Web Intelligence

Navigating large and complex indoor spaces can be difficult for visitors unfamiliar with the layout, especially when network access is limited. Although technologies like Bluetooth beacons, Wi-Fi positioning, NFC tags, and augmented reality have been used for indoor navigation, this study introduces a quick response (QR) code-based indoor navigation system that operates entirely offline and requires minimal infrastructure. QR codes are placed in key locations with embedded static metadata, including floor and directional information, which is read by a cross-platform mobile app built using React Native. Field testing in a multifloor environment showed that the system reliably provides clear, step-by-step instructions without requiring internet connectivity. Overall, the proposed solution is cost-effective, scalable, and practical, offering a strong alternative to conventional indoor navigation technologies. When compared with other indoor navigation approaches, our system demonstrated 92% overall accuracy along with negligible deployment cost, highlighting its reliability and cost-effectiveness.

Big data classification has become popular for classifying important data such as healthcare, stock market, and other fields’ data for effective handling of many classical databases. In recent years, deep learning and machine learning techniques have become more reliable for such classification, which has resulted in effective results with better accuracy. In this survey, articles from around 10 years are taken for analyzing various techniques recently for big data classification, and this study helps to improve the future evolution of fine, adequate, and highly acceptable models for classification-based approaches. This survey contains the advantages and disadvantages of several methods, which aid in overcoming the challenges of building new ensemble models for classification. Moreover, this research provides complete knowledge about the existing methods and emphasizes the ideas for creating high-potential models as well as paving the way for inventing a real-world application to classify big data. The performance metrics to measure the classification techniques are accuracy, sensitivity, and specificity, and also provide improvement for future classification processes.

Software Defect Prediction is a critical challenge in programming language research and software development aimed at enhancing software quality and reliability. Accurately identifying faulty source code can be difficult, given the complexity of the task. Existing software defect prediction models, designed to estimate the precise number of faults in software applications, failed to deliver precise results due to the presence of noisy input data. Thus, the research proposes the Black-Faced Arachnid Optimized Deep Long Short-Term Memory (LSTM) model for the accurate detection of software defects. In essence, the model leverages the deep LSTM to capture relevant patterns over long sequences and to mitigate the vanishing gradient problems effectively. Additionally, the research proposes the Black-Faced Arachnid (BFA) optimization algorithm to perform an effective hyperparameter tuning process. Moreover, the BFA-optimized feature selection process is introduced to effectively identify the highly relevant features related to defects and non-defects. Furthermore, the experimental results demonstrate that the model achieved a greater accuracy of 98.54%, F1-score of 98.64%, precision of 98.97%, sensitivity of 98.32%, and specificity of 98.81%, for a training percentage of 90 using the PROMISE Dataset, respectively.

Every citizen has the right to control their own data in this digital society. For data privacy, policies are created by social media applications. The users often skip the entire policies of websites or applications to save energy and time without realizing the main points of these policies. Moreover, most of the hypermedia users won’t read the policies because of the verbose explanations and obscure language. If these privacy policies are summarized and classified into useful data, that will be beneficial for users. For that reason, text summarization models are used, which improve social media privacy by automatically summarizing very long privacy policies for users. Recently, many machine learning-based text summarization models have been introduced by researchers. However, these models suffer from issues like inaccuracies, scalability issues, and difficulty in tuning. To solve this problem, in this work, a hybrid classifier (transfer learning (TL)-based modified long short-term memory (LSTM) + bi-directional gated recurrent unit (Bi-GRU)) based policy categorization and text summarization model is proposed, starting with policy categorization as the initial step. During this phase, the data annotation process takes place using an Improved fuzzy C-means algorithm to provide an accurate policy categorization by dividing the policies into paragraphs. After clustering, a preprocessing phase is applied to each cluster, and features such as smoothed dispersion coefficient (SDC)-based term frequency-inverse document frequency (TF-IDF), Thematic features, and Bag of Words are extracted. These features are then used in the text summarization phase, where a hybrid classifier, combining TL-based modified LSTM and Bi-GRU models, is utilized. The fusion of TL-based modified LSTM and Bi-GRU produces a single model that generates the final summarized text. Analysis shows that the proposed hybrid classifier (TL-based modified LSTM + Bi-GRU) achieved superior accuracy of 95% and 95.6% on both APP-350 and BillSum datasets.

This study proposes an improved random forest algorithm for an educational resource course recommendation network based on boundary value for the problems of low accuracy of educational resource recommendation network in universities, including weak ability to deal with boundary value, and poor adaptability in the face of complex educational environments and diversified user needs, the use of this network can be recommended for better educational resources. The research findings demonstrate that the algorithm error value of the improved random forest algorithm is better in the analysis of the matrix, the algorithm has the best algorithmic performance when the number of forests is 300, at the same time in the system test the algorithm can be smooth and safe through the test, the system in the home page resource recommendation and network performance, the improved algorithm test is good, the uploading and running time is less than 1 s, and the memory accounted for less than 40%. Algorithm model threshold at 0.10 and 0.15, the accuracy trend is the same as the threshold 0.05, while the larger the threshold the higher the accuracy. It can be seen that the improvement of the random forest algorithm can improve the accuracy rate of the current course recommendation, and at the same time, it can complete the course recommendation of the current educational resources, which has certain research significance for the research in this direction.

This study introduces an optimization-based approach to improve the longevity and energy efficiency of wireless sensor networks (WSNs) while ensuring comprehensive target coverage. The suggested method categorizes sensor nodes into separate coverage sets to decrease duplicate data transmission and lower total energy consumption. During each operational period, a single coverage set is active, and a mixed integer linear programming (MILP) model is employed to optimize the selection of cluster heads and multi-hop routing. The model concurrently addresses clustering, routing, and variable sensing radii to enhance energy distribution among nodes. Simulation results indicate that the suggested strategy markedly prolongs network longevity compared to traditional strategies, such as T-LEACH. The analysis of the influence of differing coverage set quantities on network performance indicates that an optimal number of sets achieves a balance between energy conservation and effective coverage. The research presents an enhanced energy consumption model that incorporates both sensing and communication expenses. In comparison to baseline techniques, the proposed framework attains more consistent energy utilization and facilitates extended network operation without compromising monitoring precision. The amalgamation of coverage-aware scheduling, energy-aware clustering, and efficient routing offers a comprehensive solution for extending the lifespan of WSNs, with applicability in energy-limited monitoring applications.

The digital transformation in education, driven by platforms such as massive open online courses and virtual learning environments, has significantly broadened access to education globally. This study explores how machine learning (ML) can enhance and predict learning outcomes by identifying and supporting at-risk students through predictive models. The goal is to improve academic achievement by analyzing student profiles and addressing multiple contributing factors. In this research, ML classification models, specifically support vector classification (SVC), were employed along with two optimizers: the Ebola optimization algorithm and the attack-leave optimizer. The aim was to enhance the model's performance and prediction accuracy. The results indicate that in the training phase, the support vector-based ebola optimization (SVEO) model achieved an accuracy of 0.900, demonstrating moderate performance compared to the SVC model, which achieved an accuracy of 0.887. The support vector-based attack-leave optimization (SVAL) model, however, outperformed the others with the highest accuracy of 0.936.

Retrieving and analyzing vulnerability reports remains a critical challenge in cybersecurity, exacerbated by the exponential growth of disclosed vulnerabilities and the increasing complexity of Proof-of-Concept (PoC) reports. Confronted with massive numbers of vulnerability reports, automated tools and models are urgently required to facilitate the understanding and analysis of vulnerability reports and PoC reports, thereby supporting security professionals in filtering similar vulnerabilities and extracting critical vulnerability attributes. Existing methods for vulnerability semantic similarity either rely on rule-based keyword matching (failing to capture contextual nuances) or generic pre-trained language models, leading to suboptimal retrieval performance. To address these gaps, this work focuses on the semantic similarity analysis of vulnerability descriptions using semantic representation learning. To fully exploit the semantic information embedded in vulnerability reports, we propose a task-specific fine-tuned Sentence Transformer based model for calculating vulnerability semantic similarity. Our approach integrates domain-specific knowledge of vulnerability reports into the model training process, enabling it to capture nuanced semantic relationships unique to cybersecurity. On this basis, we further construct an end-to-end vulnerability retrieval system that uses our fine-tuned similarity model with Elasticsearch vector indexing, realizing intelligent retrieval of both vulnerability and PoC reports. Experimental results demonstrate that the proposed model captures domain knowledge more effectively, and the enriched semantic information significantly enhances the effectiveness of vulnerability report retrieval.

In recent years, machine learning (ML) models have become the top prediction options in agriculture, cybersecurity, healthcare, and finance, among other fields. ML models have assisted in the study of numerous disorders in medical research. Early diagnosis and health schedule management may prevent severe heart disease in many people. This study uses national government repository data to apply ML models to early heart disease detection. This research also introduces two novel models based on swarm intelligence, called ACOKNN (ant colony optimization with K-nearest neighbors) and ACOWNNBoost (ant colony optimization with weighted K-nearest neighbors), to improve heart disease early detection models. The suggested models pick features using ant colony optimization with traditional k-nearest neighbor and weighted k-nearest neighbor with XGBoost. The suggested models are evaluated with two datasets of varying capacity, and results are compared to various ML techniques based on precision, accuracy, recall, F1 score, and receiver operating characteristic area under the curve score. The investigation shows that the suggested model achieved 92% and 98% accuracy with Dataset-1 and 92.6% and 98.6% with Dataset-2.

Autism spectrum disorder (ASD), a neurodevelopmental condition, affects approximately 1% of children globally and their social and cognitive abilities. This leads to difficulties in communication, repetitive behaviors, psychomotor skills, and eye contact maintenance. In recent years, there has been an increased utilization of Artificial Intelligence (AI) for the early detection of autism. Using knowledge gathered from 116 peer-reviewed publications, this study assessed algorithmic efficacy, model performance, multimodal data integration, classification metrics, generalization ability, and clinical usefulness. Machine Learning, Deep Learning (DL), Graph Neural Networks, Federated Learning, auto encoders, and Natural Language Processing, the Attention Mechanism seemed to be among the several AI techniques that were investigated in this study. To protect a child's developmental progress, this survey investigates how early detection of autism facilitates prompt therapeutic treatments, minimizes intellectual disabilities, improves mobility, and supports customized care. The research emphasizes the effectiveness of predicting ASD with minimal time using different data modalities, including EEG microstates, ABIDE I & II, to assess DL models such as GoogleNet, Xception, AlexNet, ResNet, VGG, and DenseNet. Three types of data were analyzed: biochemical (biomarkers and physiological metrics), behavioral (facial features, eye gazing, and audio-video cues), and structural and functional (MRI, EEG, and ECG) images. The research demonstrated strong diagnostic performance with models attaining accuracy rates ranging from 90% to 96% across diverse datasets. Classification measures, such as accuracy, sensitivity, specificity, precision, recall, and F1-score, were used in the performance evaluation. Error and statistical metrics, such as RMSE, MSE, R2, Kappa, and G-mean, were also employed. The dependability and efficiency of the models in detecting ASD were enhanced by validation methods, such as confusion matrix, receiver operating characteristics (ROC) curves, and AUC. Based on the evaluated studies, transfer learning with diverse datasets and modalities has great promise for early ASD diagnosis. Even with a minimal data size, these techniques increase robustness, accuracy, and generalization. For real-time clinical applications, a hybrid transfer learning-based framework is advised to assist clinicians and therapists in accurately diagnosing and assessing ASD severity.

The importance of quality of service (QoS) in categorizing cloud services has been increasing as the quantity of services offered by the cloud continues to grow. Cloud Computing is an internet technology that allows customers to use computing resources and pay based on demand in real-time. Various cloud service providers now offer similar services at different prices and performance levels, making it crucial but difficult for consumers to choose the best cloud service. The current techniques for selecting a cloud service allow users to provide choices in a quantifiable way. However, because many QoS variables are connected and not independent, the combined weights approach does not consider correlations between QoS attributes and can generate unreliable results. Our solution to this challenge involves implementing a cloud service architecture that considers customers’ choices and selects the best cloud service based on QoS limitations. We propose a hybrid cloud service selection approach that integrates principal component analysis (PCA) with a combined weighting mechanism incorporating both objective and subjective criteria. This dual strategy offers two key advantages: (1) it achieves a more well-rounded weighted result through the combined weighting strategy, and (2) it employs PCA to eliminate redundancy by removing correlations among QoS criteria, thereby enhancing the robustness of the selection process. The effectiveness and reliability of the proposed approach have been validated through simulations using the real-world QWS dataset, demonstrating its feasibility and superior performance in identifying the best cloud services.

Multimodal emotion recognition comprises speech utterances with corresponding images in a discrete form that strengthens the combined knowledge, captures the importance of image and speech features, and recognizes particular emotions accurately. Several studies have been undertaken in audio–visual model emotion recognition and faced specific challenges such as missing features, misalignment of modalities, inefficient generalization, and inaccurate detection results. To solve these problems, a Taylor Cooperative Share Hunt Optimizer with Deep Quantum neural network (TCoSH–DQNN) is proposed, in which both speech utterance and facial expressions are effectively combined. Furthermore, the proposed model achieves effective multimodal emotion recognition with the combined representation of audio and visual features extracted from videos. The proposed TCoSH–DQNN classifier extracts the human expression in an informative aspect with enhanced performance and improved recognition accuracy. Additionally, the TCOSH–DQNN model generates better communication by resolving conflicts and effectively recognizing feelings. In this perspective, the model reduces the computational complexity and achieves effective recognition with a better convergence rate. The effectiveness of the model is improved by validating the dataset and achieving significant evaluation metric outcomes with an accuracy of 99.97%, a Cohen kappa score of 98.61%, a recall of 99.70%, an F1-score of 99.81%, a f-beta score of 98.67%, and a precision of 99.96%, with the evaluation of the Enterface’05 dataset.

Knowledge tracing (KT) is a core component of intelligent education systems, which aims to predict students’ future performance from their historical learning behaviors. Despite the remarkable progress brought by deep learning, existing models often struggle to effectively capture the dynamic evolution of students’ knowledge states, particularly the intertwined processes of knowledge-acquisition and -forgetting. To address this challenge, we propose the Dynamic Knowledge Perception for Temporal Knowledge Tracing (DKPKT) model. DKPKT integrates two complementary modules: the interaction perception module employs a dynamic attention mechanism to extract key interaction features that signal shifts in knowledge states, and the knowledge perception module refines these representations by incorporating knowledge-forgetting and knowledge-acquisition factors, which enable more accurate modeling of knowledge dynamics over time. We evaluate DKPKT against several mainstream baseline models on three real-world educational datasets. Experimental results demonstrate that DKPKT achieves superior predictive performance, validating its effectiveness in modeling knowledge states and enhancing the accuracy of student performance prediction.