Emerging technologies have accelerated the digitalization of smart grids, improving demand-side management, sustainability, and operational efficiency. The attack surface is widened by this interconnection, though, leaving vital smart grid data and systems vulnerable to online attacks. Single points of failure, privacy violations, and a lack of robustness against sophisticated attacks persist in centralized data processing. Traditional cryptographic techniques are further threatened by the development of quantum computing, which raises significant security risks for smart grids. With a focus on post-quantum cryptography (PQC) resilience, this study examines 206 peer-reviewed research articles on blockchain-based federated learning (BFL) in smart grids that were published between January 2023 and July 2025. It assesses the advantages, limitations, and compromises of the current BFL models in this field. The paper suggests a unique post-quantum secure BFL (PQS-BFL) framework that integrates federated learning (FL), lightweight PQC protocols, and a scalable blockchain architecture to solve the vulnerabilities that have been uncovered. This design enables decentralized, private, and impenetrable cooperation among grid nodes. The results demonstrate that the system mitigates quantum-resilient attacks and inference threats while improving data integrity, key management, and secure model aggregation. A path for creating safe, scalable PQS-BFL solutions for upcoming smart energy systems is provided in the paper's conclusion, along with an overview of the main research issues. This study shows that using PQC, blockchain, and FL to secure next-generation smart grids is both feasible and important.

As digital threats become increasingly sophisticated, user authentication has become vitally important in cybersecurity. Traditional authentication methods such as passwords are under increasing assault from a range of attacks. Behavioral biometrics, such as mouse dynamics, have the potential to address these attacks in a way that is largely passive and continuous. In this paper, we present a new solution that rests on mouse dynamics behavior together with a lightweight deep learning model inspired by EfficientNet, specifically designed for Behavioral Assessment of Numerical Data (BAND). The SapiMouse dataset, consisting of mouse tracking data from 120 actual users, is harnessed. By applying preprocessing techniques such as Quantile Transformation and Min-Max Encoding, along with encoding, the raw data were prepared for model training. The modified EfficientNet model retains its computational efficiency while also being tailored to work with numerical input. Its structure uses compact convolutions along with compound scaling to capture time-series mouse data discriminative features, lowering the processing burden while maintaining accuracy. Moreover, to stabilize training and enhance generalization, dropout and batch normalization layers were added, ensuring robustness to overfitting, even when using data generated by a model. CGAN’s capacity for class sample synthesis was harnessed towards improving recognition of unused user profiles, resulting in a total of 240 unique classes (120 real + 120 synthetic). The model reached an accuracy of 99.24% for classification and a macro-averaged F1-score of 0.991 on the testing set. An inference time of only 0.2331 seconds per sample, alongside a cumulative training duration of 158.25 seconds, suggests real-time applicability. These findings support the promise of repurposing advanced deep learning models for behavioral biometrics, providing affordable, scalable, and efficient user verification for sensitive security contexts.

The trends of resource consumption and optimization mechanisms for blockchain-enabled security in edge-fog computing environments. While blockchain provides robust security for fog networks in a decentralized fashion, its demand for resources creates tremendous challenge in resource-constrained settings. Through in-depth examination of a Practical Byzantine Fault Tolerance PBFT-based blockchain deployment across 50 edge devices and 10 fog nodes. The study reveals the most critical resource bottlenecks and proposes an adaptive resource management framework that maximizes the tradeoff between security requirements and operational efficiency dynamically. The proposed work shows that data-type-based optimization and intelligent workload distribution can reduce CPU utilization by 27%, memory by 22%, and network bandwidth by 38% without sacrificing security assurance. The introduction of a novel dynamic resource allocation algorithm that adjusts consensus participation and cryptographic strength to current system conditions, demonstrating that security-performance trade-offs can be optimally resolved through context-sensitive optimization. These advancements are a move towards resource-constrained security architectures for edge-fog computing, enabling the broader applicability of blockchain security in resource-poor IoT environments.

Automatic Text Summarization is a crucial feature for managing the ever-increasing volume of textual data. However, existing methods often struggle with feature identification for sentence importance, which leads to a lack of maintained narrative coherence and accuracy. In this proposed approach, the summarization process leverages the Chi-square Binary Cuckoo Search (Chi-BCS) method for feature selection, this optimizes text features enhance the summary content and utilizes insights from classification to ensure summaries are contextually relevant and concise. Feature selection aims to improve the performance of machine learning models by reducing the dimensionality of the input data and removing irrelevant or redundant features. Classification, on the other hand, contributes to better summarization by distilling lengthy or redundant content into key points, thereby enhancing both efficiency and accuracy. The proposed approach implements a model that leverages advanced Natural Language Processing and machine learning techniques for effective extractive summarization on both BBC and CNN/DailyMail datasets. Key features extracted from the text include Named Entity Recognition, Cue phrases, TF-IDF, Sentence position, sentiment analysis, etc. Various algorithms are employed to improve classification performance, such as Decision Trees, Support Vector Classifier, Gradient Boosting, Random Forest, K-Nearest Neighbors, and Logistic Regression. Among all the methods evaluated, the Random Forest and Ensemble Hard Voting approach achieved the highest F-score of 96.26 and 0.9322 respectively on the BBC and CNN/DailyMail dataset. In the text summary evaluation, the ensemble method also delivered exceptional results, with ROUGE-2 and ROUGE-L F1 scores reaching 0.799 and 0.818, respectively on BBC. While our ensemble model achieved to high score on ROUGE1 and ROUGE 2 reaching 0.275, 0.5017, respectively on CNN/DailyMail when compared with state of art highlighting the model's strong performance. These findings demonstrate that the proposed model is highly effective for both the classification and summarization of large-scale textual data.

Blockchain has become a critical enabler of secure data sharing in domains such as healthcare, finance, and digital identity. However, its reliance on classical cryptographic schemes (e.g., RSA, ECDSA, SHA-256) makes current systems vulnerable to emerging quantum computing attacks, raising risks to data confidentiality, integrity, and long-term trust. This paper addresses this challenge by proposing a modular hybrid framework that integrates post-quantum cryptographic (PQC) techniques into blockchain-based personal data sharing. The framework combines lattice-based encryption for protecting off-chain data, hash-based signatures for smart contract authentication, and quantum-safe zero-knowledge proofs and trusted execution environments (TEEs) for privacy-preserving verification and secure key management. To ground this design, we conducted a systematic literature review of 35 studies published between 2018 and 2025, analyzing security, scalability, interoperability, regulatory alignment, and user autonomy. Findings reveal that only 5 out of 35 studies (14%) explicitly addressed quantum threats, with over 80% focusing on theoretical resilience without testing implementation constraints. Furthermore, 90% of proposals neglected smart contract compatibility, and only 8% (3/35) incorporated TEEs, underscoring implementation barriers in contract execution, secure key management, and performance integration. Prototype evaluation demonstrated that the framework sustained 1,500 TPS on Hyperledger Fabric, achieved a 75% reduction in storage bloat using IPFS, and supported GDPR-aligned workflows with 99.98% audit log completion and 95% successful erasure requests. Privacy was further strengthened through zk-STARK proofs, which reduced unauthorized access by 40%, while TEEs improved key management efficiency by ~28%. Although PQC introduced 5–12 seconds of latency, consent revocation was processed in under 2.1 seconds, highlighting both the feasibility and trade-offs of practical post-quantum deployment. This work demonstrates a clear pathway toward quantum-resilient blockchain infrastructures that safeguard personal data, comply with regulatory standards, and maintain user trust in the quantum era.

This paper presents a newly collected and highly relevant dataset on students' abnormal behavior in online exams. This dataset focuses on assisting research in building machine-learning models that allow for maintaining academic integrity during the era of online exams. Properly, more than 8,500 annotated images of normal and abnormal behaviors of students during remote examination are held in the dataset hosted at the Harvard Dataverse repository. The dataset has two versions: the original and the augmented. We utilize semantic segmentation and deep learning techniques in the applied data augmentation; this dataset provides a crucial foundation for developing and benchmarking intelligent proctoring systems. We evaluate the dataset using YOLO5 and our improved SPL-YOLO5 model, and the resulting mean average precision (mAP) is close to 1.0.

The continuous COVID-19 pandemic, caused by the SARS-CoV-2 virus, required fast and efficient diagnostic tools. This work presents a deep learning-based system, using convolutional neural networks, for the detection and diagnosis of COVID-19 through computed tomography tests, aiming to assist specialized medical professionals. A total of 746 Computed Tomography images (CT), were used in this work, one of the largest publicly available chest computed tomography dataset for research into COVID-19. Our proposed technique showed the accuracy of more than 99% for the training set, with high sensitivity and specificity, and achieved 97% on the validation set. Such results would hint at the very possible implementation of our deep CNN approach in clinical diagnostic settings, particularly for COVID-19 testing, to enhance early detection and management for patients.

Diabetes has been recognized as a major cause of death. Diabetes is a chronic disease. In recent years, the impact of diabetes has increased dramatically, and it has become a global threat. Machine learning is a part of computational algorithms designed to imitate human intelligence by learning from the surrounding environment. Type 2 diabetes is indicated by deviation high blood glucose levels attributable to insulin resistance and reduced pancreatic insulin production. In this study, two diabetes datasets are used, the Pima Indians diabetes and Iraqi Society Diabetes ISD datasets. They are collection of data on diabetes which characterized by an imbalanced distribution and the presence of outliers. The diabetes data sets are preprocessed. Many methods, including data resampling have been proposed to address the data sets imbalance issue. We utilized the resampling SMOTE-ENN technique to address the imbalance diabetes datasets issue and imputation. The classification of imbalanced datasets is a crucial field in machine learning. The machine learning approach that is used in this study is the Least Square Support Vector Machine LS-SVM to categorize the diabetes patients. Machine Learning ML algorithms are constructed by a set of hyperparameters. Thus, hyperparameters values should be carefully chosen. We used grid search algorithm to optimize LS-SVM algorithm hyperparameters. The classification results were improved. In addition, we could enhance the performance of the fine-tuned LS-SVM with the used resampling technique, SMOTE-ENN, that processes diabetes datasets. The performance metrics that evaluate the proposed algorithm SMOTE-ENN and fine-tuned LS-SVM are accuracy, recall and precision. The metrics measurements obtained were much better and higher when the proposed algorithm was used to categorize diabetes patients.

Land Cover and Land Use studies play an important role in regional socioeconomic development and natural resource management. They support sustainable development by tracking changes in vegetation, freshwater quantity and quality, land resources, and coastal areas. Iraq's Land Use and Land Cover Monitoring with Remote Sensing Data in the Period 2019–2023. This paper performed land use/land cover LULC type classification and time series analysis using Sentinel-2 satellite imagery for the years 2019 and 2023 to identify changes over time. Remote sensing data is used in this paper to address the challenge of detecting land cover change in Iraq through SVM classification. This goal aims to develop a fundamental method of mapping and monitoring these changes, encouraging sustainable land use practices, and achieving the United Nations Sustainable Development Goals. Land cover classes were categorized into five main types: Water, Barren, Building, Vegetation, and Rangeland. The study showed a marked increase in urbanization, and most of this occurring in previously bare soils at the edges of cities. This urbanization was primarily driven by population growth and economic development. What is beneficial for the environment can also be beneficial for us as people humanity as these findings have major implications for urban planning, green space management, and sustainable city development. It seems that there was no change to the existing barren land and buildings, which increased by 8% and 11% respectively, as noted from the data up to October 2023. However, vegetation coverage decreased by 27%, indicating a significant loss of green area. The water category was also up 9%. Results showed satisfactory accuracy assessment (OA: 93.11%) from applying a Support Vector Machine SVM for the LULC classification. The study lays the foundation for ongoing monitoring of LULC changes in Iraq.