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.
