Recent Articles

Dr. K. IYNA  ·  International Journal of Law, Politics and Governance  ·  02 Oct 2026

Abstract Gender inequality, poverty and economic inequality are interconnected dimensions of disadvantage that cannot always be adequately understood when examined separately. This paper examines the intersectionality of gender, poverty and economic inequality in the Indian context. The study adopts a secondary-data and conceptual research approach, drawing upon published reports, official statistics and scholarly literature relating to gender, labour-force participation, unpaid care work, poverty, access to economic resources and social inequality. The intersectionality perspective highlights how gender interacts with socioeconomic position, location, education, employment status and other structural conditions to influence women's economic opportunities and well-being. The analysis indicates that although India has experienced substantial economic growth and a reduction in poverty, unequal access to employment, productive assets, economic opportunities and decision-making continues to affect women differently across social and economic groups. Official labour-market statistics also indicate a substantial gender gap in labour-force participation. The unequal distribution of unpaid domestic and care work further constrains women's time and economic choices. The paper argues that policies addressing poverty and inequality should incorporate gender-responsive and intersectional approaches rather than treating women as a homogeneous category. Strengthening access to decent employment, education, social protection, productive assets, financial resources and care infrastructure can contribute to more inclusive and equitable economic development. Keywords: Gender inequality; Poverty; Economic inequality; Intersectionality; Women’s economic empowerment; India

Mulugeta Tilahun Bekele  ·  International Journal of Electrical and Electronics Engineering  ·  01 Oct 2026

Abstract: Conventional smart-grid systems often rely on fixed control strategies that have limited ability to respond dynamically to changing electricity demand, renewable-energy variability, equipment degradation, and unexpected faults. This study proposes an AI-Driven Adaptive Smart Grid (AI-ASG) for real-time energy optimization and automated fault detection. The framework integrates machine learning, real-time sensing, adaptive demand-response control, and intelligent fault classification to improve grid efficiency, reliability, and operational responsiveness. A mixed-methods quantitative and qualitative research design was adopted. Smart-grid operational data, including load demand, voltage, current, frequency, power factor, renewable-energy generation, and equipment-fault indicators, were processed through preprocessing, feature extraction, and AI-based prediction. Adaptive optimization was evaluated using energy consumption reduction, peak-load reduction, renewable-energy utilization, power-loss reduction, voltage stability, fault-detection accuracy, precision, recall, F1-score, false-alarm rate, detection latency, computational overhead, and system scalability. Qualitative assessment considered interpretability, operator usability, interoperability, adaptability, and decision-support effectiveness. Performance was compared with conventional rule-based control, non-adaptive machine-learning control, and the proposed adaptive AI approach. The proposed AI-ASG demonstrated improved operational performance across energy-management and fault-detection measures. Quantitative evaluation indicated reductions in unnecessary energy consumption and peak demand, improved renewable-energy utilization and voltage stability, and faster fault identification. The adaptive model also achieved higher fault-classification accuracy, precision, recall, and F1-score while reducing false alarms and detection latency compared with conventional approaches. Qualitative findings indicated improved system interpretability, operator confidence, adaptability, and real-time decision support. The proposed AI-driven adaptive architecture provides an integrated approach for intelligent energy optimization and real-time fault management, supporting more responsive, reliable, efficient, and scalable smart-grid operation.

Satish Gajawada  ·  International Journal of Philosophy, Ethics and Humanities  ·  01 Oct 2026

This biography of Satish Gajawada (IIT Roorkee Alumnus) is generated by Artificial Intelligence.

Kondru Hemalatha, Pilla Jyotshna, Sabbisetti Suryakala, Praveen Kumar, Maddi Ramaiah  ·  International Journal of Medical and Health Sciences  ·  01 Oct 2026

Kyasanur Forest Disease (KFD), better known as monkey fever, is a tick-borne virus that causes a zoonotic disease endemic to the Western Ghats of India. The disease is caused by Kyasanur Forest Disease virus (KFDV) belonging to the genus Flavivirus. Humans are often infected by the bite of infected Haemaphysalis ticks during work or travel in forested environments. The disease was first reported from Karnataka in the year 1957 and has slowly spread to a few neighboring states. The disease is an important public health problem. KFD is naturally transmitted between ticks, wild animals and humans. Monkeys, particularly bonnet macaques and gray langurs, are very susceptible to infection and often die during outbreaks, serving as early indicators of viral activity. Human cases are most frequently reported January through June when tick populations are highest. The distribution of infected ticks and the persistence of the disease are influenced by environmental factors, such as forest vegetation, climate and wildlife movement. Patients with KFD usually present with sudden onset of fever, severe headache, myalgia and gastrointestinal symptoms, although a minority may develop bleeding or neurological complications. Illness and deaths can be reduced by early detection, supportive medical care and preventive measures. Efective control depends on continuous disease surveillance, vaccination of high-risk groups, public awareness, protection against tick bites, and coordinated vector control. Continued research and improved public health programs are still needed to control KFD spread in endemic areas.

Muhammad Akram, Aiman Yaseen  ·  International Journal of Oncology Research  ·  28 Sep 2026

Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies due to the complex biology, genetic factors, over - expression of multiple biomarkers leading to multiple signaling pathways, immersed nature of tumor, lessen survival incidence from surgical resection and delayed diagnosis. The conventional treatment methods with less efficacy include chemotherapy and combinational therapy of gemcitabine with other chemotherapeutic drugs. These are some factors which demands advanced therapeutic regimens which includes oral drug which can stop progression at early stages. To address this concern, different drugs have been identified through virtual screening and experimented in-vivo and in-vitro that showed effective treatment outcomes. Capecitabine combination with different drugs has shown promising results in case of combinational therapies usage against PDAC. Advanced treatment strategies includes immunotherapy, use of monoclonal antibodies, immune checkpoint inhibitors, cancer vaccines, personalized medicine and nanotechnology based drug delivery system (DDS) which opens doors for rapid and promising results to alleviate progression of pancreatic ductal adenocarcinoma. Drug delivery through nanotechnology based drug delivery system (DDS) has been showing effective anti-tumor results to stop the progression of cancer at advanced stage of PDAC. Overall, this review suggested that advanced technologies could serve as a robust way to treat this solid malignancy as well as development of drugs (oral / effective chemotherapeutic drug) can stop this disease at early as well as at advanced stages.

Ajay Bisht  ·  International Journal of Sociology and Social Research  ·  25 Sep 2026

The glacier temporarily blocked the Lhende Khola River, then released a destructive debris flow that travelled through the Bhote Koshi and Trishuli corridors into the districts of Rasuwa, Nuwakot, and Dhading in Nepal. This paper relies on secondary data from government disaster management bureaucracies and reporting from the United Nations, multilateral banks, and print and broadcast media. It focuses on the events surrounding the disaster, analysis of causal factors, casualties, damage to infrastructure and the economy, and response efforts by the government and international agencies. On August 29, 2026, the toll in Nepal was reported to be 675 dead with 2,478 missing and over 93,000 affected, while Tibet reported 7 dead and 560 missing persons. The calamity destroyed about 430 megawatts of hydropower capacity, 19 bridges, and 40 kilometres of road, while obliterating the vital Gyirong border crossing for Nepal-China trade. This paper discusses the event in the context of available scientific literature on glacial lake outburst floods (GLOFs) and ice–rock avalanches in High Mountain Asia, highlighting that the same Bhote Koshi corridor faced a similar transboundary glacial lake outburst event in 2016, which points to an issue of recurrent and unmitigated risk. The study finds there are still gaps in cross-border early detection, information sharing across borders, and infrastructure management based on climate risks, and gives some suggestions for governance and engineering. This study provides an early and organised description of a quickly changing disaster which will be useful for investigators, practitioners in civil defence planning, and policy-makers, but it also notes the limits of this investigation using secondary sources.

Vydeti Mounika, Eegala Sirisha, Srikakulapu Balu, Suddapalli Akhila, Praveen Kumar Uppala, Prof . Maddi Ramaiah  ·  International Journal of Medical and Health Sciences  ·  25 Sep 2026

Dengue is among the fastest-growing mosquito-borne viral infections worldwide and continues to pose a major public health challenge because no approved antiviral treatment is available. As a result, vaccination has become a cornerstone of dengue prevention. Several vaccine candidates have progressed through clinical development; however, questions remain regarding their efficacy, long-term safety, and ability to provide consistent protection against all four dengue virus serotypes. Over the last five years, findings from phase II and phase III clinical trials, post-licensure safety monitoring, and real-world effectiveness studies have substantially advanced the understanding of vaccine performance and influenced both regulatory recommendations and future vaccine development strategies. Despite these advances, important immunological issues remain unresolved, including the induction of balanced and durable immunity against all serotypes, identification of reliable correlates of protection, and clarification of how age and pre-existing dengue immunity affect vaccine responses. Clinical studies have reported variable outcomes, with several vaccines offering moderate protection against symptomatic dengue while showing differences in efficacy among serotypes and lower effectiveness in individuals without prior dengue exposure. Consequently, vaccination policies have evolved toward more targeted approaches, including pre-vaccination serological testing in selected settings, preferential use among seropositive populations, and integration of immunization programs with vector control and disease surveillance measures. Current research is focused on developing next-generation vaccines that provide stronger tetravalent immune responses, longer-lasting protection, and improved safety for seronegative individuals. In parallel, innovative clinical trial methodologies and detailed immunological analyses are being employed to better define the mechanisms underlying protective immunity. This review summarizes recent clinical evidence, emerging immunological knowledge, and evolving public health policies to provide an updated assessment of dengue vaccines and to identify key research priorities for achieving safe, effective, and equitable dengue prevention through vaccination.

Jennifer Natnat  ·  International Journal of Electrical and Electronics Engineering  ·  14 Sep 2026

This study presents an automated cocoa bean classification and defect detection system developed in accordance with the ASEAN Standard for Cocoa Bean (ASEAN Stan 34:2014). Implemented in MATLAB, the system utilizes digital image processing and computer vision techniques to segment individual cocoa beans, extract geometric, color, and texture features, and classify them into quality categories: Extra Class, Class I, Class II, and Non-Compliant. The image processing workflow integrates color space transformations (RGB to HSV), adaptive thresholding, and morphological filtering to ensure accurate segmentation. Feature extraction targets parameters such as area, length, aspect ratio, texture entropy, and color uniformity to identify specific defects including moldy, slaty, insect-damaged, and germinated beans. Validation against independent expert grading achieved an overall reliability rate of 96.2%, with high precision (0.93) and recall (0.91), and an average processing speed of 1.3 seconds per image. The developed analyzer provides an objective, rapid, and repeatable tool to support standardization and postharvest cocoa quality control across the ASEAN region.

Rekha Agarwal, DIVYANSH MISHRA, RAJESH KUMAR MISHRA  ·  International Journal of Oncology Research  ·  14 Sep 2026

Breast cancer remains one of the most commonly diagnosed malignancies worldwide, and timely, accurate discrimination between benign and malignant breast masses is critical to reducing unnecessary biopsies while ensuring early treatment of true malignancies. This study presents a comprehensive, methodologically rigorous machine learning (ML) analysis of six supervised classifiers — Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting, k-Nearest Neighbors (k-NN), and a shallow Artificial Neural Network (ANN) — for binary classification of breast masses using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, a well-established, publicly available benchmark comprising 569 cases and 30 real-valued nuclear morphometric features computed from digitized fine-needle aspirate (FNA) images. Beyond standard accuracy benchmarking, this study contributes four analyses that are comparatively underreported in the WDBC literature: a feature-domain ablation quantifying the diagnostic contribution of mean-value, standard-error, and worst-value feature subsets; a class-imbalance handling comparison; a clinically motivated decision-threshold optimization using the Youden index; and a dual global-interpretability analysis combining Random Forest Gini importance with SHapley Additive exPlanations (SHAP). Each classifier was tuned via 5-fold cross-validated grid search and evaluated on a stratified 75/25 held-out split. Logistic Regression and k-Nearest Neighbors jointly achieved the highest held-out test accuracy (97.90%), with all six classifiers exceeding 95.8% accuracy; pairwise paired t-tests over cross-validation folds found only one statistically significant difference among fifteen pairwise comparisons, indicating that classifier choice for this task is largely accuracy-neutral. The best-performing model achieved a receiver operating characteristic area under the curve (ROC-AUC) of 0.997, an average precision of 0.998, and a Matthews correlation coefficient of 0.955. Feature-domain ablation showed that worst-value features alone recovered 96.50% accuracy versus 97.90% for the full feature set, while standard-error features alone achieved only 86.71%, and SHAP analysis corroborated Random Forest's Gini-based ranking, jointly identifying worst area, worst perimeter, and worst/mean concave points as the dominant diagnostic drivers. Youden-index threshold optimization recovered two additional correctly identified malignant cases relative to the default 0.5 probability threshold, at a small, quantified cost to specificity. These results, obtained on real diagnostic data rather than simulated signals, corroborate and extend a substantial body of prior work, and reinforce the broader case for feature-based, interpretable, computationally lightweight ML as a viable decision-support tool for cytological breast mass classification, while underscoring that any such tool requires prospective, multi-institutional clinical validation before informing real diagnostic decisions.

Dr Nellutla Sasikala  ·  International Journal of Computer Science and Artificial Intelligence  ·  14 Sep 2026

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have evolved from rule-based symbolic systems into data-driven computational methods capable of perception, prediction, decision support, and content generation. This chapter presents an accessible yet technically grounded overview of the relationship among AI, ML, and DL, tracing their historical development from early symbolic reasoning and theoretical foundations to modern neural networks and transformer-based systems. It explains the principal ML paradigms supervised, unsupervised, reinforcement, semi-supervised, and self-supervised learning and introduces widely used algorithms including regression, decision trees, ensemble methods, support vector machines, k-nearest neighbors, Naive Bayes, gradient boosting, and k-means clustering. The chapter then examines deep-learning architectures such as Convolutional neural networks, recurrent and long short-term memory networks, transformers, generative adversarial networks, and diffusion models, together with training concepts including back propagation, gradient descent, regularization, transfer learning, and evaluation metrics. Applications across healthcare, finance, transportation, manufacturing, agriculture, education, cyber security, and creative work are discussed, alongside challenges involving bias, explainability, privacy, misinformation, employment, and computational and environmental costs. The chapter concludes by emphasizing human-centered deployment, responsible governance, interpretability, and continuous evaluation as AI systems become increasingly integrated into high-impact domains.

Kanchuboyina Divya, Kandi Vanitha, Akali Sowjanya, Praveen Kumar, Ramaiah Maddi  ·  International Journal of Medical and Health Sciences  ·  14 Sep 2026

Melioidosis is a serious infectious disease caused by the environmental Gram-negative bacterium Burkholderiapseudomallei. The organism is naturally found in soil and stagnant water, particularly in tropical regions. The disease is a major public health challenge, but it is frequently unrecognized in endemic areas such as Southeast Asia and northern Australia. Individuals with occupations that involve agricultural work pose a greater risk to infection, with exposure to contaminated environments being common. However, despite many people having contact with the pathogen, only a small proportion develop melioidosis. This evidence suggests the presence of both bacterial and individual host factors that influence the risk of infection and the severity of the disease. Recent advances in genomic research provide new opportunities to explore the interplay between bacterial variation and human genetic diversity in infectious diseases. Ongoing research investigates combined analyses of the pathogen and the host in the context of integrated genomic approaches. Projects such as BurkHostGEN obtain biological samples from infected patients but also healthy populations and analyze host DNA, gene expression, and the genetic variation of the bacterium. Researchers investigate environmental sources, such as the presence of B. pseudomallei in household water, to determine the presence and diversity of the pathogen. Integrating microbial genome-wide association studies in conjunction with host genetic data represents an emerging strategy to identify the genetic factors associated with the risk for infection, disease progression, and clinical outcomes in patients with melioidosis. In the long term, such comprehensive genomic studies can improve the overall understanding of melioidosis and further contribute to research of infectious diseases with environmental transmission.

Divyansh Mishra, RAJESH KUMAR MISHRA, Rekha Agarwal  ·  International Journal of Electrical and Electronics Engineering  ·  14 Sep 2026

Unplanned failure of three-phase induction motors is one of the leading causes of unscheduled downtime, safety hazards, and maintenance expenditure in industrial and power-system installations. This paper presents an extensive, reproducible machine learning (ML) framework for the automatic detection and classification of the three most prevalent electromechanical fault types bearing defects, broken rotor bars, and stator winding faults together with the healthy operating condition, using stator current signatures. Ten time- and frequency-domain features are extracted from simulated current waveforms generated with motor current signature analysis (MCSA)-informed fault models under randomized severity and realistic sensor noise. Four classifiers Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Random Forest (RF), and a shallow feed-forward Artificial Neural Network (ANN) are tuned via 5-fold cross-validated grid search and evaluated on a stratified 1,200-sample dataset (300 samples per class). After hyperparameter optimization, the Random Forest and proposed ANN classifiers achieve the highest test accuracy (98.33% each), followed by SVM (97.67%); k-NN trails substantially (76.67%). Paired t-tests over cross-validation folds confirm no statistically significant difference between ANN, RF, and SVM (p > 0.05), while the gap to k-NN is highly significant (p < 0.001). Beyond headline accuracy, this study contributes a feature-ablation analysis that identifies the sideband-energy ratio and harmonic ratio as indispensable features (their removal drops accuracy by up to 26.7 percentage points), a noise-robustness sweep showing Random Forest degrades most gracefully under increasing measurement noise, learning curves characterizing data efficiency, receiver operating characteristic (ROC) and precision-recall analyses per fault class, and a computational cost comparison (training time, per-sample inference latency, and parameter count) relevant to embedded deployment. The methodology, mathematical formulation of each classifier, dataset-generation procedure, and full evaluation protocol are documented to support reproducibility, benchmarking, and extension to experimentally acquired data.

Vivek Kashyap, Dr. Manoj Kumar Mittal  ·  International Journal of Electrical and Electronics Engineering  ·  14 Sep 2026

This research paper investigates the generation of electrical energy from mechanical stress, especially from footsteps and vehicle pressure, using piezoelectric transducers. The main objective is to experimentally analyze how force, repeated loading, arrangement of sensors, and electrical conditioning circuits influence the voltage and current output of the system. The project is relevant because large amounts of mechanical energy are wasted daily in crowded locations such as railway stations, shopping malls, footpaths, parking areas, toll plazas, and roads. The report discusses the physical principle of piezoelectricity, the design of a prototype system, the bridge rectifier and storage circuit, data collection methods, and performance evaluation. It also compares practical limitations such as small output power, dependency on intermittent loading, durability issues, and conversion losses. Along with experimental observations, the project presents tables, calculations, graphs to be drawn manually if needed, applications, future scope, and safety aspects. The report concludes that piezoelectric mechanical energy harvesting is feasible for low-power applications like LEDs, sensors, counters, and wireless monitoring systems, but it is not yet suitable for large-scale bulk power generation without significant optimization.

Dr. Kusham Lata  ·  International Journal of Communication, Media and Linguistics  ·  14 Sep 2026

Social media is widely seen as a low-cost channel through which women entrepreneurs with limited capital or formal infrastructure can reach customers, but little is known about how women entrepreneurs in a specific regional Indian context actually behave on the platforms they use. This study addresses that gap through a quantitative content analysis of ten women-led YouTube business channels selling clothing in Haryana, coding 290 posts across twenty-two variables covering platform choice, channel growth, posting behaviour, and customer-relationship indicators. YouTube was the dominant platform, used exclusively by 70% of sampled channels and combined with Instagram or Facebook by the remainder. Subscriber bases fell predominantly between 1 lakh and 50 lakh (60% of channels), and per-post views most often fell between 10,000 and 100,000 (50.3%). Recorded content (60%) was used more than live broadcasts (22.4%), and business-oriented posts dominated the sample (79.7%). Engagement was largely favourable, with positive comments (34.1%) far outweighing negative comments (0.7%), and most channels offered periodic discounts (58.5%) or free delivery (46.5%). The study concludes that social media, and video-based platforms in particular, function as low-cost, accessible infrastructure through which rural and semi-urban women entrepreneurs in Haryana build visibility, sustain customer relationships in the local Haryanvi language, and grow micro-enterprises largely without formal institutional support, even as gaps in digital and cybersecurity literacy continue to constrain further growth.

Ankita Chhikara  ·  International Journal of Technology and Emerging Research  ·  14 Sep 2026

Background: Nursing interns frequently work long night shifts, predisposing them to fatigue, reduced alertness, and a higher risk of clinical errors. Strategic power naps are underutilized in Indian healthcare settings despite evidence of their benefits. Objective: To evaluate the effect of a short power nap on the alertness level of night duty nursing interns using a reaction time test. Methods: A quantitative research approach with a quasi-experimental (one-group pre-test post-test) design was adopted. A purposive sample of 70 nursing interns working night shifts at Sharda Hospital was selected. Data were collected using a demographic profile sheet and the Psychomotor Vigilance Test (PVT) to assess reaction time before and 30 minutes after a scheduled 10-15 minute nap. Results: The majority of participants were female (70%) and aged 23-25 years (82.9%). The mean pre-nap reaction time was 645.73 ± 93.09 ms, which significantly improved to 327.30 ± 43.21 ms post-nap. The paired t-test demonstrated a highly significant statistical difference (t = 26.291, df = 69, p < 0.001). Conclusion: A 10-minute power nap significantly reduces reaction time and improves alertness among nursing interns during night shifts. Incorporating brief, scheduled naps into hospital policies is recommended as a cost-effective strategy to minimize fatigue and enhance patient safety

Shubham Marbade  ·  International Journal of Medical and Health Sciences  ·  08 Sep 2026

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are well established for the management of type 2 diabetes mellitus and obesity. However, growing evidence suggests that their therapeutic effects extend beyond glycemic control and weight reduction. This review summarizes the emerging pleiotropic effects of GLP-1 RAs on cardiovascular, renal, neurological, inflammatory, dermatological and vascular systems. Evidence indicates that GLP-1 RAs may improve endothelial function, reduce oxidative stress and inflammation, promote natriuresis and vasodilation, and provide cardioprotective and renoprotective effects. Potential benefits have also been reported in heart failure, aging-related cellular dysfunction, psoriasis, neurodegenerative disorders, cancer biology, and vascular smooth muscle dysfunction. These effects are associated with modulation of signalling pathways including cAMP/PKA, PI3K/Akt and AMPK, along with regulation of oxidative and inflammatory processes. Although cardiovascular and renal benefits have comparatively stronger clinical evidence, several other applications remain emerging and require further clinical investigation. Overall, GLP-1 RAs represent a promising multifunctional class of drugs with therapeutic potential beyond diabetes and obesity.

Lovedeep Kaur, PARMINDER SINGH, Dr. Naveen Dhillon  ·  International Journal of Computer Science and Artificial Intelligence  ·  06 Sep 2026

Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is an important task in computer-aided diagnosis because accurate identification of tumor regions supports clinical assessment and treatment planning. However, the complex structure, irregular shape, intensity variation, and heterogeneous appearance of brain tumors make automated segmentation challenging. This study presents a comparative deep learning framework for brain tumor segmentation using the BraTS 2020 dataset and two-dimensional (2D) MRI images. In this study, we explore 2D deep learning architectures for automated tumor segmentation, focusing on U-Net, Vision Transformer (ViT), and Res-ViT models. U-Net, with its encoder–decoder design and skip connections, has been widely adopted for medical image segmentation due to its ability to capture fine-grained spatial features. ViT, leveraging self-attention mechanisms, introduces a global receptive field that enhances contextual understanding across slices. The Res-ViT hybrid combines residual learning with transformer-based attention, aiming to balance local feature extraction and long-range dependency modelling. Preprocessing steps, including skull stripping, intensity normalisation, and bias field correction, were applied to ensure consistency across scans. Data augmentation techniques such as rotation, flipping, and elastic deformation were employed to mitigate overfitting and improve generalisation. The models are evaluated using important segmentation metrics, including Intersection over Union (IoU), accuracy, precision, recall/sensitivity, loss, and 95th-percentile Hausdorff Distance (HD95). The comparative analysis aims to identify the strengths and limitations of convolutional and transformer-based approaches for 2D brain tumor segmentation. The study demonstrates the potential of combining local feature extraction and global contextual learning to achieve more accurate and robust brain tumor segmentation from multimodal MRI images.

Dr. Tangutur Aparna  ·  International Journal of Law, Politics and Governance  ·  02 Sep 2026

Abstract India's shift towards digitally mediated governance has reshaped the citizen-administrative state dynamics. Biometric identity framework (Aadhaar), faceless assessment in the income tax administration, direct benefit transfer system and the Digital Personal Data Protection Act, 2023 are expected to be efficient, leak-proof and targeted, but leave questions unanswered before the court. This article questions whether the digital governance ecosystem in India has sufficiently harmonized the efficiency of administration with the fundamental rights of privacy, equality of substantive rights, and procedural rights in Article 21, 14 and due process. The article uses the doctrinal approach to examine the jurisprudence of the Supreme Court of India on 'informational privacy' and 'proportionality', as well as the statutory framework of the Aadhaar Act, 2016, the Information Technology Act, 2000, and Digital Personal Data Protection Act, 2023; and the administrative practice of algorithmic and automated decision-making in welfare and taxation. It believes that the judiciary has created a functional test of proportionality for privacy interests, but this test continues to be under-respected when it comes to algorithmic exclusion and automated adjudication, where reasoned decision making and meaningful review are often lacking. The article then envisions a calibrated approach that combines proportionality review with a compulsory duty of explaining algorithms and a system of independent institutional oversight, ensuring that enhanced efficiency in government does not sacrifice constitutionally protected rights.

M Razia Begum  ·  International Journal of Law, Politics and Governance  ·  02 Sep 2026

The rapid advancement of artificial intelligence has enabled the creation and dissemination of highly realistic deepfake content, creating significant challenges for electoral integrity and constitutional democracy. This paper critically examines the impact of deepfakes on democratic elections, with particular emphasis on the constitutional tension between safeguarding electoral processes and protecting freedom of expression. Adopting a qualitative, interpretivist and inductive approach, the research relies on secondary data comprising academic literature, legislation, judicial decisions, governmental reports and policy documents, analysed through thematic analysis. The paper finds that deepfakes can facilitate political misinformation, voter manipulation, identity impersonation and declining public trust, while existing legal frameworks remain insufficiently specific to address election-related synthetic media. A comparative assessment of India, the European Union, the United Kingdom and the United States demonstrates significant differences in legislative clarity, platform accountability and election-specific regulation. The research further establishes that excessive regulation may suppress legitimate political expression, whereas inadequate regulation may undermine informed democratic participation. It therefore advocates a proportionate constitutional approach based on clear and dedicated deepfake election legislation, stronger platform accountability and transparency, technological detection, digital literacy, judicial oversight and international regulatory cooperation. Such measures can strengthen electoral integrity while preserving the fundamental democratic value of freedom of expression in the digital era.

Joseph Kortu, Lahai Brima, Sundufu Alphansu Kamara  ·  International Journal of Computer Science and Artificial Intelligence  ·  02 Sep 2026

This study examined the impact of Information and Communication Technology (ICT) infrastructure on the productivity of academic staff at Eastern Technical University (ETU), Sierra Leone. Guided by three objectives: to examine the availability and types of ICT infrastructure, to assess the extent of its utilization, and to evaluate its impact on teaching, research, and related activities, the study adopted a descriptive survey design. Data were collected from forty-nine academic staff using a structured, five-point Likert-scale questionnaire and analyzed with descriptive statistics (frequencies and percentages) in SPSS and Microsoft Excel. The findings reveal a clear paradox. Availability of ICT infrastructure was limited and inconsistent, with unreliable electricity and the absence of digital learning platforms (both 44.9% disagreement) identified as the weakest areas, while utilization was moderate to low and fewer than half of respondents (44.9%) reported adequate ICT skills. In sharp contrast, an overwhelming majority affirmed that ICT strongly enhances productivity improved teaching effectiveness (91.9%), faster task completion (91.8%), and improved overall job performance (89.8%). Respondents also strongly affirmed persistent barriers, including unreliable internet connectivity (83.7%), inadequate training (85.7%), and lack of technical support (83.7%). The study concludes that although staff clearly recognize the value of ICT, its realization is constrained by infrastructural, skills-related, and support-related gaps. It recommends targeted investment in reliable electricity and internet, deployment of digital learning platforms, regular capacity-building, and a dedicated technical support unit.