Volume 13 | Issue 12 |

 Abstract

Reliable detection of malware is a fundamental component of cybersecurity in the modern world. Models have to be able to identify new and sophisticated malware strains, even among large and complex datasets, while having accuracy, efficiency, and understandability. Review of previous research, based on direct comparison, of Quantum Support Vector Machines, Quantum Neural Networks,and hybrid modeles such-as Quantum Multilayer Perceptron is the basis of the proposed clear and concise QML framework for malware detection. Basic research by Cai et al. on QSVM demonstrates high classification accuracy. In exploration of bases, QNN has passed the trials and indicated the need for improvement due to data re-uploading. Finally, further experiments on QMLP and QCNN researched the relationship between classification accuracy and model training cost. XAI added the level of interpretability and the analysis demonstrated an O(log n) computational leverage, which is the key ingredient that maintains this field. These combinations of research construct the unified framework of the strong classification strength of QSVM, the architecture flexibility of QNN, and the insights from XAI. This leads to a more robust accuracy, explainability, and reliability of existing and future malware detection systems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Quantum Machine Learning , Malware Detection , Cybersecurity , Quantum Based Neural Network , Quantum>Support Vector Machine , Quantum Convolutional Neural>Network, Explainable AI, QMLP, LIME.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Diabetes mellitus, a widespread worldwide health issue marked by high blood glucose levels, requires novel treatment strategies for increased effectiveness and better patient compliance. Drug delivery systems could be revolutionized by nanotechnology, which operates at the nanoscale (1-100 nanometers) and offers a revolutionary paradigm in medicine called nanomedicine. The potential of nanotechnology to transform diabetes care is examined in this review, with an emphasis on improving medication delivery for diabetes control. Liposomes, polymeric nanoparticles, and dendrimers are examples of nanoparticles that provide targeted delivery, enhanced bioavailability, and regulated release kinetics. These formulations selectively accumulate therapeutic agents in diabetic tissues by taking advantage of improved permeability and retention. With their sensors and feedback systems, smart nanoparticles react to changes in blood sugar levels to deliver individualized treatment in real time. Nanosensors that identify biomarkers linked to diabetes provide insights into the course of the disease in addition to drug delivery. By customizing treatments to each patient's unique profile, this integrated approach is consistent with personalized medicine. In order to advance diabetes management into a new era of precision medicine for better patient outcomes and fewer treatment-related burdens, this review critically examines current knowledge in the fields of materials science, pharmacology, and bioengineering. The objective is to introduce novel therapeutic approaches while emphasizing how nanotechnology is revolutionizing the treatment of diabetes.[1]

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Nanotechnology, Diabetes, Drug-Delivery, Liposomes, Nano-Particles

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Background: Muscular dystrophies (MDs) refer to a group of inherited disorders that cause progressive weakness and degeneration of skeletal muscles. Conventional management focuses on physical therapy, medication and supportive care, but yoga has emerged as a complementary therapy offering holistic healing. Objectives: This paper aims to explore how the Integrated Approach of Yoga Therapy (IAYT) can support patients with Muscular Dystrophy by improving physical function, emotional stability and overall quality of life. Methods: The review of the literature is compiled from authentic sources, including PubMed, Google Scholar, Web of Science, Yoga Research Journals and traditional yogic texts. The paper interprets the application of the IAYT model--Annamaya, Pranamaya, Manomaya, Vijnanamaya, and Anandamaya Kosha--for holistic management of Muscular Dystrophy. Results: The available studies are small, heterogeneous, and mostly pilot or add-on designs; however, they consistently demonstrate feasibility and signal benefits, particularly in pulmonary function and specific measures of mobility and quality of life. Evidence quality is low to moderate; randomised, adequately powered trials are lacking. IAYT's person-centred framework fits the multi-domain needs of MD patients. Conclusion: Yoga, delivered within an IAYT framework and integrated with physiotherapy and respiratory care, is a promising adjunct for MD rehabilitation. We present a replicable Integrated Yoga Module (IAYM-MD) and recommend priority research designs to evaluate safety, efficacy and mechanisms.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Muscular Dystrophies (MDs), Duchenne Muscular Dystrophy (DMD), Yoga Therapy, IAYT Model, Panchakosha, and Rehabilitation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The COVID-19 pandemic has favourable impact on improving public hygiene standards. Like other businesses, street food stall vendors are also positively affected by the pandemic in terms of better hygiene standards. The study reveals that food stalls established post pandemic exhibit improved hygiene practices, in turn contributing public health and reduced disease risk. Another significant outcome from the point of following hygiene practices is positive association between hygiene practices followed by the street food stalls and their revenue further motivating the new entrants to maintain good hygienic practices. Besides, longer duration of the operation of the food stall has also generated higher revenue for the vendor. Consumers' perception of a hygienic street food stall is aligned with the increased revenue for the one following hygienic practice. A successful street food business requires both environmental factors as well as personality traits along with interpersonal skills. Selection of an appropriate location for business with absence of flies, garbage heaps near the selling place, open drainage near the selling place is one of those factors. Sustained efforts and hard work are of paramount importance for a successful business. Building strong interpersonal relationships is critical for trust and loyalty.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Street food stalls, hygiene, revenue, successful entrepreneur, consumers' perception of hygiene

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Timely and accurate diagnosis of brain tumors plays a crucial role in neurological treatment. MRI imaging is the most reliable means of identification, but manual interpretation can be time-consuming and expert-dependent, leading to errors. To address this issue, this research developed a hybrid, interpretable, and resource-efficient framework based on Scale-Invariant Feature Transform (SIFT)-based feature extraction, Principal Component Analysis (PCA)-based dimensionality reduction, and Support Vector Machine (SVM) and Artificial Neural Network (ANN)-based machine learning classification. Experimental evaluation on the BraTS 2021 and Kaggle Brain MRI datasets showed that the proposed SIFT-PCA-ML model achieved 96-97% accuracy and 0.98-0.99 AUC, comparable to modern deep learning models, but with significantly lower computational complexity. Additionally, SIFT keypoint mapping and Grad-CAM heatmaps of the ANN clearly depicted tumor-related critical regions, enhancing the model's interpretability. This study demonstrates that the combination of handcrafted features and machine learning, especially SIFT-PCA-ANN-based models, can provide an effective, reliable, and clinically applicable solution for brain tumor detection--especially in medical centers that lack high-end GPUs or big data resources.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Brain Tumor Detection, Machine Learning, SIFT, PCA, SVM, ANN, MRI Classification, Feature Extraction

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Brain tumor segmentation is a crucial task in medical image analysis, essential for accurate diagnosis and treatment planning. Manual segmentation of MRI scans is time-consuming and prone to human error due to tumor heterogeneity, irregular boundaries, and imaging artifacts. This study proposes an advanced automated segmentation model using Multi- Modal Graph Convolutional Networks (M2GCNet) for accurate and efficient brain tumor detection. The system integrates multiple MRI modalities--T1, T2, FLAIR, and T1c--to extract complementary information and enhance feature representation. It employs Spatial Graph Convolution Modules (SGCM) and Channel Graph Convolution Modules (CGCM) to capture spatial and inter-modal relationships. Multi-scale feature extraction with dilated convolutions improves tumor boundary detection, while an efficient decoder with skip connections ensures spatial consistency. The proposed model was evaluated on BraTS 2018 and 2019 datasets, achieving superior segmentation results in terms of Dice Similarity Coefficient (DSC) and accuracy. By combining deep convolutional and graph-based feature learning, M2GCNet demonstrates robust performance across diverse MRI modalities. This work highlights the effectiveness of integrating graph-based reasoning and multi-modal fusion in medical image analysis, paving the way for improved diagnostic support systems in healthcare. Furthermore, the model exhibits strong generalization capabilities, performing reliably across different patient datasets and imaging conditions. The modular architecture allows easy adaptation to other medical imaging tasks, enhancing segmentation accuracy while remaining computationally efficient, and demonstrates robust performance.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Whole Tumor, Tumor Core, Enhancing Tumor

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Colonialism in India's northeastern states brought about significant changes in politics, the economy, and culture. British policies like annexation, land revenue systems, the inclusion of chiefs in government, and missionary work weakened traditional ways of governing, subsistence economies, and social structures among indigenous people. This paper analyzes the transitions experienced by the Nagas, Mizos, Khasis, Garos, and other tribal groups, focusing on their responses such as resistance, adaptability, and cultural preservation. Colonial governance provided improved infrastructure, educational institutions, and commercial opportunities; but, it concurrently rendered the economy and government increasingly interdependent and hierarchical. The research demonstrates the impact of tribal resilience on the region's enduring social, political, and cultural dynamics. Colonialism in Northeast India significantly impacted the tribes' political, economic, and cultural dynamics. British activities, such as annexation, land revenue systems, the administrative co-option of chiefs, and missionary intervention, eroded traditional governance, subsistence economies, and indigenous social structures. This paper analyzes the transitions experienced by the Nagas, Mizos, Khasis, Garos, and other tribal groups, focusing on their responses such as resistance, adaptability, and cultural preservation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

tribal communities, Northeast India, colonialism, governance, cultural change, resistance, postcolonial studies

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract The imposition of trade tariffs by the United States has had far-reaching implications across global financial systems, particularly for emerging economies such as India. This study examines the impact of U.S. trade tariffs on India's capital market and financial services sector. The research analyses how tariff-induced changes in global trade dynamics influence investor sentiment, foreign portfolio investments (FPI), exchange rate volatility, and overall market performance. Furthermore, it investigates the indirect effects on the financial services sector, including banking, insurance, and investment services, through shifts in global capital flows and risk perception. The findings suggest that while India experiences short-term volatility due to global trade uncertainty, its capital market demonstrates resilience in the long term, supported by strong domestic fundamentals and policy interventions. The paper concludes by emphasizing the need for diversified trade partnerships and prudent monetary measures to mitigate external shocks stemming from global tariff policies. Challenges and Opportunities of a trade deal with U.S by November 30th 2025 an Indo-U.S. trade deal by November 30, 2025, presents a complex mix of challenges and opportunities, primarily influenced by ongoing geopolitical dynamics and domestic political interests. The current landscape is marked by the U.S. imposing heavy tariffs on India over its Russian oil imports, while both sides are under pressure to finalize an agreement to address economic disruptions.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: 1. Trade Tariffs, 2. Capital Market, 3. FDI 4. IRR Model

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Kimera is the science fiction novel in Malayalam language which explores the dark side of Genetics. This article is intended to examine and introduce the extent to which this novel faithfully presents scientific matters.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

malayalam language

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper analyses the morphology of Motihari, the district headquarters of East Champaran, Bihar. It explores the physical, historical, and socio-economic factors that have shaped the town's spatial form, identifies critical urban issues such as congestion, encroachment, and environmental degradation, and proposes sustainable morphological interventions. The study combines both primary and secondary data, GIS-based mapping and field observations to produce existing and proposed urban structure. The results reveal that Motihari's urban core around Motijheel Lake has expanded in a concentric yet unplanned manner toward the north-west and south-east fringes, producing spatial inequality and ecological pressure. Future morphological improvement depends on reclaiming public spaces, protecting the lake, improving drainage, and establishing a clear growth boundary.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Urban morphology, land use, sustainable planning, flood risk, fringe development

  License

Creative Commons Attribution 4.0 and The Open Definition