Volume 13 | Issue 12 |

 Abstract

The proposed project is a Ransomware Detection System developed using Python, TensorFlow/Keras, Pandas, and NumPy. It employs lightweight deep learning models, specifically a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN), to accurately detect and classify Ransomware samples while remaining suitable for resource-limited environments. The MLP model captures nonlinear feature interactions, while the CNN model processes feature vectors as sequences to extract local patterns. Together, these architectures enable robust Ransomware detection with high accuracy and efficiency. To enhance performance, the system incorporates feature selection using mutual information, reducing input dimensionality while preserving critical information. The models are trained and evaluated on a balanced Ransomware dataset, with results measured using Accuracy, Precision, Recall, F1-score, and ROC-AUC. Additionally, a generalization test is performed using a hold-out subset of unseen Ransomware samples, demonstrating the models' ability to adapt to novel attack variants. By combining lightweight deep learning architectures with effective preprocessing, this proposed system provides practical and efficient solution for Ransomware detection in resource-constrained environments such as IoT devices, mobile systems, and embedded platforms.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Ransomware Detection, Cyber security, Lightweight Deep Learning, Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), Feature Selection, Efficient Classification, Resource-Constrained Systems, Generalization, Binary Classification.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The increasing reliance on Medical Internet of Things (MIoT) devices for patient monitoring and healthcare delivery has introduced significant security challenges, necessitating advanced threat detection mechanisms. This review provides a comprehensive analysis of deep learning approaches for MIoT threat detection, while also considering complementary techniques such as machine learning, anomaly detection, blockchain-based security, hybrid models, and cloud-edge integration. Traditional machine learning methods like SVM, Decision Trees, Random Forests, and k-NN are effective for structured data but struggle with complex healthcare datasets, whereas deep learning models such as CNNs, RNNs, and LSTMs excel in feature extraction and sequential data analysis, achieving higher accuracy in detecting malicious activities. Anomaly detection techniques, including autoencoders and clustering methods, offer the ability to identify zero-day attacks but often suffer from false-positive rates. Blockchain integration enhances trust, transparency, and data integrity, though at the cost of latency and energy efficiency. Hybrid frameworks that combine ML/DL with anomaly detection or blockchain provide a balanced approach to security, and cloud-edge computing synergy further improves scalability and real-time responsiveness. Collectively, these approaches highlight the growing importance of deep learning in enabling secure, reliable, and intelligent threat detection in Medical IoT environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MIoT, Deep Learning, Accuracy, Security

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study explores the historical evolution and cultural significance of masks by examining their roles across diverse civilizations and time periods. From early ritual practices to classical theatre, healing traditions, funerary customs, and modern artistic and protective uses, masks have served as powerful tools for expressing identity, mediating spiritual beliefs, and supporting social cohesion. Although extensive research exists on masks within individual cultures, comprehensive cross-cultural analyses remain limited. This paper addresses that gap by integrating perspectives from anthropology, performance studies, and material culture to trace how masks have transformed in form, function, and symbolic meaning. Through comparative historical analysis, the study reveals how masks embody themes of transformation, protection, and performance, and how they continue to influence contemporary expressions of identity and creativity. Ultimately, the paper argues that masks are more than ornamental objects they are dynamic cultural artefacts that reflect the evolving relationship between humans, society, and the sacred.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Masks Cultural significance Cross-cultural analysis Identity Ritual and performance

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Women constitute an eternal and indispensable component in the growth and development of any nation. With the passage of time , the role of women in society and nation-building has been increasingly recognized and substantially expanded across the world. It is now widely acknowledged that national development cannot be achieved without ensuring the holistic development and empowerment of women. Among the various dimensions of empowerment, political empowerment holds a central position, as it enables women to participate directly in decision-making processes and democratic governance. In India, Panchayati Raj Institutions (PRIs) serve as a vital mechanism for promoting women's political empowerment at the grassroots level. The 73rd Constitutional Amendment Act, which mandates reservation for women in PRIs, has significantly enhanced their representation and participation in local self-governance. This study focuses on Himachal Pradesh, where PRIs have emerged as an important platform for strengthening women's leadership and political awareness.

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 Keywords

Keywords: Women, Empowerment, Panchayati Raj Institutions, Himachal Pradesh

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Natural indicators extracted from plant sources offer an economical, environmentally sustainable, and readily available alternative to synthetic chemical indicators in pharmaceutical and analytical applications. This project focuses on the systematic isolation and comprehensive evaluation of natural acid-base indicators from selected plant species including Hibiscus rosa-sinensis, Curcuma longa (turmeric), Lawsonia inermis (henna), and other flowering plants rich in anthocyanins and flavonoid pigments. The isolation was performed using aqueous and methanolic extraction techniques followed by evaluation through pH titration studies and colorimetric analysis. Results demonstrate that natural indicators exhibit sharp color transitions comparable to synthetic indicators (phenolphthalein and methyl orange) across various pH ranges, with minimal standard deviations. The extracted indicators successfully performed acid-base titrations involving strong acid-strong base, strong acid-weak base, weak acid-strong base, and weak acid- weak base combinations. Natural indicators represent a viable, cost-effective, pollution- free replacement for conventional synthetic indicators, offering superior stability and reliability for pharmaceutical quality control and academic laboratory applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

natural indicators, anthocyanins, plant extraction, acid-base titration, pH indicators, pharmaceutical applications

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In this era of a politically unstable world, there is a growing demand for the use of military robots to aid the soldiers to perform perilous missions. This paper focuses on the design and build of a semi-autonomous, unmanned robotic system used for various military and rescue operations. Dangerous tasks such as bomb disposal, enemy territory surveillance, search and rescue can be efficiently carried out by the Military Support and Rescue Robot(MSRR). This reduces the risk of losing the lives of both soldiers and civilians. With the help of live feed from the wireless camera and data analysis of environmental composition by various sensors of the area under surveillance the soldiers can better prepare for their missions. Using technology, the above-mentioned tasks can be achieved. The different sensors of the robots are connected to the Arduino UNO which in turn is connected to the bluetooth. Data transmission and receiving are through bluetooth technology. This prototype design overcomes the weakness of the existing models and thus provides better support for military operations.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Arduino UNO, MSSR, EOD, Rescue Robot

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research presents a real-time driver drowsiness detection and alert system using Python-based computer vision and machine learning techniques. The system combines several detection methods--including Eye Aspect Ratio (EAR), facial landmark tracking, head pose estimation, and temporal behavioral analysis--to accurately identify signs of fatigue. Using OpenCV, dlib's 68-point landmark model, and scipy for signal analysis, it provides multi-modal alerts through audio, visual, and haptic feedback. Tested on 150 participants under varied lighting, demographics, and driving conditions, the system achieved 96.7% accuracy, 94.3% sensitivity, 97.8% specificity, and a rapid 0.98-second response time. It also maintains real-time performance at 35 FPS and reduces false positives to 2.1%, outperforming existing commercial solutions. Additional features include adaptive user calibration, environmental condition adjustments, and APIs for seamless integration into vehicle systems. Overall, the system demonstrates strong reliability and suitability for widespread use in advanced driver assistance systems (ADAS).

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Alert system, vehicle, Android, Automation NLP, Face recognition.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Computational Chemistry, Drug Design, Molecular Positioning, Computational Screening, Pharmaceutical Characteristic Prediction, Structure-Activity Relationship

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cryptocurrency has emerged as a revolutionary form of digital currency, challenging traditional financial systems and legal frameworks across the world. Its decentralized nature, anonymity in transactions, and borderless functioning raise significant legal implications that demand critical examination. In India, the status of cryptocurrency remains uncertain due to the absence of specific legislation and the fluctuating judicial and regulatory approach. While the Supreme Court in Internet and Mobile Association of India v. Reserve Bank of India (2020) struck down the RBI's banking ban on virtual currencies, the lack of comprehensive regulatory guidelines continues to pose challenges. The legal implications primarily revolve around issues of taxation, money laundering, investor protection, cybersecurity, and the possible conflict with existing financial laws. Moreover, the potential misuse of cryptocurrency in terror financing and illegal trade further complicates the regulatory landscape. This paper seeks to analyze the evolving legal status of cryptocurrency in India, compare it with international frameworks, and suggest reforms that balance innovation with legal certainty and consumer protection.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cryptocurrency, Blockchain, Legal Implications, Cyber Security, Regulation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Sepsis, a life-threatening organ dysfunction due to a dysregulated host response to infection, continues to pose a significant clinical challenge despite advances in supportive care. The review synthesizes findings from randomized controlled trials and cohort studies to evaluate the impact of vitamin C--administered alone or in combination with thiamine and hydrocortisone--on key outcomes such as mortality, organ dysfunction (SOFA scores), vasopressor requirements, and safety profiles. The evidence regarding vitamin C monotherapy is mixed: several meta-analyses suggest possible reductions in mortality and improvements in SOFA scores and vasopressor duration, but these findings are not consistently statistically significant, and some large trials even indicate potential harm. Combination therapies, particularly the "metabolic resuscitation cocktail" (vitamin C, thiamine, hydrocortisone), have not demonstrated significant mortality benefit in high-quality trials, though modest improvements in organ dysfunction metrics are observed. Vitamin C is generally well tolerated, but rare adverse effects--such as increased renal replacement therapy or hypernatremia--have been reported in certain studies, warranting caution in patients with specific comorbidities. Substantial heterogeneity in dosing regimens, timing, patient populations, and study design complicates interpretation, and the overall certainty of evidence for major outcomes remains low to moderate according to GRADE criteria. Current guidelines do not recommend routine intravenous vitamin C use in sepsis outside of clinical trials. The review underscores the need for large, well-designed randomized studies to identify patient subgroups most likely to benefit, determine optimal dosing strategies, and clarify the role of vitamin C--alone or in combination--in improving clinically significant outcomes in sepsis. This systematic literature review critically examines the efficacy and safety of intravenous vitamin C administration in the management of sepsis and septic shock.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

vitamin C; sepsis; literature; mortality; hydrocortisone

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Creative Commons Attribution 4.0 and The Open Definition