Authors - Sandeep Shinde, Parth Kedari, Atharva Khaire, Shaunak Karvir, Omkar Kumbhar Abstract - With the use of cutting-edge technologies like Flask, web technology, API rendering, Make It Talk, and machine learning (ML), an AI smart tutor bot is being implemented with the goal of giving users an engaging and customized learning experience. The bot uses machine learning techniques to analyze responses and generates quiz-style questions with multiple-choice possibilities and extended answers. This allows for quick feedback. Additionally, it has an interview mode in which the user engages with an AI avatar that reads their body language and facial emotions. Using written material and specialized alphabets, the AI avatar is dynamically educated, gaining comprehensive knowledge and an accurate evaluation of user performance. The research article goes into detail about the system architecture, how different technologies were integrated, and the process for training the avatar and gauging user response. Through user feedback and experimental trials, the AI Smart Tutor Bot's performance is assessed, showcasing its potential as an advanced teaching tool that can adapt to each student's unique learning needs while boosting comprehension and engagement.
Authors - Bhagyashree D. Lambture, Madhavi A. Pradhan Abstract - Phytochemical qualities, geographic information, environmental conditions, and traditional medicinal knowledge are some of the sources of information that are incorporated into this research project, which presents a comparative examination of machine learning (ML) algorithms for the qualitative evaluation of medicinal plants. In order to categorize and forecast the medicinal value of plants based on multi-modal data, the purpose of this study is to investigate the effectiveness of various machine learning algorithms. For the purpose of determining which method is the most effective for evaluating complicated and diverse datasets, a full evaluation is carried out utilizing well-known machine learning models. These models include decision trees, random forests, support vector machines, and deep learning algorithms. Key criteria including as accuracy, precision, recall, F1-score, and computing efficiency are utilized in order to evaluate the levels of performance achieved by each method. For the purpose of gaining a deeper comprehension of the role that each data source plays in determining the medicinal potential of plants, the value of features and their interpretability are also investigated. A basis for the ongoing development of AI-driven tools in pharmacological research and plant-based drug discovery is provided by the findings of this comparative analysis, which offer vital insights into the usefulness of machine learning for medicinal plant assessment. Contributing to the expanding fields of computational botany and natural product science, the purpose of this study is to improve the precision and effectiveness of the evaluation of medicinal plants.
Authors - Azhar Abbas, Farha Abstract - Fraudulent claims in the insurance industry lead, to significant financial losses and negatively affect both policyholders and insurance firms. Machine learning has proven to be revolutionizing fraud detection since it is more than just performing the ordinary rule-based systems while automating and optimizing detection processes. The current work proposes a novel hybrid approach that combines supervised and unsupervised techniques in machine learning with applications in accurately and robustly detecting insurance fraud. Three primary models include in the framework are Decision Tree, Random Forest, and Voting Classifier, which improve detection performance on real-world datasets. In addition, an embedding-based model interprets sequential claims data, and a statistically validated network is used to detect patterns of collusion and fraud among related entities. Extensive experimentation was conducted using large-scale motor, and general insurance datasets and showing that the proposed hybrid model achieved an accuracy of 89.60%. Hyperparameter tuning and data preprocessing were used to further refine the model's performance; it was able to counterbalance all issues brought forth by imbalances, complexities, and complexities due to variations in fraud types. The methodology outperformed the existing models, better at identifying rare sophisticated cases of fraud. The practical implications of deploying machine learning models in the insurance sector are also discussed from the angle of best practices for data governance, model interpretability, and stakeholder trust. In Future this work will be improved by incorporating real-time analytics to provide quicker detection, enhancing interpretability features, and adapting the model to emerging fraud patterns in evolving data environments.
Authors - Poonam Yadav, Meenu Vijarania, Meenakshi Malik, Neha Chhabra, Ganesh Kumar Dixit Abstract - Parkinson's disease is aging-associated degenerative brain illness that results in the degeneration of certain brain regions. Early medical diagnosis of Parkinson's disease (PD) is difficult for medical professionals to make with precision. Magnetic Resonance Imaging (MRI) and single-photon emission computed tomography, or SPECT are two medical imaging strategies that can be used to non-invasively and safely assess quantitative aspects of brain health. Strong machine learning and deep learning methods, along with the efficiency of medical imaging techniques for evaluating neurological wellness, are necessary to accurate the identification of Parkinson's disease (PD). In this study, we have used dataset of MRI images. This study suggests three deep learning models: ResNet 50, MobileNetV2 and InceptionV3 for early diagnosis of PD utilizing MRI database. From these three models, MobileNetV2 demonstrated superior accuracy in training, testing and validation with a rate of 99%, 94%, and 96%, respectively. With its effectiveness and precision, MobileNet V2 has a lot of potential for PD identification using MRI scans in the future. We may further advance the development of dependable and easily accessible AI-powered solutions for early diagnosis and better patient care by tackling the issues and investigating the above-mentioned future paths.
Authors - Aye Thiri Nyunt, Nishi Vora, Devanshi Vaghela, Brij Kotak, Ravi Chauhan, Kirtirajsinh Zala Abstract - This paper is an AI and Machine Learning Algorithm - based dualistic Gesture-to-Speech and Speech-to-Gesture framework. The core of this initiative is to enable machines and humans to converse with each other by enabling the translation of physical body movements into reasonable speech and vice versa. We used deep learning models- Convolutional Neural Networks (CNN)- to train our system using a dataset consisting of human gestural movements and the relevant speech patterns. For the Gesture-to-Speech module, real-time gesture recognition and interpretation were used, which involved computer vision and were implemented to interpret gestures into speech output containing words and phrases representing the message illustrated by the gestures. The Speech-to-Gesture module, on the other hand, uses speech as input to produce context-related gestures-the main purpose of which is to improve user interaction and experiences. In the system, multiple applications were tested, including sign language and webcams. Further research will try to extend the flexibility of the system to include various languages, cultural backgrounds and characteristics of individual gesture styles which eventually has a high level of customization. We had designed the CNN architecture for real-time gesture recognition and taken care of data preprocessing as well to increase accuracy concerning different types of gestures. We created Gesture-to-Speech translation with the use of an LSTM, then added in a Text-to-Speech engine for it to have a very natural sound. We then worked on Speech-to-Gesture and even refined the gestures through a CNN-based network, to ensure transitions are very fluid. Everything was coordinated such that there would be synchronous gestures and speech for extremely natural real-time interaction. We coached on how one would integrate, test, and further optimize models with dropout and batch normalization for higher performance.
Authors - Varun Maniappan, Praghaadeesh R, Bharathi Mohan G, Prasanna Kumar R Abstract - This paper constitutes a comprehensive review of how language models have changed, focusing specifically on the trends toward smaller and more efficient models rather than large, resource-hungry ones. We discuss technological progress in the direction of language models applied to attention mechanisms, positional embeddings, and architectural enhancements. The bottleneck of LLMs has been their high computational requirements, and this has kept them from becoming more widely used tools. In this paper, we outline how some very recent innovations, notably Flash Attention and small language models (SLMs), addressed these limitations by paying special attention to the Mamba architecture that uses state-space models. Moreover, we describe the emerging trend of open-source language models, reviewing major technology companies efforts such as Microsoft’s Phi and Google’s Gemma series. We trace here the evolution from early models of transformers to the current open-source implementations and report on future work to be done in making AI more accessible and efficient. Our analysis shows how such advances democratize AI technology while maintaining high performance standards.
Authors - S.K. Manjula Shree, Shreya Vytla, J. Santharoopan, Harisudha Kuresan, A.Anilet Bala, D.Vijayalakshmi Abstract - The goal is to use a Random Forest classifier to categorize future price movements as "up" or "down" in order to forecast stock market trends. In order to guide investing strategies, this model will examine pertinent attributes and previous stock data. The effectiveness of Logistic Regression, Support Vector Machines (SVM), and Random Forest Classifier in forecasting stock market movements is compared in this study. The ensemble approach Random Forest is very resilient under erratic market situations since it is excellent at handling noisy, complex data and capturing non-linear patterns. SVM performs best on smaller, more structured datasets, however noise and non-linearity might be problematic. Despite its simplicity and interpretability, logistic regression is constrained by its linear character and finds it difficult to account for the dynamic, non-linear behavior of stock prices. In recall focused tasks, logistic regression is helpful because it performs well in identifying true positives (such preventing missed opportunities in stock predictions). SVM's reliance on kernel functions makes it computationally expensive, but it can also be helpful when handling smaller datasets with clear patterns and where accuracy is needed. All things considered, Random Forest offers the greatest results around 99% especially for difficult stock market prediction assignments.
Authors - Umakant Singh, Ankur Khare Abstract - This paper aims to find the optimal model for stock price forecast. In examining the different approaches and aspects that need to be considered, it is exposed the methods decision tree and Gradient Boosted Trees Model. This paper aims to propose a more practical approach for making more accurate stock predictions. The dataset including the stock bazaar values from the prior year has been considered first. The dataset was optimized for actual analysis through pre-processing. Therefore, the preprocessing of the raw dataset will also be the main emphasis of this work. Again, decision trees and gradient tree models are used on the pre-processed data set and the results thus obtained are analyzed. In addition, forecasting papers also address issues related to the usage of forecasting systems in actual situations and the correctness of certain normal value. This paper also presents a machine learning model for predicting stock stability in financial markets. Successful stock price forecasting greatly benefits stock market organizations and provides real solution to problems faced by investors.
Authors - Aneesh Kaleru, Chaitanya Neredumalli, Mrudul Reddy, Ramakrishna Kolikipogu Abstract - One major risk factor that contributes to traffic accidents globally is poor visibility in foggy situations. Drivers are seriously threatened by fog because it weakens contrast, hides important objects, and makes lane markings almost invisible. Recent developments in visibility enhancement methods for foggy circumstances are summarized in this paper, with a focus on picture defogging combined with object detection and lane aid. We analyze the application of models such as Conditional Generative Adversarial Networks (cGANs), Single Shot Multibox Detectors (SSD), All-in-One Defogging Network (AODNet), and You Only Look Once (YOLO) from the perspective of deep learning and computer vision. These methods have the potential to increase driver safety in inclement weather by identifying impediments, improving visibility, and offering lane guidance. The review also covers the limitations of these solutions, such as computational demands and requirements for real-time processing. Our goal is to provide researchers and practitioners with a comprehensive understanding of the current methods and their uses, allowing for the development of effective visibility enhancement systems that can prevent accidents and save lives.
Authors - Sindhu C, Taruni Mamidipaka, Yoga Sreedhar Reddy Kakanuru, Summia Parveen, Saradha S Abstract - India is a country with very rich ancient historical legacy. It preserved vast cultural and linguistic knowledge through stone inscriptions. Extracting text from ancient stone inscriptions and translating it into a language which is understandable by everyone is a very challenging task due to script variations, natural wear, and the uneven degraded surfaces of stone carvings. Our idea is to build a model which can extract the text from these stone inscriptions which were written in Telugu language and translate them into other Indian local languages. The Region-Based Convolutional Neural Network (R-CNN) model which is integrated with Tesseract OCR is trained on a custom dataset of 30,000 labelled images of Telugu script, encompassing Achulu (vowels), Hallulu (consonants), and Vathulu. By achieving a 96% accuracy in character detection, this model demonstrates significant reliability in recognizing Telugu characters from degraded and complex inscriptions. Data augmentation techniques, including rotations, flips, and shifts were used to further enhance the model’s robustness to different orientations and environmental conditions encountered in historical artifacts. The text which is extracted from the image is ultimately translated into Indian local languages using an API-based translation module, enabling a seamless interpretation of ancient content. This research contributes a comprehensive and automated solution for cultural preservation, giving us a scalable method to digitize and make historical inscriptions accessible to everyone which are integral to Telugu heritage and linguistic history.
Authors - Satya Kiranmai Tadepalli, Sujith Kumar Akkanapelli, Sree Harsha Deshamoni, Pranav Bingi Abstract - This paper in detail analyzes how generative AI and encoder-based architectures are drastically changing the realm of video generation with multimodal inputs such as images and text. The application of CNNs, RNNs, and Transformers so neatly serves to encode divergent modalities that blend into the seamless synthesis of realistic video sequences. It is based on the up-and-coming fields of generative models like GANs and VAEs, in bridging from static images to video generation. However, this represents a significant leap forward in the technology of video creation. It also goes into great detail on the complexities of multimodal input, working to balance coherence over time as well as semantic alignment of what's being produced. In the above-described context, it can now be realized how the role of encoders translates visual and textual information into actionable representations for generating video. What follows is a survey on recent progress in adopting Generative AI and multimodal encoders, discussions on what challenges are encountered today, and possible future directions that ultimately lay emphasis on their potential to assist video-related tasks and change the multimedia and AI communities.
Authors - Vathsal Tammewar, Bharat Sharma, Dharti Sorte, R. Sreemathy Abstract - Accelerated facial aging using GANs has been the key interest area in generative modeling and facial analysis fields, which offers significant breakthrough in age progression and regression solutions. This survey conducted an extensive review on techniques of GAN- based approaches for accelerated facial aging, emphasizing highly realistic and controllable aging transformations. Many of these methods applied in forensic investigations, entertainment industries, or age-invariant facial recognition systems, which are vivid demonstrations of the versatility and practical relevance of such methods. While such recent breakthroughs hold great promises, several issues remain; namely high-fidelity transformations to preserve important facial details do not fully diminish biases due to imbalanced datasets, and temporal consistency when age progressions or regressions consist of sequential ages is also critical. Computational efficiency and real-time applicability are still the most critical areas of focus. This paper probes into the strengths, limitations, and open challenges of existing approaches, while emphasizing the importance of innovations such as improved loss functions, diverse and representative training datasets, and hybrid architectures. Thus, this survey contributes to synthesizing current progress and outlining future research directions for advancing the field of GAN-based facial aging technologies.
Authors - Khushi Mantri, Abhishek Masne, Shruti Patil, Girish Mundada Abstract - In medical diagnostics, identifying bone fractures is a crucial task that is traditionally dependent on radiologists deciphering X-ray pictures. However, human factors like experience or exhaustion can occasionally cause delays or inaccuracies in diagnosis. The construction of an automated system for bone fracture identification utilizing Convolutional Neural Networks (CNN), a deep learning method that performs especially well in picture processing, is examined in this research. With the use of a tagged dataset of X-ray pictures, the suggested method can efficiently and accurately detect fractures. Prior to feature extraction using CNN layers which are trained to distinguish between fractured and non-fractured bones the images are pre-processed to improve clarity. In order to assist medical practitioners in making prompt, correct judgments, the final classification attempts to increase diagnostic accuracy while decreasing the amount of time needed for analysis. The potential of incorporating machine learning into healthcare to lower diagnostic errors and enhance patient outcomes is also discussed in this overview paper, which includes examines recent developments in CNN-based medical picture categorization.
Authors - Shradha Jain, Sneha Suman, Insha Khan, Ashwani Kumar, Surbhi Sharma Abstract - With continual advancements in deep learning, the potential misuse of deep fake is increasing and its detection is in a major scope of work. A model is trained to recognize the patterns in input data, deep fake recognize those patterns in a fabricated way. Sometimes a small, intentional change is added in the data points, these changes are undetectable to humans and confuse the learning model. Those changes are called adversarial perturbations. Compressive adversarial perturbations aim to make those changes even smaller and harder to detect. Authors explore a sophisticated framework - ComPAD (Compressive Adversarial Perturbations and Detection) which is used to detect adversarial attacks. This paper explores the strategies, and provides comparative analysis of methods used by different researchers. Various datasets including UADFV, DeepfakeTIMIT, LFW, FF++, and Deeperforensics are evaluated to achieve the highest metrics. Methods based on convolution neural networks, particle swarm optimization, genetic algorithm and D4 (Disjoint Diffusion Deep Face Detection) are used for detection. Authors also discuss the challenges such as generalization of models across the new data, the continuous evolution of adversarial perturbations that leads to consistent attacks, and the scalability issues for the real time deep fake. Concluding that models can significantly improve the accuracy, robustness and generalization.
Authors - Krisha Zalaria, Jaitej Singh, Priyanka Patel Abstract - The ubiquitous use of mobile phones in modern society has sparked increasing concern in environments where their usage is restricted, such as hospitals, schools, religious sites, and hazardous zones. Mobile phones, although integral to daily life, pose risks such as privacy breaches, interference with sensitive equipment, and even serious safety hazards. In response, this paper investigates the efficacy of various state-of-the-art object detection models for real-time mobile phone detection in restricted areas. We benchmarked YOLOv8, YOLOv9, EfficientDet, Faster R-CNN, and Mask R-CNN to identify optimal solutions balancing speed, accuracy, and adaptability. This study introduces a two-class detection framework to distinguish between individuals texting or talking on the phone, catering to differing levels of restriction. Evaluations using a customized, diverse dataset reveal YOLOv8 and YOLOv9 as superior, achieving high precision and recall, thus positioning these models as effective solutions for scalable, real-time surveillance systems in sensitive environments. Our research contributes significant insights into mobile phone detection, paving the way for enhanced safety and privacy in restricted zones.
Authors - Aniket Gupta, Chris Dsouza, Sarah Pradhan, Amiya Kumar Tripathy, Phiroj Shaikh Abstract - This paper is based on the development of voice chatbots and their configuration to make sure that e-commerce websites are in compliance with all the customer care requirements. The authors talk about the introduction of natural language processing in an e-commerce company and provide a review of recent developments in that area. The research specifically focuses on natural language processing techniques, steps involved in developing a chatbot, problems encountered during design, and functions and benefits of voice-based chatbot in e-commerce. This A study emphasizes chatbots as tools to support customer service systems. Keywords: Machine Learning, Natural Language Processing, Data Analysis, Customer support system, and CHATBOT.
Authors - Siddharth Lalwani, Abhiram Joshi, Atharva Jagdale, M.V.Munot, R. C. Jaiswal Abstract - Empathetic response generation is a rapidly evolving field focused on developing AI systems capable of recognizing, understanding, and responding to human emotions in a meaningful way. This paper investigates the integration of the Big Five OCEAN personality model with generative AI to generate emotionally relevant, personalized responses tailored to individual users' personality traits. The Big Five model categorizes individuals into five core personality dimensions—Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. By combining this model with advanced generative AI techniques, the system can deliver empathetic responses aligned with users' emotional states and personality profiles. Through the use of various machine learning algorithms, the study demonstrates that incorporating personality traits significantly improves the quality, accuracy, and emotional resonance of AI-generated responses, leading to more effective human-AI interactions.
Authors - Shaga Anoosha, B Seetharamulu Abstract - Brain tumor segmentation is a critical task in medical imaging, essential for accurate diagnosis and treatment planning. Recent advancements in federated learning (FL) and deep learning (DL) offer promising solutions to the challenges posed by traditional centralized learning methods, particularly regarding data privacy and security. This review paper delves into state-of-the-art approaches that FL and DL to enhance brain tumor segmentation. Each institution trains a deep learning model, typically a Convolutional Neural Network (CNN) or a specialized architectures like U-Net on its local dataset. U-Net, particularly effective for image segmentation tasks, consists of an encoder that extracts hierarchical features from MRI scans and a decoder that reconstructs the segmented output, creating a segmentation map outlining tumor boundaries. Instead of sharing raw MRI scans, federated learning allows each institution to share model updates with a central server. The central server aggregates the updates from all participating institutions to create a global model using Federated Averaging, which averages the weights of the local models. The updated global model is then sent back to each institution, which continues training on their local data using this improved model. This iterative process ensures high accuracy, robustness, and privacy preservation, making it a promising approach for collaborative brain tumor detection and segmentation. By combining the strengths of federated learning and deep learning, these state-of-the-art methodologies provide a powerful solution to the challenges posed by traditional centralized models. This integration not only improves segmentation performance but also ensures that sensitive patient data remains secure. As advancements in this field progress, the collaborative use of these state-of-the-art techniques is poised to significantly enhance diagnostic accuracy and improve patient outcomes in medical imaging.
Authors - Adarsh Singh Jadon, Rohit Agrawal, Aditya A. Shastri Abstract - This study investigated the efficacy of various deep learning models in performing sentiment analysis on code-mixed Hinglish text, a hybrid language widely used in digital communication. Hinglish presents unique challenges due to its informal nature, frequent code-switching, and complex linguistic structure. This research leverages datasets from the SemEval-2020 Task 9 competition and employs models such as RNN (LSTM), BERT-LSTM, CNN, and a proposed Hybrid LSTM-GRU with 1D-CNN model. Combining the strengths of LSTM and GRU units along with 1D-CNN, demonstrated superior performance with an accuracy of 93.21%, precision of 93.57%, and recall of 93.02%, along with Sensitivity & Specificity of 93.62% and 93.24% respectively. It also achieved F1 Score of 93.44%. We also evaluated the model on some other parameters, such as PPV, PNV, RPV, and RNV. This model outperformed existing approaches, including the HF-CSA model from the SemEval-2020 dataset, which achieved an accuracy of 76.18%.
Authors - Chetana Shravage, Shubhangi Vairagar, Priya Metri, Akanksha Madhukar Pawar, Bhagyashri Dhananjay Dhande, Siddhi Vaibhav Firodiya, Tanmay Pramod Kale Abstract - Emotion Recognition has gained significant popularity, driven by its wide range of applications. Emotion recognition methods use various human cues such as use of speech, facial expressions, body postures or body gestures. The methods built for emotion recognition use combinations of different human cues together for better accuracy in results. This paper explores different methods which use different human cues.
Authors - Ravin Kumar Abstract - This paper introduces the Adaptive Base Representation (ABR) Theorem and proposes a novel number system that offers a structured alternative to the binary number system for digital computers. The ABR number system enables each decimal number to be represented uniquely and using the same number of bits, n, as the binary encoding. Theoretical foundations and mathematical formulations demonstrate that ABR can encode the same integer range as binary, validating its potential as a viable alternative. Additionally, the ABR number system is compatible with existing data compression algorithms like Huffman coding and arithmetic coding, as well as error detection and correction mechanisms such as Hamming codes. We further explore practical applications, including digital steganography, to illustrate the utility of ABR in information theory and digital encoding, suggesting that the ABR number system could inspire new approaches in digital data representation and computational design.
Authors - Aarya Pendharkar, Tanmay Pampatwar, Mrunal Zombade, Ashwini Bankar Abstract - This study offers an effective approach for forecasting changes in stock prices using a binary classification model that makes use of sentiment analysis, technical indicators, and historical stock data. The model forecasts whether a stock will gain or lose the following day, rather than predicting actual stock prices. Technical indicators including moving averages, the Relative Strength Index (RSI), and Bollinger Bands are among the input elements, along with historical price data (open, close, high, low, and volume). Market news and social media data are subjected to sentiment analysis, which produces sentiment ratings (positive, neutral, or negative) in order to identify general patterns in market sentiment. When combined with technical indicators, these mood scores provide additional context for stock movements. The model uses machine learning techniques like XGBoost, SVC, Logistic Regression, and Random Forest, and it outputs a confidence score and a binary forecast. Performance indicators like accuracy, precision, recall, and F1 score are used to assess the model's efficacy. Back testing is also done to evaluate the robustness and performance of the past. The suggested model offers a comprehensive perspective of stock movements by integrating technical and sentimental aspects, producing better prediction skills than conventional models that only use past price data.
Authors - Divyashree H.B., Shirshendu Roy, Supraja Eduru, Dev Sharma, Prathamesh M.Naik Abstract - In today's tech scenario maximum farmers are practicing unconventional farming which needs hard work, in detail if say it is physical practicing. Especially the day-to-day work if talk about that watering the crop manually without measuring the temperature or having the knowledge of soil moisture in the field. As this is practiced from generation to generation, instead of any gain they are losing manpower, water loss which leads to low production and lower the income of farmer. The development of smart agriculture which is built, gives the surety about the soil's water level and fertility outcome by using several sensors. The sensors which are included is temperature sensors, soil moisture sensors and humidity sensors. The coordinated work with these sensors integrated with IoT and raspberry pi will make it convenient and limits the excessive work of the farmers. The integrated sensor will be placed on the water tank and interconnected with pump source, will give alert notification to the farmer phone about the need of water supply. Most the problems are related to electricity is there this issue can be resolved by connecting the sensors with power source and integrating it with cloud so that every controls of the farm will be in the fingertips of farmers. Similarly for soil moisture sensors in case of water requirement by the soil will be directly reach to users phone. So they can perform irrigation. Cattles responsibility is there, farmers owns livestock in the time of grazing, it may lost or distracted from the pathway. Collar tracker with map support will be beneficial at that time. Livestock abnormal behaviors can be detected, there feeding and water tank refilling can be done by just one click. Cows milk thickness health issue and certain things can be managed. Not only limited to cow but for other livestocks. Climate and weather conditions will be directly updated on the applications. Data analytics support for managing expenses. Graph guidance for the soil moisture, temperature and irrigation support. Water tank percentage filled, air composition whether drop irrigation or sprinkler irrigation needed, temperature, humidity cattles live location on custom based maps will be displayed on the dashboard. Application usage guidance and query support will be there for smooth use of application.
Authors - Ankit Patne, Hritika Phapale, Kaushik Aduri, Hemantkumar B Mali Abstract - Cloud-based Learning Management Systems (LMS) are secure online platforms that enable L&D professionals to upload their resources and build a comprehensive suite of learning materials. This paper presents an overview of the cloud LMS technologies landscape and examines architecture, scalability solutions, and security perspectives on deploying these tools. We take a look at how these platforms are also incorporating machine learning into their personalization of learning experiences. If you investigate some of the case studies on platforms like Coursera, you will get a sense of practical ways to implement and maintain performance improvements. The present paper, by reviewing current literature reviews major benefits of cloud technologies in improving educational outcomes, which include reducing cost, better scalability, and enhanced security. Such a study contributing to the evolving knowledge base of cloud-based education is shedding new light into the possibility of how cloud LMS can revolutionize IT security education delivery.
Authors - R.Mehala, K.Mahesh Abstract - The Content Preserve for 3D Video Stabilization using Warping Techniques for making a hand-held video camera captured using a guided camera motion. This technique enables the simulation of 3D camera movements by modifying the video look it was captured from adjacent views. Its algorithms successfully reproduce dynamic scenes from a single source video by focusing solely on perceptual plausibility rather than perfect reconstruction. The method that modifies a hand-held video camera's output to make it look as though it captured using a directed camera motion. This technique enables the simulation of 3D camera motions by modifying the video to look as though it was captured from adjacent views. It is possible to automatically select a particular wanted camera path. The warp calculated the content maintains the video frame while adhering to sparse deletions suggested by the restored 3D structure. This method works well as seen by the experiments stabilizing difficult movies with dynamic sceneries.
Authors - K.Sarvani, Dinesh, Bijith Narayanan, Aayush Rai Abstract - Digital finance has become a buzzword in every financial service to identify any country's solvency position and competitive environment. This study emphasizes the performance of the banking sector with respective to macro-economic variables to assess the solvency and profitability position of commercial banks in India. Two macroeconomic variables namely gross domestic product, and inflation were considered to identify the performance of nonperforming assets of the public sector banks. There are twelve public sector banks in India as of 2013-24 as per the RBI database. All the public sector banks were considered for the study for ten years. The data was collected from PROWSSIQ for the financial data of public sector banks. Macroeconomic variables were taken from Economic Times data from the published data from web sources. The findings of the study are that non-performing is negatively correlated to inflation and GDP growth rates. The adjusted R Squared value is 61 percent implying that the regressors are perfectly explained that the dependent and independent had a relation. Forecasting the performance of non-performing was done using the SARIMA model. It is found that for all the select banks, non-performing assets are continuously increasing which implies that the recovery of bad debts may be done by the adoption of new fintech apps and it is a positive sign for the performance of the banks in coming years.
Authors - Anudeep Arora, Neha Arora, Neha Tomer, Ranjeeta Kaur, Vibha Soni, Lida Mariam George, Anil Kumar Gupta, Prashant Vats Abstract - Effective human resource management is a major issue for the Business Process Outsourcing (BPO) business, which is marked by a high staff turnover rate and a dynamic operating environment. These issues are frequently not adequately addressed by traditional HR management techniques, which results in inefficiencies and higher expenses. BPO companies may improve employee engagement, optimize staffing levels, and anticipate workforce demands with the use of predictive analytics, which makes it a potent option. The use of predictive analytics for efficient HR utilization in the BPO sector is examined in this article. It explores important technologies, tools, and processes; talks about the advantages and difficulties of implementation; and provides case studies of effective deployments. BPO firms may increase labor productivity, lower attrition, and boost overall company success by utilizing predictive analytics.
Authors - Kavita Patil, Rohit Patil, Vedanti Koyande, Amaya Singh Thakur, Kshitij Kadam, Kavita Moholkar Abstract - This paper evaluates a chatbot system designed for personalized business interactions using advanced Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). The system combines proprietary business data with external databases to improve contextual relevance. A comparative analysis of leading LLMs—Gemini Pro, GPT-4, Claude 2, GPT-3.5, and LLaMA 2—was conducted across benchmarks like MMLU, GSM8K, BigBench Hard, HumanEval, and DROP. Gemini Pro outperformed the others, with scores of 88.9% on MMLU, 86.3% on GSM8K, 78.1% on BigBench Hard, 73.5% on HumanEval, and 79.2% on DROP, showcasing its strength in complex reasoning and long-context retrieval. Fine-tuned with business-specific data, Gemini Pro sets a new standard for high-accuracy, scalable chatbot solutions, ideal for enterprise applications.
Authors - Darshana Naik, Aishwarya Bhagat, Amman Baheti, Atharva Kulkarni, Hitesh Kumar Abstract - This paper examines the potential of AI-powered chatbots to address the growing global need for accessible and effective mental health support. It traces the evolution of chatbots, from rudimentary systems to sophisticated AI-driven platforms, emphasizing advancements in artificial intelligence and natural language processing that enable personalized responses. Driven by the need to overcome barriers of cost, availability, and stigma in mental health care, the paper explores chatbot integration strategies. These include using chatbots for screening and triage, extending therapist reach, bridging care gaps, reaching underserved populations, and leveraging data for personalized interventions. While chatbots show promise in delivering therapeutic support and improving symptoms, they are envisioned as a complement to, rather than a replacement for, traditional therapy. The paper advocates for leveraging AI to enhance the scalability, reach, and personalization of mental health care, ultimately aiming to improve global mental health outcomes. By exploring both the potential and the challenges of AI-powered chatbots, this paper contributes to the ongoing dialogue about the future of mental health care in an increasingly digital world.
Authors - Ashwini Bhosale, Laxmi Patil, Gitanjali Netake, Sakshi Surwase, Rutuja Gade, Prema Sahane Abstract - This paper discusses a project that aims to create a system for translating sign language into spoken words while also recognizing the emotions of the signer. The goal is to make communication easier for Deaf and hard-of-hearing individuals by converting hand gestures into speech and reflecting the signer’s emotional tone in the voice output. This would make conversations feel more natural and expressive, enhancing interactions in both social and work environments. The project uses computer vision and Convolutional Neural Networks (CNNs) to accurately recognize various sign language gestures. To identify emotions, it uses deep learning models like VGG-16 and ResNet, which focus on facial expressions. It also uses Long Short-Term Memory (LSTM) networks to analyze audio input and detect emotional tones in speech. For turning sign language into spoken words, the system employs Text-to-Speech (TTS) technologies like Tacotron 2 and WaveGlow. These tools create natural-sounding speech, and the detected emotions are added to the voice by adjusting tone, pitch, and speed to match the signer’s feelings. With real-time processing and an easy-to-use interface, this system aims to provide quick translation and emotion detection. The expected result is a fully functional system that not only translates sign language into speech but also effectively conveys emotions, making communication more inclusive for Deaf and hard-ofhearing individuals.
