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检索条件"机构=Artificial Intelligence and Machine Learning Faculty of Engineering and Technology"

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Deep Reinforcement learning In Autonomous Cars To Preventing Road Accident 2

Deep Reinforcement Learning In Autonomous Cars To Preventing...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Thote, Rushikesh Barhate, Aditya Tale, Abhay Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331528713

The integration of autonomous vehicles (AVs) into modern transportation systems presents both unprecedented opportunities and challenges, particularly in ensuring road safety and reducing accidents. This paper explores the application of Deep Reinforcement learning (DRL) techniques to enhance decision-making processes in AVs, aiming to proactively prevent road accidents. By simulating complex driving environments, DRL algorithms enable AVs to learn optimal driving strategies through continuous interaction and feedback. We introduce a DRL-based framework that incorporates sensor data fusion, real-time environment analysis, and adaptive control mechanisms. Experimental results from simulated urban and highway scenarios demonstrate a significant reduction in collision rates and improved response to unforeseen obstacles compared to traditional rule-based systems. The findings underscore the potential of DRL in fortifying the reliability and safety of autonomous driving technologies. © 2024 IEEE.

关键词： Deep reinforcement learning

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Prediction of Heart Disease Using machine learning Algorithms 2

Prediction of Heart Disease Using Machine Learning Algorithm...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Pacharaney, Utkarsha Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350360523

In India, heart disease claims the lives of about one person every day. The human body's most vital organ is the heart. The heart has two primary purposes: first, it gathers blood from body tissues and pumps it to the lungs;second, it gathers blood from the lungs and pumps it to all body *** disease has claimed the lives of many people. Predicting that disease at the appropriate moment is crucial, for this reason. Making use of machine learning Additionally, illnesses are easily identified and anticipated. It also covers the use of many algorithms on the Heart Disease Dataset. The dataset used in this research was obtained from Kaggle. Analyzing fundamental machine learning algorithms for the prediction of heart disease, such as Random Forest, K-Nearest Neighbours, Decision Tree Classifier, and Support Vector machine, is the primary goal of this study. Our Acquired Model for The Decision Tree Classifier has a score of 79.0%. And the Random Forest Classifier has an 84.0% score. With a linear kernel, the Support Vector Classifier scores 83.0%. Next, K Neighbours Classifier~ outperformed all previously employed methods, achieving the maximum score of {87%} with ~8 nearest result. RF (Random Forest), K-Nearest Neighbours Algorithm, and Decision Tree, together with Python programming and Visual Studio, which excels at both data and code. © 2024 IEEE.

关键词： Heart

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Automatic Human Life Rescue Analysis Model with Incorporation of Deep learning Algorithm 5

Automatic Human Life Rescue Analysis Model with Incorporatio...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kalaiselvi, V.K.G. Prithikhaa, S. Divya Darshini, J. Reddy, S Bharath Anandkumar, B. Hariharan, Shanmugasundaram Sri Sairam Engineering College Department of Information Technology Tamilnadu India KG Reddy College of Engineering and Technology Department of CSE Telangana India Vardhaman College of Engineering Department of Artificial Intelligence and Machine Learning India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India

ISBN: (纸本)9798331530013

In today's fast-paced world, every passing moment can mean the difference between life and death. To address this critical issue, we have developed an innovative application designed to provide rapid emergency medical response. Ambulance services play a pivotal role in emergency healthcare by offering vital transportation and care. However, limited access to ambulance contact information often hampers patients' ability to secure timely assistance during emergencies. With the widespread adoption of smartphones and mobile applications, the proposed solution integrates React Native with a Convolutional Neural Network (CNN) to create an advanced mobile app for law enforcement, capable of real-time car accident detection. Utilizing datasets from Kaggle, images are resized to optimize CNN training. Through transfer learning, the CNN architecture effectively extracts spatial features, enabling accurate accident prediction, validated by robust evaluation metrics such as precision and recall. The React Native app facilitates swift notifications to law enforcement, utilizing cutting-edge deep learning technology to enhance road safety and improve emergency response efficiency. This project aims to streamline emergency services, minimizing response and transport times, thereby saving lives and fostering safer communities. © 2024 IEEE.

关键词： Convolutional neural networks

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Anti-Accident Mechanism to Detect Driver Drowsiness Integrated with Alerting Mechanism: An IoT-based Model

Anti-Accident Mechanism to Detect Driver Drowsiness Integrat...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Katukam, Srujan Potluri, Sai Vinay Abburi, Sunell Dutt Cherukuri, Rahul Sreeka Parinishta, C. Peddineni, Mohith Ram Kumar, R.P. Polepaka, Sanjeeva Gokaraju Rangaraju Institute of Engineering and Technology Department of AI & ML Engineering Hyderabad India Department of Artificial Intelligence and Machine Learning Engineering Hyderabad India

ISBN: (纸本)9798350348217

According to the report released by the National Safety Council (NSC), about 1,00,000 accidents, 71,000 injuries, and 1,550 deaths are caused due to Drowsiness while driving each year. According to a study by the AAA Foundation for Road Safety, Drowsiness is responsible for up to 9.5% of accidents and 10.8% of those involving airbag deployment, injury, or significant property damage. The paper aims to build a real-time system to prevent accidents caused due to Drowsiness. It implements the features of Computer Vision, Deep learning, and Internet of Things (IoT) tools to detect Drowsiness and alert the Driver. The various alerting techniques adopted to alert the Driver includes Alarming, Vibration, and Water Sprinkler are implemented using IoT. © 2023 IEEE.

关键词： Internet of things

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Predictive Modeling and Analysis of Campus Placements Using machine learning Techniques 5

Predictive Modeling and Analysis of Campus Placements Using ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Gaurkhede, Sanskruti Burle, Rushikesh Gudadhe, Amit Chopra, PKP Gaitry Datta Meghe Institute of Higher Education and Research Computer science And Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331530013

A prediction method for campus placements is designed to determine the likelihood that a student will be hired by a firm after completing a campus placement. Numerous parameters are taken into account by the model, which may be used to estimate the student's ability level. Some information is gathered from examinations administered through the placement management system, while other information is gathered from college-level sources such as academic performance, CGPA, pointers, projects, internships, HSC and SSC marks, etc. This placement prediction technique aims to assist students in enhancing their academic performance and outcomes while also helping them to acquire a soft skill that would increase their placement chances. The reputation and annual admissions of the institute are always contingent upon the student placements made by the institute. Thus, in an effort to enhance their organisation as a whole, the majority of schools diligently work to grow their placement department. The goal of this study is to evaluate student placement data from the previous year and use it to calculate the likelihood that current students will be placed on campus. Four distinct machine learning algorithms Logistic Regression, Decision Tree, Support Vector machine, K Neighbour Classifier, and Random Forest have been tested for this purpose. © 2024 IEEE.

关键词： Self-supervised learning

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ML for Sustainable Solutions: Applications in Renewable Energy Optimization and Climate Change Prediction 4

ML for Sustainable Solutions: Applications in Renewable Ener...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Awachat, Arya Dube, Ananya Chaudhri, Shivnath Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

This paper delves into the transformative potential of ML in addressing some of the world's most pressing challenges in optimizing renewable energy and mitigating climate change. How ML algorithms work to improve efficiency within solar and wind energy systems, smart grids, and energy storage technologies is revealed by large data sets and predictive analytics. As ML improves the forecasting of energy, minimizes waste, and allows for better grid integration, its contribution to cutting carbon emissions significantly is immense. In addition, it facilitates climate adaptation by accurately predicting extreme weather occurrences and climate variability. Challenges facing the approach include data paucity, computational cost, and integration with older systems. Interdisciplinary collaboration and newer ML models tackle these challenges well. The study recognizes the significant role of machine learning in creating a sustainable environment and notes its ability to actually reconcile environmental, economic, and social objectives toward a robust and greener future. © 2025 IEEE.

关键词： Clean energy

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CampusSight—College Management Automation Using Full Stack and RPA 12th

CampusSight—College Management Automation Using Full Stack ...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Manohar, Eashaan Duraimurugan, N. Vijay, K. Narayana, K.E. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The college management diaspora encapsulates various stakeholders such as students, faculty members, administrative personnel, and other staff. Unfortunately, most of the management workflows are undertaken manually, leading to silos, long paper work and admin overhauls. The pivotal aim of this research project is to digitalize the college management by bringing the entire setup into an intelligent application, leveraging key technologies like Automation, data analytics, full stack development. This application ultimately streamlines cumbersome tasks and enhance working across the college environment, promoting transparency and facilitating seamless interfaces for the entire user base. Swapping the monotonous manual workings with state-of-art digital platform will elevate the overall student experience by bringing down the high latency issue with the manual undertakings. Prime features of the application features management in terms of attendance, absences, on-duty requests, result disbursement, course registration, and facilitation of placement processes. Also, the system will promote enhanced organizing of events related to campus, industrial visits, increasing the net student engagement within the college and opening new opportunities. The system prioritizes core functionalities such as maintaining clean design and nimble experience, aligning with the increasing trend of digital transformation, granting greater college management autonomy. The application not only brings down the complexities prevailing in the existing college administration but also helps the higher education and placement processes alike, by leveraging key concepts like Data Analysis, machine learning and artificial intelligence from the data and insights stored, thereby adopting agile college management practices and emphasizing rapid digital growth, contributing to the entire education community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Optimizing CNNs for Leaf Disease Detection: A Comparative Analysis of Algorithms, Activation functions, and Performance Metrics

Optimizing CNNs for Leaf Disease Detection: A Comparative An...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Nitisha, S. Mohith, N. Vinay, R. Agarwal, Monika Sinha, Aparajita Mokshitha, M. Dayananda Sagar University Dept. Artificial Intelligence and Machine Learning Bangalore India National Institute of Technology Dept. Computer Science Engineering Agartala India

ISBN: (纸本)9798331540821

Agriculture plays an important role in ensuring food security. However, the emergence of plant diseases causes significant economic losses. The routine method of detecting crop diseases is time consuming, requires technical skill and can be inaccurate, especially when new diseases are introduced. To solve these problems, we present a new and effective method for the early identification of leaf diseases using computer vision and machine learning. Extensive testing has been done on the widely used Plant Village dataset to ensure accurate findings. Our plans focus on disease diagnosis and classification. This study demonstrates the pros and drawbacks of this method. The testing process includes data collection, data training, feature extraction, model testing, and classification. The main processing techniques include image acquisition, image preprocessing, extraction and the use of neural networks. Combining computer vision and machine learning, our approach holds great promise for efficient and reliable detection of crop diseases. This has important implications for improving disease control, reducing economic losses and increasing food security. © 2024 IEEE.

关键词： Agriculture Classification CNN Computer Vision Plant diseases

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A Secure and Efficient Framework for Storing Medical Data using Blockchain and Cloud Servers 8

A Secure and Efficient Framework for Storing Medical Data us...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Lakshmanan, M. Mala, G.S. Anandha Sivakumar, G. Divya Sundar, V.S. Kesavan, Rupa Ajoe Sweetlin Jeena, A. Dayananda Sagar University Department of Artificial Intelligence and Machine Learning Bangalore India Easwari Engineering College Anna Univeristy Department of Computer Science and Engineering Chennai India Gnanamani College of Technology Anna University Department of Artificial Intelligence and Machine Learning India Anna Univeristy Saveetha Engineering College Chennai India Sri Venkateswara College of Engineering Computer Science and Engineering Sriperumbudur India

ISBN: (纸本)9798350367904

The digitization of Electronic Health Records (EHRs) has brought a revolution in health care delivery and even practices in reporting key organizational HR processes. However, these advancements have their merits and demerits particularly in regard to data security, the speed of data retrieval, and how data transactions are performed. This paper presents a solution to this problem in the medical world. It presents a unique architecture that connects cloud servers with Blockchain technology for better and secure medical databases. Using Merkle trees to protect health information and advanced heuristic algorithms to optimize operations, the system deals with main issues of transaction time, retrieval rate, and data privacy. The method is tested on a realized medical database and shows that the performance indicators increased substantially. It would seem that Blockchain assures that data cannot be altered once uploaded, while cloud servers provide flexibility to users and allow faster data retrieval. Proposed system improves the transaction time by 30 percent and the retrieval rate by 40 percent compared with traditional methods and supports strategic management of EHR systems. © 2024 IEEE.

关键词： Electronic health record

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Self Defence and Safety Monitoring System 4

Self Defence and Safety Monitoring System

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Suhas, M.L. Kashyap, Akshat Devadiga, Dhanush Ghoshal, Manish Roopashree, S. Deemed-to-be University Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Jain Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474368

In the modern world, the safety of women has been compromised. Domestic violence, sexual assault, and murder against women are more common than they are supposed to be. Women's safety is a major concern in India and there are many laws and measures in place to stop them, but the number of such incidents is only increasing by the day. This is not just limited to women as men face a lot of harassment too. In response to these problems, we aim to create a smart safety device that provides some security to the user. Many such smart devices are already available in the market but are not proven to be effective and often provide serious damage to the attacker. We aim to create a safety device that is capable of alerting the user or taking actions automatically without or with very little involvement from the user to activate it. This helps when there is a situation where the user doesn't get enough time to react. Different models available are analysed and the proposed solution is explained in detail. The drawbacks and future works are also included in the article. © 2022 IEEE.

关键词： Emotion Recognition

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