Volume 13 | Issue 11 |

 Abstract

Machine vibration-based health monitoring has emerged as a vital technique for predictive maintenance and fault diagnosis in rotating and industrial machinery. This review conducts a detailed comparative analysis of recent research papers focusing on vibration signal processing and machine learning-based diagnostic models. Studies published between 2017 and 2025 demonstrate a significant evolution from traditional signal and frequency domain methods toward data-driven frameworks leveraging deep learning (DL), convolutional neural networks (CNN), long short-term memory (LSTM), and transfer learning approaches. Benchmark datasets such as the Case Western Reserve University (CWRU) and Paderborn datasets remain the primary sources for model evaluation. Despite the impressive accuracy achieved by deep models, several challenges persist, including handling noisy sensor data, ensuring model generalization in varying operational conditions, and achieving real-time deployment on low-power devices. Moreover, there is limited exploration of multimodal sensor fusion and domain adaptation techniques. The study concludes that future research should focus on integrating IoT-enabled vibration monitoring systems with edge AI, adaptive learning, and cloud-based predictive analytics to advance intelligent fault diagnosis and ensure industrial reliability in the era of Industry 4.0.

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 Keywords

Vibration Analysis; Condition Monitoring; Fault Diagnosis; Machine Learning; Deep Learning; CNN;Transfer Learning; Predictive Maintenance; IoT; Edge Computing;

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Dowry practices remain one of the most deeply entrenched social issues in many cultures, often resulting in severe forms of discrimination and hardship for brides and their families. Despite legal bans and ongoing awareness campaigns, dowry continues to influence marriage negotiations and perpetuate gender-based inequality. This paper explores the social, psychological, economic, and cultural injustices experienced by brides and their families as a result of dowry traditions. It further highlights the systemic roots of the practice and proposes pathways toward sustainable reform.

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 Keywords

Dowry System Gender Inequality Social Injustice Patriarchal Norms Economic Exploitation Cultural Traditions Dowry-Related Violence Legal Framework Women's Rights Social Reform

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The analysis reviews the combination of Bayesian classifiers with Gabor filters together with Support Vector Machines (SVMs) for enhancing secure face authentication throughout cloud-based systems. The paper stresses the importance of Gabor wavelets because these filters extract face features and retain their discrimination properties despite lighting and positioning changes. The SVM method provides reliable classification functions and it enables separation of face entities from non-face elements in simplified representation areas. A study of Bayesian approaches enables the enhancement of authentication system reliability by dealing with probabilistic uncertainties present in encrypted facial templates. The framework combines those techniques with principles of homomorphic encryption to establish protected storage and processing of biometric data in cloud systems that guarantee privacy. This paper examines computational complexity alongside adversarial vulnerabilities through an analytical assessment before providing security methods and efficiency enhancement solutions. The method combines these technologies to build a solution for secure medical data manipulation that can expand within decentralized cloud networks.

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Feature extraction via Gabor wavelets; SVM classification robustness; Bayesian uncertainty modelling; Homomorphic encryption integration; Privacy-preserving cloud authentication

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This study investigates the effectiveness of selected technical indicators in decoding market trends of five major NSE-listed companies: Reliance Industries, Infosys, Tata Motors, State Bank of India, and Larsen & Toubro. Using six months of historical price data sourced from leading trading platforms, the analysis applies Relative Strength Index (RSI), Simple Moving Averages (SMA), Bollinger Bands, and Standard Deviation to evaluate trend direction, momentum strength, overbought and oversold conditions, and market volatility. The study finds that SMA crossovers serve as reliable signals of trend reversals, while RSI effectively identifies momentum shifts and potential entry/exit points. Bollinger Bands reveal periods of volatility contraction and expansion, highlighting breakout zones and price extremes. Standard deviation further validates fluctuations in volatility across the selected stocks. Collectively, these indicators provide meaningful insights into market behavior, enabling traders to interpret price movements with greater precision. The findings reinforce that while technical indicators do not guarantee price prediction, their combined application significantly enhances decision-making in dynamic market environments. The study concludes that multi-indicator strategies offer a more robust approach to understanding stock market behavior, particularly in fast-moving sectors, and recommends integrating technical analysis with fundamental perspectives for improved investment outcomes.

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Technical Analysis , Relative Strength Index (RSI), Simple Moving Average (SMA), Bollinger Bands, Standard Deviation, Market Trends

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This research paper examines India's derivatives market in 2025, focusing on emerging market trends, risk-return dynamics, retail participation patterns, regulatory reforms, case-based evidence, and global comparative perspectives. The study integrates secondary data drawn from authoritative sources such as the Securities and Exchange Board of India (SEBI), the National Stock Exchange (NSE), the Reserve Bank of India (RBI), the Bank for International Settlements (BIS), and verified media reports. The analysis reveals that retail investors continue to face substantial financial losses primarily due to speculative trading behavior, high leverage exposure, and limited understanding of derivative instruments. In contrast, professional investors benefit from disciplined strategies and advanced risk-management frameworks that yield sustainable returns. Regulatory interventions introduced during FY 2024-2025, including enhanced surveillance, revised margin norms, and structured investor education programs, have contributed to improved market transparency and systemic stability. Nevertheless, the study emphasizes that long-term market sustainability depends on the alignment of regulation, education, and behavioral reform. Continuous investor awareness, disciplined trading practices, and robust governance mechanisms are critical for transforming India's derivatives market into a balanced and inclusive financial ecosystem. The paper concludes with recommendations for effective risk management, policy refinement, and investor capacity-building to ensure long-term market stability and inclusivity.

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Keywords: Indian Derivatives Market, Retail Participation, Regulatory Reforms, Risk Management, Behavioral Finance, Market Stability, Investor Education.

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Nurses have a critical role in tracheostomy care. By its very nature, it is a technical procedure, requiring expertise to manage the safety and patency of the patient's airway, and avoid complications. Nurses support patients with tracheostomies whilst in the care and emergency setting. Yet studies indicate widely differing levels of knowledge, predominantly as a result of lack of training and absence of established educational programmes. This review synthesizes results from national and international studies to explore the efficacy of various types of educational interventions including structured teaching programs, workshops, simulations, and online modules in enhancing staff nurses' knowledge and skill level when performing tracheostomy care. The evidence base indicates that use of these educational techniques will greatly enhance both theoretical knowledge, clinical confidence and adherence to the principles of evidence based practice. Though such practises can yield very favorable short-term results, long-term knowledge retention, the lack of standardised training protocols and that information is not shared as widely - including through advanced digital media like virtual and augmented reality - are providing reasons for concern. This review highlights significant research areas of concern, future directions, and potential implications for nursing education and policy.

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Tracheostomy, Education, Training, Simulation, Knowledge

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ABSTRACT: This article makes a humble attempt to discuss on the topic: "DANDA NACHA: AN INSTRUMENT OF SOCIAL UNITY." For this purpose, the present study comprises four parts only. In first parts, it deals with the origin and significance of Dandanacha. The second parts deals with the suffering: the path of renunciation. The third parts deals with Saivism and its positive impact. Lastly, it deals with the concluding position of the essay. An attemp is made in this article to know how Danda Jatra is connected to various theories of punishment and especially Reformative and Preventive theory of punishment. A new terminology and theological theory of punishment is assumed to collogical form of dance. Dandanacha is one of the most important traditional dance festivals organised in different parts of South Odisha and particularly in the Ganjam district, the Heartland of ancient Kalinga Empire. The Dandanacha is celebrated in the odia month of Chaitra (March- April) of every year. It's an ancient festival of the Kalinga Kingdom and still alive in and around the Odisha, particularly at Ganjam district. It is performed for over a period of 13,15 or 21 days.

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KEY WORDS: Danda Nacha, Punishment, Saivism, Unity, Danduas, Social

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Drug discovery and development is a complicated, slow, and expensive process. It goes through several steps, from identifying targets to clinical evaluation. This review summarizes each step and outlines major challenges. These include high attrition rates, long timelines, and regulatory obstacles. It also highlights new strategies like artificial intelligence, nanotechnology, and personalized medicine that can improve efficiency and success in modern drug development.

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Drug discovery, drug development, clinical trials, challenges, artificial intelligence, nanotechnology, personalized medicine

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This paper presents a system based on Convolutional Neural Networks (CNNs) for detecting deepfake videos. It tackles the growing threat of AI-generated visual manipulation. Deepfake technology allows for the creation of very realistic videos that can spread false information, lead to identity theft, and harm reputations. The proposed system examines extracted video frames to find pixel-level inconsistencies and visual flaws that separate real content from fake. By using a lightweight, frame-based CNN architecture, the model achieves high accuracy and keeps computational demands low. This approach addresses the shortcomings of traditional deepfake detection methods. The system is trained on various datasets that include both authentic and manipulated videos. This helps improve its ability to generalize across different deepfake generation techniques. Experimental results show strong performance and scalability, confirming the model's ability to accurately detect deepfakes. This method provides a practical and efficient way to fight the misuse of deepfake technology in areas like cybersecurity, media verification, and digital forensics

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Deepfake Detection, Convolutional Neural Networks (CNN), Artificial Intelligence (AI), Video Forensics, Digital Media Security. _____________________________Deepfake Detection, Convolutional Neural Networks (CNN), Artificial Intelligence (AI), Video Forensics, Digital Media Security. __________________________________________________________________________

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Abstract: The therapeutic potential of Syzygium cumini (black plum or Jamun) seed powder (JSP) in managing type 2 diabetes mellitus (T2DM) is widely recognized in traditional medicine, largely attributed to its rich phytochemical composition of polyphenols, flavonoids, and tannins. These compounds confer a range of benefits. Mechanistically, JSP appears to exert its effects through multifaceted pathways: it can stimulate insulin secretion from pancreatic beta-cells, improve peripheral insulin sensitivity, inhibit key carbohydrate-digesting enzymes (like alpha-amylase and alpha-glucosidase), and actively mitigate oxidative stress-induced beta-cell damage via its significant antioxidant capacity. A review of available clinical evidence indicates that limited human studies support these preclinical findings. Trials have reported promising outcomes, including notable reductions in both fasting and postprandial blood glucose levels in T2DM patients. Furthermore, JSP consumption has been associated with improved lipid profiles, addressing the common comorbidity of dyslipidemia. While these results suggest significant potential as a complementary therapy, the existing clinical data remains sparse. Further rigorous, large-scale, and well-controlled clinical trials are imperative to standardize the effective dosage, establish long-term efficacy, and confirm safety across diverse patient populations.

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Syzygium cumini, type 2 diabetes mellitus, black plum, phytochemicals, insulin sensitivity, antioxidants.

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