Volume 13 | Issue 4

 Abstract

Retinopathy of Prematurity (ROP) is a significant cause of visual impairment among preterm infants, but its impact extends beyond vision to broader neurodevelopmental outcomes.(1) This narrative review explores findings from an observational study conducted in Belagavi City, assessing the cognitive, language, motor, social-emotional, and adaptive behavior development of children with ROP at three years of age using the Bayley Scales of Infant and Toddler Development IV (BSID-IV). Results indicate that while social-emotional domains are relatively preserved, significant delays are evident in cognitive, language, and motor domains. Risk factors such as severe ROP, low birth weight, consanguinity, and maternal factors like preeclampsia were associated with poorer developmental outcomes. Early neurodevelopmental screening and targeted interventions are recommended to improve the developmental trajectories of these children.

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Keywords: Retinopathy of Prematurity, Neurodevelopment, BSID-IV, Cognitive Delay, Motor Delay, Belagavi

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The rise of autonomous and semi-autonomous machinery in industrial settings necessitates advanced safety mechanisms to ensure smooth operation while preventing collisions and protecting nearby workers. This project proposes an AI-based Extreme-Edge Collision-Avoidance System utilizing a Temporal Convolutional Network (TCN) deployed on an STM32 microcontroller. The system integrates multiple sensing technologies, including LIDAR, Camera Modules, Ultrasonic Sensors, and Infrared Sensors, to comprehensively monitor the machine's surroundings. At the system's core, the STM32 microcontroller processes real-time data from the sensors via a driver circuit, ensuring ultra-low-latency response. AI-based monitoring runs on a Raspberry Pi, analyzing time-series sensor data using the TCN model to detect potential hazards in real-time. The AI algorithm predicts collision risks and enhances situational awareness, ensuring timely interventions critical for industrial safety. Upon detecting an obstacle, the STM32 microcontroller triggers immediate corrective actions, controlling machinery operations while engaging an alarm and alert system to notify nearby workers. The integration of Raspberry Pi extends computational flexibility, supporting data logging, visualization, and remote monitoring, while enabling machine learning model updates. The system ensures robust performance in noisy and dynamic industrial environments by leveraging sensor fusion and AI. Extreme-edge processing minimizes latency, optimizes energy consumption, and maintains a compact memory footprint. Designed for seamless integration into industrial applications, this real-time collision avoidance system significantly enhances workplace safety, reduces accidents, and improves operational efficiency.

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Collision avoidance, Industrial safety, Edge computing, Ultrasound sensors, Temporal Convolutional Network (TCN), Machine learning, Low-power MCU, Raspberry Pi, Real-time processing, Sensor fusion, Acoustic noise robustness, Embedded systems, Proximity sensing, Smart manufacturing, Industrial automation.

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Automatic License Plate Recognition (ALPR) is an advanced computer vision technology that extracts vehicle registration numbers from images and videos for various applications, including security, traffic management, and automated parking systems. This research focuses on developing an efficient and accurate ALPR system leveraging Optical Character Recognition (OCR) and Fuzzy Searching techniques to enhance accuracy, even with noisy or unclear images. The system follows a structured approach where license plate images are first captured through a camera, processed using OCR to extract alphanumeric characters, and further refined with fuzzy searching algorithms to handle variations caused by lighting, font differences, and image distortions. The extracted license number is then matched against a database to identify the vehicle owner and generate real-time reports for security personnel, administrators, or authorities. A key contribution of this research is the integration of real-time analytics and a user-friendly dashboard for monitoring visitor logs, security access, and traffic trends. The proposed model is tested on a dataset of license plate images under varying environmental conditions to evaluate performance in terms of accuracy, processing time, and robustness. Experimental results demonstrate that our approach significantly improves recognition accuracy compared to traditional OCR-based models, particularly in challenging scenarios. This work has broad applications in smart parking systems, automated toll collection, vehicle entry management in organizations, and traffic surveillance. The future scope includes integrating deep learning-based recognition methods, expanding the dataset to accommodate multiple regional plate formats, and enhancing security features with blockchain-based data integrity.

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License Plate Recognition, OCR, Fuzzy Searching, Image Processing, ALPR, Smart Security, Automated Vehicle Identification.

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Federalism, as a system of governance, distributes power between a central authority and constituent political units, such as states or provinces. This research paper provides a detailed analysis of federalism, evaluating its pros and cons in contemporary governance. Drawing on scholarly literature and theoretical frameworks, the paper investigates how federalism promotes democracy, diversity, and accountability, while also exploring its challenges in coherence, efficiency, and governance. Additionally, the paper discusses relevant topics such as fiscal federalism, intergovernmental relations, and the role of federalism in managing crises and fostering innovation.

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Federalism, Authority, Governance, power, state, intergovernmental

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In today's digital world, typing efficiency and accuracy are crucial for seamless communication. Users often struggle with slow typing speeds and frequent errors, especially in text-based interfaces. Next Word Prediction is a critical application of Natural Language Processing (NLP) that enhances user experience in text input systems by suggesting the next probable word based on the context. It focuses on implementing a Next Word Predictor using Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN) designed to handle sequential data efficiently. LSTMs are well-suited for capturing long-term dependencies in text, enabling context-aware and accurate predictions. The model is trained on a large text collection, allowing it to learn linguistic patterns, grammatical structures, and semantic relationships between words. Preprocessing steps include tokenization, embedding, and sequence generation, which prepare the data for training. The system is evaluated using perplexity and accuracy metrics to ensure performance, improving both typing speed and accuracy. LSTM-based Next Word Prediction models can be integrated into various applications, including virtual keyboards, chatbots, and assistive writing tools, enhancing user interaction.

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Natural Language Processing (NLP), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Linguistic Patterns, Grammatical Structures.

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ABSTRACT:- Brand loyalty plays a massive role by acting as a critical determinant of consumer behaviour thereby influencing purchase patterns and long-term brand attachment. Specially in an environment which is increasingly shaped by rapid dissemination of information and negative word-of-mouth (NWOM). Understanding the protective function of brand loyalty has become crucial in a world where negative word-of-mouth (NWOM) and the quick spread of information are shaping society more and more. The goal of this study, "Shielded by Loyalty: Analyzing Consumer Resistance to Negative Word-of-Mouth," is to investigate how consumer resistance to NWOM and brand loyalty are related. The study's goals are to determine how loyal consumers are to a brand, how much they are exposed to unfavorable word-of-mouth, and how loyalty levels relate to resistance behaviors. To determine whether loyalty serves as a protective barrier against adverse influences, special consideration is given to extremely devoted customers. A structured questionnaire that can be accessed by a wide range of people, including common and semi-literate consumers, is used to gather primary data. To analyze the data, statistical tools are used, with an emphasis on finding trends, correlations, and differences among various loyalty segments. It is anticipated that the results will show a positive correlation between resistance to NWOM and brand loyalty, emphasizing loyalty as a moderating factor in consumer decision-making. The study adds to the body of knowledge on consumer behavior and offers useful advice for brand strategists who want to improve customer retention and brand resilience against reputational risks.

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Key words:- Consumer Behaviour, Brand Loyalty, Negative Word-of-Mouth, Consumer retention.

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Mergers and acquisitions (M&A) have repeatedly emerged as a favored strategy for organizations seeking to expand beyond their organic capacities. In contrast to internal growth, which can be slow and limited by resource availability, mergers and acquisitions provide companies with a more rapid and significant means of expansion. Through these strategic agreements, corporations might penetrate new markets, expand their product offerings, gain technological competencies, or remove competition. M&A serves as a potent instrument for corporate restructuring, enabling firms to reorganize activities, optimize processes, and enhance overall efficiency. The incentives for mergers and acquisitions are primarily economic, typically based on the desire for enhanced profitability, expanded market share, and enduring sustainability. Companies may opt to merge with or purchase others in reaction to evolving industry dynamics, new consumer needs, or macroeconomic concerns. Every transaction is distinct, shaped by a confluence of strategic foresight, fiscal preparedness, and regulatory structures. This research seeks to examine the financial ramifications of M&A activities, specifically analyzing the impact on the financial performance of acquiring corporations before and after the merger or acquisition. The paper examines a series of chosen M&A deals in India throughout two notable periods to accomplish this objective. The research employs a series of essential financial ratios--namely profitability, liquidity, leverage, and efficiency ratios--to assess the performance of acquiring firms during these intervals. These financial indicators provide a thorough overview of the companies' operational and financial well-being. Additionally, the paired t-test at a 5% significance level is utilized to evaluate the statistical significance of the variations in financial performance pre- and post-merger. This meticulous methodology guarantees that the results are both sound and dependable, enhancing the comprehension of the actual financial implications of M&A selections inside the Indian business environment.

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Mergers and Acquisitions (M&A), Inorganic Growth, Financial Performance, Strategic Restructuring, Acquirer Company, Pre-Merger Analysis, Post-Merger Analysis, Financial Ratios, Statistical Analysis, Indian Corporate Sector, Profitability, Liquidity, Leverage

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In today's connected world, companies can do business in many countries. This has made corporate tax dodging a pressing issue. Big firms often move their profits between different places paying much less tax than you'd expect based on their work in a country. While these tax tricks might follow the rules, they often go against the idea of fairness that tax systems are built on. In India where public money is key to build roads run social programs, and keep the government going, these practices hit harder. To tackle these issues, India brought in the General Anti-Avoidance Rule (GAAR), which kicked off in 2017. GAAR aimed to give tax authorities the ability to overrule deals that, while within the law, were set up to dodge taxes. The thinking behind GAAR is straightforward but important: tax should be based on what's happening, not just what's on paper. But putting the rule into action hasn't been easy. It showed up after years of hold-ups and arguments, and even now many taxpayers and experts aren't sure how it'll work in real life. This research paper digs deep into the legal structure and real-world workings of GAAR in India's tax system. It looks at why it was created how it's been put into action (or not), and breaks down important cases and expert opinions that have shaped how people understand it. To give a wider view, the paper also stacks up India's GAAR against similar setups in other countries--Australia, the UK, and Canada--which have been using general anti-avoidance rules for longer. These side-by-side looks help show what's good and what's missing in India's approach. The research points out that GAAR could help stop aggressive tax planning, but it's got some problems right now. It's not clear enough, doesn't get used much, and courts haven't explained it well. If it's not used the same way all the time and there's no clear guidance, it might end up being just for show instead of doing something. The paper wraps up by saying that for India to tackle tax avoidance, it's not just about having tough laws written down. It's about how people understand those laws how they're explained, and how they're put into action. So, for GAAR to work well, there needs to be a good mix of legal power, the ability to run it, and people trusting that the tax system is fair.

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 Keywords

Corporate Taxation, Tax Avoidance, Tax Havens, GAAR, India,

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Chronic Kidney Disease (CKD) is a significant global health issue with increasing prevalence and limited early detection methods. Traditional diagnostic practices often identify CKD at advanced stages. This project proposes a data-driven approach using machine learning algorithms to detect early-stage CKD from patient health records. The system utilizes medical datasets and applies classification models like Random Forest, SVM, and Logistic Regression to predict CKD occurrence. By integrating data analytics with healthcare diagnostics, this approach aims to improve early detection accuracy, enabling timely interventions and better patient outcomes.

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Chronic Kidney Disease (CKD) Kidney failure End-Stage Renal Disease (ESRD) Nephrology Kidney function Glomerular filtration rate (GFR) Proteinuria Creatinine levels Renal insufficiency Dialysis Kidney transplant ? Medical and Scientific Terms Uremia Hemodialysis Peritoneal dialysis Nephrons Electrolyte imbalance Hypertension Diabetes mellitus Renal biopsy Albuminuria eGFR (estimated GFR)

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Indigenous art form is a cultural practice related to the way of life of a traditional group. It includes painting, sculpture, music, dance, and more. This art form is passed down through generations. Rajbanshis are a group of ethnic people in the northern part of West Bengal, the Lower part of Assam, and some parts of Bangladesh, Nepal and Bhutan. They belong to the Royal family of Kamta / Kamrup Dynasty. Over time, they have mixed with other communities, yet some have preserved their identity through their traditional culture. Rajbanshi women initially represented themselves for the welfare of society and to welcome guests. Over a period, this representation evolved into an art form. K?ti g?na is a ritual based on the worship of God named "K?ti". Only women artists perform the song of K?ti along with the dance. The study will focus on the contributions of women artists, the process of acquiring this art form, and its cultural relevance. The research will follow qualitative research methodologies. The findings of this research Women perform around the K?ti deity. The song describes the K?ti deity, K?ti's birth story. In their performances, they describe the various parts of K?ti's body part like the hand, nose, ears, etc, which have been created. Then prays to K?ti for a son.

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Keywords: Kati Gana, Rajbanshi women, Women's art form, Rajbanshi

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The agricultural sector is the backbone of our economy, providing food, clothing, shelter, and employment opportunities to the people, as well as a significant source of income to the state. Karnataka state is a predominantly agriculture-oriented economy where nearly 65% of the population follows agriculture and allied activities as their major occupation. In this context, agricultural sector development always depends on governmental effort to extend supportive policies and programs; simultaneously, farmers' active participation assists in achieving targeted goals for development is an essential precondition required.But while delivering governmental services through electronic means, we face many issues and challenges from different perspectives, which hinder the effective implementation of E-governance in the farming sector. In this context, this paper focuses on the E-governance initiatives of the Karnataka government for agricultural sector development and also issues and challenges faced while delivering E-governance in the agrarian sector. The paper also analyses remedial measures to be undertaken for the successful E-projects to be effectively implemented and sustained for a longer time.

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E-governance initiatives, Agricultural development, issues and challenges

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The study mostly focuses on the use of machine learning techniques to find fraudulent behavior in financial facts. This is the main challenge in the financial sector, where it is important to recognize and prevent fraud. Images are hyperparameters of tuning class as a method for increasing fraud detection. These settings improve fraud detection system by helping the version of the extra precisely distinguish between real and fraudulent transactions. The work deliberately uses three machine learning techniques: XGBoost, LightGBM, and CatBoost. Every method has sure advantages; their combined use seeks to improve the general fraud detection method performance. The research includes deep learning algorithms to adapt to hyperparameters. This connection improves the effectiveness and adaptability of fraud detection systems, and increases the efficiency of identifying modified fraud strategies. The effort employs actual data to conduct comprehensive analyses. The findings indicate that Lightgbm and XGBOOST outperformed the contemporary method when assessing numerous factors. This suggests that, among other strategies, the suggested one is more a success in spotting fraudulent behaviour. It incorporates a Stacking Classifier, which combines with precise parameters Random forest and LightGBM classifier predictions. Through using the strengths of numerous models, this ensemble technique improves prediction accuracy by means of a GradientBoostingClassifier as the final estimator.

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Bayesian optimization, data mining, deep learning, ensemble learning, hyper parameter, unbalanced data, machine learning".

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Data Science plays a crucial role in Business Analytics by enabling data-driven decision-making through statistical analysis, machine learning, and predictive modeling. Businesses leverage data science techniques to identify trends, optimize operations, enhance customer experiences, and gain competitive advantages. This paper explores the integration of data science in business analytics, covering key concepts such as data collection, preprocessing, exploratory data analysis, and advanced predictive modeling. Real-world applications in marketing, finance, supply chain, and customer insights demonstrate how businesses harness data science to drive strategic growth and efficiency. The study also highlights challenges such as data quality, security, and ethical considerations in business analytics.

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- Data Science, Business Analytics, Data-Driven Decision-Making, Statistical Analysis, Machine Learning, Predictive Modeling, Data Collection.

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Women have an important place in social and family life, without them a family or society cannot be imagined. Their contribution to family and society is incomparable. Women are the guides of society and family, the audience of the success of the young generation, the guide of the family. Women have always had an inventive mind. But with time the mind stopped thinking like this, now they have to start this inventive thought again and show the path to the society created by them. Only then will equality come in society. Nari Shakti Bandhan Act is an important legal step for the empowerment of Indian women. Through this, women get assurance of protection of their rights and freedom. However, for this Act to be completely successful, there is a need to change the thinking towards women in the society. Women can be empowered through awareness, education, and legal protection, so that they can live freely and with respect in the society

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Bharteey samaj me mahila sashktikaran yew sarkari prayash

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This paper explores the lived experiences of a transgender individual in kerala, India within the broader context of the LGBTQ movement. This paper delves into the life of Deepu, a transman from kerala, to shed light on the personal and social challenges faced by transgender individuals. Based on an in-depth interview , Deepu's story is presented as a reflective narrative that highlights his journey through gender identity, social stigma, family dynamics and personal resilience. The paper also focuses on the transgender rights and the initiatives undertaken by the government of kerala such as the 2015 Transgender policy to promote transgender welfare and inclusion.

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Transgender- transman- social stigma- queer- LGBTQ

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Wireless Sensor Networks (WSNs) are utilized in a variety of applications, including environmental monitoring, disaster management, and smart cities. In these networks, a collection of sensor nodes gathers and transmits data to a central unit for analysis. However, one of the main challenges in WSNs is achieving energy efficiency. Since sensor nodes are usually battery-powered, energy depletion directly impacts the network's lifespan and performance. Traditional data collection methods often lead to premature energy exhaustion of nodes located near the base station due to the heavy communication load. This creates a bottleneck that limits the scalability of the network. To tackle this problem, this project proposes an optimized energy-efficient path-planning strategy that employs multiple mobile sinks to distribute the data collection load more evenly across the network. In this strategy, mobile sinks move throughout the network to collect data from sensor nodes, which reduces the distance that the nodes need to transmit data. This effectively minimizes energy consumption. The project focuses on developing an optimal path-planning algorithm that ensures each mobile sink follows an efficient route to maximize data collection while minimizing redundant movements and energy usage. Simulation results indicate that the proposed approach significantly reduces energy consumption, enhances network longevity, and balances the load across the network when compared to traditional stationary sink methods.

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Wireless Sensor Network, Network Simulation 2, Internet of Things, Mobile Ad Hoc Networks

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Bhartiya Dak Pranali ki Etihashik Vivechana

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Bhartiya Dak Pranali ki Etihashik Vivechana

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Sewage treatment plants are vital infrastructures that safeguard public health and protect the environment by treating and purifying wastewater. The design of these plants plays a crucial role in ensuring efficient and effective treatment processes. This research paper focuses on the design principles and considerations for sewage treatment plants and explores various methods for improving existing facilities. By implementing innovative design strategies and adopting optimization techniques, wastewater treatment plants can enhance their performance, increase treatment capacity, and achieve higher levels of environmental sustainability.

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Sewage treatment plant, wastewater, design principles, optimization, environmental sustainability.

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Mental health disorders, particularly depression, are on the rise due to factors like excessive social media use, stress, and unhealthy lifestyle habits. To tackle this issue, we've developed a groundbreaking system that combines artificial intelligence, natural language processing, and personalized insights to provide real-time mental wellness support. Our Deep Learning-Powered Holistic Mental Health and Wellness Analyzer is designed to assess user mood, detect depression symptoms, and offer tailored recommendations. Using advanced sentiment analysis and emotional intelligence, our system achieves an impressive 98.9% accuracy in depression detection. But that's not all. Our platform also includes a personalized nutrition module that explores the link between diet and mental well-being, as well as Fit-Harmony music therapy, which creates customized playlists to promote relaxation and balance. With accuracy rates of 78.5% in mood-based music recommendations and 82.3% in dietary suggestions, our system provides users with a comprehensive and supportive mental health journey. By harnessing the power of machine learning, predictive analytics, and real-time monitoring, our platform offers an interactive and user-friendly mental health tracking experience.

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 Keywords

Deep Learning, Mental Health Analysis, Depression Detection, Sentiment Analysis, Natural Language Processing (NLP), Recurrent Neural Network (RNN), Two-State LSTM (TS-LSTM), Support Vector Machine (SVM), Emotion Classification, Personalized Mental Health Recommendations, Fit-Harmony Music Therapy, Personalized Nutrition Insights, Machine Learning in Healthcare, Mental Wellness Companion, Healthcare Data Analysis.

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Low back pain is a significant and common public health issue. Low back pain (LBP) is a public health issue that has reached epidemic proportion. Most of patients suffering from back pain practice yoga. Astang yoga comprised of eight limbs Yama (rule of moral conduct),Niyama(rule of personal conduct),Asana(postures),Breath control(pranayama),Pratyahara(sense withdrawal),Dharma(concentration),Dhyana(meditation)and Samadhi(self-realization).out of many style of yoga, Iyenger Yoga has applied yoga pose emphasis on precise structural alignment and has a good result in low back pain.

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Back pain; Yoga; Stiffness and Pain

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