Haseeb Javed

The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; we also examine security concerns related to adversarial attacks that aim to deceive models along with privacy attacks that seek to extract sensitive information. Researchers have discussed various defenses to these attacks to enhance model robustness, such as adversarial training and input preprocessing, along with mechanisms like data augmentation and uncertainty estimation. Tools and packages that extend the reliability features of deep learning frameworks such as TensorFlow and PyTorch are also being explored and evaluated. Existing evaluation metrics for robustness are additionally being discussed and evaluated. This paper concludes by discussing limitations in the existing literature and possible future research directions to continue enhancing the status of this research topic, particularly in the medical domain, with the aim of ensuring that AI systems are trustworthy, reliable, and stable.

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation.

Bankruptcy analysis is needed to anticipate bankruptcy. Errors in predicting bankruptcy often cause bankruptcy. Machine learning with high accuracy to analyze reversal must continuously improve its accuracy. Many machine learning models have been applied to predict bankruptcy. However, model improvisation is still needed to improve prediction accuracy. We propose a combination model to improve the accuracy of bankruptcy prediction based on a genetic algorithm-support vector machine (GA-SVM) and stacking ensemble method. This study uses the Taiwanese Bankruptcy dataset from the Taiwan Economic Journal. Then we implement a synthetic minority over-sampling technique for handling imbalanced datasets. We select the best feature using GA-SVM, adopt a new strategy by stacking the classifier, and use extreme gradient boosting as a meta-learner. The results show superior accuracy obtained by the stacking model-based GA-SVM with an accuracy of 99.58%. The accuracy obtained is higher than just applying a single classifier. Thus, this study shows that the proposed method can predict bankruptcy with superior accuracy. Received: 11 January 2023 | Revised: 8 March 2023 | Accepted: 14 March 2023 Conflicts of Interest Much Aziz Muslim is an Editorial Board Member of Journal of Data Science and Intelligent Systems and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request.

Binary code analysis is essential in modern cybersecurity by examining compiled program outputs to find vulnerabilities, detect malware, and ensure software security compliance. However, the field faces significant challenges due to the scattered nature of existing research and the lack of unified analytical frameworks, which hinder a comprehensive understanding and practical application. To address these gaps, we conducted a thorough systematic review of current binary code analysis techniques across six key areas, analyzing 239 research papers published between 2007 and 2025. Our work addresses significant gaps in current research by offering: (1) a comprehensive overview of methods for binary code similarity; (2) a detailed examination of binary code fingerprinting techniques across various scenarios, from malware detection to digital forensics; (3) a systematic review of vulnerability analysis methods, including control flow graphs, taint analysis, and symbolic execution; (4) an assessment of clone detection strategies, such as text-based, token-based, structural, and behavioral approaches; (5) an in-depth study of authorship attribution techniques, with emphasis on malware attribution methods used in real-world cybersecurity cases; and (6) a thorough review of evasion and anti-analysis strategies, along with their countermeasures. In addition to highlighting the strengths and applications of these approaches, the study also identifies limitations in current methods, such as challenges in malware analysis, vulnerability analysis, and authorship attribution. Finally, we discuss future research directions, including the development of more robust analytical tools, enhancements to attribution models, and the creation of scalable solutions. Overall, this survey provides a foundation for advancing binary code analysis and fostering innovation to enhance software security and resilience by leveraging insights from previous research.

The integration of large language models (LLMs) into digital healthcare has the potential to significantly improve access to accurate and timely medical advice, especially in underserved areas. However, serious privacy concerns hinder the widespread adoption of LLM-based medical consultation systems, as they often require users to disclose private health information, risking unauthorized exposure and non-compliance with regulations. To address these issues, we introduce MediGuard, a new privacy-preserving LLM framework that dynamically protects sensitive healthcare data throughout the consultation process. MediGuard employs adaptive information obfuscation, combined with secure access protocols and robust auditing mechanisms, to process only non-sensitive information while preserving the necessary semantic integrity for precise medical inference and decision-making. Extensive testing across multiple medical question-answering datasets demonstrates that MediGuard consistently outperforms existing methods in both privacy protection and clinical accuracy, even under stringent privacy constraints. Our findings suggest that MediGuard provides safe, trustworthy, and clinically reliable medical consultations, setting a new standard for privacy-aware healthcare AI.