Association of Hypertension and Diabetes Mellitus with Reperfusion Timelines in STEMI Patients Undergoing Primary PCI at a Tertiary Care Cardiac Centre in Jodhpur, Rajasthan: A Prospective Observational Study
Background ST-Elevation Myocardial Infarction (STEMI) remains one of the leading causes of cardiovascular morbidity and mortality worldwide. Timely diagnosis and early reperfusion therapy are the cornerstones of management and significantly influence clinical outcomes. Evaluation of demographic characteristics, cardiovascular risk factors, and reperfusion timelines is essential for improving quality indicators in acute cardiac care. Aim: To evaluate the demographic profile, cardiovascular risk factors, STEMI patterns, culprit vessel distribution, and reperfusion timelines among STEMI patients presenting to a tertiary care cardiac centre. Materials and Methods This prospective observational study was conducted at Trinay Hospital, Jodhpur, Rajasthan. A total of 60 consecutive patients diagnosed with STEMI and undergoing primary percutaneous coronary intervention (PCI) were included. Data regarding demographic profile, cardiovascular risk factors, STEMI type, culprit vessel, door-to-ECG time, door-to-balloon time, and total ischemic time were collected using a structured STEMI data collection tool. Statistical analysis was performed using descriptive statistics. Statistical Data Analysis: Chi-Square Analysis of Hypertension and Diabetes Mellitus Correlation. A chi-square test was performed to determine the association between hypertension and diabetes mellitus among STEMI patients. Table 1: Correlation Between Hypertension and Diabetes Mellitus Variable Diabetes Present Diabetes Absent Total Hypertension Present 28 11 39 Hypertension Absent 9 12 21 Total 37 23 60 Statistical Findings • Chi-square (χ²) value = 5.84 • Degrees of freedom = 1 • p-value = 0.015 Interpretation A statistically significant association was observed between hypertension and diabetes mellitus among STEMI patients (p < 0.05). Patients with hypertension were more likely to have coexisting diabetes mellitus. Graphical Analysis Histogram Analysis of Reperfusion Timelines The histogram demonstrated that total ischemic time remained substantially higher than door-to-ECG and door-to-balloon times. Although in-hospital management timelines were within acceptable international standards, delayed patient presentation contributed significantly to prolonged ischemic duration. Comparative Demographic Analysis Gender-wise Comparison Variable Male Female Mean Door-to-Balloon Time 79.4 minutes 84. Interpretation of Comparative Analysis Patients with diabetes mellitus and hypertension demonstrated relatively prolonged ischemic times and delayed reperfusion compared to patients without these risk factors. Female patients also showed slightly prolonged treatment timelines compared to male patients. Results: The mean age of patients was 56.7 ± 8.7 years. Male patients constituted 68.3% of the study population. Hypertension was present in 65%, diabetes mellitus in 61.7%, smoking history in 53.3%, alcohol consumption in 46.7%, and prior coronary artery disease in 25% of patients. Inferior wall STEMI was the most common presentation (45%), followed by anterior wall STEMI (36.7%). The right coronary artery and left anterior descending artery were equally involved as culprit vessels (43.3% each). Mean door-to-ECG time was 12.48 minutes, while mean door-to-balloon time was 81.18 minutes. Mean total ischemic time was 282.6 minutes. Conclusion: STEMI predominantly affected middle-aged male patients with multiple cardiovascular risk factors. Early reperfusion metrics observed in the present study were within acceptable international standards for primary PCI centres. Continuous monitoring of STEMI timelines and quality indicators can further improve patient outcomes.
Published by: Dr. Dhruva Sharma
Author: Dr. Dhruva Sharma
Paper ID: V12I3-1199
Paper Status: published
Published: May 28, 2026
Cyber Security Challenges and Protection Strategies in the Modern Digital Era
The rapid expansion of digital technologies across the globe has made cybersecurity an essential component of modern society. As individuals, organizations, and governments increasingly rely on digital platforms, the frequency and complexity of cyber attacks have grown significantly. Threats such as ransomware, phishing schemes, zero-day vulnerabilities, and artificial intelligence–driven attacks continue to challenge existing security frameworks. This review paper examines the major cybersecurity challenges faced in the contemporary digital environment and evaluates current protection mechanisms, including artificial intelligence–based threat detection, encryption techniques, zero-trust security models, and blockchain-oriented solutions. The study adopts a comprehensive research approach that integrates technical analysis, threat modeling, real-world case studies, and human-factor considerations. The paper further highlights existing limitations in current security practices and identifies future research directions required to build secure and resilient digital ecosystems.
Published by: Rutuja Kamble, Swapnil Jagtap, Manisha Gadekar, Dr. Vilas Wani
Author: Rutuja Kamble
Paper ID: V12I3-1183
Paper Status: published
Published: May 28, 2026
Security Challenges in Cross-Chain Asset Transfer Systems
The rapid growth of blockchain technologies and decentralized finance has significantly increased the demand for secure interoperability solutions between independent blockchain networks. Cross-chain asset transfer systems, commonly known as blockchain bridges, enable the movement of digital assets and data across multiple blockchain ecosystems, improving scalability, liquidity distribution, and usability of decentralized applications. However, the increasing adoption of cross-chain infrastructures has also introduced substantial security risks. In recent years, bridge-related exploits have resulted in financial losses exceeding billions of US dollars, making interoperability systems one of the most vulnerable components of decentralized ecosystems. This paper analyzes the primary security challenges associated with cross-chain asset transfer systems and examines the architectural characteristics of modern blockchain bridge solutions. The study reviews major bridge architectures, including lock-and-mint bridges, burn-and-release bridges, liquidity pool bridges, validator-based bridges, and light-client bridges. In addition, the paper investigates common attack vectors such as smart contract vulnerabilities, replay attacks, validator compromise, oracle manipulation, multisignature weaknesses, consensus desynchronization, and liquidity draining attacks. The research further evaluates several major bridge exploits, including the Ronin Bridge, Wormhole, and Nomad incidents, in order to identify recurring security weaknesses and operational failures. The paper also discusses mitigation strategies such as decentralized validation mechanisms, threshold signature schemes, formal verification, anomaly detection systems, transaction monitoring, and rate-limiting approaches. Finally, the study explores future research directions related to zero-knowledge interoperability systems, AI-based fraud detection, trust-minimized bridge architectures, and quantum-resistant cryptographic mechanisms. The findings demonstrate that achieving secure and scalable interoperability remains one of the central challenges in modern blockchain infrastructure development.
Published by: Kyrylo Sotnykov
Author: Kyrylo Sotnykov
Paper ID: V12I3-1196
Paper Status: published
Published: May 27, 2026
Hybrid Machine Learning and Deep Learning Approaches for Network Traffic Anomaly Detection: A Literature Review
Network traffic produces large volumes of data every second, and traditional security tools often struggle to detect new or unknown attacks hidden within this traffic. Anomaly-based intrusion detection systems address this problem by learning normal network behavior and identifying suspicious deviations. This literature review examines recent studies that use machine learning, deep learning, and hybrid machine learning-deep learning approaches for network traffic anomaly detection. The review focuses on feature selection, model complexity, dataset use, evaluation metrics, and the practical challenges that still limit real-world deployment. The reviewed studies show that traditional machine learning models can remain efficient when supported by careful feature selection, while deep learning models are useful for learning more complex spatial and temporal traffic patterns. Hybrid approaches often report stronger performance because they combine the speed and simplicity of machine learning with the representational power of deep learning. However, the literature also shows continuing weaknesses, including reliance on static benchmark datasets, class imbalance, computational cost, limited explainability, and uncertainty about performance in live networks. The review concludes that hybrid approaches are promising, but their future value depends on making them lighter, more explainable, and more reliable outside controlled experimental settings.
Published by: Abdulhaq Nabizoi
Author: Abdulhaq Nabizoi
Paper ID: V12I3-1174
Paper Status: published
Published: May 26, 2026
A Review of Explainable Federated Learning Frameworks for Chest X-ray Diagnosis under Heterogeneous Hospital Data
The application of deep learning in chest X-ray diagnosis has demonstrated promising results in detecting multiple thoracic diseases. However, traditional centralized approaches face significant challenges, including limited generalization across hospitals with heterogeneous patient populations and imaging protocols, compounded by strict privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) that prevent data sharing between institutions. Although centralized deep learning approaches perform well and achieve high local accuracy on their predictions, they are often “black boxes,” which limits clinical trust and interpretability. This review examines existing explainable federated learning frameworks for chest X-ray diagnosis under heterogeneous data conditions. Current approaches enable decentralized training across non-independent and identically distributed (non-IID) hospital environments, utilizing robust aggregation strategies such as Federated Averaging (FedAvg) and Federated Proximal (FedProx) to address label, quantity, and feature skew. To establish clinical trust, explainable Artificial Intelligence (XAI) techniques, such as Gradient weighted Class Activation Mapping (Grad CAM) and SHapley Additive exPlanations (SHAP), have been incorporated to generate interpretable visual explanations. The reviewed frameworks are evaluated on classification performance, robustness under heterogeneity, and stability of generated explanations. However, this review reveals significant gaps: the types of heterogeneity are addressed in isolation, XAI evaluation remains largely qualitative, and explanation stability under non-IID conditions lacks rigorous validation. These findings collectively highlight the need for federated frameworks that unify heterogeneity handling across all its forms simultaneously rather than addressing each in isolation, quantitative XAI assessment, and validation of explanation consistency across diverse hospital environments to enable trustworthy and interpretable clinical deployment.
Published by: Muhammad Auwal Yusuf
Author: Muhammad Auwal Yusuf
Paper ID: V12I3-1172
Paper Status: published
Published: May 26, 2026
Edge-Optimized Pre-Trained Deep Learning Models for Real-Time Detection of Red Palm Weevil and Date Palm Diseases: A Review
Date palm (Phoenix dactylifera L.) constitutes one of the most economically and culturally significant crops across arid and semi-arid regions, yet its productivity faces existential threats from the Red Palm Weevil (Rhynchophorus ferrugineus, RPW) and a spectrum of fungal and bacterial diseases. While deep learning has demonstrated remarkable classification accuracies exceeding 97% in controlled laboratory environments, the transition from academic prototypes to deployable, real-time agricultural solutions remains critically underdeveloped. This comprehensive review systematically examines recent studies, synthesizing the current landscape of deep learning applications for RPW detection and date palm disease classification. Our analysis reveals a persistent disconnect between architectural sophistication and practical deployability. Furthermore, the literature exhibits a pronounced fragmentation between pest detection and disease classification, with few studies addressing the integrated palm health ecosystem. This review identifies critical research dimensions where the current state-of-the-art falls short. By mapping these interconnected gaps across the evaluated literature, this review establishes a structured roadmap for developing lightweight, accurate, and interpretable AI systems that bridge the gap between theoretical accuracy and operational feasibility in precision agriculture.
Published by: Umar Faruk Ibrahim
Author: Umar Faruk Ibrahim
Paper ID: V12I3-1170
Paper Status: published
Published: May 26, 2026
