Journal of Computational and Cognitive Engineering
The Journal of Computational and Cognitive Engineering (JCCE) is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles on the recent trends in computational and cognitive engineering. The goal of the journal is to provide a new platform for the dissemination of the research and current practices in the emerging disciplines of the cognitive and computational engineering to solve the real-world problems. The journal welcomes submission related to various computational and cognitive modelling approaches in the areas of health, education, finance, environment, engineering, commerce and industry. The journal is a Gold Open Access journal, online readers don't have to pay any fees.
Deep Learning Approaches for Detecting Cyberbullying on Social Media
The widespread use of social media has brought many challenges, mainly due to a misconstrued interpretation of the right to freedom of expression. Cyberbullying is a particularly noteworthy issue with far-reaching global implications for both its victims and the wider community. It takes the form of bullying that happens on several social media websites. This paper's goal is to develop a deep learning model capable of recognizing cases of cyberbullying on social media. Four models, such as bidirectional long short-term memory (BiLSTM), convolutional neural network with bidirectional long short-term memory (CNN-BiLSTM), bidirectional long short-term memory with gated recurrent unit (BiLSTM-GRU), and artificial neural network (ANN), will be evaluated in a multiclass classification difficulty context. The results showed that the BiLSTM model outperformed the other models by achieving the highest accuracy in 91% of cases, while the CNN-BiLSTM and ANN models demonstrated relatively lower performance. In addition to determining the efficacy of the deep learning techniques, the work highlights the urgent requirement for strong systems to resist cyberbullying. By enhancing detection accuracy, the proposed model can contribute significantly to providing a safer digital environment for further studies in this field. Received: 24 August 2024 | Revised: 16 December 2024 | Accepted: 25 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Ghaith Jaradat: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mohammad Shehab: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Dyala Ibrahim: Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Project administration. Saif Najdawi: Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization. Rami Sihwail: Software, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
Strengthening Security in Clouds Through Cloud Computing Authentication Using Facial Image Forensics
Numerous cyber threats may succeed in cloud platforms owing to unproductive authentication methods. Multiple credential authentication is a vital precautionary measure that helps reinforce cloud security for warding off imminent data breaches and illegal access. This kind of authentication approach strongly guarantees that trustworthy clients are only ratified to get cloud services, making it less tiresome for clients and more secure for organizations. This study proposes an Image Forensics-based Dual Credential Authentication (IF-DCA) based on username (i.e., preferably email ID to avoid replica) and client photographs as password. Along with a username, a rapid and acquainted user action of capturing a photograph using a web camera is adequate for this method; hence it does not need any expensive, special hardware devices. During the registration phase, along with the username, the user's face image is stored on the cloud server as a password. When registered clients need to access cloud resources or data, they should log in on the server with their username and their photograph. To improve the authentication approach, this work proposes an Attribute-controlled Conditional Generative Adversarial Network (ACC-GAN) to generate face images of the same user at various age groups. ACC-GAN includes an additional attribute control unit and an age prediction unit to synthesize photo-realistic face images with aging effects. After matching the username and face image with the registered username and ACC-GAN-generated images, the face identification module provides access to the authorized cloud user. The performance of IF-DCA is assessed through the Cross-Age Celebrity Dataset using the MATLAB R2018b/deep learning toolbox. The empirical analysis reveals that the ACC-GAN achieves better performance measures such as 98.40% accuracy, 99.3% sensitivity, 90.0% specificity, and 95.7% precision. The performance of IF-DCA is analyzed using the verification time for username, user image, and complete authentication against the number of users. Received: 4 September 2024 | Revised: 14 October 2024 | Accepted: 7 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Cross-Age Celebrity Datasets that support the findings of this study are openly available at https://paperswithcode.com/dataset/cacd. Author Contribution Statement Pranali Dahiwal: Conceptualization, Software, Formal analysis, Resources, Writing – original draft, Visualization, Project administration. Vijay Khare: Methodology, Validation, Investigation, Data curation, Writing – review & editing, Supervision.
Adaptive Gaussian-Based Kernel K-Means: Scalable Adaptive Kernel-Based Clustering for Big Data
In the field of big data, clustering remains a fundamental component of data mining and knowledge discovery, particularly in high-volume, heterogeneous environments. Although traditional clustering algorithms have been widely used, they often exhibit poor scalability, limited robustness, and sensitivity to noise when applied to large industrial-scale datasets. To address these limitations, this paper proposes a Spark-based clustering framework that incorporates an Adaptive Gaussian-Based Kernel K-Means (A-GBK-Means) algorithm, designed to mitigate performance degradation in the presence of noisy and nonhomogeneous data. The A-GBK-Means algorithm integrates fuzzy logic with adaptively determined kernel widths, using local density estimation, thereby enhancing both clustering accuracy and flexibility. Extensive experiments were conducted on real-world industrial sensor data as well as synthetic benchmarks, and performance was evaluated using multiple metrics, including execution time, Silhouette Score, Davies–Bouldin Index, and noise resilience. The comparative analysis demonstrates that the A-GBK-Means algorithm consistently outperforms classical approaches—namely K-Means, Density-Based Spatial Clustering of Applications with Noise, Fuzzy C-Means, and standard GBK-Means—in terms of clustering quality and computational efficiency. Furthermore, the proposed framework exhibits superior scalability, owing to its distributed architecture built on Apache Spark. This study provides practical insights into scalable and interpretable clustering methods for intelligent manufacturing and sensor-based systems and highlights the effectiveness of adaptive kernel learning in modern big data applications. Received: 18 June 2025 | Revised: 8 August 2025 | Accepted: 16 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/dnkumars/industrial-equipment-monitoring-dataset. The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/mohammedarfathr/sensor-data-from-an-industrial-machine. Author Contribution Statement Bakhshali Bakhtiyarov: Investigation, Data curation, Writing – original draft, Project administration. Aynur Jabiyeva: Conceptualization, Validation, Formal analysis. Anakhanim Mutallimova: Software, Resources. Rukhsara Novruzova: Methodology, Writing – review & editing, Supervision. Mahabbat Khudaverdiyeva: Visualization.
A Novel Approach to Model the Economic Characteristics of an Organization by Interval-Valued Complex Pythagorean Fuzzy Information
The success of a nation mainly depends on its economic power. The economic growth plays an important place in enhancing the caliber of life as it produces job opportunities. In this research, the effect of uncertainty on economic growth and investments is examined with gross domestic product (GDP). Currently, the relationship between financial market and economic growth is one of the most important issues in economics. These departments are often accompanied by uncertainty. Through that regard, the idea of interval-valued complex Pythagorean fuzzy relation (IVCPFR) is introduced that is handy in such situations. Moreover, the Cartesian product between interval-valued complex Pythagorean fuzzy sets and verities of IVCPFRs was described. Furthermore, these IVCPFRs are applied to analyze GDP, unemployment, price, demand, supply, interest rate, investment, and money supply effecting business markets. The possibility of the Hasse diagram is more suitable to make sense of the relationship between various economic factors. IVCPFRs will clearly help to set up the impact of one component on the other and change the grades of supportive and distinctive outcomes concerning the time. IVCPFR is used for tracking various multidimensional issues and produces quick and better outcomes rather than the previous methodologies. Finally, the proposed method is the best method to model uncertainty in economics rather than previous methods. Received: 12 May 2022 | Revised: 12 August 2022 | Accepted: 17 August 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
interval-valued complex Pythagorean fuzzy set
interval-valued complex Pythagorean fuzzy relation
Copyright (c) 2022 Authors https://creativecommons.org/licenses/by/4.0/
Switching-Algebraic Calculation of Banzhaf Voting Indices
This paper employs switching-algebraic techniques for the calculation of a fundamental index of voting powers, namely, the total Banzhaf power. This calculation involves two distinct operations: (a) Boolean differencing or differentiation, and (b) computation of the weight (the number of true vectors or minterms) of a switching function. Both operations can be considerably simplified and facilitated if the pertinent switching function is symmetric or it is expressed in a disjoint sum-of-products form. We provide a tutorial exposition on how to implement these two operations, with a stress on situations in which partial symmetry is observed among certain subsets of a set of arguments. We introduce novel Boolean-based symmetry-aware techniques for computing the Banzhaf index by way of two prominent voting systems. These are scalar systems involving six variables and nine variables, respectively. The paper is a part of our on- going effort for transforming the methodologies and concepts of voting systems to the switching-algebraic domain, and subsequently utilizing switching-algebraic tools in the calculation of pertinent quantities in voting theory. Received: 13 February 2023 | Revised: 5 April 2023 | Accepted: 19 May 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Copyright (c) 2023 Authors https://creativecommons.org/licenses/by/4.0/
Digital Transformation and Corporate ESG Performance – Evidence from China
The focal point of this paper is to study environmental, social, and governance (ESG) performance consequences of digital transformation for companies, as well as green technological innovation, corporate social responsibility, and internal control as mediating variables. A set of digital transformation indicators was developed utilizing data from 355 listed industrial companies in China between 2011 and 2019 and vector arithmetic. The study’s findings suggested that digital transformation has considerably raised ESG performance, with green technological innovation, corporate social responsibility, and internal control as significant mediating variables. The paper has several contributions; for example, it provides theoretical and empirical evidence for ESG improvement in Chinese firms and an essential factor associated with ESG performance, among others. The non-economic impacts of digital transformation on ESG performance are essential insights offered by this study. They also provide insight into these impacts' mechanisms, including green technology, corporate social responsibility, and internal control. Received: 9 May 2024 | Revised: 5 August 2024 | Accepted: 31 August 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Wenxin Zhang: Conceptualization, Methodology, Investigation, Resources, Data curation, Writing – original draft. Xiangyu Li: Software, Writing – review & editing.
Copyright (c) 2024 Authors https://creativecommons.org/licenses/by/4.0/
An Improved Computational Model Based on Cardiac Imaging Data for the Quantitative and Qualitative Assessment of the Mechanical Properties of the Human Left Ventricle
Computational modeling has contributed to many fields of medicine and has been proven very useful for diagnosing complex diseases. A special case is that of myocardial infarction (MI), a prevalent cardiovascular disease where computational modeling approaches have been useful for detecting abnormalities. The aim of this paper is to propose enhanced computational models for the human left ventricle (LV) to estimate the strain and stress distributions in healthy and diseased subjects. Computational models were developed and evaluated using human LV of 20 patients with MI and 20 healthy controls using cardiac magnetic resonance imaging acquisitions and simulation tools. The finite element technique was employed for LV modeling. Comparative analysis revealed higher global strains in healthy subjects compared to MI patients, particularly global circumferential, longitudinal, and radial strains. The average stress distributions were 67.9 ± 5.01 kPa in healthy models and 78.3 ± 8.21 kPa in infarcted regions. Model-derived strain data indicated an overall average of −0.15 ± 0.06 for healthy models and 0.2 ± 0.04 for infarcted regions. LV strain values were compared with those obtained from two feature-tracking algorithms to validate the proposed models, resulting in very promising findings. The work presented here highlights the importance of computational modeling for quantitative and qualitative analyses of heart disease and the potential of using such models in other organs. When combined with imaging data, the proposed models can have significant implications for improved patient care and treatment strategies. Received: 3 June 2025 | Revised: 30 July 2025 | Accepted: 8 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Rania Awadi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Narjes Benameur: Methodology, Validation, Investigation, Writing – review & editing, Visualization, Supervision, Project administration. Wafa Baccouch: Formal analysis, Writing – review & editing, Visualization. Mohamed Deriche: Methodology, Writing – review & editing, Visualization, Project administration, Funding acquisition. Arnab Palit: Validation, Visualization. Moncef Aloui: Resources, Data curation. Nadjia Kachenoura: Software, Validation, Resources, Data curation, Writing – review & editing, Visualization. Salam Labidi: Methodology, Investigation, Writing – review & editing, Visualization, Supervision, Project administration.
AI-Driven Prediction of Greenhouse Gas Emissions in Livestock Supply Chains: Towards a Data-Driven Model for Sustainable Agri-food Systems
This greenhouse gas (GHG) emissions mainly from enteric fermentation and manure management in livestock are mostly from copious methane (CH₄) and some nitrous oxide (N₂O). IPCC (International Panel on Climate Change) Tier 1 and Tier 2 emission prediction methods cannot provide details about where the emissions come from and are ineffective for different forms of agriculture. In our research, a machine learning model based on national agricultural data forecasts how much CH₄ and N₂O will be emitted from U.S. manure management. The performance of Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) was tested by using RMSE, MAE and R² as performance metrics. XGBoost performed better than SVR since its predictive results were better than reaching R² = 0.98. The analysis of feature importance found that livestock type, methods of managing manure and population density are the main factors leading to emissions. The models resulted in information that communities in various locations could use to improve their sustainability. An adaptable decision-making procedure is proposed by the research to assist environmental planning in the agri-food sector and to ensure that intelligent agricultural platforms can better manage GHG emissions. More research is needed to improve the model by studying additional aspects from the supply chain, covering both its upstream and downstream operations, to obtain a complete analysis of environmental results. Future work should aim to incorporate additional stages of the livestock supply chain and adopt explainable AI techniques to improve transparency and support realtime decision-making. Received: 22 April 2025 | Revised: 31 July 2025 | Accepted: 3 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in USDA at https://agdatacommons.nal.usda.gov/search?itemTypes=3&categories=30761,30746,30689,30821,33323. Author Contribution Statement Baha M. Mohsen: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Mohamad Mohsen: Methodology, Validation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Early-Stage Diabetic Retinopathy Diagnosis with Feature Pyramid Networks and Spatial Pyramid Pooling Utilizing Full-Field Optical Coherence Tomography (FF-OCT)
Diabetes is a disease that is growing at the highest rate globally, and it has several impacts. One of such complications is diabetic retinopathy (DR), which causes damage to the retina and vision impairment. DR can be mild, and it can also be severe, and there are many shades in between. Therefore, it is possible to avoid vision loss if DR is diagnosed and treated in its early stages. Diagnosis of DR today is still a tedious process as only ophthalmologists can diagnose it using digitized colorful retinal fundus images. This article describes a new strategy for the early identification of DR that combines full-field optical coherence tomography (FF-OCT) with sophisticated deep learning (DL) algorithms, notably Feature Pyramid Networks (FPNs) linked with spatial pyramid pooling (SPP). FF-OCT produces high-resolution, cross-sectional pictures of the retina, revealing precise structural changes that occur before apparent symptoms of DR. We created a multiscale DL framework that takes advantage of the capabilities of FPNs and SPP to efficiently process and evaluate the fine features of FF-OCT images. The FPN design detects changes in the retinal structure at different scales, which improves the network's performance in detecting early signs of DR. On the other hand, the SPP module collects contextual information from several sub-regions of the image to provide a stable and accurate feature representation regardless of the size and location of the lesion. These models were trained and validated on this dataset using performance indicators such as sensitivity, specificity, and area under the ROC curve (AUC). The findings of this paper suggest that the FPNs with the SPP model are superior to the traditional image analysis methods and the standard Convolutional Neural Network in diagnosing early-stage DR. Received: 9 November 2024 | Revised: 10 March 2025 | Accepted: 3 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/paultimothymooney/kermany2018. Author Contribution Statement Anitha Jaikumar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Sreenivasa Chakravarthi Sangapu: Conceptualization, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision, Project administration.
full-field optical coherence tomography (FF-OCT)
Optimized Ensemble Machine-Learning-Driven Transparent Thyroid Cancer Prediction Using Explainable Artificial Intelligence
Thyroid cancer disease diagnosis is a critical medical challenge, requiring accurate and reliable predictions to support clinical decision-making. Patients may suffer from an incomplete diagnosis while using traditional machine learning models. In this study, we present an optimized ensemble machine-learning framework for predicting thyroid cancer disease. The methodology integrates multiple classifiers, including support vector machine, random forest, Naïve Bayes, K-nearest neighbors, and decision tree. The final classification decision is determined with the help of soft voting by a total predictive support vector machine, which selects the classifier with the highest confidence score among the ensemble models. The ensemble strategy enhances predictive accuracy and robustness by combining the strengths of individual classifiers. The model was trained and evaluated, achieving an impressive accuracy of 0.9633 and an area under the receiver operating characteristic curve of 0.9914. The proposed method of this study is very accurate, but there is still a black box problem. To overcome this issue and to ensure interpretability, Explainable Artificial Intelligence techniques, including Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations, are implemented, providing insights into feature contributions towards the performance of the proposed method and model decisions. The dataset contains a total of 30 features with 3,772 different cases consisting of two classes. Received: 16 June 2025 | Revised: 14 August 2025 | Accepted: 24 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/bidemiayinde/thyroid-sickness-determination. The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/bhargavchirumamilla/thyroid-cancer-risk-dataset. The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/sikandaraidev/thyroid-dataset. Author Contribution Statement Syed Younus Ali: Conceptualization, Software, Data curation, Writing – original draft, Writing – review & editing. Bilal Shoaib Khan: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision. Abdul Hanan Khan: Software, Validation. Muhammad Adnan Khan: Formal analysis, Investigation. Asghar Ali Shah: Validation, Investigation. Sagheer Abbas: Formal analysis, Project administration. Khan Muhammad Adnan: Methodology, Resources.
Explainable Artificial Intelligence (XAI)
From Tissue Image to Transcriptomics: Dual Modality Deep Learning Model for Classification of Cancer Using Histopathological Images and Gene Expression Pattern
A primary method for cancer detection involves the examination of histopathological images. However, traditional approaches to analyze these images are time-consuming and prone to errors. With recent advancements in deep learning, researchers are increasingly leveraging these models to enhance the accuracy and efficiency of histopathological image analysis. In this study, a deep learning-based model is proposed for multi-class cancer classification that integrates gene expression prediction with histopathological image analysis to enhance diagnostic precision and streamline the detection process. The model is intended for use in real-world clinical settings where it is a challenge to classify data correctly because predefined sequences of gene expression and cancer labels are often missing. Fourier Neural Networks (FNN) and EfficientNetB0 are utilized to get a full set of spatial and frequency-based features from Whole Slide Images (WSIs). To select the best set of features, Incremental Principal Component Analysis (IPCA) was used, and the resulting representations were subsequently reconstructed from patch-level features to WSI representations. A DeepONet model, one of the advanced deep learning models, was selected for mapping the generated histopathological image features to predict gene expression patterns. After training, the model achieved 93% accuracy in classifying cancer types with a 0.92 precision value indicating acceptable performance with respect to multiple cancer classifications. Additionally, the model achieved strong performance in gene expression prediction, with a Mean Absolute Error (MAE) of 0.033 and an R² score of 0.765, demonstrating its reliability in capturing gene expression patterns. Addressing real-world challenges such as missing gene expression data or ambiguous cancer-type classifications, the proposed Dual Deep Learning Model enhances cancer diagnosis by improving accuracy in oncology. By integrating histopathological image analysis with gene expression prediction, the model enables automated clinical decision-making, offering a robust solution for distinguishing between cancerous and non-cancerous cases. Received: 11 June 2025 | Revised: 15 August 2025 | Accepted: 13 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Anju Das: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Neelima Nizampatnam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Somnath Ganguly: Investigation, Data curation, Writing – review & editing, Visualization, Supervision, Project administration. Joon Ho Choi: Validation, Investigation, Writing – review & editing, Visualization, Supervision, Project administration.
Fake News Detection with Deep Learning: Insights from Multi-dimensional Model Analysis
This study aims to systematically evaluate and compare various deep learning models in terms of accuracy, efficiency, and interpretability for fake news detection. Leveraging recent advancements in pretrained models (e.g., BERT, RoBERTa) and lightweight frameworks (e.g., TextCNN), we implemented and optimized multiple detection models. Comparative analysis was conducted on a dataset containing approximately 40,000 news texts. Results revealed that BERT Large significantly outperformed other models, achieving an accuracy of 99.33%, attributed to its extensive semantic understanding capabilities. Conversely, TextCNN, despite its simpler architecture, achieved competitive accuracy (98.77%), demonstrating substantial practical value for resource-limited environments. Interpretability analysis via attention visualization highlighted distinct cognitive strategies of pretrained models when classifying real versus fake news. While the study addresses critical technical challenges in fake news detection, limitations related to potential dataset biases and domain specificity were acknowledged, suggesting opportunities for future research on multimodal and cross-domain adaptations. This research contributes substantially by providing practical benchmarks and interpretability insights, significantly enhancing real-world fake news detection systems, thus aiding platforms in combating misinformation effectively. Received: 30 April 2025 | Revised: 11 June 2025 | Accepted: 7 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement QiuPing Li: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Fen Fu: Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Yinjuan Li: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Bhunnisa Wisassinthu: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Wirapong Chansanam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Tossapon Boongoen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
An Efficient Recommendation System in E-commerce Using Passer Learning Optimization Based on Bi-LSTM
Online reviews play a crucial role in shaping consumer decisions, especially in the context of e-commerce. However, the quality and reliability of these reviews can vary significantly. Some reviews contain misleading or unhelpful information, such as advertisements, fake content, or irrelevant details. These issues pose significant challenges for recommendation systems, which rely on user-generated reviews to provide personalized suggestions. This article introduces a recommendation system based on a Passer Learning Optimization-enhanced Bidirectional Long Short-Term Memory network classifier applicable to e-commerce recommendation systems with improved accuracy and efficiency compared to state-of-the-art models. More specifically, the proposed model achieves a high accuracy of 98.03%, F1 score of 98.03%, precision of 98.49%, recall of 97.57%, and minimum mean square error of 1.97 based on training percentage using the patio lawn garden dataset. These results, made possible by advanced graph embedding for effective knowledge extraction and fine-tuning of classifier parameters by the hybrid PLO algorithm, establish the suitability of the proposed PLO-Bi-LSTM model in various e-commerce environments. Received: 9 April 2025 | Revised: 26 June 2025 | Accepted: 24 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Amazon product data that support the findings of this study are openly available at https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html. Author Contribution Statement Hemn Barzan Abdalla: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Mehdi Gheisari: Conceptualization, Software, Data curation. Awder Ahmed: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Bahtiyar Mehmed: Formal analysis, Investigation, Resources. Maryam Cheraghy: Validation, Resources, Writing – review & editing. Yang Liu: Validation, Investigation, Resources.
Multitask Virtual Keyboard Controlled by an Eye-Gaze-Based Intelligent Entry System
In modern society, computers and cell phones play an integral role in individuals' everyday lives. Individuals utilize messaging platforms such as WhatsApp, WeChat, and Facebook daily to communicate with others. Virtual keyboards are a significant tool in assistive technology because they aid individuals with significant motor disabilities in making effective contact with computers. This study proposes an eye-gaze-controlled virtual keyboard using a new method called Distance Eyelid-Iris-MediaPipe, which enables individuals with disabilities to write effectively. The suggested virtual keyboard is an affordable assistive device because it only necessitates a webcam for operation. The proposed virtual keyboard consists of six supplementary menus: two menus for English letters, one for numerical digits and mathematical operations, one for important text operations, one for Latin symbols, and an emoji menu. The suggested keyboard has special characteristics such as auditory feedback and visual highlighting of pressed keys. The testing of the proposed system received positive feedback from several users, and the empirical results of the proposed system were better than those of the previous models. It achieved this with an average typing rate of 18 characters per minute and 4 words per minute, and it had a NASA-TLX score of 10% and a system usability scale score of 93. Received: 27 April 2025| Revised: 25 June 2025| Accepted: 11 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Amal Hameed Khaleel: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Thekra Abbas: Methodology, Validation, Resources, Writing – original draft, Writing – review & editing, Supervision. Abdul-Wahab Sami Ibrahim: Conceptualization, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision.
A Study on Zeek IDS Effectiveness for Cybersecurity in Agricultural IoT Networks
As agriculture moves toward Agriculture 4.0, which uses Internet of Things (IoT) devices to collect data in real time and monitor things from a distance, these networks are becoming increasingly vulnerable to cyberattacks. A common method used to protect against these kinds of threats is the use of intrusion detection systems (IDS). However, the agricultural environment is often changing and has limited resources, which makes cybersecurity challenging. Several available IDS tools are not designed to work properly in places with few resources, intermittent access, and unpredictable network conditions. This paper investigates the performance of Zeek, an open-source IDS, in identifying potential threats in agricultural IoT networks. We performed both offline and real-time experiments: offline analysis used pcap files from the Stratosphere Laboratory dataset, and real-time evaluation involved simulated live attack scenarios, focusing on unauthorized access attempts and distributed denial-of-service (DDoS) attacks. Zeek's performance was assessed based on CPU and memory utilization, as well as quality of service (QoS) metrics. From the experimental results, we found that Zeek was quite effective in protecting agricultural IoT networks against typical threats. Memory usage remained stable around 5% during offline analysis and under 20% during active attacks. However, CPU usage was more volatile, peaking at 120% during DDoS events. In terms of QoS, the system maintained a good throughput (1,375 kbits/s) with minimal packet loss (0.000186%). Among the attack types that we tested, brute force attacks, which represent attempts at unauthorized access, had the strongest effect on network performance, increasing delay to 2.159 ms and jitter to 0.793 ms. It seems clear that a heavier traffic load during such attacks can interfere with QoS. On the basis of our observation, we recommend practical deployment strategies for agricultural IoT systems that take these limitations into consideration, aiming to keep networks both secure and efficient under pressure. Received: 2 June 2025 | Revised: 2 September 2025 | Accepted: 13 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Stratosphere Laboratory at https://www.stratosphereips.org/datasets-overview, in GitHub at https://github.com/zeek/zeek, https://github.com/vanhauser-thc/thc-hydra, and https://github.com/antirez/hping. Author Contribution Statement Samsul Huda: Conceptualization, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Muhammad Bisri Musthafa: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – review & editing, Visualization. S. M. Shamim: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – review & editing, Visualization. Yasuyuki Nogami: Supervision, Funding acquisition.
Micro-expression and Masked Expression Classification Using Neural Networks
Micro-expressions are short, involuntary facial expressions unnoticeably occurring for a fraction of a second. They are highly valuable in various real-life situations since the facial muscles associated with micro-expressions cannot be consciously controlled. They can also aid psychiatrists in diagnosing patients or police officers in detecting the true emotions of suspects. In contrast, masked expressions are intended to hide genuine emotions, and these occur when someone uses a falsified expression to conceal their true feeling. In this study, a custom Micro Mask Emotion Dataset-Face Speech Text was developed for both micro-expressions and masked expressions using questionnaires and images as stimuli. The study participants were 18–25 years old and did not wear any face mask. The dataset was compiled and evaluated using various neural network models. The results indicate that females are more expressive and the questionnaire-induced dataset showed better prediction ability than the image-induced dataset on a similar set of neural network models. Received: 13 May 2025| Revised: 13 August 2025 | Accepted: 2 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Karan Suresh: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Shamyuktha Ramachandran Sugumaran: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Suja Palaniswamy: Conceptualization, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Manju Priya Arthanarisamy Ramaswamy: Writing – review & editing, Visualization.
A Computational Complexity-Based Framework for Predicting the Difficulty of Sudoku Puzzles Using CSP-Based Symbolic Modeling and Constraint Metrics
This paper provides a logic-based method for the categorization of the puzzle difficulty levels of Sudoku using deterministic measures of computational complexity. Shifting from heuristic, statistical, or machine-based paradigms, the method presents four symbolic measures: constraint density metric (CDM), logical inference depth (LID), guessing complexity (GC), and constraint graph tightness (CGT). These are derived from the Constraint Satisfaction Problem theory and implemented using R programming without the application of data-driven inference. The new method assesses puzzle complexity based on pre-solving properties yielding consistent outputs that align with theoretical NP-completeness measures. Difficulty levels are grounded on strict numerical criteria, e.g., CDM over 700, LID 14 or higher, GC over 6, and CGT approaching 30, indicative of increased structural complexity. The method is compared with rule-based, backtracking, and solver-based methods using benchmarks and shown to run with linear time and space complexity O(n) compared with traditional methods with time and space complexity O(n²) or exponential growth O(2ⁿ). The method presents a reproducible, scalable method for analytically assessing puzzles and provides a formal, non-heuristic method for evaluating complexity within combinatorial logic systems. Received: 12 May 2025 | Revised: 29 July 2025 | Accepted: 17 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/bryanpark/sudoku. Author Contribution Statement Rajan Thangamani: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization, Project administration. Pallavi Regulagedda: Software, Validation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision.
Factors Affecting the Adoption of Generative AI Tools Among Information Technology Employees: A UTAUT3, TTF, and SOR Perspective
This empirical study examined the components that affect the adoption and actual use of generative AI tools among information technology (IT) employees in Hyderabad. This study also attempted to unravel the SOR, UTAUT3, and TTF models by integrating them for practical application in industry and by IT sector employees. Data were gathered from 470 employees working in several IT industries in Hyderabad by adopting a quantitative method for this investigation. IBM-AMOS was used to test the hypotheses. Thirty-six variables were used to measure the following 10 reflective constructs: optimism, innovativeness, trust, performance expectancy, effort expectancy, hedonic motivation, social influence, task–technology fit, adoption intentions toward generative AI tools, and actual use of generative AI tools. EFA and CFA analyses were conducted to unravel the structural relationships between constructs, and hypotheses were tested using SEM. A 12% variance in the adoption intention of generative AI tools by IT industry employees was explained by optimism, innovativeness, trust, performance expectancy, effort expectancy, hedonic motivation, social influence, and task–technology fit, and a 5% variance in the actual use of generative AI tools was explained by the adoption intentions. The construct trust fully mediated the nexus between adoption intentions to use generative AI tools and actual use. All constructs, except hedonic motivation, statistically significantly influenced the adoption intentions of generative AI tools. In turn, adoption intentions to use generative AI tools influenced the actual use of generative AI tools. A study involving Hyderabad IT industry employees revealed that they could adopt useful technology to improve performance. Received: 10 May 2025 | Revised: 13 August 2025 | Accepted: 9 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://figshare.com/s/cad95b195de4aa53e43a. Author Contribution Statement KDV Prasad: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft. Shivoham Singh: Conceptualization, Methodology, Writing – original draft. Ved Srinivas: Formal analysis. Hemant Kothari: Validation, Resources, Writing – review & editing, Supervision, Project administration. Ankita Pathak: Methodology, Investigation, Data curation, Visualization. Devendra Shrimali: Investigation, Resources, Writing – review & editing.
AI-Driven Augmented Software Engineering: Leveraging Cognitive Models for Enhanced Code Generation
Artificial intelligence (AI) is transforming the software engineering landscape that allows for new development approaches. This paper proposes a framework that integrates cognitive models with AI-driven code generation to enhance the software development process. By leveraging cognitive principles, the proposed system performs human-like decision-making to optimize code generation, refactor existing code, and fix bugs. The framework was evaluated based on code quality, developer productivity, usability, and system adaptability. Results demonstrate improvements by AI-driven system such as speed of code generation increased by 10% compared with human-written baseline and complexity reduced by 15% compared with human-generated code. Developers using the system reported a 25–29% reduction in task completion time, and errors were minimized by 60–67%. Usability feedback indicated that the system integrated seamlessly into developers’ workflows but requires further development, including enhanced personalization and a better understanding of complex code contexts. This study highlights the potential of AI-driven systems to assist developers in producing high-quality software more efficiently and provides a foundation for future research in AI-enhanced software engineering tools. Received: 10 May 2025 | Revised: 24 September 2025 | Accepted: 11 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the CodeSearchNet repository at https://github.com/github/CodeSearchNet. Author Contribution Statement Ameen Shaheen: Conceptualization, Software, Writing – original draft, Visualization. Mohammad Al Khaldy: Methodology, Writing – review & editing, Supervision, Project administration. Wael Alzyadat: Validation, Formal analysis, Resources, Writing – review & editing. Aysh Alhroob: Investigation, Data curation, Writing – review & editing.
Modular Federated Cross-Domain Recommendation (MFCDR) System with a Projected Attention Network
In today's digital age, data is considered a new currency. It drives many aspects related to research, business strategies, and decisions across various industries, providing recommendations. As data becomes more valuable, the privacy of user data becomes crucial. This article introduces an innovative, novel privacy-preserving federated learning approach, with the advancement of attention networks for cross-domain recommendation. A decentralized approach to federated learning has replaced traditional machine learning to enhance user privacy and data security. The research employs a projected attention network (PRADO) within a real federated environment to improve local device training. The proposed framework is examined through a use case of book recommendation based on emotions extracted from social media reviews, utilizing GoEmotions and customized book dataset. The results showed assurance of the system with better performance in terms of precision, recall, and the F1-score over benchmarking models such as Embedding and Mapping framework for Cross-Domain Recommendation (EMCDR), Cross-Domain based on Latent Feature Mapping (CDFLM), Aspect-based Neural Recommender (ANR), and Meta-learning-based model for Federated Personalized Cross-Domain Recommendation (MFPCDR), with the suggested system reaching a precision of 0.96 and an F1-score of 0.89. The findings indicate that the real implementation of a federated learning environment is modular, scalable, efficient, and preserves privacy. Received: 8 May 2025 | Revised: 31 July 2025 | Accepted: 13 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Manisha Shrirang Otari: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Dipika Patil: Validation, Formal analysis, Supervision. Mithun Basawaraj Pati: Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Revolutionizing Agriculture Through Smart Farming by Employing Advanced Machine Learning Techniques for Optimal Crop Selection
Precision agriculture relies heavily on crop selection to improve sustainability and increase productivity. The increasing problems of soil erosion, climate change, and water shortages have made it crucial to optimize crop selection through cutting-edge methods to increase farm yields and enhance resource efficiency. This research attempts to develop machine learning (ML) models, such as logistic regression (LR), Gaussian Naive Bayes (GNB), support vector classifier (SVC), K-nearest neighbors (KNN) classifier, decision tree (DT) classifier, extra tree (ET) classifier, random forest (RF) classifier, and bagging classifier, to optimize crop selection for precision agricultural systems. A large dataset comprising information on crop recommendations, weather patterns, and soil characteristics was used in this research. The data is preprocessed using the interquartile range (IQR) method to remove outliers and ensure that all features contribute equally to the model. Linear discriminant analysis (LDA) is used to extract the important features for feature extraction. The RF classifier was determined to be the most effective method for raising the precision of crop selection forecasts. This framework is designed to provide actionable insights for selecting optimal crops based on environmental conditions and resource availability. The suggested model is a valuable tool for crop selection optimization in precision agriculture, as preliminary results show that it outperforms traditional ML models in terms of F1-score, precision, accuracy, and recall at 99.88% (all measures). According to the research, modern ML algorithms can revolutionize agricultural practices and provide a sustainable solution to increase crop output while reducing resource wastage. Received: 29 April 2025 | Revised: 13 August 2025 | Accepted: 19 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Crop Recommendation datasets that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset. The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/shekharyada/crop-soilcsv. Author Contribution Statement Rajesh Natarajan: Conceptualization, Methodology, Validation, Writing – original draft, Writing – review & editing, Funding acquisition. Sujatha Krishna: Validation, Formal analysis, Writing – original draft, Writing – review & editing. Pradeepa Ganesan: Software, Formal analysis, Visualization. Amna Al Kaabi: Writing – review & editing, Supervision. Rajesh Kala: Resources, Data curation. Anthony Mendoza Madlambayan: Resources, Data curation, Visualization.
AI-Driven Diagnosis of Autism Spectrum Disorder Using Retinal Fundus Imaging: A Comparison of Traditional and Deep Learning Feature Extraction Methods
Autism spectrum disorder (ASD) is a complicated neurodevelopmental disorder. There is no definitive or easily interpretable medical test that aids in the early diagnosis of ASD, which leads to delays in its detection. In this study, we compared deep learning and traditional feature extraction methods used to detect ASD using retinal fundus images. The authors implemented convolutional neural networks (CNNs) such as ResNet50, EfficientNet, and vision transformers (ViTs), apart from the hybrid CNN + ViT model, for automated feature extraction. In addition, classic methods such as the gray level co-occurrence matrix for texture analysis, Frangi filters for measuring vessel density, and cup-to-disc ratio estimation were used to extract clinically relevant retinal features. To evaluate the discriminative power of the features obtained by each technique, classification models such as support vector machines, random forest, and XGBoost were implemented. Among the models used, hybrid CNN + ViT obtained the highest accuracy, which suggests that combining spatial and contextual retinal information enhances the detection of ASD. This study examined various feature extraction approaches in detail and elucidated the advantages of deep-learning-based approaches to enhance ASD diagnosis using retinal images. The results contribute to ongoing research on AI-supported ASD detection and provide crucial insights into the selection of optimal feature representation methods for future clinical applications. Received: 29 April 2025 | Revised: 14 July 2025 | Accepted: 18 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Ayain John: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Santhanalakshmi S.: Validation, Formal analysis, Resources, Writing – review & editing, Supervision, Project administration.
Multi-sequential Neural Network Models for Stock Price Forecasting
Deep learning techniques are transforming stock market forecasting by significantly improving the accuracy of predicting price movements and market patterns. In this research work, we propose two novel hybrid architectures – MSLSTM (Multivariate Sequential Long Short-Term Memory) and MSLSTMA (Multivariate Sequential Long Short-Term Memory Autoencoder). Both models leverage the Long Short-Term Memory (LSTM) ability to capture complex temporal dependencies in sequential financial data. Our method performs well in a variety of industries and surpasses a few other LSTMs and their autoencoder-augmented variations, as well as conventional deep learning models like CNNLSTM. MSLSTMA achieves the highest sector-wise winning rates, notably 95.83% in the telecommunication sector, 87.5% in technology, and 83.33% in industrials. Model performance was rigorously evaluated using mean squared error, root mean squared error, mean absolute error, and mean absolute percentage error. Across all these metrics, MSLSTMA consistently delivered the lowest error rates, showcasing its superior accuracy and practical relevance for real-world financial forecasting. MSLSTM also demonstrated strong performance, with winning rates of 50% in consumer staples and 45.83% in the healthcare sector. This research introduces an effective and scalable tool for investors, with the potential to enhance investment decisions through precise stock price prediction. Received: 18 March 2025 | Revised: 17 June 2025 | Accepted: 11 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Sheba Elizabeth Thomas: Methodology, Validation, Investigation, Data curation, Writing – review & editing, Visualization, Supervision. Rubell Marion Lincy George: Conceptualization, Validation, Resources, Writing – original draft, Project administration. Nevin Selby: Conceptualization, Software, Formal analysis, Writing – original draft, Project administration. Aditya Taparia: Software, Formal analysis, Supervision. Jobish Vallikavungal: Methodology, Validation, Investigation, Resources, Data curation, Writing – review & editing, Visualization, Supervision.
Object-Based Spatial Analysis of Airbnb Homes in Istanbul
This study investigates the sociocultural and functional organization of residential interiors in Istanbul through an object-based spatial analysis. Utilizing a dataset of over 46,780 photographs from 1,968 Airbnb listings in Istanbul, the research employs computational methods, specifically web scraping with Python for data acquisition, and advanced computer vision and artificial intelligence models (MiniCPM-V:8B and LLaVA-Llama3:8B) for identifying objects and mapping their spatial relationships. The study aims to reveal how the interconnections between objects within distinct room types–dining room, kitchen, living room, bathroom, and bedroom–shape spatial integration and influence daily life activities. Key findings demonstrate the central role of the dining table in dining rooms through strong connections with chairs, the critical function of the sink in kitchens linked to preparation areas and appliances, the dominant relationship between the coffee table and sofa in living rooms reflecting comfort and social interaction, and the distinct functional priorities of hygiene in bathrooms and comfort in bedrooms. The analysis reveals that the spatial structure of domestic environments in Istanbul is increasingly characterized by object clusters and user-object interactions, moving beyond traditional physical boundaries. These findings underscore the significant influence of cultural values, hospitality practices, and social interaction patterns on the spatial configuration and integration of contemporary homes, offering insights into the evolving nature of domestic space in an urban context. Received: 20 March 2025 | Revised: 23 June 2025 | Accepted: 11 July 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Mansur Beştaş: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
Predicting k-Barriers for Intrusion Detection Through LSTM-Based Deep Learning in Wireless Sensor Networks
This paper presents a deep learning-based mechanism to predict k-barriers in intrusion detection systems (IDSs) for wireless sensor networks (WSNs) using long short-term memory (LSTM) networks. This study targets intrusion analysis and preprocessing in WSNs, feature extraction, and the construction of a full set of features to enhance detection performance. We propose an LSTM model to predict k-barriers; this also helps in reducing false alarms and improving detection accuracy. One of the major contributions of this study is the comparative testing with other classic intrusion detection models of the proposed Deep BarrierNet based on LSTM. The proposed method achieves better and more reliable accuracy compared to traditional methods. To accelerate the training process, a correlation-based feature selection model is incorporated to identify the most relevant features for LSTM-based intrusion detection. This indicates that LSTM-like networks can efficiently predict k-barriers and will contribute to the final performance of intrusion detection for WSNs. This paper highlights the prospects of using LSTM networks for securing WSNs and proposes a scalable, reliable IDS mechanism to address current security challenges in wireless communication systems. Received: 21 April 2025 | Revised: 20 August 2025 | Accepted: 9 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Ganga Bhavani Thsaliki: Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Ali Altalbe: Writing – review & editing. Prasanna Kumar Rangarajan: Conceptualization, Validation, Visualization, Supervision, Project administration.
An Extreme Gradient Boosting Feature Selection–Based GAN-ELM for Classification of Imbalanced Big Data
The high-volume, velocity, and variety nature of Big Data introduces tremendous difficulties in accurate classification, especially when dealing with class imbalance. Traditional computational methods often fail to handle the imbalanced nature of datasets that may result in predictions biasing toward majority classes and poor model accuracies. This work presents a novel classification framework that combines state-of-the-art methodologies to provide a solution for the classic problem of class imbalance in Big Data environments. The proposed method is based on Generative Adversarial Network with Extreme Learning Machine for classification, Extreme Gradient Boosting with Bayesian Hyperparameter Optimization for feature selection, the Coati Optimization Algorithm for gradient optimization, and Fuzzy Adaptive SMOTE for oversampling. In addition to this, we put a Physics-Informed Policy Gradient to achieve interpretability of the model and classification decisions with respect to the domain classification rules. This framework provides better performance in terms of accuracy, robustness and scalability than other approaches for various types of imbalanced medical imaging datasets, such as histopathological images. The collaborative use of these state-of-the-art algorithms, taking into consideration common challenges like noisy data, redundant samples, and overfitting, will result in improved classification and provides a feasible solution for Big Data problems. Received: 21 April 2025 | Revised: 30 June 2025 | Accepted: 5 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Breast Cancer Histopathology image data that support the findings of this study are openly available at https://www.kaggle.com/code/paultimothymooney/predict-idc-in-breast-cancer-histology-images/notebook. The CelebA data that support the findings of this study are openly available at https://www.kaggle.com/datasets/jessicali9530/celeba-dataset. The ImageNet-LT data that support the findings of this study are openly available at https://www.tensorflow.org/datasets/catalog/imagenet_lt. Author Contribution Statement Rithani Mohan: Conceptualization, Software, Validation, Resources, Data curation, Writing – original draft. Prasanna Kumar Rangarajan: Methodology, Formal analysis, Investigation, Writing – review & editing, Visualization, Supervision, Project administration.
A Hybrid Framework Integrating QML, AI, and Quantum-Safe Cryptography for Cybersecurity
The rise of quantum computing poses threats to traditional cryptographic systems, thereby requiring security measures to safeguard against traditional and quantum-attacker cyber insecurity. This framework uses quantum machine learning (QML) algorithms along with quantum-safe encryption to boost security measures. The proposed system combines QML anomaly detection models variational quantum classifier (VQC) with quantum support vector classifier (QSVC) as well as quantum neural network and examines them based on the BB84 QKD protocol for information safety. This model was evaluated using three datasets of HIKARI Flow intrusion detection records, phishing activity logs, and malicious URLs. This includes all high-dimensional input by extensive application of feature engineering, which merges entropy scoring combined with keyword extraction and domain analysis to transmogrify it into suitable inputs for quantum processing. The QML models outperformed the traditional models with a maximum phishing detection accuracy of 97.75% by QSVC implementation. With the BB84 protocol, its eavesdropping detection was proved by quantum interference detection upon testing on IBM's Qiskit and Google's Cirq systems while the operations were secure and in attack scenarios. This system combines the latest features to address the limitations of the current AI security model and incorporates post-quantum cryptography to protect against quantum threats. In conclusion, QML and quantum cryptography work efficiently with operational cybersecurity platforms. Received: 31 December 2024 | Revised: 10 April 2025 | Accepted: 7 May 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Ramasubramaniyan Gunasridharan: Methodology. Ali Altalbe: Methodology. Bharathi Mohan Gurusamy: Conceptualization, Writing - review & editing. Gundala Pallavi: Writing - original draft. Prasanna Kumar Rangarajan: Supervision.
Comparing BERT Against Traditional Machine Learning Models in Text Classification
The BERT model has arisen as a popular state-of-the-art model in recent years. It is able to cope with NLP tasks such as supervised text classification without human supervision. Its flexibility to cope with any corpus delivering great results has make this approach very popular in academia and industry. Although, other approaches have been used before successfully. We first present BERT and a review on classical NLP approaches. Then, we empirically test with a suite of different scenarios the behaviour of BERT against traditional TF-IDF vocabulary fed to machine learning models. The purpose of this work is adding empirical evidence to support the use of BERT as a default on NLP tasks. Experiments show the superiority of BERT and its independence of features of the NLP problem such as the language of the text adding empirical evidence to use BERT as a default technique in NLP problems. Received: 10 March 2023 | Revised: 4 April 2023 | Accepted: 20 April 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
An Efficient Attention Model with Critical Frames Identification for Sign Language Recognition (CRAM-SLR)
Sign language recognition (SLR) plays a crucial role in enhancing communication accessibility for individuals who are deaf or hard of hearing. This paper introduces the convolutional recurrent attention model (CRAM), a novel deep learning framework specifically designed to improve recognition performance in low-resource sign languages such as Indian Sign Language (ISL) and Arabic Sign Language (ArSL). CRAM features a Critical Frames Identification algorithm that leverages the histogram of oriented gradients descriptor to extract the most informative key frames from sign videos, thereby reducing computational overhead while retaining essential gesture information. The model architecture combines convolutional layers to extract rich spatial features, bidirectional long short-term memory networks for effective temporal sequence modeling, and an attention mechanism to dynamically prioritize crucial frames. This integration enables CRAM to capture complex spatial-temporal dependencies inherent in sign gestures. Extensive experiments conducted on ISL and ArSL datasets validate the model's effectiveness, with CRAM achieving state-of-the-art accuracy, precision, and recall. The results highlight CRAM's potential in advancing robust and inclusive SLR solutions for underrepresented sign languages, promoting more effective gesture-based human-computer interaction. Received: 23 January 2025 | Revised: 13 May 2025 | Accepted: 7 June 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Renjith Sasidharan: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Aneesh Varghese: Software, Resources, Visualization. Manazhy Rashmi: Writing – review & editing, Supervision, Project administration. Poorna S. Surendran: Writing – review & editing, Project administration.
Spatio-temporal Learning for Trajectory Prediction in Full Self-Driving Cars
Autonomous driving technology is advancing quickly. Full self-driving systems aim to navigate safely in complex and busy traffic conditions. A key challenge for these systems is predicting how nearby vehicles will move. Accurate trajectory prediction helps the vehicle make better decisions. It improves motion planning and reduces the risk of collisions. This study introduces a deep learning model that improves trajectory forecasting. The model uses convolutional neural networks (CNNs) to learn spatial features from the traffic scene. It also uses long short-term memory (LSTM) networks to understand how vehicle movement changes over time. Together, these methods help the system predict future paths more effectively. The model was tested in various real-world traffic scenarios. It performed well across different conditions and showed strong accuracy in its predictions. These results show that combining CNN and LSTM networks can help autonomous vehicles better understand their surroundings. This approach supports safer and smarter decision-making on the road. Received: 12 April 2025 | Revised: 11 July 2025 | Accepted: 5 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Sreenivasa Chakravarthi Sangapu: Conceptualization, Formal analysis, Investigation, Writing – review & editing, Visualization, Supervision, Project administration. Amirthavarshini Venkadesh: Methodology, Software, Validation, Data curation, Writing – original draft. Jayant Toleti: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Visualization. Sountharrajan Sehar: Resources, Data curation, Writing – review & editing.