Volume 13 | Issue 5 |

 Abstract

Abstract: The IoT-Based Shoot Sight Missile System is a cutting-edge solution designed to automate target detection and engagement with high precision. At the core of the system is an ultrasonic sensor-- consisting of both a transmitter and receiver--that detects the distance and position of potential targets by analyzing reflected sound waves. This information is then processed by an Arduino microcontroller, which precisely adjusts the launcher's aim using a servo motor.The missile launcher itself is mounted on a rotating platform powered by stepper motors, giving it a wide range of motion for detecting and locking onto targets. Bluetooth connectivity allows for remote monitoring and control, while an onboard LCD screen displays real-time system updates. To enhance situational awareness, a buzzer is triggered to alert users when a threat is detected.Built for reliability in various environmental conditions and terrains, this system combines automation, real-time data processing, and wireless communication to provide a powerful solution for military defense, border security, and autonomous surveillance. By merging IoT technology with intelligent targeting capabilities, the system offers a flexible and scalable approach to modern security challenges.

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 Keywords

Internet of Things (IoT), Smart Missile System, Ultrasonic Sensing, Arduino Microcontroller, Automated Targeting, Military Defense, Surveillance Technology.

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The present study is examining the financial literacy of teaching professionals in Assam. The study is conducted in Jorhat District with a sample size of 201respondents. This study reveals that a fair proportion of teachers are in the higher levels of financial knowledge, financial behaviour and financial attitude. The concept of compound interest is appeared as most difficult financial concept among salaried employees. On the other hand, in risk diversification highest correct response is generated. Among the various financial behaviour indicators, regular bill payment is mostly followed behaviour, where as, maintaining record of expenses is the least practiced financial behaviour. Saving in retirement plans, maintaining emergency fund is also not adequately found among the respondents. It is found that 50.7 per cent of the respondents (102 respondents) falls in financially literate group and the 49.30 per cent (99 respondents) falls in financially illiterate group. It means an almost equal chance of a teacher to fall in either of the financially literate or illiterate group. It implies there is need of improving financial literacy among teachers. It is satisfactory to find that with higher financial knowledge, behaviour and attitude overall financial literacy improves.

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 Keywords

Financial knowledge, Behaviour, Attitude , Financial Literacy, Teachers

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As the digital era reshapes access to information, library stack management must evolve to meet the demands of both physical and virtual spaces. Innovative models for library stack management in the digital age focus on integrating emerging technologies, optimizing user experiences, and enhancing operational efficiency. Key advancements include the development of hybrid collections that seamlessly blend physical and digital resources, the use of automation and artificial intelligence for resource retrieval and cataloging, and the implementation of smart shelving systems with RFID and real-time data tracking. Cloud-based cataloging systems enable collaborative sharing of resources, while AI-driven personalization offers tailored recommendations to users. Furthermore, libraries are embracing immersive technologies such as virtual reality and 3D printing, expanding their roles as community hubs and educational centers. Sustainability and decentralized management, facilitated by blockchain and energy-efficient infrastructure, further complement these innovations. These models reflect the ongoing transformation of libraries, positioning them as dynamic spaces that foster knowledge access, collaboration, and lifelong learning in a rapidly changing digital landscape.

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Key Words: Library, Dynamic, RFID, Digital, Hybrid

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 Abstract

The Andaman and Nicobar Islands, a union territory of India, are home to a diverse population, including indigenous tribes such as the Jarwas, Onges, Great Andamanese and Sentinelese. While the region enjoys the legal framework of the Indian Constitution and international human rights commitments, unique challenges persist in the protection of promotion of human rights. These includes issues of displacement, loss of traditional land, restricted access to healthcare and education and cultural erosion among indigenous communities due to tourism and development projects. Additionally developmental policies sometimes conflict with tribal rights, leading to tensions. Law enforcement and governance mechanisms, though present, often face limitations in addressing these complex, context-specific human rights concerns.

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Indigenous, Tribes, Jarwas, Commission, Assault, Harassment

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Agriculture remains the cornerstone of rural economies but faces persistent challenges such as plant diseases and soil nutrient deficiencies. Early detection of crop diseases and appropriate soil management can drastically improve yield and reduce losses. This paper presents "Krishisuvidha," a user-friendly mobile and web-based application powered by the Random Forest algorithm, aimed at diagnosing plant diseases from leaf images and providing soil fertility-based crop recommendations. Using a dataset of over 25,000 plant images, the system preprocesses data via normalization and resizing and provides real-time feedback via an intuitive interface built with Streamlit and React Native. Our results demonstrate high prediction accuracy, fast inference times, and positive user reception. The application holds promise for empowering farmers through technology, improving decision-making, and fostering sustainable agriculture.

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Plant disease detection, Random Forest, soil fertility, crop recommendation, Streamlit, agriculture, machine learning.

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The current study set out to determine how leg strength and resting pulse rate were affected by varying plyometric training volumes and terrains. Fifty male handball players from Annamalai University's various colleges participated in this study. Group I received low volume plyometric training on grass terrain (LVPTGT), Group II received low volume plyometric training on hard terrain (LVPTHT), Group III received medium volume plyometric training on grass terrain (MVPTGT), Group IV received medium volume plyometric training on hard terrain (MVPTHT), and Group V served as the control group (CG), engaging in no special training. They were split up into five equal groups, each with ten subjects. For twelve weeks, the study's training session lasted three days a week. The individuals' resting heart rate and leg strength were measured both before and after the training session. A dynamometer was used to test leg strength, and an arterial pressure pulse count was taken for one minute while the subject was at rest in order to determine the resting pulse rate. To determine if there were any significant differences between the experimental and control groups on certain criteria variables, the analysis of covariance (ANCOVA) was employed. The Scheff? S test was employed as a pos-hoc test because there were five groups involved. The study's findings indicated that the experimental group had improved the criteria variables, including resting pulse rate and leg strength.

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plyometric training, different volume and terrains, leg strength and resting pulse rate.

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The burden of retirement preparedness has increased in recent years. This study determined the significance of the mediating effect of financial literacy on the correlation between financial management practices and retirement preparedness among public school teachers. Using descriptive-correlational design, and involving 200 samples selected through purposive sampling technique, it was found that financial literacy accounting 35.29% partially mediates the correlation between the predictive variable and criterion variable, supporting the Theory of Planned Behavior. Future studies may be conducted in other areas with different groups to validate the role of financial literacy. Further, teacher training on financial literacy and management may be prioritized.

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Mediating effect of financial literacy, correlation between financial management practices, retirement preparedness, public school teachers

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Sanskrit, the ancient Indian language, has played a pivotal role in the transmission of knowledge across various disciplines. This paper explores the significance of Sanskrit in the dissemination of knowledge, tracing its historical trajectory, linguistic features, and impact on Indian and global intellectual traditions. By examining the language's role in the preservation and transmission of ancient texts, its influence on the development of various branches of knowledge, and its continued relevance in modern times, this research aims to highlight the enduring legacy of Sanskrit as a vehicle of knowledge transmission.

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Sanskrit, Science, Education, Pedagogy, NEP

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Readiness of teachers working in inclusive schools and general schools for students with disabilities- a comparative study

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Readiness of teachers working in inclusive schools and general schools for students with disabilities- a comparative study

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Breast cancer remains a major health concern globally, ranking among the leading causes of cancer-related deaths in women. Timely and accurate diagnosis plays a pivotal role in increasing survival rates and reducing the risk of advancedstage complications. Conventional diagnostic procedures, such as physical examination, mammography, and biopsy, though clinically effective, often depend heavily on the expertise of medical professionals and may be prone to human oversight. The integration of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, has shown great promise in supporting medical decision-making by automating the diagnostic process and minimizing errors.This research presents a comparative study of several ML and DL models applied to a real-world breast cancer dataset to evaluate their effectiveness in distinguishing malignant tumors from benign ones. The dataset, obtained from a public repository, consists of various cell-level measurements derived from breast tissue samples. Features such as mean radius, texture, perimeter, area, and smoothness are used as input for classification algorithms. These attributes are considered clinically significant indicators of tumor characteristics and form the basis for model training. To begin with, data preprocessing steps were carried out to ensure consistency and quality. This included handling missing values, feature scaling using standardization techniques, and splitting the dataset into training and testing sets. Following this, a range of classification algorithms were implemented, including Logistic Regression, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), and XGBoost. In addition to these traditional ML approaches, a Deep Neural Network (DNN) model was designed and trained using the TensorFlow framework to analyze how neural architectures perform on structured medical data.Each model's performance was evaluated using widely accepted metrics: accuracy, precision, recall, F1-score, and balanced accuracy. In summary, this work demonstrates the potential of machine learning and deep learning models to support early breast cancer detection with high reliability. The results suggest that combining robust data preprocessing with well-tuned models can yield predictive tools that complement traditional diagnostic practices. As part of future efforts, exploring more advanced neural networks, incorporating larger and more diverse datasets, and developing explainable AI solutions can further enhance the applicability of such systems in real-time clinical environments. Ultimately, this research underscores the transformative role of AI in advancing personalized healthcare and improving patient outcomes.

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 Keywords

Breast Cancer Detection, Machine Learning, Deep Learning, Classification, Medical Diagnosis, Neural Networks

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