Authors - Keerthi AJ, Kalyanasundaram V, Srinivasa Perumal R Abstract - Individuals who are visually impaired or have dual sensory impairments, such as both hearing and vision loss, face significant challenges in navigating public spaces. These challenges often limit their independence and pose risks of unintentional harm to themselves and others. While traditional mobility aids like canes or guide dogs provide some assistance, they lack the ability to deliver real-time, comprehensive awareness of the user's surroundings. To address these limitations, The Intelligent system-powered Smart Device designed to enhance mobility and safety for visually impaired individuals. This device leverages advanced object detection technology to enable users to navigate public spaces more effectively and confidently. The solution employs a SSD MobileNetV3 Convolutional Neural Network (CNN) model for real-time, efficient, and accurate object detection. Integrated with the Ov7670 for computer vision tasks and an Arduino microcontroller for hardware coordination, the system captures live video through a mounted camera to detect and classify obstacles. Users receive instant alerts via auditory or haptic feedback, promoting safer navigation. To ensure robust performance, Azure Custom Vision is used to evaluate and visualize the precision, recall, and average precision (AP) using the COCO dataset. By offering enhanced mobility and reducing risks, this innovative device fosters independence and inclusivity for visually impaired individuals in public environments.
Authors - K.L.Sailaja, Gollapudi Vanditha, Goriparthi Krishna Swapnika, Mohammad Sania Sultana, Madala Pavani Abstract - The aim of this project is to create an effective machine learning model for the detection and forecast of Urban Heat Island (urban heat islands) phenomenon in the mid region of Andhra Pradesh state specifically Vijayawada. High temperatures in urban areas relative to their rural areas are called Urban Heat Islands. The negative impacts include increasing energy use, health risks, and environmental destruction. The satellite imagery and Random Forest models, in particular, have a long-standing reputation of being inaccurate when it comes to geolocalization and even when time-based forecasts are provided, they are mostly misleading. Thus, this gives rise to inaccuracies and inconsistencies in hotspot identification and forecasting’s metrics. This Project suggests an improved Recurrent Neural Network (RNN) model that incorporates Long Short-Term Memory (LSTM) algorithms, driven by the need for more precise and reliable predictions. The proposed LSTM-based model targets the traditional approaches shortcomings of being spatially and temporarily inaccurate in the detection of hotspots. The patterns of temperature, humidity, and soil moisture in city regions can be explained better by this model. It increases the model's predictive capability and explains urban island’s patterns. The project uses data obtained through NASA/POWER CERES/MERRA2 Native Resolution Daily Data, which provides an extensive collection of temperature, humidity, and soil moisture records. These factors will be used to develop forecasting and predictive models of the Urban Heat Islands hotspots. Normalization is one of the methods employed even during advanced data preprocessing.
Authors - Payal Khode, Shailesh Gahane, Arya Kapse, Pankajkumar Anawade, Deepak Sharma Abstract - The technology behind cryptocurrency is secure and transparent. Currently, numerous investors are attracted to cryptocurrencies because of their transparent and safe technology. Additionally, investors find cryptocurrencies fascinating due to their high return potential and innovative possibilities. To optimizes trading and predict prices for investment strategies, some artificial intelligences are required. As of 2024, the global cryptocurrency market capitalization has exceeded 2.5 trillion dollars. Since then, cryptocurrency has established itself in the financial arena, with daily transaction volume reaching enormous heights. Navigating investment strategies are one of the main challenges for investors. There for, leveraging artificial intelligence for optimal investment decisions stands as effective solutions. The study examines recent developments in the field of artificial intelligence methods for cryptocurrencies investment, focusing on trading digital currencies such Bitcoin, Altcoin, Meme coin, and others. Even though price prediction for investing strategies has been the subject of extensive research, notable gaps remain in enhancing cryptocurrency trading through AI for successful investment outcomes. This paper reviews these gaps by examining the role of AI in accurately predicting cryptocurrency prices to enhance optimal investment. The study's findings demonstrate the critical role that precise price forecasting plays in developing adaptable and cautious trading and investing methods, which are essential in the erratic cryptocurrency market. Additionally, the study highlights current issues and suggests future research possibilities, highlighting the importance of ethical issues and multidisciplinary methods in the investment. By filling the knowledge vacuum and providing direction for future study, this synthesis hopes to promote more advanced and successful investment techniques in the crypto space.
Authors - Grishma Bobhate, Pawan Bhaladhare Abstract - Internet of behavior is a primitive approach to study behavior analysis and predictive learning system to understand user experience and interpret their psychological patterns for betterment of the society. Basically, Internet of Things and Internet of Behavior are closely related to each other and can offer different techniques in various areas for developing technology and significant applications. Major psychological disorder, or depression, is a common but fatal neurological condition that has a disruptive impact on feelings, actions, and ways of seeing actuality. Various Machine learning algorithm have been implemented to detect depression through fusion modalities applied on different parameters such as visual, textual and gaze movement. To ensure preventive measure and provide ethical frameworks, this study aims to identify Internet of behavior technology that can have crucial importance in the comprehensive study of depression detection in healthcare sectors. With the assessment in health monitoring systems, the main objective is to explore and analyze the strategies in the Internet of behavior technology for understanding patient behavior and mental health to detect depression and mood. Various challenges towards Internet of behaviors has discussed. In order to ensure the reliability of the system, it also explores the different machine learning and deep learning approaches to determine depression with the performance validation. This will help to assist medical personnel in acquiring details and evaluating the actions of patients for an effective regimen of treatments. This study outlines the strategies to adopt the behavioral analysis for effective learning of depression detection model.
Authors - Balasubbareddy Mallala, Azka Ihtesham Uddin Ahmed, P. Kowstubha, T. Murali Krishna Abstract - The world is now transiting towards Renewable Energy sources (RES) at a rapid pace to overcome the limitation of fossil fuel and generate Green Energy. But due the irregular generation of power in RES (like Solar PV Plant) throughout the day is making it less reliable. This paper integrates RES with an Energy Storage System (ESS) and Fuel Cell to overcome this disadvantage. With the help of this system the dependence on conventional energy sources can be reduced, the cost of generation of power can be brought down to 1/4th compared to an existing traditional system and also increases energy independence. During the morning hours, Combined with the fuel cell, the solar photovoltaic plant will supply power. Any excess power generated will be stored in the energy storage system (ESS). This way, when sunlight is unavailable, the ESS can meet the load demand, ensuring continuity and making the system more efficient and reliable.
Authors - Pradeepkumar G, Priya Devi T, S A Suje, Gobinath S, M Dhanapal Abstract - Nowadays self-driving cars are gaining attraction globally but their implementation in India faces significant hurdles due to the inadequacies of existing approaches reliant on GPS and sensor technologies. The erratic nature of Indian roads, characterized by variable road conditions and inaccuracies in mapping, renders conventional methods unreliable. To address these challenges, propose a novel approach utilizing pattern matching techniques for autonomous navigation. The solution involves deploying specialized patterns on the road surface, facilitating accurate detection and identification of pathways suitable for autonomous driving. By utilizing a modelled car equipped with a Raspberry Pi for image processing, the system captures road imagery via onboard cameras. These images are then transmitted to a remote computer for analysis and subsequent navigation instructions. Additionally, an array of sensors is deployed to detect and avoid obstacles in the vehicle's vicinity. The key innovation lies in the hybrid approach, which combines traditional sensor-based navigation with the novel pattern matching methodology. By leveraging these complementary technologies, the prototype aims to provide robust autonomous navigation tailored to the unique challenges of Indian roads.
Authors - Dhanalakshmi R, Prashaanth S, Hari Prasath S, Dhanaselvam J, Harish R Abstract - India is mainly an agricultural country, where almost three-fourths of the country's population works on farms. Several crops are grown according to regional situations. High-quality production of these crops can be achieved only with new techniques. The appropriate management of crops and identification of diseases and their respective treatments are very significant to prevent losses after harvesting as it usually happens. Diseases in crops deviate from their normal functions and show symptoms that hinder growth. Pests and insects always devastate major crops like rice, wheat, maize, and soyabeans. Consequently, productivity becomes low. With the adoption of deep learning technologies, pest infestation detection and management in agriculture have accuracy and efficiency. A solution is proposed in this paper that integrates image processing techniques with the MATLAB platform for the classification of pests and the proper fertilizers and pesticides to be applied. An autonomous robotic sprayer is used by this system to remotely traverse crop fields, ensuring pinpoint treatment applications. On the other hand, the infrastructure cost is reduced by the proposed solution. The camera setup density in an agricultural IoT monitoring system is minimized by it. Thus, the advanced technology is integrated with agricultural practice by this approach to promote sustainable farming. A validation accuracy of 99.80% is achieved by it to maximize crop production while minimizing losses due to pests and diseases.
Authors - M.Kavitha, N.Revathy Abstract - Forest cover prediction has applications in environmental monitoring, forest management, and land-use planning. Governments and conservation organizations can use it to assess forest cover types and predict land changes. The research article depicts the use of the XGBoost algorithm applicable in forest cover prediction, focusing on evaluating model performance through key metrics like Mean Squared Error (MSE), Logarithmic Loss (Log Loss), and confusion matrices. The XGBoost model, optimized through hyperparameter tuning, demonstrates robust performance with a relatively low MSE, indicating accurate predictions. The Log Loss value of 0.5786 suggests that while the model's classifications are reasonably confident, there is room for refinement. The confusion matrix reveals strong performance for certain classes, such as class 1, but highlights significant errors in others, particularly class 5, which shows a high error rate of 60.93%. The proposed model effectively captures underlying data patterns and performs well across most classes. However, further enhancements, such as addressing class imbalances and refining hyperparameters, are needed to improve accuracy in challenging cases. The model's high hit ratios, where the correct class is often among the top predictions, indicate its reliability in multi-class classification tasks, making it a valuable tool for forest management and environmental monitoring.
Authors - Shailesh Gahane, Payal Khode, Arya Kapse, Deepak Sharma, Pankajkumar Anawade Abstract - Nowadays, many universities and information technology (IT) institutes throughout the world provide online courses, tests, and certificates. In order to administer the tests from any location, delivery technologies have been developed. Putting this into practice will result in time and travel cost savings. Due to the COVID-19 epidemic, there is currently a significant demand for online courses and exams. The majority of universities presently use a variety of assessment methods to evaluate their pupils. These include of oral, paper-based, electronic, and electronic-paper. To help identify the most secure and acceptable assessment method, a survey was carried out. Participants were selected from One Universities in India and Uganda. Population Sample Participants were selected from One Universities in India and Uganda. Using the Krejcie and Morgan formula, a sample of 98 participants was drawn from the 110 research participants. Data was gathered using a questionnaire instrument, and descriptive statistics were generated through data analysis using SPSS software.
Authors - Pradeepkumar G, Pavithramathi R, Jahina J, Tamilselvan K, Arulanantham D Abstract - This work presents an innovative system designed for remote monitoring of landslides using IoT technology. The solution uses a wireless underground sensor network (WUSN), a cloud computing platform and a dedicated mobile application to provide real-time monitoring capabilities. In this system, a sensor network uses Arduino components connected via Wi-Fi modules to collect data on soil moisture levels. This collected data is then transferred to a cloud computing environment for secure and permanent storage. In addition, the cloud platform hosts the model that can trigger alarms when potential landslides are detected. In addition, the system includes a user-friendly mobile application that facilitates real-time data visualization and alerts on potential landslides. This end-to-end solution, from humidity sensor data collection to citizen-facing data visualization, is particularly suitable for smart cities and IoT environments. The effectiveness of the system was evaluated with both real-life tests and simulated scenarios. The results show that the network of sensors accurately measures soil moisture, while the landslide monitoring model continuously sends alerts when necessary.
