Year 2025 | Volume - 2 | Issue – 2 [July - December 2025]

An Efficient Hybrid Machine Learning Approach for Phishing Detection using Semantic and Feature-based Optimization2 (2) 47-54

Author Name: Suyog Vilas Patil, Dr. Vijay Pal Singh

Paper id: 25305

Abstract:

Phishing continues to be a major cyber threat targeting individuals and organisations by stealing sensi tive information such as passwords and financial details. Traditional signature-based approaches fail to detect new and obfuscated phishing techniques. This paper presents a lightweight hybrid machine learn ing model that combines supervised and heuristic components enhanced with semantic analysis. The system integrates lexical, content-based, and technical attributes to identify phishing websites and emails effectively. Natural Language Processing (NLP) techniques, including transformer-based em beddings, are used for extracting textual semantics, while feature optimisation is achieved using a sim plified clustering-based selection method. Experimental results on benchmark phishing datasets demon strate an overall accuracy of 97.1%, precision of 96.8%, recall of 97.3%, and a False Positive Rate (FPR) of only 2.0%. The proposed framework offers improved adaptability, low computation time, and potential for real-time deployment in institutional and enterprise-level environments.

Prevalence of Fatigue and poor Quality of Life in Hemodialysis Patients: hospital – based, cross-sectional study 2 (2) 55-68

Author Name: Dr. Prerna Khati, Dr. Shivraj Singh Tyagi, Dr. Helen Mariadoss, Dr. Rohit Dhanuka, Dr. Ajit Singh, Dr. Vivek Gaurav

Paper id: 25306

Abstract:

Fatigue and poor quality of life (QoL) are important burdens of hemodialysis (HD) patients, but definitive data on these issues in developing countries is scarce Aim: Identify the prevalence of fatigue and its association with QoL in patients with HD in North Bengal. Methods: Cross-sectional study carried out in Dr. Chhang’s Super-specialty Hospital, Siliguri with 165 HD patients and convenience sampling technique was used. Tool used for data collection were Standardized: Fatigue Severity Scale (FSS) to assess the level of fatigue and SF-36 questionnaire for (QoL). Statistical analysis involved frequency, percentage, mean and standard deviation, chi-square and Pearson correlation Findings: The results showed that there is a high prevalence of fatigue among patients (79.39% of patients were reported as having severe fatigue (Mean=5.84±0.25) and 9.70% of the patients reported clinically significant fatigue). Energy/fatigue domains were most affected by QoL (Mean=38.39±7.16). There was a significant negative relationship between fatigue and overall QoL (r = -0.47, p=0.001). The severity of fatigue was significantly correlated with marital status (χ²=97.13, p=0.00001), dialysis duration (χ²=26.18, p=0.0002), and intradialytic physical complaints (χ²=52.68, p=0.00001). Conclusion: fatigue is prevalent and negatively correlated with QoL in this population. Factors that relate to treatment and psychosocial factors also play a central role in determining the level of fatigue. These findings demonstrate the necessity of the incorporation of systematic fatigue evaluation and management guidelines into the routine HD care to enhance patient outcomes and overall well - being.

A Deep Reinforcement Learning Approach for Proactive Cardiovascular Risk Prediction in IoT-Enabled Cloud Systems 2 (2) 69-83

Author Name: Rohit S. Raut , Aasheesh Raizada

Paper id: 25306

Abstract:

In recent years, cardiovascular diseases (CVDs) have emerged as a leading cause of mortality worldwide, necessitating advanced and proactive health monitoring systems. This paper presents the design and implementation of an IoT-based cardiovascular health monitoring system that leverages cloud computing and artificial intelligence for real-time analysis. The system integrates IoT-enabled wearable sensors to continuously capture vital signs, such as heart rate, blood pressure, transmitting the data to a cloud-based infrastructure for processing. A novel Deep Deterministic Policy Gradient (DDPG)-enabled model is employed to predict potential cardiovascular anomalies, providing personalized insights and early warnings to patients. The DDPG model enhances the system's decision-making by enabling continuous learning and adaptation to individual health patterns, leading to more accurate predictions and recommendations. The cloud architecture ensures scalability, data security, and real-time access to health data, that leads to low-latency responses for critical alerts. The proposed system's performance is evaluated through simulations and real-world testing, demonstrating its efficacy in early detection of cardiovascular events, reduced false alarms, and improved patient outcomes. This proactive monitoring solution represents a significant step forward in leveraging IoT, AI, and cloud computing for personalized healthcare and disease prevention.

Novel Multiparticulate Sublingual Approach for Rapid Delivery of Rizatriptan Benzoate2 (2) 84-94

Author Name: Ram Gopal Singh and Sunil Gupta

Paper id: 25308

Abstract:

The objective of the present study was to develop and evaluate sublingual multiparticulate granules of rizatriptan benzoate with the aim of achieving rapid onset of action, improved patient compliance, and enhanced therapeutic effectiveness in the acute treatment of migraine. Sublingual multiparticulate granules containing rizatriptan benzoate at a therapeutic dose of 10 mg were formulated using mannitol, starch powder, and starch paste. Mannitol was selected as the primary diluent to improve palatability and mask the bitter taste of the drug. The granules were prepared and optimized by varying formulation and process parameters such as mixing time, drying conditions, and concentrations of binder and disintegrant. The prepared formulations were evaluated for physical characteristics, disintegration behavior, drug content, content uniformity, mouthfeel, and solubility in simulated saliva. A stability-indicating HPLC method was developed and validated in accordance with ICH guidelines, and forced degradation studies were conducted to assess method specificity. The optimized multiparticulate granules exhibited rapid disintegration, acceptable mouthfeel, uniform drug distribution, and drug content within pharmacopeial limits. Solubility studies in simulated saliva supported rapid drug availability, and stability studies demonstrated predictable degradation behavior with effective protection provided by aluminium sachets.The study concludes that sublingual multiparticulate granules of rizatriptan benzoate offer a scientifically sound and patient-friendly formulation approach, providing rapid drug release, consistent dosing, and suitability for fast-acting migraine therapy.

Phytotherapeutic Approaches to Letrozole-Induced Polycystic Ovary Syndrome in Female Rats2 (2) 95-110

Author Name: Bhumika Varshney, Soni Singh and Amarjeet Singh

Paper id: 25309

Abstract:

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder affecting women of reproductive age, characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovaries. Letrozole, an aromatase inhibitor, has been extensively utilized to induce PCOS in female rats, providing a valuable model for investigating the pathophysiology of PCOS and evaluating potential therapeutic interventions. This review aims to comprehensively summarize the existing literature on the phytotherapeutic approaches to letrozole-induced PCOS in female rats, with a focus on the efficacy of various medicinal plants and their bioactive compounds in alleviating PCOS-associated complications. A thorough analysis of the current evidence reveals that medicinal plants such as Aloe vera, Cinnamomum verum, Trigonella foenum-graecum, and Nigella sativa exhibit promising therapeutic potential in improving hormonal balance, reducing ovarian cysts, and alleviating metabolic disorders in letrozole-induced PCOS rats. The bioactive compounds present in these plants, including flavonoids, alkaloids, and glycosides, have been found to exert various mechanisms, including hormonal regulation, antioxidant activity, and insulin sensitization. This review highlights the potential of phytotherapeutic approaches as adjunctive treatments for PCOS, emphasizing the need for further research to confirm the efficacy and safety of these plants in human clinical trials.