Authors - Manohar R, N Abhishek, Nagesh S, Sumith R, C Balarengadurai Abstract - Water quality monitoring is essential for public health and environmental stewardship. Conventional methods, while effective, are often costly, time-intensive, and require specialized skills. In response to these limitations, this paper explores machine learning as a rapid, scalable solution to classify water quality using key parameters, including pH, turbidity, organic carbon, and contaminants. By implementing algorithms such as Random Forest, SVM, and other advanced models, we seek to enhance the precision of water purity assessments. This paper shows the potential of ML applications in real-time monitoring, addressing the need for accessible, cost-efficient, and accurate water quality solutions suitable for broad deployment across diverse environments.
Authors - Hrudai Aditya Dharmala, Ajay Kumar Thallada, Kovvur Ram Mohan Rao Abstract - Recent advances in vision-language models have demonstrated remarkable multimodal generation capabilities. However, their typical reliance on training large models on massive datasets poses challenges in terms of data and computational resources. Drawing inspiration from the expert-based architecture of Prismer, we propose a novel framework for contextual visual question answering specifically tailored to remote sensing imagery. Our methodology extends the Prismer architecture through a two-stage approach: first, by incorporating a domain-specific segmentation expert trained on remote sensing datasets, and second, by integrating a fine-tuned Large Language Model (Mistral 7B) optimized using Parameter-Efficient Fine-Tuning (PEFT) with QLoRA for remote sensing terminology, with hyperparameters optimized with help of Unsloth framework. The segmentation expert performs the analysis of remote sensing imagery, At the same time, the language model acts as a reasoning expert, combining domain-specific knowledge with natural language understanding to process visual contexts and generate accurate responses. In our framework, the use of the Unsloth fine-tuning approach for the language model helps maintain high performance within the defined scope of remote sensing classes and terminology while avoiding hallucination or deviation from established classification schemas. This opens an exciting direction for making the use of Earth observation data more accessible to end-users, demonstrating significant improvements in accuracy and reliability compared to traditional approaches. Experimental results validate that this architecture effectively balances domain expertise with computational efficiency, providing a practical solution for remote sensing visual question answering that requires substantially fewer computational resources compared to end-to-end training of massive models.
Authors - Jenat Arshad, Afruja Akter, Tanjina Akter, Kingkar Prosad Ghosh, Anupam Singha Abstract - Cryptocurrencies have emerged as a significant financial asset class, attracting global attention for their potential to disrupt traditional financial systems. Due to its extreme price volatility and ability to be traded without the assistance of a third party, cryptocurrencies have gained popularity among a wide range of individuals. This paper presents a comprehensive study of machine learning techniques, particularly deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Artificial Neural Network (ANN) in predicting cryptocurrency prices. Furthermore, this study addresses the security and privacy challenges inherent to blockchain technology, upon which cryptocurrencies operate. We predict the prices of popular cryptocurrencies like Bitcoin, and Ethereum, and lesser-known ones like Binancecoin, Litecoin, and Ripple through a hybrid deep learning model. This paper also compares cryptocurrency price prediction with machine learning models like GRU, ANN, and our proposed model Hybrid LSTM-GRU. The results demonstrate the efficacy of machine learning in price prediction, highlighting blockchain's potential to enhance security and privacy in financial transactions. Our model gives the value of MSE, RMSE, MAE and MAPE to determine the forecasting. We’ve also added the manual calculation for each metric and compared the actual price with the predicted price that our model gave.
Authors - Harsha S Khurana, Payal D Joshi Abstract - Stress is a major health concern that significantly affects mental stability and can have adverse effects on physical well-being if prolonged. Early detection of stress can help and prevent health-related issues. Individual stress patterns are detected using a variety of bio-signals, including thermal, electrical, auditory, and visual cues which are invasive methods. But according to the well-known saying statement, "Face is a mirror of mind," one can observe one’s emotion or mental state on one’s face. Based on this, Investigated the potential of using facial expressions as a non-invasive method to detect stress levels. Facial expressions could be analyzed and classified as stress and non-stress by examining facial expressions. To solve this problem we have used pretrained network models - Inception, Xception, MobileNetv2, Vgg19, EfficientNet deep learning models, and Affectnet Dataset for stress detection and also represent the comparative study of networks based on confusion and performance metrics. Testing on a separate set of data of images indicates that the MobileNetv2 and Xception models give more accuracy for stress detection.
Authors - Rinkesh N Parmar, Payal D Joshi Abstract - Cotton, an essential crop for the textile industry and millions of farmers, is vulnerable to diseases that can significantly affect yields and profitability. Traditional methods of disease detection, relying on expert visual inspections, are labour-intensive, time-consuming, and prone to errors, often causing delays in addressing problems. This study investigates the use of Convolutional Neural Networks (CNNs) for automated, early, and accurate detection of cotton diseases. CNNs are effective at extracting hierarchical features from raw image data, making them ideal for image classification tasks. In this approach, a labelled dataset of cotton plant images is utilized to train the CNN model, incorporating data augmentation to enhance variability and generalization. The model employs convolutional layers for feature extraction, max-pooling layers for dimensionality reduction, dropout layers for regularization, and fully connected layers for classification. The Adam optimizer, known for faster convergence, is used during training, along with categorical cross-entropy loss. The evaluation is based on accuracy, precision, recall, and F1-score. The model showed significant improvements in performance. The baseline CNN achieved 92.34% accuracy, but advanced architectures like Hybrid CNN-LSTM, DenseNet-121, ResNet-50, and InceptionV3 enhanced accuracy by 2-3%, along with increased precision, recall, and F1-score. The Hybrid CNN-LSTM model outperformed others, achieving 94.5% accuracy, 93.5% precision, 93.2% recall, and 93.3% F1-score. These results suggest that CNN-based models, particularly Hybrid CNN-LSTM, offer substantial improvements in cotton disease detection. The incorporation of data augmentation and dropout regularization strengthens the model, making it effective for real-time agricultural disease management. Future work will focus on expanding the dataset, improving the model, and implementing it in real-world cotton farming practices.
Authors - Joven A. Tolentino Abstract - The growing demand for electricity necessitates effective monitoring and forecasting of consumption trends. This study employs ARIMA modeling, using data from the Department of Energy, Philippines, to analyze and predict electricity consumption. The forecast for the next two years indicated an 18.99% increase in consumption between 2016 and 2017.To enhance analysis, the predicted data was clustered using the K-Means algorithm to group months with similar consumption patterns. This approach identified periods of high, medium, and low electricity usage, providing valuable insights into peak demand months. Such data-driven findings can guide electricity providers in prioritizing resources and implementing strategies to address fluctuations in consumer demand effectively. This study emphasizes the importance of forecasting and clustering as tools for decision-making to mitigate challenges arising from increasing electricity demand.
Authors - Manmeet Borkar, Suneeta Raykar Abstract - Monitoring biomarkers is essential for patients to effectively manage their health profiles and prevent potential complications. Regular tracking of these indicators allows for timely interventions and better control over one’s health, particularly when the methods employed are non-invasive and grant convenience and comfort to the patient. Conventionally, this monitoring is accomplished in pathology laboratories, by collecting blood samples or finger-pricking, which can be distressing and impractical for regular use. Given the growing need for more accessible and affordable healthcare solutions, the development of a cost-effective non-invasive method has become crucial. We propose the use of machine learning models to enable non-invasive measurement of biomarkers such as Total Cholesterol, Uric acid and Blood Sugar. Several Machine learning algorithms, including Linear Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest and Support Vector Regression (SVR), were applied to the datasets constructed using the MAX30102 sensor. The metrics used to evaluate regression models were Mean Square Error (MSE) and Coefficient of determination (R²). The final prediction model was built using the algorithm that yielded the highest Coefficient of determination (R²). A user-friendly interface was developed using Tkinter, allowing the input of sensor values from the MAX30102 sensor. The prediction of biomarker values promotes health awareness and timely alerts against potential complications. The results obtained using this approach were validated against laboratory blood reports, revealing an average offset of less than 10% in the predictions.
Authors - Kirthika. P, M. Suresh, S. Kanagaraj Abstract - This paper explores the social barriers faced by Indian plantation communities. It focuses on how these obstacles impact their well-being, productivity, and social mobility. By analyzing historical, socioeconomic, and cultural factors, the study uncovers the multifaceted challenges plantation workers encounter, including income, education, social position in the community, social networks, migration, exploitation, and working and living conditions. The DEMATEL approach identifies the barriers and analyzes the interrelationships among those that impact social barriers among plantation workers. This paper identified seven barriers of impact from a literature review followed by interviews with experts to interpret the interconnection of barriers and investigate the interrelationships. The result says that income and education are the key barriers impacting the lives of plantation workers in their society. The present study incorporates the DEMATEL approach model to analyze the critical barriers in mapping the social barriers of plantation workers. The DEMATEL approach model is the first attempt to study the interrelationship among the barriers. The research overviews the prevailing issues through field surveys, interviews, and literature reviews. The paper will conclude with actionable recommendations aimed at policymakers, community leaders, and stakeholders to mitigate these barriers and promote a more inclusive and equitable environment for plantation workers.
Authors - Aishani Das, Sobitha Ahila, Sreyashi Dey Abstract - Sentiment analysis within the food industry offers essential insights into customer satisfaction, product perception, and emerging concerns. A novel sentiment classification model is developed for Amazon food reviews, leveraging Sentiments are categorized as positive, neutral, or negative using techniques from Natural Language Processing and Machine Learning. Traditional ML algorithms, such as Logistic Regression, Naive Bayes, and Support Vector Machines, are combined with the BERT deep learning model to enhance classification accuracy. With a dataset of over 500,000 reviews sourced from Kaggle, the methodology includes data cleaning, feature extraction, exploratory data analysis, model training, and evaluation. Initial findings demonstrate SVM’s high predictive accuracy in sentiment classification, while BERT’s advanced contextual understanding suggests further enhancements. Applications of this model extend to real-time feedback systems that assist businesses in identifying and addressing customer sentiments promptly. Future developments aim to improve accuracy, incorporate a diverse range of datasets, and integrate real-time processing and multilingual analysis for broader, more effective sentiment analysis capabilities.
Authors - Tinoy Santra, Sahil Neekhra, Ritik Gupta, Gunabalan Ramachandiran Abstract - With the rising environmental degradation and increasing global warming, electric vehicles are the promising concept in the automobile industry. Different sources of energy are available for giving power to drive the vehicle. Sunlight being an efficient and abundant resource, the world is moving towards solar energy leaving behind conventional power resources. Moreover, battery based electric vehicles have short driving range and speed which is not acceptable in the dog-eat-dog market. This paper discusses a simple approach for BLDC motor driven electric vehicle powered by buck-boost converter. The primary energy source is solar energy, and the PI controller holds the DC-DC converter's output constant. A 660 W, 48 V BLDC motor driven electric bike system is worth an alternative when it is solar powered which solves utmost all the problems faced in usage of EVs. The circuit is simulated in MATLAB environment and output parameters are observed for different load conditions. Overall, the motive is to prove that electric vehicles are more efficient and cost effective than the conventional ones.
Authors - A.Kousar Nikhath, J.Ananya Reddy, P.Aishwarya, S.Maadhurya Sri, P.Gowthami Abstract - Sign language is the medium between the people who can hear and speak and those who cannot. This project is set to be used in the development of technologies that are beneficial for the lives of individuals with disabilities. The project studies in-depth the use of computer vision and deep learning. The accurate and the regional language translation began with the gestures of the sign languages as the input information, and finally the software produced the accurate translation in the regional language. I am inspired by the prospect of using Artificial Intelligence technology in developing hereditary transmission from a worldwide venue and health diagnosis in a timely manner. Convolutional Neural Network (CNN) is employed to pick up characteristics from hand movement that belongs to the sign language. These attributes are used in the training set as features, themselves in the classification of the gesture, and the process is the learning of this model for the recognition of gestures in real-time. Further, there is an inclusion of computer vision for preprocessing and the sake of accuracy prediction of the recognition process. The functionality of the sign language recognition system is assessed by using a variety of experiments, including accuracy and speed. In general, the developed Sign Language Recognition System with integration of deep learning and computer vision techniques facilitates the precise and quick recognition of sign language gestures. Integration with a translator in addition to this not only makes it multi-language support but also guarantees the correct translation into regional languages.
Authors - Hritesh Kumar Shanty, Padirolu Moses, Tulasiram Nimmagadda, Samson Anosh Babu Parisapogu Abstract - In today’s digital world, combining image editing with secure NFT trading is essential. Imagify addresses this need by offering a unified platform with advanced artificial intelligence tools for image enhancement, recoloring, restoration, and object removal, empowering users to customize images to their preferences. Imagify also simplifies the NFT creation process, allowing users to seamlessly transform their edited images into NFTs that can be bought and sold on a blockchain-secured marketplace. This ensures transparent and secure transactions, providing peace of mind for both creators and buyers. With a flexible, credit-based system, users pay only for the features they choose, making it a cost-effective option. By merging intuitive image editing with a streamlined NFT marketplace, Imagify offers an accessible, user-friendly platform where creators and collectors can engage in digital image trading confidently. This integration creates an efficient and transparent process, supporting both casual creators and seasoned collectors seeking a secure, comprehensive solution for managing and trading digital images.
Authors - R V S S Surya Abhishek, T Sridevi Abstract - This paper provides an overview of the current state of AI-based approaches in virtual fitness coaching, focusing on posture estimation and exercise tracking along with real-time feedback. Advances in pose estimation models, including OpenPose, MediaPipe, and AlphaPose, are boosting personalized exercise correction and injury prevention within the sphere of fitness applications. Current literature varies from 2D to 3D pose estimation that includes action recognition and deep learning framework for specific inputs toward movement analysis and user engagement. There is still much room for improvement in current models, with regards to adaptation to individual needs and environments, such as the real-time accuracy that often has not been matched by the personal feedback and robustness of exercise variations. It discusses the approaches currently in use, their applications, and challenges, and by looking at the topic, this paper insinuates the improvement in the adaptability and customization of AI fitness solutions to perfectly emulate human trainers.
Authors - Mrudul Dixit, Rajiya Landage, Prachi Raut Abstract - The paper presents a comprehensive comparison of Speech-to-Text (STT) and Text-to-Speech (TTS) models, two foundational technologies in the field of natural language processing and human-computer interaction. The paper examines the evolution of these models, focusing on state-of-the-art approaches such as Whisper Automatic Speech Recognition (ASR), DeepSpeech, and Wav2vec, Kaldi, SpeechBrain for STT, and Tacotron, WaveNet, gTTS and FastSpeech for TTS. Through an analysis of architectures, performance metrics, and applications, the paper highlights the strengths and limitations of each model, particularly in domains requiring high accuracy, multilingual support, and real-time processing. The paper also explores the challenges faced by STT and TTS systems, including handling diverse languages, background noise, and generating natural-sounding speech. There are recent advances in end-to-end models, transfer learning, and multimodal approaches that are pushing the boundaries of both STT and TTS technologies. By providing a detailed comparison and identifying future research directions, this review aims to guide researchers and practitioners in selecting and developing speech models for various applications, particularly in enhancing accessibility for specially-abled individuals.
Authors - A.Kousar Nikhath, Aanchal Jain, Ananya D, Ramana Teja Abstract - The project focuses on creating an advanced system for visual speech recognition by performing lipreading at the sentence level. Traditional approaches, which were limited to word-level recognition, often lacked sufficient contextual understanding and real-world usability. This work aims to overcome those limitations by utilizing cutting-edge deep learning models, such as CNNs, RNNs, and hybrid architectures, to effectively process visual inputs and generate coherent speech predictions. The system's development follows a systematic approach, beginning with a review of existing solutions and their shortcomings. The proposed framework captures both temporal and spatial dynamics of lip movements using specialized neural networks, significantly enhancing the accuracy of sentence-level predictions. Extensive testing on diverse datasets validates the system’s efficiency, scalability, and practical applications. This study underscores the critical role of robust feature extraction, sequential data modeling, and hierarchical processing in achieving effective sentence-level lipreading. The results demonstrate notable improvements in performance metrics. Additionally, the project outlines future advancements, including optimizing the system for real-time processing and resource-constrained environments, paving the way for practical implementation in multiple fields.
Authors - Randeep Singh Klair, Gurkunwar Singh, Ritik Verma, Satvik Rawal, Rajan Kakkar, Agamnoor Singh Vasir, Nilimp Rathore Abstract - The most accurate way to measure galaxy redshifts is using spectroscopy, but it takes a lot of computer power and telescope time. Despite their speed and scalability, photometric techniques are less precise. Thanks to large astronomical datasets, machine learning has become a potent technique for increasing cosmology research’s scalability and accuracy. On datasets such as the Sloan Digital Sky Survey, algorithms such as k-Nearest Neighbors, Random Forests, Support Vector Machines, Gradient Boosting, and Neural Networks are assessed using metrics like R-squared, Mean Absolute Error, and Root Mean Square Error. Ensemble approaches provide reliable accuracy, whereas neural networks are excellent at capturing non-linear correlations. Improvements in feature selection, hyperparameter tuning, and interpretability are essential to improving machine learning applications for photometric redshift estimation and providing deeper insights into cosmic structure and development.
Authors - Sudha K L, Navya Holla K, Kavita Guddad Abstract - The antenna is a vital component of the Magnetic Resonance Imaging (MRI) machine which receives the radio frequency signals emitted by the protons in the body after the RF pulse is turned off. Specialized high frequency antennas can improve the quality, clarity, and resolution of the resulting MRI images. This paper deals with the design of Bow Tie antenna for X-Band in the frequency range 8–12 GHz, used in ultra-high field MRI systems. Using the Ansys HFSS tool, the antenna is designed and simulated and analysed. The fabricated antenna with the design specifications is tested in anechoic chamber for its working. Reflection coefficient at 10.5GHz is found to be around -14 dB for simulated antenna and -12 dB for fabricated antenna, which is satisfactory for practical application. Differences between the measured and simulated values were seen in results which are caused by cable loss in the measuring apparatus.
Authors - Bharati B Pannyagol, S.L Deshpande, Rohit Kaliwal, Bharati Chilad Abstract - The Internet of Things has revolutionized markets by connecting previously isolated devices, but this integration raises security risks from malicious nodes that can corrupt data or disrupt operations. This evaluation of Federated Learning's possible application as a decentralized node identification technique highlights its advantages over standard machine learning approaches. Internet of Thing devices may collaborate on model training while protecting sensitive data and reducing network use. Federated Learning and Blockchain interactions creates a robust framework addressing critical IoT challenges like data privacy, security, and trust. Blockchain enhances this system by providing a decentralized, tamper-resistant ledger that ensures data integrity and transparency. Automated processes, including model validation and incentive distribution, are facilitated by smart contracts. While this integrated approach improves data protection and scalability, challenges such as computational demands and consensus delays remain. The survey discusses practical applications, challenges, and future research directions for combining Federated Learning and Blockchain in IoT systems.
Authors - Bhadouriya Khushi Mukeshsingh, Rajput Adityasingh Shashikantsingh, Patel Swayam Vinodkumar, Ashish P. Patel, Nirav D. Mehta, Anwarul M. Haque Abstract - As digital infrastructure becomes more interconnected, effective cyber security has never been more important. This article explodes how advances in power electronics technology can support and improve cyber security frameworks. Energy management strategies, control systems, and semiconductor technologies can be used to increase the systems resilience to potential vulnerabilities that serve as possible entry points for cyber attackers. The research discussed in this article seeks to demonstrate that optimized distribution systems with adaptive control techniques can improve the stability and reliability of critical infrastructure, even in the face of cyber threats. This article discusses the inter-relationship between energy management and cyber security, showing the reader how power electronics can be important in developing a holistic security strategy. It describes a proposed approach to integrating power electronics into cyber security to create an adaptive, robust defence mechanism. This study provides valuable insights into the design of systems that are not only efficient but also fortified against evolving cyber threats, contributing to the broader understanding of how technology convergence can enhance overall infrastructure security.
Authors - Madhura Shankarpure, Dipti D. Patil Abstract - This paper presents a robust framework for YOLO (You Only Look Once) algorithm- based orange detection and localization in photos and videos is presented. The system combines contour-based bounding box localization with deep learning-based item recognition for increased accuracy. Transfer learning was used to refine a pre-trained YOLOv10 model on a Fruit 360 dataset. Data augmentation techniques such as random rotations, brightness changes, and scaling were applied to improve the model's resilience. Bounding boxes are created around identified oranges with a confidence threshold greater than 0.5 as part of the real-time video processing methodology. The model performed well on a balanced test dataset, achieving 95% accuracy, 92% precision, and 90% recall. These findings show how well YOLO works when combined with conventional computer vision methods for real-world uses like automated fruit sorting, fruit harvesting, and real-time market monitoring. The processed video output confirms the system's suitability for real-world situations.
Authors - Manjusha Pandey, Rajeev Kumar, Satyam Tiwary, Yuvraj Singh, Oindrella Chatterjee, Siddharth Swarup Rautaray Abstract - This paper delves into the complexities of providing equitable access to multimedia content across India's diverse linguistic landscape. It proposes innovative strategies for translating English video content into Indian regional languages, leveraging cutting-edge technologies such as machine translation, speech recognition, and text-to-speech synthesis. The suggested approach involves a systematic four-phase process, encompassing audio separation, text conversion, machine translation, and speech synthesis. [1] By utilizing open-source tools like IBM's Watson supercomputer and the Flite engine from Carnegie Mellon University, the system achieves a commendable 79% accuracy in terms of naturalness and fluency, as evaluated by native speakers. However, challenges persist in handling multi-speaker conversations and accommodating a broader range of Indian languages. Despite these limitations, the research lays a solid foundation for future advancements in the field. By fostering cross-cultural communication and knowledge dissemination, the proposed solution holds the potential to bridge linguistic barriers, empower marginalized communities, and foster an inclusive digital ecosystem in India.
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari Abstract - This paper explores the phenomenon of Artificial Intelligence (AI) transformation in organizational culture evaluation, discussing capabilities, advantages, obstacles and future direction. While traditional means of mining forms like surveys and interviews are often lengthy and flawed due to human biases, AI tools rely on real-time data, natural language processing, and predictive analysis to deliver objective insights instantly. Such applications, including sentiment analysis, behavioural analytics, and cultural diagnostics, allow organizations to mitigate cultural misalignments in advance at the organizational level or within specific teams, idem for the employee's engagement and inclusivity. Nonetheless, ethical issues related to data privacy, security and algorithmic wage discrimination continue to pose significant challenges. The implications of this study highlight the increasing importance of artificial intelligence in enabling organizations to build dynamic, resilient, and agile organizational cultures.
Authors - Sakshi Sharma, Tanisha Verma, Shailesh D. Kamble Abstract - Accurate, timely detection of plant disease is critical to protect crop from being damaged and increase agricultural productivity. Many disease identification methods are labor intensive and only practical with an expert set of trained eyes. A mobile application for real time plant disease detection using CNNs presented in this paper allows farmers to have a simple yet powerful access to a diagnostic tool. CNN was trained on a big collection of plant leaf images to discriminate between diseases using Keras and TensorFlow. The application was built using Flutter for cross platform mobile development, trained model deployed on mobile devices using TensorFlow Lite, which allows offline inference. Users can capture images of affected plant leaves and get immediate diagnostic feedback as to the potential disease involved. Following data preprocessing and model optimization, the application uses a lightweight architecture that achieves high accuracy while meeting requirements for mobile deployment. This research shows integration of AI with mobile technology can provide a scalable, efficient and accessible solution to crop disease detection. The system as proposed is capable of improving crop health management, reducing losses, and working towards global food security.
Authors - Utkarsha Wanjari, Shubham Kadam Abstract - Gamification in HRM through AI is thus a total revolution that can maximize the engagement and productivity of employees. Game-like qualities such as rewards, badges, leaderboards, and challenges incorporated in the HR processes create a captivating environment that motivates and pushes an employee into an achievement culture. AI amplifies the effect of gamification: it enables data-driven insights, personalized experience, and real-time feedback loops. The paper also looks into the psychological underpinnings of gamification intrinsic and extrinsic motivation and their alignment with the organizational goals. It analyzes some of the challenges in incorporating gamification, including ethical considerations, potential overuse, and the balance between entertainment and productivity. It also reflects on some success stories and presents a pathway to implementing gamified AI solutions into the existing HR framework. This is because gamification, combined with AI, will alter the way human resource practice prevails, uplift employee productivity, boost employee satisfaction, and contribute to the long-term success of an organization. The present research study aspires to provide business organizations with the actionability of a very innovative method to remain ahead of their game in the changed wilderness of the workplace.
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari Abstract - The paper investigates Green ICT leadership in e-governance towards carbon footprint mitigation from the digital government. E-governance uses information and communication technology (ICT) to deliver administrative services through enhanced technology in this service chain, thus increasing the efficiency of their services, which is guided by an aim for complete transparency that requires accurate information. However, digitalization is responsible for environmental problems such as carbon emissions produced by data centres, digital infrastructure, and devices. The paper emphasizes the importance of vision-oriented leadership in promoting sustainability through processes of strategic thinking, collaboration and innovation. The Guide presents a series of critical strategies, including energy-efficient data centres, virtualization and cloud computing, sustainable procurement, and citizen engagement to build green practices. Innovative technologies such as AI, IoT, and blockchain are labelled enablers for optimizing energy consumption and increasing transparency.
Authors - Alpa R. Barad, Ankit R. Bhavsar Abstract - Analysis of grass quality is essential to improve cattle health. To improve animals' health and productivity, it is necessary to survey quality food. Grass is a primary and major source of food for every cattle. As a part of vegetation quality of grass is decreasing day by day, and it’s also not possible to survey fresh grass on a daily basis. Proposed research is used to analyze the quality of grass based on its color space. The quality of grass differs over the grass species and weather, and it's become more difficult with a single model to recognize its quality. To solve this problem proposed research uses machine learning based hybrid approach. The proposed research uses Median filter with kmeans clustering. Based on the clustering, the Simulation uses color deflection code to identify threshold values for a given species of grass. Proposed research finds the remarkable performance of three different qualities of grass. Simulation of study uses a Wiener filter and data augmentation to identify the impact of the proposed k-means based hybrid approach for grass quality recognition.
Authors - Aryan Jain, Shrirang Joshi, Vatsal Jain, Dinesh Kumar Saini Abstract - 5G network roll-out is expanding globally, which further shows that low-cost and good modem design remains to be absolutely integral. Scaling here is tough, not to mention the complexity and cost of production involved in traditional hardware-based 5G modems. This analysis explores how advances such as Open Radio Access Networks (Open RAN), Software-Defined Networking (SDN) and Network Function Virtualization (NFV) could reduce the hardware requirements, leading to lower costs for 5G modems. We marvel over the functionalities which we take for granted in a modem, such as digital signal processing and base-band processing, are being virtualized so that it is done on general-purpose hardware rather than on parts custom designed to do these particular tasks. Adopting cloud-native and software-based solutions for these traditional hardware-driven processes can bring huge savings without compromising on performance. In addition, we discuss Dynamic Resource and Change in Network efficiency which are improved by Modem Allocation, Edge Computing, Network Slicing — SDN NFV open day light. This collection of methods is described in a comprehensive article on the application of virtual network technologies to improve 5G modem design, reduce deployment costs, and enable more flexible, scalable, and energy-efficient 5G solutions.
Authors - Mallu Praneeth Reddy, T. A. S. Vardhan, Kura Bhargava Gupta, Nagireddy Deekshitha, Pudari Shrainya Goud, Khalvida Pamarty, Sushama Rani Dutta Abstract - This paper aims to predict the calories burnt by a person using machine learning models built on several regression algorithms like Linear, Random Forest, XGBoost,and CatBoost based on gender, age, height, weight, duration of exercise, body temperature, and heartbeat of the person. In addition, the analysis compares the algorithms based on performance metrics like MAE (Mean Absolute Error), MSE (Mean Square Error), and R2 score and determines the most effective algorithm for calorie prediction.
Authors - Shailender Vats, Prasadu Peddi, Prashant Vats Abstract - Blockchain technology's explosive growth has created previously unheard-of potential in several industries, but it has also revealed fresh security flaws. To improve threat detection and response mechanisms, this paper provides a complete intrusion detection system (IDS) designed especially for distributed blockchain ledger security. It makes use of sophisticated smart contracts. We demonstrate the efficacy of the suggested IDS in detecting possible intrusions while preserving the integrity of the blockchain environment by validating it using simulation-based scenarios. According to the research, combining IDS with blockchain technology and smart contracts greatly improves security and is a viable way to address current cybersecurity issues.
Authors - M Nanda Kumar, Harsh Sharma, Rajan Kakkar, Tushar Naha, Atul, Rishabh Yadav, Naveen Abstract - The demand for autonomous vehicles (AVs) has grown rapidly due to their potential to revolutionize transportation by enhancing safety, efficiency, and convenience while reducing human error, a leading cause of road accidents. AVs leverage advanced technologies like machine learning, LIDAR, GPS, cameras, RADAR, and ultrasonic sensors for precise navigation, obstacle detection, and real-time decision-making. However, their reliability and safety in di-verse environmental conditions remain a significant challenge. Extreme weather events such as heavy rain, snow, fog, ice, hail, and dust storms can impair sensor performance, reducing visibility, traction, and the ability to detect road markings, obstacles, and other vehicles. These conditions degrade the accuracy of critical systems like LIDAR, RADAR, and cameras, raising concerns about AVs’ reliability, particularly in emergencies or unpredictable scenarios. This review paper explores the effects of adverse weather on AVs’ performance, analyzing the limitations of key sensors and assessing various mitigation strategies to enhance their resilience. By identifying technological gaps and emphasizing the need for weather-resilient solutions, the paper aims to guide future research and innovation to improve AVs’ safety and reliability in challenging real-world conditions, ensuring their readiness for broader deployment.
