optimization (HPSO) and Whale Optimization Algorithm (WOA) are used to optimize energy consumption in the network. The analysis reveals that the proposed scheme makes the cognitive radio network more energy efficient than conventional schemes.

A wireless sensor network consists of a large number of sensor nodes. The key parameters of the wireless sensor network are limited energy, network lifetime, limited ability, secure communication, quality of service, data aggregation, and synchronization. In wireless sensor network when the single base station multi-hop communication model is used, the adjacent nodes to the base station transmitted all the data to the base station. Thus the adjacent nodes deplete their energy earlier than other nodes and create the energy holes near the base station. These energy holes minimize the lifetime of the network. The primary objective in large-scale wireless sensor networks is to increase the lifetime with limited energy resources. This can be achieved by placing the multiple base stations using an intelligent clustering technique in a wireless sensor network. In this paper, an intelligent clustering technique has been proposed to choose the optimal position of multiple base stations with the help of k-means++ clustering technique in conjunction with the local+ scheme. The simulation result shows that the proposed method provides minimum energy consumption with an extended lifetime in comparison to the two individual clustering techniques.

 Generally, several IoT (Internet of Things) applications employ cloud data centre for processing the data generated by edge devices like smartphones and tablets. Due to the increasing use of the IoT devices, the demand for higher computational and communication capabilities are also increasing. With the advent of Edge Computing and given the fact that computational capabilities are currently untapped, a part of the computational load can be offloaded to the edge nodes. In this paper, a Grefenstette bias based Genetic Algorithm for MultiSite Offloading (GGA-MSO) is proposed. This algorithm decides the schedule of the application that could be offloaded. The proposed algorithm provides a solution which has convergence in lesser time by employing diversification of initial population using the Grefenstette’s Bias method. Besides, the container based lightweight virtualization is analyzed for offloading code and data to the nearby devices. The evaluation of the proposed work on random graphs shows that the proposed method starts to converge with significantly lesser iterations than its counterpart with undiversified population. The test bed results on Single Board Computers (SBC) like Raspberry Pi setup indicates that by adapting container virtualization in the edge environment, the performance of the IoT devices is improved and the communication overhead is reduced.

In the year 2015 per person is having 3.47 devices that will be around 25 billion devices, In future years 2020 expected devices per person are 6.58 which is around 50 billion devices. From this statistic, major problems for IoT devices are scalability and survivability was identified. To address these problems a novel architecture design was proposed named as TGO (Trimet graph optimization) topology for Wireless sensor networks and IoT. As the number of devices increases, scalability increases leads to decrease of Quality of service (QoS). In this paper graph based solution was given for scalability and survivability using TGO topology. In Scalability model an algorithm and flowchart was proposed. Experiments are conducted on Star of star, TGO of TGO, Wheel of wheel networks. In analysis part we observed TGO consumes less power than star and wheel networks. For parameters like received packets, lost packets, hops etc. TGO lies between star and wheel. In Survivable model Hexagon and TGO are 2 edge survivable networks are compared, whereTGO shows better performance in all parameter.

Mobile internet protocol (MIP) is a client based mobility management protocol, and it is used to provide continuous internet service for the user. The mobile user forwards the control and data packets to the home agent whereas, in network-based localized mobility management known as proxy MIP version 6 (MIPv6), the network itself manages the functionality of the mobile user. In proxy MIPv6, the continuous services between the mobile users and the correspondent entities are provided with the help of mobile access gateway via localized mobility anchor. Though, latency and packet loss has been minimized in proxy MIPv6; it still faces the issue of handoff latency, single point of failure, and non-optimized routing. It can suffer from many security threats due to the inadequacy of the protection of proxy update and acknowledgement messages. Also, it impacts on the challenges such as high scalability, congestion and the cost of the network. Therefore, it is necessary to construct a secure and efficient scheme for proxy MIPv6 while updating the location of the mobile users to the participating principals. Hence, the present paper proposes a new secured scheme for the location update of the mobile host. The security properties of the proposed scheme are validated using the model checker - AVISPA. The numerical results demonstrate that the proposed scheme surpasses the existing approaches regarding enhanced security, significant reduction in communication payload.

In this paper, the judgment of fuzzy bases of a closed G-V fuzzy matroid is studied and a necessary and sufficient condition of judging fuzzy bases is presented. Based on the results, an intuitive tree structure of a closed G-V fuzzy matroid is proposed and some of its properties are obtained.

This study proposes a fuzzy Cerebellar Model Articulation Controller (CMAC) using a dynamic Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique for dealing with the metallic sphere position control of a magnetic levitation system (MLS). The proposed Dynamic TOPSIS Fuzzy CMAC (DTFCMAC) incorporates a multi-criteria decision analysis with a fuzzy structure to decrease the computational load for parameter learning and to enhance the fuzzy reasoning inference for a CMAC. The Shannon entropy index is used to derive the objective weights for the evaluation criterion. By combining entropy weight and TOPSIS, the optimal threshold value for suitable firing nodes is determined automatically and easily. In the proposed method, the dynamic back-propagation algorithm is applied to train the proposed DTFCMAC online. Moreover, to guarantee the convergence of output tracking error for periodic command tracking, analytical methods developed from a discrete-type Lyapunov function are used to determine the optimal learning-rate parameters for the proposed DTFCMAC. The proposed DTFCMAC is applied to the MLS, and its performance is verified through simulations and experiments. Our findings indicate that the proposed DTFCMAC control system achieves stability and desired control performance for the MLS.

 Housing around 17% of the earth’s total population, the Republic of India is a place where 91.6 million of its total population is above 60 years old. Elderly people in India has always been taken care of by their children; however, due to the rampant urbanization and employment constraints, children along with their own families move to the places of their employment leaving old parents alone in their houses, unattended. In this paper, a fuzzy probability transformation based system is proposed to ensure that the elderly are safe in their houses. The scope of this work is limited to learn the bathroom usage pattern of an elderly individual. If an unusual pattern is detected, the system will inform the caregivers immediately. To deal with the ambiguities, the concept of fuzzy numbers is used in the system. The proposed system utilizes the idea of the Internet of Things (IoT) to collect, transmit and present data to the stakeholders. Bayesian reasoning is used as a rider to the proposed methodology for robust decision making in cases of deep uncertainties. An alternative analysis is also performed on the same data using logistic regression and time-series analysis. The results show that the proposed method learns well with time than the other methods and provides superior accuracy.

 Similarity measure is an important uncertainty measurement in intuitionistic fuzzy set (IFS) theory. In this study, a novel similarity measure is presented by the combination of the information carried by hesitancy degree and the endpoint distance of membership and nonmembership, respectively. Moreover, a numerical example is used to verify the reasonable of the proposed similarity measure. After that, the similarity measure is applied to construct the IF decision-theoretic rough set (IF-DTRS) model and multigranulation IF decision-theoretic rough set (MG-IF-DTRS) model. Some properties of IF-DTRS and MG-IF-DTRS are also investigated. Thirdly, based on granular significance, a novel approach of optimal granulation selection is formulated. Finally, a heuristic algorithm is designed and the effectiveness of this algorithm is demonstrated by an illustrative example.

 Due to advancement in technology, surveillance systems have become more automated without manual assistance. This paper presents a new method of gait feature extraction and fusion algorithm to identify the individuals irrespective of the walking style of the person in various occlusion states. This is done in addition to their walking speeds and varying clothing style. The Gait Recognition is performed based on the bio mechanics approach. The kinematics features are also called as dynamic features such as joint angles are extracted from the video sequences. The novelty of this paper lies in the extraction of static and dynamic features and feature fusion methodologies, which involves model-based parameters. The static features are measured from the distance function and the dynamic features such as joint angles are extracted from the transformation matrix. The fusion of both features forms a final feature vector for every person to reveal the identity of person irrespectively based on various factors such as occlusion state: static and dynamic occlusion, normal, fast and slow walking speeds and different clothing. The system has been analyzed using CMU motion capture dataset, TUMIIT KGP database and real time videos for person identification by various factors. This smart system can be used in apartments to identify the entry of unauthorized people and to avoid theft and burglary cases.

 A table is a compact, effective and structured way of representing information in any document. Automatic localization of tables in scanned handwritten document images, and extracting the information are very critical and challenging task for applications like Optical Character Recognition, handwriting analysis, and auto-evaluation systems. The same task becomes more complex, when the handwritten document images are acquired through handheld mobile-cameras, because the captured images naturally get distorted due to poor illumination, device vibration, camera-angle, camera-orientation, camera-movement, and camera-distance. In this research article, a novel technique of automatic localization and segmentation of tables in handwritten document images which are captured using a handheld mobile-camera is proposed. Generally, ruling lines are used for structuring tables, sketching figures, and scribing scientific equations. In the current research work, tables are detected and extracted based on edge features of the ruling lines subjected to three main stages. Firstly, block– wise mean-computed fuzzy based binarization technique is proposed for analyzing the distortion in the acquired image, and subsequently the background surface that envelops the document area of the image is removed. Secondly, horizontal and vertical granule or strip-based technique is proposed for fast edge-feature extraction from the ruling lines of the table in the binarized image. Finally, entropy quantifiers are employed for segmenting the table in the image. The performance of the proposed technique is evaluated and reported using the proposed composite handwritten benchmark daset. Linear computational benefit 0 (h × w) is observed in the worst-case tolerance.

To solve the problem of Social Stability Risk (SSR) assessment for Not In My Back Yard (NIMBY) major projects, first, we established an SSR assessment index system in terms of legality, rationality, feasibility, and controllability. Then, we used the cloud model to determine the value of SSR assessment indicator and the Ordered Weighted Averaging (OWA) operator to weight SSR indicators. Finally, the SSR levels of NIMBY major projects were measured by the matter-element model. The case study shows that the SSR indicator system and assessment model developed in this work can assess the SSR of NIMBY major projects and provide a reference for the investment decisions of government departments.

Target frequency response estimation in the case of stochastic extended targets is an essential part of a cognitive radar system. A new efficient and practical target spectrum estimation method is proposed based on orthogonal frequency division multiplexing (OFDM) signals. The proposed estimation procedure can entirely suppress any unwanted signal and intelligently estimate the target spectrum in predefined subcarrier frequencies. We show that the proposed estimator is capable to exploit the important target frequency characteristic with high precision in heavy clutter and noise. Computer simulations express a promising precision by comparing the relevant Cramer-Rao bound and the mean square error (MSE) of numerical experiments.

 In this paper, we introduce and study the concept of generalized intuitionistic fuzzy hypergraph (shortly, g-if-hypergraph) and establish a relation between generalized if-hypergraph and if-hyperstructures. Further, we introduce partial if-hypergroupoid and extend the results for higher order if-hypergroupoids and study their properties.

This paper considers a new fuzzy sliding mode control (NFSMC) of a 7 Degree of Freedoms (7-DoF) upper-limb exoskeleton robot. The proposed exoskeleton robot has a complex structure, an appropriate control method should be chosen for this robotics system. Therefore, a new sliding mode controller (NSMC) is applied for controlling of this robot conveniently. For this task, a novel sliding surface is selected, which is robust against external disturbances and unknown dynamics such as friction forces, different upper-limb’s mass, backlash, and input saturation. In order to improve the control method, fuzzy control is applied. Simulation results clearly validates that the proposed control method has some advantages such as high trajectory tracking, robustness, and zero tracking error.

Dynamic time warping has attracted wide attention in various fields for its high matching accuracy. In time series data mining, dynamic time warping is a robust similarity measure of multivariate time series. However, the high computational cost of dynamic time warping restricts its applications in large scale data sets. In this paper, we propose a novel approach to speed up dynamic time warping of multivariate time series. Multivariate time series are fitted with multidimensional piecewise lines; and then, important points are extracted as features to reduce the dimensions of multivariate time series; finally, the features are imported to dynamic time warping to measure the similarity of multivariate time series. Extensive empirical results indicate that the proposed method can effectively improve the efficiency of dynamic time warping for multivariate time series, and obtain satisfactory matching accuracy.

Fuzzy soft set theory is applied to hyper BCK-algebras. The notion of fuzzy soft positive implicative hyper BCK-ideals is introduced, and several properties are investigated. The relation between fuzzy soft positive implicative hyper BCK-ideal and fuzzy soft hyper BCK-ideal is considered. Characterizations of fuzzy soft positive implicative hyper BCK-ideal are provided. Using the notion of positive implicative hyper BCK-ideal, a fuzzy soft weak (strong) hyper BCK-ideal is established.

The existing variable control charts using the classical statistics are designed under the assumption that no uncertainty, fuzziness or indeterminate observations or parameters. In practice, the data obtained from the measurement process may have indeterminate or unclear values. In later the situation, it may not possible to apply the variable control chart designed using the classical statistics. The neutrosophic statistics which is the extension of the classical statistics is more suitable to deal with uncertainty/indeterminate observations/parameters issues in the industry. In this paper, we will originally design a variable control chart for the monitoring of data having indeterminate observations using the neutrosophic interval statistics method. The neutrosophic control charts coefficients will be determined and used to calculate the neutrosophic average run length (NARL). A simulation is study is also given to show the efficiency of control chart designed using neutrosophic interval statistics method over the control chart designed using the classical statistics. A real example from the industry is also given. From the comparative study, it is concluded that the proposed control chart under the neutrosophic interval statistics method is more effective and adequate than the chart based on the classical statistics under the uncertainty environment.

 For given two families B ˜ R , B ˜ C of fuzzy subsets of a fuzzy hyperring R, we obtain some sufficient conditions such that two fuzzy geometric spaces ( Δ , B ˜ R ) , ( Δ , B ˜ C ) are strongly transitive and Δ be a nonzero fuzzy subset of R . Moreover, we show that the relation α and Γ on a fuzzy hyperfield R are transitive and obtain some related basic results.

In this paper, we introduce the notions of right and left closure systems on generalized residuated lattices. In particular, we study the relations between right (left) closure (interior) operators and right (left) closure (interior) systems. We give their examples.

 In decision-making for major emergencies, the risk of a decision is caused by both the uncertainty of the decision maker and his or her deviation from group consistency. In this paper, a two-stage risk emergency decision-making method for large groups based on social media big data is hence proposed. In this method, the user-generated content related to major emergencies is first collected from social media. Then, an emergency decision attribute system is constructed based on the public opinion information about events, which is obtained by natural language processing. Term frequency-inverse document frequency and expert evaluation are used to determine the weights of the attributes. Second, an open two-stage decision-making process is designed to quantify decision risk according to the reliability and accuracy of decision makers’ opinions. Here, clustering is used to calculate the value of group members. Then, the technique for order preference by similarity to an ideal solution (TOPSIS) method is employed to rank decision alternatives. Finally, a case analysis and comparison of the major explosions in Tianjin Port on August 12, 2015, demonstrates the feasibility and effectiveness of the proposedmethod.

 The changing process of facial expression contains dynamic texture information, and its accurate extraction is crucial to expression recognition. In this paper, a weighted adaptive symmetric center binary pattern from three orthogonal planes (ASCBP-TOP) is proposed for facial expression recognition. First, facial expression image sequences are partitioned into sub-blocks on different scales to establish a multi-scale space, and ASCBP-TOP is used to extract the dynamic texture features of facial expression image sequences in each scale space and obtain the corresponding feature histograms. Then, these feature histograms are connected in series with different weights to obtain an overall feature histogram for describing the dynamic texture features of facial expression sequences. Finally, support vector machine (SVM) is used to classify and recognize facial expressions. Experiments on Cohn-Kanade and JAFFE databases show that the proposed method is superior to the state-of-the-art methods, and it can extract the dynamic texture information more effectively. In addition, the proposed method is more robust to illumination, expression and pose variations, and it has higher expression recognition rate.

By using fuzzy set theory (FS), interval-valued fuzzy set theory (IVFS) and nonparallel support vector machine theory (NPSVM), the fuzzy nonparallel support vector machine (F-NPSVM) and interval-valued fuzzy nonparallel support vector machine (IVF-NPSVM) are constructed. Both F-NPSVM and IVF-NPSVM consider the membership degree of the training points in their models and the difference is the method to determine them. Then the solutions to them are derived. The experiments on both artificial data set and benchmark data sets show that most of the classification results by using the F-NPSVM and IVF-NPSVM are more accurate than NPSVM, support vector machine (SVM), interval-valued fuzzy support vector machine (IVF-SVM), generalized eigenvalue proximal support vector machine (GEPSVM) and twin support vector machine (TWSVM). Finally, Friedman test is used to verify that there is a significant difference between the two new models and the previous ones.

Education is a collective intelligence system where a group of persons ranging from students to management thinks and work together to achieve institutions’ goals. The primary goal of every institution is to accomplish excellent end-semester examination results. A good result is achieved through proper training given by the educators and in response to the performance of students in the examination. Training is cost accounting, whereas students’ performance is unpredictable. Outlier analysis in the education system has been stipulated in recent decades to predict the students’ uncertain behavior in learning activities which are utilized to alert the education systems. Fuzzy Logic System can handle such uncertainties in learning activities. The major issues that affect the accuracy of fuzzy based outlier detection methods are fixing appropriate membership function and validating the fuzzy rules before extracting outliers. To remedy these issues the proposed Fuzzy Temporal Outlier Detection (FTOD) method detects outliers from mid-semester examination results using fuzzy logic based associative classifier with optimal membership functions. The resultant outliers distinguish the slow learners from spurious-slow learners with high accuracy than the existing FARIM and modified-FARIM algorithms. Thus, educators can provide cost-effective training to enrich the slow learners’ cognition to score high in end-semester examinations.

In this paper we give a natural method of constructing a bi-stratified [0, 1]-convexity by a [0, 1]-valued interval operator, where the concept of bi-stratified [0, 1]-convexities is firstly introduced. And its convex hull operators can be characterized by [0, 1]-valued interval operators. Finally, we discuss the relationship between bi-stratified [0, 1]-convexities and [0, 1]-valued interval operators from a categorical aspect.

Cloud computing relates to the storage and accessing of data as a service from Internet for any organizational infrastructure at-any-time. The delivery of some of the services related to computing such as servers, networking, storage, software, etc., is made possible with the use of cloud computing. Companies offer these services in terms of cloud service providers (CSPs) who charge for the services they provide to the users. When a request is made to use the services, the service provider allocates a feasible number of virtual machines (VMs). Determining optimum amount of resources required at runtime to satisfy the user’s request is not a trivial task. Therefore, in cloud ecosystem the cardinal issue is the management of resource allocation to an application in order to abide by the service level agreements (SLAs). The fundamental objective of cloud service management is to design a self-adjustable auto-scalar to respond to elastic workload and optimizing the allocation of resources with reduced cost. The notable issue is how and at what time resources are to be allocated/de-allocated in order to follow agreed SLAs. In this paper, we propose a resource provisioning framework based on the integrated concepts of autonomic computing with Fuzzy Q Learning and Chebyshev’s Inequality principle. The concept of auto-scaling mechanism is commonly implemented in four phases of proposed autonomic MAPE loop framework: Monitoring, Analysis, Planning and Execution. The proposed framework follows the control MAPE loop structure with the inclusion of Chebyshev’s inequality for prediction in the analysis phase and fuzzy Q-learning in planning phase, where human intervention in the form of fuzzy rules ensures efficacious provisioning of VMs. A comparative analysis has been performed with a different combination of (i) LRM in the analysis phase with FBQ-LA in planning phase, ii) Chebyshev’s Inequality in the analysis phase with FBQ-LA in planning phase, and iii) Chebyshev’s Inequality in the analysis phase with Q-Learning in planning phase. Experimental results prove that the proposed autonomic model based on Chebyshev’s inequality and FBQ-LA outperforms the existing model in terms of improved VM provisioning, minimized costs as well as reduction in response time.

In this study, hesitant fuzzy graph (HFG) structure and some concepts related to HFGs such as homomorphism, isomorphism, weak isomorphism and co-weak isomorphism are introduced. Also, operations of Cartesian product, direct product, lexicographical product and strong product between two HFGs are defined, and examples are given related these operations. Finally, a decision making algorithm and an application of HFGs in decision making are given.

To address the problem that the existing time-aware methods always recommend out-of-fashion items to users, this paper proposes a novel method named Item Life Cycle based Collaborative Filtering (ItemLC-CF), taking both user’s preference and item’s popularity into consideration. This method presents a life cycle function to simulate the variation of item’s popularity and integrates such variation with CF to learn the relative preference for items by reranking the candidate set of CF according to item’s vitality. Given that it is extremely complicated to construct a life cycle function for each item, items are clustered into different categories via their popularities instead of original product categories. Meanwhile, a SVM ensemble classifier based on AdaBoost is employed to find an appropriate category for new arrival items. Evaluation on MovieLens-1M dataset demonstrates that the proposed method can improve the performance of recommendation list, in turn providing more reasonable and popular recommendation lists for users.

 The aim of this paper is to correct the assertions (3) and (4) of Proposition 3.6 proposed by Alkhazaleh [Journal of Intelligent and Fuzzy Systems 32 (2017) 4311-4318]. Every notions involved are extend to the arbitrary set case in a clear, rigorous, and non-burdensome manner. A counterexample is given to illustrate the flaw of the assertions. Then we introduce two new notions describing ‘subset’ of n-valued refined neutrosophic soft sets (n-VRNSs for short) and ‘equal’ of n-VRNSs, and give some examples and related propositions. Finally, we use these results to remedy the flaw of the assertions, and to improve the work of Alkhazaleh.

A complex fuzzy set, characterized by complex-valued membership functions, is a generalization of a fuzzy set. In this paper, we present complex fuzzy arithmetic aggregation (CFAA) operators, complex fuzzy weighted arithmetic aggregation (CFWAA) operators. Based on the partial ordering of complex fuzzy values, we develop complex fuzzy ordered weighted arithmetic aggregation (CFOWAA) operators. Highly pertinent to complex fuzzy operations is the concept of rotational. This concept has been developed for complex fuzzy aggregation operators. We show that CFAA, CFWAA and CFOWAA operators possess the property of rotational invariance. Moreover, based on the relationship between Pythagorean membership grades and complex numbers, these operators are studied under Pythagorean fuzzy values.

The conditional distributivity, also called restricted distributivity, is crucial for many different areas such as utility theory and integral theory. This is the because it weakens distributivity on the domain. This paper is focused on and fully characterizes the conditional distributivity for a uni-nullnorm with continuous and Archimedean underlying t-norms and t-conorm over a continuous t-conorm or a uninorm from U_min ∪ U_max with continuous underlying operators.

We study the Yager OWS aggregation and the Irregular normalized Yager OWA aggregation under infinite environment. Two evaluation problems are proposed to motivate their applications. Few distinguishing properties are discussed. We show that such aggregations use the same type of weights, infinite weights (sequence), to embody the involved preferences. Their respective applications in the fields of Scientometrics and Gradual Confidence Aggregation are proposed and analyzed.

Rough set theory proposed by professor Pawlak in 1982 is an important tool to solve uncertain problems. In order to make rough sets deal with uncertain problems better, by considering the stringent notions of mathematical equality and inclusion, the definitions of rough equality and rough inclusion were introduced by Pawlak. But the researches of rough equality and rough inclusion are on a certain granularity space. In this paper, some change rules will be given with respect to the relations of rough equality and rough inclusion between two uncertain target sets in multi-granulation spaces, and the similarity degree between two roughly equal sets is proposed to describe the similarity of two uncertain sets in multi-granulation spaces. In order to describe two uncertain sets which are rough equality by an approximation set at the same time, the definitions of optimistic λ-approximation set and pessimistic λ-approximation set will be defined from the points of optimism and pessimism, and some properties of them are discussed in detail.

In this paper, Nystrom method was used to solve the Fredholm integral equations of the second kind under interval data. The interval analysis is first presented along with integral equations of the second kind and then Nystrom method is used to find solution of integral equations in the intervals instead of nodes. Also the existence of solution and error analysis is presented. Numerical examples show that the method is efficient and demonstrate a good accuracy.

The issues of security and privacy in multimedia data have become of most extreme significance particularly in the applications associated to medical and military sectors. In this paper, an innovative encryption algorithm for real time videos is proposed. The proposed algorithm consists of two phases in both sender and receiver sides. In these two phases, the tasks of compression/encryption and decompression /decryption are performed at sender and receiver sides respectively. The proposal uses H.261 codec in compression/decompression processes and chaotic logistic map system in encryption/decryption processes for private – key generation. The suggested algorithm takes the features of H.261 codec into consideration to achieve high security with less processing time by encrypt the produced intra frame (I frame) and leaving inter-frame (P-frame) without any changing. Proposed algorithm has innovated new strategy in selecting bytes for encryption by encrypt half of bytes in each I-frame or all of them. Most common statistical tests were performed on the obtained results from applying the proposed algorithm and the results were promising in terms of security level and required time for performing two states.

The key objective of the present proposed work in this paper is introduced a new version of picture fuzzy set so called spherical fuzzy sets (SFS). spherical fuzzy set is a new extension of picture fuzzy sets and Pythagorean fuzzy sets. In spherical fuzzy sets, membership degrees are gratifying the condition 0 ⩽ P² (x) + I² (x) + N² (x) ⩽1 instead of 0 ⩽ P (x) + I (x) + N (x) ⩽1 as is in picture fuzzy sets. In this paper, we investigate the basic operations of spherical fuzzy sets and discuss some related results. We extend operational laws to aggregation operators and introduce weighted averaging and weighted geometric aggregation operators based on spherical fuzzy number’s. Further a multi attribute decision making method is developed and these aggregation operators are utilized. Finally, we constructed a numerical approach for implementation of proposed technique.

Surge arresters are essential equipment for power system protection against transient overvoltages. Therefore, their operating condition and fault diagnosis are very important. Leakage current analysis is a conventional method of surge arrester condition monitoring. In this paper, the impacts of disruptive factors on harmonic components of surge arrester current have been evaluated experimentally. Experimental tests have been done on different surge arresters to investigate disruptive factors effects on surge arresters leakage current. To show the ability of introduced indicators, obtained dataset was applied to fuzzy network for recognition task and classification. In order to increase the accuracy of proposed system, the optimum vector of radius has been found using the optimization algorithm. Bees algorithm, genetic algorithm, imperialist competitive algorithm and particle swarm optimization have been applied to evolve adaptive network based fuzzy inference system. Also the performance of adaptive network based fuzzy inference system has been compared with other classifiers to investigate the capability of the proposed classifier. Results show that the success rates of Bees-ANFIS is higher than the performance of other systems.

Investigation of the environmental/economic optimal operation management of a microgrid (MG) as a case study for applying a novel modified multi-objective grey wolf optimizer (MMOGWO) algorithm is presented in this paper. MGs can be considered as a fundamental solution in order for distributed generators’ (DGs) management in future smart grids. In the multi-objective problems, since the objective functions are conflict, the best compromised solution should be extracted through an efficient approach. Accordingly, a proper method is applied for exploring the best compromised solution. Additionally, a novel distance-based method is proposed to control the size of the repository within an aimed limit which leads to a fast and precise convergence along with a well-distributed Pareto optimal front. The proposed method is implemented in a typical grid-connected MG with non-dispatchable units including renewable energy sources (RESs), along with a hybrid power source (micro-turbine, fuel-cell and battery) as dispatchable units, to accumulate excess energy or to equalize power mismatch, by optimal scheduling of DGs and the power exchange between the utility grid and storage system. The efficiency of the suggested algorithm in satisfying the load and optimizing the objective functions is validated through comparison with different methods, including PSO and the original GWO.

Navigable environment can impact on the energy efficiency of ships. The reasonable operation with ships can be achieved the purpose of energy saving and emission reduction under different navigation environment. In this paper, a bulk carrier is chosen as the research object. According to experimental data, the fuel consumption level is analyzed in different wave height, wind speed and ship operation mode by using the fuzzy clustering method. The data analysis will show that in different navigation environments, the reasonable operation of ships can significantly improve the ship energy efficiency.

In this paper, S₀, S₁ and S₂ separation axioms are introduced in M-fuzzifying convex spaces. Each M-fuzzifying convex space can be regarded to be S₀, S₁ and S₂ separated to some degree. Some properties of them are investigated. The relations among them are discussed.

Due to the radical changes in the global economy and internationalization of markets around the world, local corporates are encountering much more severe challenges and uncertainties than ever before. Hence, how to reliably and effectively evaluate whether corporates will exhibit substantial troubles/difficulties in the near future turns out to be an attractive investigative issue. This study introduces a fusion mechanism that gives decision makers a comprehensive description on a corporate’s operation status so as to prevent a biased judgment from occurring. The introduced mechanism consists of three main procedures: (1) Performance rank determination through the integration of balanced scorecards (BSC) and two-level DEA with a weighting adjusted strategy; (2) Forecasting model construction by combining core vector machine (CVM) with the support vectors (SV)-based online learning strategy; and (3) Knowledge extraction by a rule-based algorithm. The experimental results show that the introduced fusion mechanism (i.e., TDMCR) reduces unnecessary information, gives a more overarching description, satisfactorily predicts a corporate’s operation status, and provides intuitive decision logics for market participators to adjust their investment portfolios for maximizing their profit margins under an anticipated risk level. Examined by real cases, the introduced fusion mechanism is a promising alternative for corporate operating performance forecasting.

Two-stage model free fuzzy adaptive control (MFFAC) has been proposed which rejects the multiplicative input disturbance while also reducing the unnecessary control activity arising due to the sensor noise. Noisy sensor measurements of the input disturbance severely degrades the control performance and might destabilize the control loop. In this work, a novel approach has been proposed which initially develops a model of the multiplicative input disturbance whereas in the second stage the measurement uncertainty is modeled. The uncertainty is ultimately represented as the fuzziness of the control signal. Adaptive conditional defuzzification is employed which utilizes the fuzziness resulting in reduced control activity. The proposed control scheme has shown good control performance in cases where the plant dynamics varies and the sensor noise in the control loop cannot be simply rejected by low pass filtering. Simulation and experimental results adequately validate the effectiveness of the proposed approach.

In this paper we study the mathematical model for diabetes in fuzzy environment. For real situation we notice that some data cannot be precisely known. For that reason we have to take the parameters as imprecise parameter. In this paper we take fuzzy number as the imprecise parameter. The diabetes model is formed by the concept of system of linear differential equation whose initial condition are fuzzy number. The solution procedure for solving governing fuzzy differential equations i.e., the fuzzy model is taken as generalized Hukuhara derivative approach. To show the biological importance the stability analysis of the model is done in fuzzy environment. The numerical results found and are illustrated through computer simulation which is followed by discussions and conclusions in an elegant way.

In order to investigate the effect of Distribution Automation (DA) on network reliability enhancement, two different planning and operation strategies are presented in this paper i.e. switch placement and Distribution Feeder Reconfiguration (DFR). Furthermore, because of increased utilization of Plug-in Electric Vehicles (PEVs) and their capability of Vehicle to Grid (V2G) technology, these intelligent bilateral energy sources can affect on switch placement problem and system reliability. Therefore, through a new failure rate reduction approach due to PEVs as well as evaluating of the System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI), system reliability is investigated in both strategies. In this regard, the objective functions to be investigated are total cost of switches and cost of PEV fleets in switch placement problem as well as SAIFI, SAIDI, active power loss and voltage deviation in DFR problem. In addition, in order to achieve more reliable results, both of strategies are presented in stochastic framework by 2m + 1 Point Estimate Method (PEM). The uncertainty of the active and reactive loads and load annual growth rate as well as active and reactive hourly loads are considered in switch placement and DFR problems, respectively. Simulation results verify the proposed strategies should be investigated in probabilistic framework and finally feasibility of the proposed method is studied by Modified Teaching Learning Algorithm (MTLA) through a 69 bus IEEE distribution test system.

In this paper, a fuzzy multi-objective mixed-integer linear programming (FMOMILP) model is proposed that integrates sustainability three pillars into the Closed – loop Supply Chain (CLSC) network design problem under uncertainty. A two-phase solution approach is proposed to solve this problem. Firstly, the presented model is converted into multi-objective mixed-integer linear programming (MOMILP) by the weighted average method. Then, three widely used interactive fuzzy programming approaches are implemented to solve the model. The significance of the proposed model as well as the fuzzy programming solution approaches are validated through a computational experiment. The obtained results demonstrate the effectiveness of proposed model and solution approaches in designing sustainable CLSC network under uncertain environment.

A new soft group definition is given by Ghosh and Samanta by using the definition of soft element which is introduced by Wardowski. In this paper we study on binary operation on a soft set by using the soft element definition. Considering this binary operation which is the generalization of the soft group definition of Ghosh and Samanta we gave the notions such as soft groupoid, soft group, soft ring and some related results are obtained.