International Journal of Pharmacy with Medical Sciences
Editor- In-Chief: Dr. Charles M Title proper: International Journal of Pharmacy with Medical Sciences (IJPAMS) Subject: Pharmacy, Medical, AI Computer Science, IOT, Science and Technology Publisher Name: Zestera Publications ISSN: 3068-2738 Dates of publication: 2021 Frequency: Quarterly Language: English Country: USA Medium: Online Indexed by: CROSSREF, MDPI scilit, Google Scholar Ext Title DOI of the Journal: http://doi.org/10.64751/issn.3068-2738 E-mail: editor@ijpams.com
STUDY ON GENETIC DIVERSITY IN RICE GERMPLASM
In 2024, the present investigation Study on Genetic Diversity in Rice (Oryza sativa L.) Germplasm. Five plants selected at random from every genotype were examined for 13 yield- related traits. ANOVA results for all traits identified considerable genetic heterogeneity favourable for fostering yield improvement through breeding. These high-yielding genotypes SHUATS DHAN-1, INDRANI, AK DHAN, D DHAN-39, and JARAVA were characterized by pronounced genetic progress, elevated heritability, and pronounced variability. Their adaptation to a variety of conditions was highlighted by the clear genetic differences found in variations in phenotypic traits, including as blooming time, maturity, plant height, and yield components. Other promising lines KHAYAM DHAN, FALGUNA, KRISHNA HAMSA, and HMT displayed outstanding performance in vital yield-related traits and are therefore recommended for direct selection and incorporation into breeding programs. Cluster analysis further emphasised the genetic diversity, noting that Cluster II possesses considerable yield potential and considerable separation from other clusters most notably from Cluster VI indicating abundant opportunity for heterotic hybrid development.
Cluster analysis, genetic diversity, grain yield, genetic advance, heritability
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
CROP TOLERANCE TO SOIL SALINITY, STATISTICAL ANALYSIS OF DATA MEASURED IN FARM LANDS.
Field data on the relation of agricultural crop yields and soil salinity, expressed in the electric conductivity and extract of saturated soil samples (ECe in dS/m) , are analysed to find the salt tolerance level. It concerns eight different crops in three countries of which wheat occurred thrice and cotton twice. A first attempt of analysis can be by means of a generalized cubic regression (GCR) to detect the trend of the relation between yield and salinity. When this trend suggests that a range of no effect may exist, this range is determined by linear regression using the condition that the regression coefficient, i.e. the slope of the regression line, does not differ significantly from zero. This method is called partial regression (PAR) because the trend of the data beyond the range is analysed separately. From the range of “no effect”, the tolerance level, i.e. the maximum salinity level at which no yield decline sets in, can be determined. This tolerance level appears as a Break-Point (BP) between the yield-salinity relations left and right of it. This breakpoint is also called threshold, tolerance or critical level. In literature, the Maas-Hofmann (MH) model has been used frequently to detect the tolerance level, but this has occurred mainly for data obtained under controlled laboratory conditions, or in pot and lysimeter experiments. Owing to the generally flat trend at the tail-end of the yield-salinity relation, the MH model, determined by the least squares method, usually produces considerably lower BP values than the PAR method as the flat tail-end draws the BP to the left. It is questionable that the trend at the tail-end should determine the salt tolerance. The van Genuchten-Gupta (vGG) model, producing a general picture of the relation between crop yield and soil salinity, has been applied less frequently. It does not yield a well defined tolerance level. In general, a polynomial or the CGR regression produces a better fit of the data to the growth curve. In all cases in this study of field data, the “range of no effect” could be clearly defined. However, in some cases the tolerance values could be higher than determined here because prolongation of the range of no effect was prohibited due to limited number of data beyond it. A comparison of results of field data and laboratory experiments is made, which shows the same orders of magnitude.
EFFECT OF CLIMATE CHANGE ON CROP WATER REQUIREMENT IN VISAKHAPATNAM DISTRICT OF ANDHRA PRADESH
Water is important for life and it is becoming a scarce resource in the future owing to climate change. To apply irrigation water efficiently, the water requirement of the crops is to be estimated accurately. The crop water requirements (CWR) may currently be estimated using a variety of computer models. A computer program called CROPWAT 8.0 was created by Joss Swennenhuis and calculates the summarized irrigation water requirements of a complex scenario. In this study, the weather data from 2011 to 2020 was collected from the Regional Agriculture Research Station, and calculated the average weather data for estimating the CWR. CWR for varietal crops in Anakapalle for Paddy (Kharif), Paddy (rabi), Green gram, Maize, Groundnut (Kharif), Groundnut (rabi), Cotton (Kharif), Cotton (rabi), Sugarcane and Small vegetables was 559.5 mm, 460.0 mm, 253.8 mm, 336.6 mm, 301.9 mm, 253.0 mm, 490.3 mm, 469.5 mm, 1284.8 mm and 346.8 mm respectively. The MarkSim weather generator forecasted the weather conditions using models HADGEM2-ES and MIROC- ESM–CHEM for the years 2030, 2040, and 2050. It was observed that there is an increase in crop water requirement with the range of 12.00 to 13.50%, 13.50 to 14.51%, and 14.51 to 15.74 percent for the years 2030, 2040, and 2050.
Crop water requirement, CROPWAT, MarkSim weather generator, and water demand
THERAPEUTIC EFFICACY OF MONENSIN ON LACTIC ACIDOSIS IN BUFFALOES
Three non-descript buffaloes were presented to Veterinary College Hospital, Bidar with the history of ingestion of large quantity of wheat based bakery products. Clinical examination revealed mild distension of left abdomen, passing semisolid pasty dung. Rumen pH, Rumen lactate and total protozoal count, Blood pH and HCO3- were analysed before and after treatment. Acidotic buffaloes were treated orally with monensin at the dose rate 3 mg/kg body weight along with fluid therapy and repeated after 24 hours. Healthy ruminal cud was given orally after 48 hours. Monensin was found effective in the treatment of lactic acidosis in buffaloes
Lactic acidosis, Buffaloes, Monensin, Rumen lactate, Blood HCO3-, Rumen protozoa
EFFECT OF NANO-FERTILIZER ON GROWTH AND YIELD OF GREEN GRAM
The present investigation aimed to find out the “Effect of Nano-Fertilizer on Growth and Yield of Green Gram was carried out at Agronomy Research Farm of Faculty of Agriculture and Veterinary Science, Mewar University, Gangrar, Chittorgarh, Rajasthan. Geographically Chittorgarh. The experiment was laid out in Randomized Block Design. Replicated thrice with 10 treatment combinations, comprising like T1 Control, T2 Foliar spray Nano Zinc @ 1 per cent, T3 2 Foliar spray Nano Zinc @ 2 per cent, T4 Foliar spray Nano DAP @ 1 per cent. T5 Foliar spray Nano DAP @ 2 per cent, T6 Foliar spray Nano Urea @ 1 per cent, T7 Foliar spray Nano Urea @ 2 per cent, T8 Foliar spray Nano Zinc+ DAP @ 1.5 per cent, T9 Foliar spray Nano Zinc+ Urea @ 1.5 per cent and T10 Foliar spray Nano Zinc+ DAP+ Urea @ 0.75 per cent. The results reveled that during experiment use of different nano-fertilizer on growth, yield and economics of green gram its effect among all treatments T10 (Foliar spray Nano Zinc+ DAP+ Urea @ 0.75 per cent) was found to significantly increase growth, yield and economics of parameters, yield as well as economic of green gram. It was found that maximum value of growth parameters viz., the plant height was found to be the maximum plant height (35.12, 47.66 and 54.03 cm) at 30 DAS, 45 DAS and at harvest chlorophyll content (1.21) at 40 DAS, dry matter accumulation (4.86 and 7.65) at 25 DAS, 50 DAS, number of branches (6.36, 11.02 and 13.85) at 30 and, 45 DAS and at harvest. Yield attributes and yield viz., grain per pod was recorded significant maximum (6.50), pod length (6.36 cm), grain yield per plot (0.810 kg), higher seed yield (13.50 q/ha), higher biological yield in T10 (38.96 q/ha), maximum straw yield was (25.46 q/ha), maximum harvest index was (34.65 q/ha) economics viz., Maximum fetched value in term of net return (75250.00 /ha) and B: C ratio (2.89) were recorded with foliar spray Nano Zinc+ DAP+ Urea @ 0.75 per cent.
Nano-fertilizer Green gram on growth, yield and economics
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
INTEGRATED ANALYSIS OF ADAPTATION AND MITIGATION ON COASTAL FLOOD
The northern coast of Central Java Province – Indonesia is considered as the critical area of flood path. The area always suffers from flood due to heavy rainfall and/or sea level rise. As the consequence, people are suffering, properties are damaged and lost. A number of efforts have been carried out to manage the flood problem, although the achievement is incomplete. It is realized that infrastructures capacity for flood control, community awareness, and other multi-factors significantly contribute in solving the flood problem. This research aimed to outline the strategies of flood adaptation and mitigation. Quantitative and qualitative method ware employed to analyze the data. The results of this study indicated that the level of vulnerability the Central Java to flood was in the medium category. The exposure and adaptive capacity variables were significantly contributed in determining the vulnerability level. Flood adaptation can be carried through anticipation and reactive adaptation, in which each type could be practiced by public and private. Flood mitigation can be carried out through structural, non-structural and community roles
Coastal, Flood, Adaptation, Mitigation, Vulnerability
A CRITICAL ANALYSIS OF THE CHANGES IN THE WORKING CONDITIONS AND MENTAL STATE OF INDUSTRIAL WORKERS AFTER COVID-19. (In The Context Of Bijauli Industrial Area)
COVID-19 pandemic has severely affected the social and economic life at the global level. In particular, the lives of industrial workers have experienced significant changes at both work and mental health levels due to this pandemic. This research paper focuses on the industrial workers of the Jhansi region, with the objective of identifying whether any notable changes have occurred in their working conditions and mental health after COVID-19.In this study, data were collected through surveys and interviews, and Chi-Square (χ²) tests were used to examine the hypotheses. The analysis clearly revealed that after the pandemic, major changes occurred in workplace situations such as the working environment, work hours, safety facilities, wage structure, and workers’ mental health conditions including stress, anxiety, and uncertainty. COVID-19 has had a strong impact on the work experience and mental health of industrial workers. This research provides useful suggestions for policymakers and industrial managers regarding the types of strategies that should be adopted in the future to improve workers’ safety and mental well-being.
Covid 19, Industrial labour, Mental health, Depression, security, working condition
IMPACT OF SOCIAL MEDIA ON MENTAL HEALTH AMONG YOUNG ADULTS
The high growth rate of the social media has greatly changed how young adults communicate, interact and what they think about themselves. Although social media does have its opportunities such as social connection, self-expression and access to information, the uncontrolled and overuse of the media has become an issue of serious concern in the context of mental health. This paper will look at how the use of social media affects the mental health of young adults, specifically anxiety, depression, stress, selfesteem, and sleep disorders. Based on the available literature and latest empirical studies, the research paper highlights both the favorable and unfavorable psychological impacts of social media use. Among the positive outcomes, one can distinguish the social support, community building, and emotional expression, and among negative consequences, there are mostly cyberbullying, social comparisons, and fear of missing out (FOMO), and addictive use habits. The results indicate that the severity, duration and intent of social media use are critical determinants of mental health outcomes of social media use. The paper emphasized the importance of digital literacy, responsible use, and special mental health assistance as a way of reducing negative outcomes and maximizing positive outcomes among young adults.
Mental health, social media, anxiety, depression, psychological well-being, young adults.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
EYELID DERMATITIS: A MULTIFACTORIAL DERMATITIS
Dermatitis means inflammation of the skin that is very frequently used as synonymous with eczema. On the basis of aetiology, it is classified as exogenous and endogenous dermatitis/eczema. There are overlaps in etiological factors for both exogenous and endogenous dermatitis. A detailed history, meticulous cutaneous examination, and specific investigations play a significant role in diagnosing the aetiology. Patients with dermatitis/eczema typically report symptoms like pruritus, burning, and irritation at specific sites. Local cutaneous findings align with the clinical stage. Eyelid dermatitis is the most common dermatological complaint and is often cosmetically very concerning for many patients, especially very young aged female patients, as it is a very concerning and distressing cosmetic problem. As the use of cosmetic agents in and around the eye is quite common in the everyday world like various creams, ointments, eyedrops. Identifying the cause is the most important factor in treating the management. The management of the eyelid dermatitis differs from the eczema/dermatitis on the other anatomical sites because skin of eyelids is thinnest leading to increased penetration of allergens and is a cosmetically concerned site. It is very much essential to timely diagnose and manage the eyelid dermatitis to avoid complications. Here we tried to compile the possible causes varying from exogenous exposure to endogenous causes, its pathophysiology and treatment in detail to meticulously understand and treat for the same. The evaluation of patients presenting with eyelid dermatitis remains a relatively complex strategy but is very rewarding when great results are achieved and improves patients’ quality of life significantly.
USE AND ABUSE OF DRUGS: A COMPREHENSIVE STUDY IN THE RAYALASEEMA REGION OF ANDHRA PRADESH
Drug abuse represents one of the most significant public health challenges in contemporary Indian society, particularly in developing regions. This comprehensive research examines the patterns, extent, and dynamics of substance abuse across the Rayalaseema region of Andhra Pradesh, encompassing four districts: Chittoor, Kadapa, Anantapur, and Kurnool. Based on empirical data from 197 respondents receiving treatment at de-addiction centres, this study presents critical findings on demographic profiles, substance types, addiction patterns, and socio-economic impacts. The research reveals a predominance of middle-aged males (mean age 42.3 years) engaging in regular alcohol and tobacco consumption, with significant ramifications for family structures, occupational performance, and community health. This paper synthesizes findings from multiple research methodologies using ethnographic approaches and statistical analysis, providing a framework for policymakers and healthcare practitioners to develop targeted intervention strategies.
drug abuse, addiction, Rayalaseema region, substance use patterns, de-addiction, public health
NOVEL METHODS FOR IDENTIFICATION AND BIOLOGICAL SIGNIFICANCE OF ESSENTIAL OILS OBTAINED FROM SECONDARY METABOLITES
Essential oils (EOs) are concentrated, volatile liquids that are extracted from various plant sections. Terpenes and terpenoids in particular have a variety of medicinal properties, including being anticancer, antibacterial, anti-inflammatory, antioxidant, and antiallergic. The research that is now available suggests that EOs have antifungal which is thought to have a substantial potential application in the pharmaceutical sector. Therefore, the objective of this review is to give a summary of what is currently known about EOs for use in the pharmaceutical and medical industries.
Essential oil, Anti-fungal, Anticancer, Antimicrobial, Anti-inflammatory,Terpenes
OXALIS CORNICUALATA: INSIGHTS AND ADVANCES IN THE CHEMISTRY AND BIOLOGY
For decades natural resources have been a genesis of medicinal plant and healthcare facilities. A number of modern drugs are inspired from natural resources. An enormous portion of world’s population depends on historical medicine. In India, the base of treatment system and remedy for several illness is herbal medicine from primordial days. From being regarded as outdated and discredited. Herbs are providing majority of population some of the very important medicine which are used in the modern era as well. There is a belief that herbal medicines are little harmful and free from risk as compared to the synthetic drugs entities in human body. Even researcher of modern medication system has realized the consequences of dietary items, in the form of nutraceuticals and in the treatment of many chronic diseases. The ethno pharmacologists, botanists, microbiologists and natural product researchers are constantly in search of efficacy and mode of action of different medicinal plants and their important medicinal substances as well
VIRTUAL REALITY AS A MODERN INTERVENTION FOR OVERCOMING SEVERE PHOBIC CONDITIONS
Phobias, when left untreated, can cause extreme avoidance behavior and significantly disrupt a person’s daily functioning and emotional stability. Emerging digital technologies are enabling new forms of psychological treatment that provide safer and more adaptable environments for patients. One such innovative method is the use of virtual environments, where individuals can gradually encounter and manage their fears without facing real-world risks. This technology-driven approach helps clinicians design tailored therapeutic experiences, closely monitor patient reactions, and adjust exposure levels to achieve improved outcomes. The effectiveness of these simulated environments has been increasingly recognized in reducing fear intensity and enabling patients to regain confidence in real-life scenarios. This paper explores the development of virtual therapeutic systems, their clinical significance for treating phobia-related disorders, and the challenges to be addressed before wider adoption in mental healthcare settings.
Virtual Reality Therapy, Psychological Disorders, Digital Therapeutic Systems, Exposure Treatment, Behavioral Intervention, Patient Engagement
INTELLIGENT ML-BASED CARDIOVASCULAR RISK PREDICTION FRAMEWORK FOR PREVENTIVE DIGITAL HEALTHCARE
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the increasing need for proactive and intelligent healthcare solutions. Early risk prediction enables timely intervention and reduces severe health outcomes. This research proposes an Intelligent Machine Learning (ML)- Based Cardiovascular Risk Prediction Framework designed to analyze clinical and lifestyle attributes to accurately forecast patient risk levels. Multiple ML models such as Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting are evaluated to determine the most reliable predictor for CVD risk forecasting, based on accuracy, sensitivity, specificity, and AUC-ROC metrics. A simulated healthcare dataset is used to validate the framework under real-world data conditions. Experimental analysis demonstrates that ensemble-based models deliver superior performance and robustness in identifying highrisk patients compared to traditional classifiers. The results confirm that ML-driven predictive analytics can play a significant role in preventive cardiology, improving early diagnosis, personalized treatment planning, and overall clinical decision-making in digital healthcare ecosystems.
Cardiovascular Diseases, Machine Learning, Risk Prediction, Healthcare Analytics, Predictive Modeling, Preventive Healthcare
AI-DRIVEN ZERO TRUST SECURITY FRAMEWORK FOR PROTECTING ELECTRONIC HEALTH RECORDS IN MODERN HEALTHCARE SYSTEMS
The rapid digitization of healthcare services has increased dependency on Electronic Health Records (EHRs), making them a prime target for cyberattacks and data breaches. Conventional perimeter-based security mechanisms fail to protect against insider threats, compromised credentials, and lateral movement within healthcare networks. This research introduces an AI-Driven Zero Trust Security Framework (ZTSF) designed specifically for EHR environments. The proposed model implements continuous verification based on user identity, device posture, behavioral analytics, and context-aware access policies. Artificial intelligence techniques, including anomaly detection and predictive threat modeling, dynamically evaluate access requests and detect deviations from normal activity in real time. Experimental evaluation demonstrates significant improvements in threat mitigation and authorization efficiency compared with static access control models. By integrating Zero Trust principles with machine learning capabilities, the framework enhances confidentiality, integrity, and regulatory compliance for patient health data. The results indicate that the ZTSF is scalable and suitable for hospitals, telemedicine systems, and distributed healthcare networks.
Zero Trust Architecture, Artificial Intelligence, Electronic Health Records, Healthcare Cybersecurity, Anomaly Detection, Access Control
A COMPREHENSIVE REVIEW ON GLOBAL ENERGY SCENARIO AND SOLAR PHOTOVOLTAIC POWER EXTRACTION PRINCIPLES
The global energy landscape is undergoing a rapid transition driven by increasing demand, environmental concerns, and the depletion of fossil fuel reserves. Renewable resources—wind, solar, hydro, and geothermal—have emerged as key contributors to sustainable power generation due to their natural abundance and declining technological costs. This review presents an overview of the world energy scenario, highlighting the availability and extraction potential of major renewable resources. Special emphasis is placed on solar photovoltaic (PV) energy, which has become the fastest-growing energy technology globally. Fundamental principles including the photovoltaic effect, I–V and P–V characteristics, and the influence of insolation, temperature, and shading are discussed. The paper reviews PV module configurations, ratings, and operational considerations in gridconnected systems. Power extraction techniques, particularly Maximum Power Point (MPP) operation, are analyzed to illustrate how modern converters and MPPT algorithms optimize energy harvest under varying environmental conditions. The review concludes by identifying research gaps in PV system modeling, environmental impact prediction, and advanced control for maximizing renewable energy extraction.
Renewable energy, solar PV, I–V characteristics, MPP, wind energy, geothermal energy, hydro power.
ML-Enhanced Web Security System for Detecting and Blocking Spoofing Attacks
Web spoofing remains one of the most pervasive cyberattacks, enabling adversaries to mimic legitimate websites to extract sensitive user information such as login credentials, financial details, and personal identity data [1]. Traditional security solutions—including signature-based detection and server-side filtering—often fail to respond effectively to newly emerging and dynamically generated spoofed URLs [4], [11]. To address this gap, the proposed ML-Enhanced Web Security System introduces a client-side defense mechanism powered by machine learning to detect and block spoofing attacks in real time [2]. The system extracts and analyzes multiple discriminative features, including URL lexical patterns, DNS registration details, webpage visual similarity attributes, SSL certificate anomalies, and DOM structural irregularities [3], [8], [17], [9]. A hybrid ML model combines lightweight ensemble classifiers to ensure high detection accuracy while maintaining low computational overhead for seamless deployment in web browsers and endpoint devices [15]. Comprehensive experiments on benchmark phishing and spoofed website datasets demonstrate that the proposed system significantly outperforms existing security filters in terms of precision, false-positive rate, and response speed [6], [19]. Furthermore, the client-side implementation eliminates reliance on external threat databases, enabling proactive protection even against zero-day spoofing websites [12]. This ML-enhanced web security solution provides a scalable and user-oriented shield against spoofing attacks, supporting safer digital experiences for users across financial, e-commerce, and enterprise environments.
Web spoofing, machine learning, client-side security, phishing detection, URL analysis, visual similarity, DOM feature extraction, TLS/SSL validation, real-time threat mitigation, cyber defense
Deep Learning–Based Monitoring of Self-Harm Indicators Across Online Social Communities
Self-harm is a rising global public health concern, and early detection of psychological distress within online communities can play a crucial role in prevention and timely intervention [1], [4]. With the rapid increase in social media usage, individuals experiencing emotional instability often express their feelings through text posts, comments, hashtags, and multimedia interactions, creating digital footprints that may reflect underlying self-harm ideation [2], [8]. However, manual monitoring of self-harm–related discourse across large-scale social networks is impractical due to the vast volume of data, rapid information diffusion, linguistic ambiguity, and privacy-preserving user anonymity [17], [19]. To address these challenges, this research introduces a deep learning–based monitoring framework designed to automatically detect and track self-harm indicators across online social platforms [1], [5]. The proposed system utilizes pretrained language models and transformer-driven architectures to extract psychological cues from user-generated content, while temporal emotional modeling captures variations in sentiment, intent, and behavioral patterns over time [3], [6], [13]. The integration of multimodal features—including text sentiment, social engagement signals, and interaction patterns—enables accurate forecasting of emerging self-harm trends at the community and population levels [2], [9], [14]. Experimental results on real-world social media datasets demonstrate that the framework surpasses traditional machine-learning baselines in precision, recall, and early-warning sensitivity [5], [7], [10]. By offering continuous and large-scale behavioral surveillance, the proposed solution supports mental-health organizations, policy makers, and crisis-response teams in identifying high-risk groups and designing proactive interventions to mitigate self-harm incidents [11], [15], [20].
Self-harm detection, Deep learning, Social network analysis, Emotion recognition, Transformer models, Behavioral trend forecasting, Mental health surveillance, Early-warning analytics.
A Comprehensive Analysis of Learning Behaviour Characteristics and Predictive Modeling of Learning Outcomes to Enhance College-Level Academic Performance
Understanding learner behavior has become increasingly important in modern higher education systems, where digital learning platforms generate vast quantities of student interaction data. This study presents an integrated framework for analyzing learning behaviour characteristics and predicting academic performance using machine learning and educational data mining techniques. Through behavioral feature profiling, engagement pattern detection, and predictive modeling, the system aims to identify at-risk learners early and recommend personalized interventions. The proposed approach leverages transfer learning, deep neural architectures, and interpretable analytics to improve prediction reliability and institutional decision-making. The findings demonstrate that incorporating behavioural metrics such as activity frequency, resource utilization patterns, and cognitive engagement signals significantly enhances prediction accuracy compared to traditional grade-based systems [1], [4], [7]. Ultimately, the research provides a data-driven pathway to improving academic success rates and optimizing college learning environments
Learning behaviour analysis, learning outcome prediction, educational data mining, student performance modeling, academic analytics, machine learning in education.
ANALYZING DARKNET TRAFFIC: IMPACT OF MODIFIED TOR TRAFFIC PATTERNS ON ONION SERVICE CLASSIFICATION ACCURACY
Darknet communication, especially through the Tor network, has become a crucial focus of cybersecurity research due to increasing misuse by malicious actors. However, identifying and classifying Onion Service traffic remains challenging because Tor encrypts packet contents and introduces obfuscation mechanisms. This study investigates how modified Tor traffic patterns, including padding variations, timing perturbations, and circuit-level manipulation, influence the accuracy of Onion Service traffic classification systems. A hybrid analytical model combining flow-level statistical features, deep learning– based traffic embeddings, and adversarial perturbation analysis is used to evaluate classification resilience. Experimental results show that even minor manipulations significantly reduce classifier confidence and increase misclassification rates, emphasizing the vulnerability of current Darknet monitoring systems [1], [3], [5], [9]. The findings provide critical insights for designing more robust traffic analysis systems capable of detecting evasion strategies within Tor-based communication.
Tor network, Darknet traffic, Onion Services, traffic classification, adversarial perturbation, deep learning, traffic obfuscation.
A TWO-STAGE MACHINE LEARNING FRAMEWORK FOR AUTOMATIC JOB TITLE IDENTIFICATION FROM ONLINE RECRUITMENT ADVERTISEMENTS
Online recruitment platforms host millions of job postings daily, making accurate job title identification critical for job matching, market analysis, and automated HR analytics. However, job titles in postings are often ambiguous, verbose, or embedded within unstructured descriptions, making extraction challenging. This study proposes a two-stage job title identification system that combines text preprocessing, semantic classification, and deep learning-based title normalization. In Stage 1, a contextual classifier identifies candidate title segments from raw advertisements. In Stage 2, a transformer-based normalization model maps extracted titles to standardized occupational taxonomies. Experimental evaluation demonstrates improved accuracy, reduced ambiguity, and enhanced generalization across diverse job categories compared to traditional rule-based or single-stage ML systems. This framework addresses inconsistencies in online job advertisements and provides scalable automation for recruitment analytics [1], [2], [3], [6], [14].
Job Title Extraction, Online Job Advertisements, Text Mining, Semantic Classification, Deep Learning, Title Normalization, NLP, Recruitment Analytics, Machine Learning, Information Retrieval.
A HYBRID ENSEMBLE LEARNING FRAMEWORK FOR ACCURATE HEPATITIS DISEASE CLASSIFICATION
Hepatitis, a severe inflammatory condition of the liver, continues to pose substantial diagnostic challenges due to its heterogeneous clinical manifestations and overlapping symptoms with other hepatic disorders. Early and accurate detection is crucial for timely intervention, effective treatment planning, and reducing the risk of progression to chronic liver disease or liver failure. Conventional machine learning models, although widely applied in medical diagnosis, often struggle to achieve high reliability due to limitations such as overfitting, feature noise sensitivity, and inability to fully capture complex nonlinear relationships present in clinical datasets. Hepatitis remains a major global health burden, requiring reliable early diagnostic support to improve clinical outcomes. Conventional single-model approaches suffer from variability in performance due to dataset imbalance, missing values, and overlapping clinical features. This paper proposes a Hybrid Ensemble Learning Framework (HELF) combining Gradient Boosting, Random Forest, and Logistic Regression through a stacked metalearner to enhance predictive accuracy for Hepatitis disease classification. The framework applies advanced preprocessing including missing-value imputation using K-Nearest Neighbours (KNN), Recursive Feature Elimination (RFE), and class rebalancing through SMOTE. Experimental evaluation using the UCI Hepatitis dataset demonstrates significant performance improvement, achieving 96.42% accuracy, 95.71% F1-score, and 0.98 AUC, outperforming baseline single models. The framework provides a reproducible, clinically feasible solution for augmenting earlystage hepatitis risk assessment.
Hepatitis Classification, Ensemble Learning, Stacking Model, Gradient Boosting, Random Forest, SMOTE, Medical Diagnosis
ASSESSMENT OF ADVERSE EVENTS ASSOCIATED WITH HOMEOPATHIC MEDICINE: A LITERATURE-BASED ANALYSIS
Homeopathic medicine is widely used across continents, with millions of individuals accessing homeopathic remedies for acute and chronic conditions. Although these products are often promoted as inherently safe due to their high dilutions, emerging evidence demonstrates that both direct and indirect harms can occur. This extended literature-based analysis reviews adverse events (AEs) reported in systematic reviews, case reports, observational studies, regulatory advisories, and public-health assessments. Findings indicate four categories of harm: (1) product-related toxicities, (2) allergic and idiosyncratic reactions, (3) quality-control and contamination-related events, and (4) indirect harms arising from delayed or foregone conventional medical treatment. Limitations in incidence estimation persist due to inconsistent reporting, publication bias, and absence of denominator data. Strengthening pharmacovigilance, regulatory oversight, and clinical counselling is essential for ensuring patient safety.
Homeopathy; Adverse Events; Safety Assessment; Pharmacovigilance; Toxicity; Contamination; Indirect Harms; Homeopathic Medicines; Quality Control; Clinical Risk; Mother Tinctures; Treatment Delay; Regulatory Oversight.
THE DOCTRINE OF MIASMS IN HOMEOPATHIC MEDICINE: A CRITICAL EXAMINATION
Miasm theory, introduced by Samuel Hahnemann in The Chronic Diseases (1828), remains a foundational yet controversial concept in homeopathy. This paper examines the historical development, theoretical foundations, clinical relevance, diagnostic indicators, and contemporary critiques of miasmatic theory. The study also compares the classical three-miasm model—Psora, Sycosis, Syphilis—with later expansions such as Tubercular and Cancer miasms. Although widely used clinically, the biological basis of miasms remains scientifically unverified. This paper synthesizes available classical literature, narrates miasmatic symptomatology, and provides a structured interpretation of miasmatic prescribing in modern practice.
Homeopathy, Miasm, Psora, Sycosis, Syphilis, Chronic Diseases.
CARICA PAPAYA IN LIVER DISORDERS: A COMPREHENSIVE SCIENTIFIC AND HOMEOPATHIC REVIEW
Background: Liver diseases are a global health challenge with rising incidence due to toxic exposures, viral hepatitis, drug-induced liver injury (DILI), and lifestyle-related conditions such as NAFLD. Natural hepatoprotective agents are increasingly explored for safety and efficacy. Carica papaya—used in Ayurveda, ethnomedicine, and homeopathy—has evidence of hepatoprotective, antioxidant, antiinflammatory, and immunomodulatory effects. Objective: To present a comprehensive, structured review of Carica papaya in liver disorders, integrating botanical, phytochemical, pharmacological, pre-clinical, clinical, and homeopathic evidence. Methods: Narrative literature review of peerreviewed experimental studies, human trials, meta-analyses, and homeopathic publications. Results: Preclinical models demonstrate significant hepatoprotection: reduction in ALT/AST/ALP, suppression of NF-κB, TNF-α, IL-6, mitigation of oxidative stress, fibrosis, apoptosis, and restoration of hepatic architecture. Leaf, seed, latex, and fruit extracts all show therapeutic potential. Human dengue studies confirm immunomodulation and thrombopoietic effects; liver benefits remain secondary observations. Homeopathic mother tincture (Carica papaya Q) shows potential adjunctive value, but controlled liver-focused trials are lacking. Conclusion: Carica papaya is a promising hepatoprotective botanical with mechanistic justification and preliminary clinical relevance. Homeopathic usage is biologically plausible at low potencies, but robust liver-specific evidence is absent. Dedicated clinical research is needed.
Carica papaya; Hepatoprotection; Liver disorders; Oxidative stress; Homeopathic mother tincture
Refrigeration and air conditioning (RAC) systems play a critical role in thermal management, industrial processes, and comfort applications. To understand their working principles effectively, a Refrigeration and Air Conditioning Trainer Kit is designed, fabricated, and analyzed in this study. The trainer kit demonstrates the vapor compression refrigeration (VCR) cycle, measuring key performance parameters such as pressures, temperatures, refrigerant flow rate, and coefficient of performance (COP). The kit is built using a hermetically sealed compressor, condenser, expansion device, and evaporator, with thermocouples and pressure gauges installed at each stage. The experimental results indicate a maximum COP of 3.1 for the refrigeration mode and 2.8 for the air-conditioning mode. The developed kit serves as an effective educational tool for understanding thermodynamic cycles, component performance, and system efficiency in real-time operation.
INTELLIGENT ECG SIGNAL ANALYSIS USING DEEP RECURRENT NETWORKS FOR HEART RHYTHM DISORDERS
Electrocardiogram (ECG) analysis is one of the most essential tools for identifying cardiac rhythm abnormalities and assessing cardiovascular health [1], [2]. Accurate interpretation of ECG signals is crucial for detecting disorders such as arrhythmias, atrial fibrillation, and other irregular heart activities that can lead to severe medical complications [3], [4]. However, traditional diagnostic methods heavily rely on manual inspection by clinicians, which is not only labor-intensive but also susceptible to subjective bias and diagnostic inconsistencies [5]. To address these limitations, this research presents an intelligent and automated ECG signal analysis framework utilizing Deep Recurrent Neural Networks (DRNNs) for reliable classification of heart rhythm disorders [6], [7]. The proposed approach leverages the temporal learning capabilities of recurrent architectures such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) networks to model the complex time-dependent characteristics inherent in ECG sequences [8], [9]. By doing so, the system effectively captures both short-term and long-term dependencies in heartbeat patterns, enabling more precise identification of abnormal cardiac activity [10]. Prior to model training, several signal preprocessing techniques—including denoising, baseline drift removal, segmentation, and feature scaling—are applied to enhance signal quality and reduce artifacts caused by noise or motion interference [11], [12]. Subsequently, a feature extraction phase converts raw ECG data into time– frequency representations to enrich the model’s understanding of rhythm morphology and improve classification accuracy [13], [14]. The proposed model is trained and validated using publicly available benchmark datasets such as MIT-BIH Arrhythmia and PTB Diagnostic ECG Database [15], [16], ensuring generalizability across diverse cardiac conditions and patient profiles. Comprehensive performance evaluation reveals that the DRNN-based system significantly outperforms traditional machine learning algorithms—including Support Vector Machines (SVMs), Random Forests, and Decision Trees—achieving superior results in accuracy, sensitivity, and specificity [17], [18]. Moreover, the system demonstrates robust generalization on unseen ECG data, confirming its potential for real-world deployment in both clinical environments and wearable health monitoring devices [19], [20]. Overall, this study presents a data-driven, intelligent ECG analysis solution capable of real-time arrhythmia detection and continuous cardiac monitoring [21], [22]. The proposed framework not only enhances diagnostic precision but also supports physicians in clinical decision-making, ultimately contributing to faster, more reliable, and accessible cardiac healthcare systems. With further refinement, integration with IoT-based platforms and edge computing technologies could transform this framework into a cornerstone of next-generation AI-assisted cardiovascular care [23]–[25].
Deep recurrent neural network; ECG classification; cardiac arrhythmia detection; intelligent signal processing; heartbeat anomaly recognition; temporal sequence modeling; automated heart diagnosis.
FORMULATION AND IN-VITRO CHARACTERIZATION OF SUMATRIPTAN SUCCINATE -LOADED MUCOADHESIVE MICROSPHERES INCORPORATED IN IN-SITU NASAL GEL
Background: Sumatriptan succinate is a selective 5-HT₁ receptor agonist used for the acute treatment of migraine attacks. However, its low oral bioavailability (approximately 15%) due to extensive first-pass metabolism and slow onset of action limits its therapeutic efficacy. Nasal drug delivery offers an alternative route that bypasses hepatic metabolism and provides rapid drug absorption through the rich vascular network of the nasal mucosa. Incorporating mucoadhesive microspheres into an in-situ nasal gel can further enhance drug residence time, control release, and improve patient compliance. Objective: The present study aimed to formulate and evaluate mucoadhesive microspheres of Sumatriptan succinate incorporated into an in-situ nasal gel for sustained release and enhanced nasal bioavailability. Methods: Sumatriptan succinate-loaded mucoadhesive microspheres were prepared using the emulsification cross-linking method with chitosan as the polymer and sodium tripolyphosphate (TPP) as the cross-linking agent. Nine formulations (F1–F9) were developed by varying the polymer-to-drug ratio. The microspheres were evaluated for percentage yield, particle size, drug entrapment efficiency, swelling index, mucoadhesive strength, and invitro drug release. The optimized microspheres were incorporated into a Poloxamer 407-based in-situ nasal gel and evaluated for pH, viscosity, gel strength, and mucoadhesive force. Results: The study revealed that increasing the concentration of chitosan improved yield, drug entrapment efficiency, swelling index, and mucoadhesive strength while reducing drug release rate. The optimized formulation (F9) showed a yield of 77.0 ± 1.8%, entrapment efficiency of 72.3 ± 2.3%, particle size of 35.0 ± 1.9 µm, and sustained drug release (40.8 ± 3.0% at 8 h). The formulated in-situ gel displayed ideal pH (6.2 ± 0.2), viscosity (860 ± 16 cps), and gel strength (43.2 ± 1.5 sec), ensuring compatibility and adequate nasal retention. Conclusion: The developed mucoadhesive microspherebased in-situ nasal gel of Sumatriptan succinate exhibited prolonged drug release, enhanced mucoadhesion, and suitable physicochemical properties for nasal administration. This delivery system could potentially overcome the limitations of conventional oral and parenteral routes, providing rapid onset and sustained therapeutic effect in migraine management.
Sumatriptan succinate, Mucoadhesive microspheres, In-situ nasal gel, Chitosan, Tripolyphosphate, Poloxamer 407, Sustained release, Migraine therapy
FORMULATION AND IN-VITRO CHARACTERIZATION OF CHLORPHENIRAMINE MALEATE-LOADED MUCOADHESIVE MICROSPHERES INCORPORATED IN IN-SITU NASAL GEL
Background: Nasal drug delivery offers rapid onset of action and avoids first-pass metabolism; however, conventional formulations suffer from rapid mucociliary clearance. Mucoadhesive microspheres incorporated into in-situ nasal gels can enhance nasal residence time and sustain drug release. Chlorpheniramine maleate, a widely used antihistamine, can benefit from such delivery to reduce systemic side effects and improve local efficacy. Methods: Chlorpheniramine maleate-loaded mucoadhesive microspheres were prepared using chitosan as the mucoadhesive polymer and sodium tripolyphosphate (TPP) as the cross-linking agent via the emulsification cross-linking method. Formulations F1–F9 were evaluated for % yield, drug entrapment efficiency, particle size, swelling index, mucoadhesion, and in-vitro drug release. The optimized microspheres were incorporated into an in-situ nasal gel and characterized for pH, gel strength, viscosity, and mucoadhesive force. Results: The microspheres exhibited % yield of 58.9–77.0%, drug entrapment efficiency of 48.6–72.3%, particle sizes of 16.2–35.0 µm, swelling indices of 22.5– 56.2%, and mucoadhesion of 45.2–75.6%. Invitro release studies showed sustained drug release over 8 hours, with the optimized gel displaying suitable pH (6.0–6.3), gel strength, viscosity, and mucoadhesive force, ensuring compatibility with nasal mucosa. Conclusion: The study demonstrates that Chlorpheniramine maleate-loaded mucoadhesive microspheres incorporated into in-situ nasal gels are a promising approach for sustained intranasal drug delivery, offering enhanced residence time, improved bioavailability, and reduced systemic side effects.
Chlorpheniramine maleate, Mucoadhesive microspheres, In-situ nasal gel, Chitosan, Sodium tripolyphosphate, Intranasal drug delivery
BLOCKCHAIN-ENHANCED HIPAA COMPLIANCE FRAMEWORK FOR SECURE AWS HEALTH DATA
This study develops a blockchain-enhanced HIPAA compliance framework for secure management of healthcare data on AWS cloud infrastructure using secondary qualitative data analysis. Three themes guide the analysis: evaluation of current cloud-based healthcare data practices, blockchain integration strategies, and design of a secure framework. Findings reveal that conventional cloud security measures are inadequate for full compliance, while blockchain provides immutable, traceable, and auditable records. The proposed framework combines encrypted AWS storage, permissioned blockchain logging, and smart contract-enabled access control to enhance data integrity, transparency, and regulatory adherence. Recoveries include improved operational efficiency, reduced breach risks, and increased patient trust.
Blockchain, HIPAA Compliance, AWS Health Data, Data Security, Healthcare Data Privacy, Cloud Computing, Secondary Qualitative Analysis, Secure Framework