Current Issue
Volume 1, Issue 1 - 2026 (Oct-Dec 2026)
Issue Details:
Volume 1 Issue 1 (Oct-Dec 2026)Issue Description:
Welcome to the 2026 issue of The Intellecta International Journal of Research and Innovation. This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.
We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.
As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.
Dr. Deshpande Madhuri Madhukar
Editor-in-Chief
The Intellecta International Journal of Research and Innovation
Articles in This Issue
Open AccessMULTI-SENSOR MULTI-SENSOR GEO-SPATIAL DEEP LEARNING MODEL (MS-GDLM) FOR AUTOMATED GLACIAL LAKE MAPPING AND GLOF SUSCEPTIBILITY FORECASTING IN HIGH MOUNTAIN ASIA
Glacial Lake Outburst Floods (GLOFs) in high mountain Asia (HMA) contains large-scale hazards which threaten downstream populations, infrastructure and ecosystems. Current automated self-supervised glacial lake mapping methods mainly rely on single-sensor data or visual interpretation, which result in boundary inaccuracies and the dif iculty of hazard dif erentiation. We present the Multi-Sensor Multi-Sensor Geo-Spatial Deep Learning Model (MS-GDLM) (MS-GFM): a joint encoder-decoder model which learns from Sentinel-1 synthetic aperture radar (SAR) and optical Sentinel-2/Landsat imagery, Copernicus DEM terrain variables, and glacier inventory data. The model utilizes a multi-task head for performing pixel-wise lake facet segmentation and GLOF susceptibility prediction in parallel. The observations described include a well characterized synthetic dataset of 320 glacial lakes across eight HMA subregions—Karakoram, Hindu Kush, Western and Eastern Himalayas, Tibetan Plateau, Pamir, Tian Shan and Hengduan—over years 2008-2017 with an associated set of 35 spectral, terrain, morphometric and temporal features. Experimental evaluation shows a mean Intersection-over-Union (IoU) of 0.82, Dice/F1 score of 0.90, general classification accuracy of 66.25% and an ROC-AUC of 0.90 in predicting susceptibility detection. Random Forest analysis with a baseline detects that glacier retreat rate, dam slope, and lake area are among the most influential predictors of GLOF. The proposed MS-GFM is able to perform well with good generalization across regions, and outperforms all single-sensor benchmarks by a considerable margin.
Contributors:
Open AccessJAIN DIETARY HABITS IN RELATION TO CIRCADIAN RHYTHM
A person's dietary habits are firmly rooted in their cultural, religious, and physiological surroundings. These habits not only have an impact on an individual's health but also on the standards that are prevalent in society as a whole. Jainism is an ancient religion that originated in India. It promotes a way of life that places an emphasis on non-violence (ahimsa) towards all living creatures. This approach extends to dietary choices, which has led to a diet that is mostly vegetarian and places stringent limitations on the intake of some foods, notably those that are thought to contain living forms. In the same way, the circadian rhythm, which is an innate biological clock, is responsible for regulating a variety of physiological processes, including as digestion and metabolism, in accordance with the day-night cycle. In this review study, the confluence between Jain dietary practices and the principles of circadian rhythm is investigated. The purpose of this research is to shed light on how adherence to Jain dietary rules may impact circadian health and vice versa. The purpose of this study is to provide insight on the complex interaction between cultural traditions and biological rhythms in the process of forming dietary habits and their consequences for health outcomes. This is accomplished by evaluating the timing of meals, practices of fasting, and the possible alignment with circadian cycles.
Contributors:
Open AccessA PREVALENCE ADJUSTED AND PROTOCOL-SPECIFIC EVALUATION OF MACHINE LEARNING-BASED INTRUSION DETECTION SYSTEMS
Machine learning intrusion detection is evaluated almost entirely through metrics computed across an entire test partition. This paper argues that the practice has two defects and demonstrates both with measurements. The first defect is protocol-level concealment: network protocols carry attack traf ic in very dif erent proportions, so an aggregate metric averages over problems of unequal base rate, and a detector that fails on a minority protocol can still report a respectable aggregate figure. The second defect is prevalence distortion: precision depends on the base rate of the positive class, and benchmark corpora carry attack prevalences one to two orders of magnitude above those of operational networks, so reported precision overstates what a deployed system will achieve. Six experiments were conducted, crossing three detectors with two benchmark corpora and evaluating 72,856 held-out records. A Random Forest reporting an aggregate recall of 0.7626 was found to attain a recall of 0.0361 within the ICMP subset, a dif erence of a factor of 21.1 that no aggregate statistic exposes. Detection quality across the five assessable protocol subsets correlated with subset attack prevalence at r = 0.7773. Holding measured sensitivity and specificity fixed, a precision of 0.9793 at benchmark prevalence corresponds to 0.3415 at an operational prevalence of one percent. The paper closes with a reporting protocol that addresses both defects and costs no additional data, training, or computation.
Contributors:
Open AccessGLOBAL TRENDS SHAPING THE FUTURE OF SUSTAINABLE ENTREPRENEURSHIP AND BUSINESS
Sustainable entrepreneurship has become more important in academics and industry among rising global issues like climate change, resource depletion, and changing consumer preferences. Emphasising important topics such globalisation, technological advancements, and corporate social responsibility (CSR), this bibliometric analysis tries to investigate international research trends in sustainable entrepreneurship. This study examines three hundred peer-reviewed papers from well-known databases in order to find clear patterns in subject clusters, citation dynamics, and publication growth. The results show that encouraging sustainability in business depends on technology innovation—especially in sectors like artificial intelligence and blockchain—which is particularly lacking in others. Moreover, Globalisation has compelled firms to integrate sustainable practices into their supply chains, therefore augmenting their competitiveness and ensuring regulatory compliance. The increasing focus on CSR and stakeholder engagement underscores the need of aligning company goals with social and environmental limitations. This report of ers essential insights into emerging trends that will influence the trajectory of sustainable entrepreneurship, presenting opportunities for innovation and enduring value creation.
Contributors:
Open AccessPROTOCOL-STRATIFIED ANOMALY DETECTION OF TCP, UDP AND ICMP ATTACKS WITH A LINEAR-ATTENTION TRANSFORMER: EVIDENCE FROM NSL-KDD AND UNSW-NB15
In general, studies of intrusion detection report only one accuracy or F1 score calculated on the entire test set. Since TCP, UDP and ICMP toxicate attack traf ic in very dif erent ratios, an average such a figure overprofiles problems of varying dif iculty and may mask a detector that collapses on a complete protocol. We introduce a protocol-aware anomaly detector, based on the Linformer, a variation of transformer that scales its attention cost linearly with the input length, and we rank it on each protocol on two benchmark corpora. On NSL-KDD (148,517 records) and UNSW-NB15 (215,757 records after protocol filtering), six experiments are done on a fully crossed design comparing the Linformer with a Decision Tree and a random forest that has 72,856 held-out records. On NSL-KDD the Linformer reached an accuracy of 0.9823, an F1-score of 0.9816 and an AUC-ROC of 0.9987; on UNSW-NB15 the corresponding values were 0.9231, 0.9344 and 0.9832. Breaking down the results according to protocol revealed a bug that the cumulative figures disguised: only 3.61 percent of the ICMP attacks were identified by the Random Forest, which has a cumulative accuracy of 0.8784 on NSL-KDD, compared to 99.30 percent by the Linformer. The protocol subsets (among five that are assessable) F1 increased with attack prevalence (Pearson = 0.7773), indicating that a direct solution is per-protocol threshold calibration. Extrapolating the measured error rates onto the prevalence values determined that accuracy reduces to 0.3415 at 1% attack traf ic, indicating the detector should not be used as a blocker, despite the fact that at 0.9793, the prevalence matches the benchmark prevalence criterion.
Contributors:
Open AccessPRECLINICAL INVESTIGATION OF CURCUMIN IN RHEUMATOID ARTHRITIS: EVALUATION OF ITS ANTI-INFLAMMATORY EFFICACY IN IN VITRO AND ANIMAL MODELS
This study is an exploration of preclinical ef icacy and safety of Curcumin compared with conventional anti-inflammatory medication in the management of Rheumatoid Arthritis (RA) as a natural replacement. The models applied in Wistar rats were Collagen-Induced Arthritis (CIA) and Adjuvant-Induced Arthritis (AIA) after which the ef ects of Curcumin (200 mg/kg) were compared to Indomethacin (10 mg/kg). The measurement of parameters was done over 28 days: paw volume, arthritis score, body weight, and pain threshold with biochemical and histopathological parameters assessments. The ef ect of curcumin therapy on the treatment of arthritis showed a strong decrease in swelling, pain, inflammation of the joints, and the restoration of joint architecture and functions. Its anti-inflammatory and antioxidant ef ects were supported by serum cytokines (TNF- a, IL-1 b, IL-6) and markers of oxidative stress (MDA, GSH, and SOD). The histopathological analysis showed that the cartilage, as well as inflammatory infiltration, was preserved in the Curcumin-treated joints with minimal inflammatory infiltration. Notably, Curcumin did not exhibit any form of hepatic or gastrointestinal toxicity as opposed to the side ef ects of the normal NSAIDs. Altogether, Curcumin proved to be a powerful therapy with the ability to reduce RA symptoms, support functional performance, and increase the level of well-being. These findings aid the applicability of Curcumin as an adjunct/alternative and safe natural agent in the treatment of rheumatoid arthritis.