Volume 14 | Issue 4

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The recent global re-emergence of Monkeypox (Mpox) necessitates rapid, non-invasive diagnostic tools to differentiate its dermatological manifestations from visually similar diseases such as Chickenpox and Measles. While deep learning models possess high theoretical accuracy for skin lesion classification, standard architectures frequently fail in clinical deployment due to "black-box" opacity and their structural vulnerability to out-of-distribution (OOD) inputs--often generating high-confidence false-positive diagnoses when presented with non-medical imagery. In this paper, we propose a robust, multi-stage Explainable Artificial Intelligence (XAI) framework to resolve these limitations. The core mathematical feature extractor utilizes an EfficientNetB0 architecture trained across five distinct classes, including a dynamic "Others" category to compartmentalize generic skin anomalies. To enforce diagnostic safety, a lightweight MobileNetV2 semantic sentry is integrated to instantaneously reject OOD inputs (such as animals or household objects) prior to inference, alongside an 80% algorithmic confidence threshold. Finally, the framework employs Gradient-weighted Class Activation Mapping (Grad-CAM) to visually project the specific convolutional features influencing the model's prediction, thereby enforcing clinical accountability. By combining high-fidelity transfer learning with explicit OOD defense mechanisms and real-time visual explainability, our proposed system bridges the critical gap between deep learning theoretical accuracy and practical, trustworthy medical deployment.

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Monkeypox Detection, Deep Learning, EfficientNetB0, Explainable AI (XAI), Grad-CAM, Out-of-Distribution (OOD) Detection, Computer-Aided Diagnosis (CAD).

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Cystic fibrosis patients frequently suffer from chronic pulmonary infections caused by multidrug-resistant pathogens, among which the Burkholderia cepacia complex represents one of the most clinically devastating. These infections are associated with rapid lung function decline, limited antibiotic responsiveness, and poor transplant outcomes, underscoring an urgent need for alternative therapeutic strategies. The present study adopts a structure-guided drug repurposing approach to identify FDA-approved drugs capable of targeting key druggable proteins of Burkholderia cepacia. Essential and virulence-associated bacterial proteins were systematically identified and evaluated for druggability using structural and functional criteria. High-confidence targets were subjected to virtual screening against a curated library of FDA-approved compounds to rapidly uncover candidates with strong binding potential. Molecular docking and interaction profiling were employed to characterize binding modes, followed by molecular dynamics simulations to assess complex stability under physiological conditions. This integrative in silico framework prioritizes clinically actionable drug candidates capable of disrupting critical bacterial pathways while bypassing early-stage drug development bottlenecks. The findings provide a rational basis for repurposing existing drugs against Burkholderia cepacia infections and offer a scalable strategy for combating antimicrobial resistance in cystic fibrosis.

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Cystic Fibrosis, Burkholderia cepacia complex, Drug Repurposing, Multidrug Resistance, Molecular Docking, Virtual Screening, FDA-approved Drugs, Antimicrobial Resistance

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Abstract Crop combination and diversification are fundamental concepts in agricultural geography that help in understanding spatial patterns of land use and regional agricultural development. This study examines crop combination and diversification patterns in Dhule district, Maharashtra, for the period 2013-2016 using Doi's modified minimum deviation method. The analysis is carried out at both tahsil and circle levels to identify dominant crop associations and regional variations. The findings indicate significant spatial heterogeneity in cropping patterns, with cotton dominating monoculture regions and diversified cropping systems prevalent in tribal and high rainfall areas. The study highlights the influence of physical and socio-economic factors such as rainfall, soil fertility, irrigation, and market accessibility on crop distribution. It also emphasizes the importance of crop diversification as a strategy for risk mitigation, resource optimization, and sustainable agricultural development.

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Keywords: Crop combination, crop diversification, agricultural regionalization, Doi's method, Dhule district

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Water scarcity and inefficient irrigation practices remain critical constraints to sustainable agricultural production, particularly in horticulture. This study examines the impact of water availability and irrigation systems on horticultural crop production in the Baruipur subdivision of West Bengal, India. Based on primary data collected from 300 farm households during 2024-2025, complemented by secondary sources, the study analyzes irrigation sources, adoption of micro-irrigation (MI), farmer perceptions, and agro-economic outcomes. Descriptive statistics and comparative analysis indicate that pond irrigation (64.33%) is the dominant source, followed by tubewells (18.67%). Approximately 43.33% of farmers reported moderate to severe water scarcity. Adoption of micro-irrigation significantly improves water-use efficiency, yield, and profitability. However, high initial costs, institutional barriers, and infrastructure limitations constrain adoption. The study suggests that strengthening policy support, improving irrigation infrastructure, and enhancing farmer awareness are essential for sustainable horticultural development.

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Keywords : Water availability; Micro-irrigation; Horticulture; Water-use efficiency; Farmer perception; Sustainable agriculture;

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Nebivolol Hydrochloride, Ramipril, first derivative, second derivative, validation, UV Spectrophotometric

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International organizations play a crucial role in maintaining global peace and security in an increasingly interconnected world. These institutions facilitate cooperation among nations, prevent conflicts, manage crises, and promote sustainable peace through diplomatic negotiations, peacekeeping missions, and humanitarian assistance. Organizations such as the United Nations, North Atlantic Treaty Organization, and European Union contribute significantly to conflict resolution and peace building initiatives. This research paper examines the role of international organizations in maintaining world peace by analyzing their functions, achievements, challenges, and future prospects. The study highlights the importance of collective security, diplomacy, and international cooperation in promoting global stability.

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International organizations United Nations, International cooperation ,peacekeeping

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The rapid growth of e-commerce platforms has resulted in the generation of large volumes of transactional data. Analyzing this data is essential for understanding business performance and improving decision-making processes. This study focuses on applying exploratory data analysis (EDA) techniques to evaluate e-commerce sales data using Python. The analysis includes monthly sales trends, category-wise and sub-category-wise performance, profit distribution, and customer segment behavior. Special emphasis is placed on the sales-to-profit ratio to evaluate the efficiency of converting sales into profit. Python libraries such as Pandas, NumPy, and visualization tools are used to process and represent the data. The results highlight that sales performance varies across different months and categories, and higher sales do not always correspond to higher profit. The study demonstrates how simple data analysis techniques can provide meaningful insights for improving business strategies.

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Consumer Behaviour, Purchase Intention, E-commerce, Machine Learning, E-commerce, Data Analysis, Sales Analysis, Profit Analysis, Data Visualization, Python, EDA.

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GoSafe is a data-driven travel safety platform designed to provide personalized risk assessments through a dynamic scoring algorithm. By transforming complex global safety data into a clear visual score out of 10, the application offers tailored advice based on specific traveler profiles, such as solo, family, or inexperienced users. Built as a high-performance static Single-Page Application using HTML5, Vanilla JavaScript, and Tailwind CSS, it utilizes a simulated client-side backend to ensure speed and security. The project addresses critical industry gaps where over 40% of travelers report disappointment due to insufficient research, ultimately empowering users to plan journeys with greater confidence.

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Travel Safety , Personalized Risk Assessment , Dynamic Scoring Algorithm , Single-Page Application (SPA) , Client-Side Data Processing , User Experience (UX) Design , Front-End Development.

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This paper seeks to examine the nuanced layers of socio-cultural exclusion in Nergis Dalal's novel Skin Deep (2005) through the lens of intersectionality and double marginalization. Set against the backdrop of the dwindling Parsi community in India, the novel moves beyond a mere domestic saga of twin rivalry to critique the systemic displacement of the female subject. By utilizing Gayatri Chakravorty Spivak's framework of the "doubly marginalized" and Naomi Wolf's "Beauty Myth," this study argues that the protagonist, Naaz, is simultaneously sidelined by her status as a member of a micro-minority and by an internal patriarchal hierarchy that commodifies female aesthetics.

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Parsi Diaspora, Double Marginalization, Nergis Dalal, Postcolonial Feminism, The Beauty Myth, Minority Identity

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Kamala Das stands as one of the most confessional and provocative voices in Indian English poetry, whose works foreground the complexities of identity, belonging, and emotional dislocation. This paper explores the intricate interplay of space and displacement in her poetry, situating her within feminist and postcolonial frameworks. It argues that Kamala Das transforms poetry into a dynamic site where personal experience intersects with broader socio-cultural tensions shaped by patriarchy and colonial legacy. The study examines how different forms of "space"--domestic, linguistic, bodily, and emotional--are continuously negotiated, revealing the fragmented and evolving nature of the self. The domestic sphere, often idealized in traditional discourse, is reconfigured as a restrictive and alienating space where the female subject struggles for autonomy and self-expression. The paper further highlights how linguistic space becomes central to identity formation, as Kamala Das's use of English alongside her cultural roots reflects both assertion and displacement. Additionally, the female body emerges as a contested site where desire, vulnerability, and resistance coexist, challenging normative expectations while intensifying the sense of isolation. Emotional estrangement and fractured relationships further contribute to the experience of dislocation, emphasizing the poet's persistent search for authentic connection and belonging. Memory and nostalgia are also examined as unstable spaces that simultaneously anchor and unsettle the self. Through close reading of select poems, this study demonstrates that dislocation in Kamala Das's poetry functions not only as a condition of loss but also as a creative and critical strategy that enables the redefinition of identity. Ultimately, her work reflects an ongoing negotiation of selfhood within shifting cultural and personal landscapes, making it highly relevant to contemporary discussions of gender, identity, and belonging.

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Kamala Das, identity, dislocation, belonging, space, feminism, postcolonialism, female subjectivity, language, selfhood

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Lichen plano pilaris (LPP) is a rare inflammatory disorder characterized by lymphocytic destruction of hair follicles leading to cicatricial alopecia and permanent hair loss. We report a case of a 14-year-old female who presented with progressive hair loss, pruritus, and hyperpigmented lesions over the scalp and multiple body sites. Clinical examination revealed patchy alopecia with perifollicular erythema and scaling, and dermoscopic findings suggested scarring alopecia. The diagnosis was confirmed by histopathological examination of a scalp biopsy. The patient was managed with a combination of systemic hydroxychloroquine, oral corticosteroids, and immunosuppressants, along with topical therapies and adjunct platelet-rich plasma (PRP). Follow-up showed symptomatic improvement with reduction in itching and stabilization of disease progression; however, residual scarring alopecia persisted. This case highlights the importance of early diagnosis and prompt management to prevent irreversible hair loss and improve patient outcomes in LPP.

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Lichen Plano Pilaris, Cicatricial Alopecia, Adolescent, Hair Loss, Biopsy, Hydroxychloroquine.

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MindPulse is a privacy-first, locally-operated, AI-powered mental wellness prediction platform built with Python (Flask), Scikit-learn, and HTML/CSS/JavaScript. The system leverages a Voting Ensemble model combining Gradient Boosting and Random Forest classifiers to categorize users' mental states across five wellness levels: Excellent, Good, Moderate Concern, High Concern, and Critical. The model is trained on 3,000 synthetically generated samples calibrated to established clinical instruments--the PHQ-9, GAD-7, and WHO-5 Wellbeing Index. Users rate ten behavioral and emotional wellness indicators on a 1-10 scale through an animated web interface, receiving a composite Wellness Score (0-100), Canvas-rendered Radar Chart, probability breakdown, and personalized therapeutic insights. Operating entirely on a local Flask server without any external API keys or cloud transmission ensures complete data privacy. Rigorous testing comprising 29 test cases across unit, integration, boundary value, and user acceptance testing yielded a 100% pass rate.

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Keywords Mental Wellness Prediction; Ensemble Learning; Gradient Boosting; Random Forest; Flask REST API; PHQ-9; GAD-7; WHO-5; Privacy-First AI; Canvas Radar Chart; Wellness Analytics

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The crises of Rohingya refugee children are the most severe humanitarian challenges in contemporary South Asia. Rohingya refugee children are a significant section of the displaced Rohingya population. This paper examines the role of digital governance in enhancing human security for Rohingya refugee children. It also focuses on access to essential services such as education, healthcare, protection, and identity. Drawing upon the human security framework, the study explores how digital tools including biometric registration, digital identity systems, data-driven governance, and e-service delivery can improve the efficiency and inclusiveness of humanitarian responses. The paper highlights the contribution of state actors, international organizations, and non-governmental agencies in integrating digital technologies into refugee management systems, particularly in the host country like Bangladesh. At the same time, it critically evaluates the challenges posed by digital exclusion, data privacy risks, and infrastructural limitations. The findings suggest that while digital governance offers transformative potential in addressing the Rohingya children's crises, its effectiveness depends on inclusive implementation, ethical safeguards, and strong institutional coordination. The study argues that aligning digital governance with human security principles is essential for ensuring sustainable and equitable outcomes for refugee children.

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Digital Governance; Human Security; Rohingya Refugees Children; Migration Governance; Digital Inclusion

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Type 2 Diabetes Mellitus (T2DM) is a chronic disease that can lead to kidney damage. Many patients do not achieve proper blood sugar control with metformin and glimepiride alone. So, a third drug like a DPP-4 inhibitor is often added to improve treatment outcomes. Teneligliptin and linagliptin are commonly used options that are generally safe for kidney patients. Both drugs helped in lowering blood sugar levels and improving kidney function. However, linagliptin showed better results in reducing HbA1c and improving kidney markers. Overall, linagliptin may be a more effective and safer choice for T2DM patients with kidney problems.

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Type 2 Diabetes Mellitus, Teneligliptin, Linagliptin, Add-on Therapy, Glycaemic Control, Renal Safety, DPP-4 Inhibitors

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The present study aimed to compare the Work Ability Index (WAI) among academic female staff across different age groups--young, middle-aged, and senior in in polytechnic colleges Bhopal. Work ability is a crucial determinant of productivity and occupational well-being, especially in academic settings where mental and physical demands coexist. A total of ninety (N=90) academic female staff members were selected using purposive sampling, with 30 participants in each age group. The Work Ability Index questionnaire was used to assess work ability. Statistical techniques such as mean, standard deviation, and one-way ANOVA were employed to analyze the data. The findings revealed significant differences in WAI among the three age groups, indicating a decline in work ability with increasing age. The study highlights the need for age-specific interventions to maintain and improve work ability among academic female professionals.

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Work Ability Index, Academic Female Staff, Age Groups, Occupational Health, Physical Activity.

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This paper proposes a secure federated learning framework enhanced with blockchain technology for use in edge computing environments. The system introduces decentralized trust management, encrypted aggregation of model updates, smart contract-based validation, a reputation-based client scoring mechanism, and tamper-resistant auditing to ensure transparency and security. It effectively addresses major threats such as model poisoning, Sybil attacks, and data integrity issues while maintaining scalability and low latency. Experimental results show that the proposed framework achieves better accuracy and stronger attack resistance compared to traditional federated learning approaches.

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Block-chain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing

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EMOTIONAL INVALIDATION IN CORPORATE CRISIS COMMUNICATION

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EMOTIONAL INVALIDATION IN CORPORATE CRISIS COMMUNICATION

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This study examines the growing complexity of modern telecommunication networks and the increasing risks associated with cyber threats. With the expansion of technologies such as 5G, cloud computing, and IoT, network infrastructures have become more vulnerable to attacks like intrusions and service disruptions. Traditional intrusion detection approaches often fail to identify new and evolving threats due to their reliance on predefined rules Traditional intrusion detection systems (IDS) rely on static rule-based and signature-based mechanisms that struggle to cope with modern cyber threats. These systems are limited in their ability to detect unknown attacks, adapt to dynamic traffic patterns, and scale with increasing data volumes. Furthermore, high false alarm rates significantly reduce operational efficiency and increase the workload of network administrators. This research proposes a Artificial Intelligence driven anomaly detection framework tailored for telecom network environments. The proposed system integrates advanced preprocessing techniques, hybrid feature selection using Weighted Adaptive Feature Selection (WAFS), Synthetic Minority Oversampling Technique (SMOTE) for data balancing, and a hybrid ensemble model combining a Deep Neural Network (DeepAnomNet) and Random Forest classifier. The UNSW-NB15 dataset is employed for training and evaluation as it represents modern network traffic and diverse attack scenarios. The model is evaluated using multiple performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Results demonstrate significant improvements in detection capability and reduction in false positives compared to conventional machine learning models. A real-time Telecom Security Information and Event Management (SIEM) dashboard is also developed to visualize anomaly probability and threat severity. The proposed system offers a scalable, adaptive, and intelligent solution for securing next-generation telecom infrastructures.

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Telecom Networks, Anomaly Detection, Artificial Intelligence, Deep Learning, LSTM, Autoencoder, Network Security, Intrusion Detection, Time Series Analysis, Predictive Maintenance, 5G Networks, Machine Learning, Big Data Analytics, Network Monitoring.

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Cloudbursts are extreme localized rainfall events that occur within a short duration and often result in severe consequences such as flash floods, landslides,and significant damage to life and infrastructure. Traditional cloudburst prediction methods based on numerical weather prediction models face limitations in accurately forecasting such sudden and small-scale events due to their complex and non-linear nature. With the increasing availability of meteorological high-resolution data from satellites, Doppler radars, and ground-based weather stations, there is a growing need for advanced data-driven approaches that can effectively analyze these datasets for improved cloudburst prediction. This study focuses on integrating meteorological data with machine learning techniques to enhance cloudburst prediction accuracy. Various atmospheric parameters such as rainfall intensity, temperature, humidity, pressure, wind speed, and cloud characteristics are analyzed using machine learning and deep learning models including Random Forest, Support Vector Machines, and Long Short- Term Memory networks. The proposed approach aims to identify hidden patterns and early indicators of cloudburst events, providing timely and reliable predictions. The outcomes of this work contribute to the development of efficient early warning systems, supporting disaster risk reduction

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Cloudburst Data, Prediction, Machine Learning, Extreme Rainfall Events, Weather Forecasting, Early Warning System, Disaster Management, Real-Time Data Analysis

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