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

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artificial intelligence and machine learning in Telemedicine: Transforming Remote Healthcare 2

Artificial Intelligence and Machine Learning in Telemedicine...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Khadse, Shrikant Verma, Prateek Lande, Vivek Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Deemed to be University Faculty of Engineering and Technology Wardha India Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Deemed to be University Wardha India

ISBN: (纸本)9798331505745

The concept of telemedicine developed before the upsurge of popularisation of telehealth services, particularly with the support of artificial intelligence and machine learning. This review aims to deeply analyse how these technologies integrate and change telemedicine, improve the diagnostics of diseases, help in the treatment process, and provide more accessible healthcare. Predictive analytics, natural language processing, and tele-therapy monitoring are examples of the ML and A.I. technologies used in patient care management. This is the time of more intelligent and more effective remote care services with the help of several innovative technologies and phenomena like A.I. health chatbots and intelligent treatment algorithms. However, several factors limit its development, including data privacy, legal issues, and users' access to technologies. Directions for further development and continuous healthcare improvement include integrating advanced 5G communication systems and wearables and exploiting blockchain technology for data interchange. These developments create prospects to eliminate current impediments and enhance the provision of healthcare services worldwide. This scope consists of mechanisms for the best present data, emphasizing growth areas of new technologies, especially artificial intelligence and machine learning, that transform healthcare systems. © 2025 IEEE.

关键词： Surgery

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Senti Comment: Event Success Prediction through Sentiment Analysis of User Feedback 5

Senti Comment: Event Success Prediction through Sentiment An...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Varma, Nadimpalli Madana Kailash Vidya, Rakapu Sri SriSaiteja, Thullimilli Teja, Chintakunta Pranay Manchala, Yugandhar Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501286 India Department of Computer Science & Engineering Vardhaman College of Engineering Telanagana Hyderabad501286 India Department of Information Technology Vardhaman College of Engineering Telangana Hyderabad501286 India

ISBN: (纸本)9798350361155

Sentiment analysis has emerged as a pivotal tool in extracting valuable insights from user-generated content, with its applications spanning various *** paper presents a novel approach to event evaluation using sentiment analysis techniques within the domain of Natural Language Processing (NLP). By analyzing user comments from diverse events such as parties, college fests, and conferences, we develop a robust NLP model to assess event success based on positive sentiment. Our model offers real-time insights into event reception and provides interpretative analysis of user feedback, including suggestions and expressions of appreciation. This research contributes to advancing sentiment analysis methodologies and equips event organizers with a valuable tool for enhancing future event planning strategies. © 2024 IEEE.

关键词： Data visualization

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A Deep Insight on Cricket Video to Text Summarization Using Neural Networks

A Deep Insight on Cricket Video to Text Summarization Using ...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： J Vijay Gopal K. Sai Prasad Dhatrika Kamal Kumar Maddhe Sai Prashanth Vuppala Praneeth Kumar I Nikhil Sri Sai Teja Computer Science Engineering Artificial Intelligence and Machine Learning MLR Institute of Technology Hyderabad India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In this modern era, the demand for efficient and automated cricket video summarization techniques is rapidly increasing. This paper introduces an innovative and advance neural network system that transforms the way cricket match videos are summarized. Cricket video-to-text summarization system overcomes the limitations of traditional manual summarization approach by utilizing various deep learning techniques to completely automate the summarization process, our system can extract crucial insights from lengthy cricket match footage and convert them into easily readable text formats. The system employs three-pronged approach which involves extraction of visual features using VGG16 Convolutional Neural Network (CNN), scoreboard information is extracted through Optical Character Recognition (OCR) technology, and Text Summarization performed by Long Short-Term Memory (LSTM) network. Our system revolutionizes the way cricket enthusiasts engage with match videos, providing a game-changing experience for fans worldwide.

关键词： Visualization Recurrent neural networks Optical character recognition Text summarization Transforms Feature extraction Convolutional neural networks Data mining Long short term memory Sports

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Edge Computing Devices Authentication using Quantum Computing 15

Edge Computing Devices Authentication using Quantum Computin...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Telsang, Vinayak A. Kakkasageri, Mahabaleshwar S. Devangavi, Anil D. Dept. of Computer Science and Engineering Biluru Gurubasava Mahaswamiji Institute of Technology Mudhol India Dept. Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote India Dept. of Artificial Intelligence and Machine Learning Basaveshwar Engineering College Bagalkote India

ISBN: (纸本)9798350370249

Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(VV), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks uses low bandwidth, and enhances the processing speed. With more number of IoT devices connected in the network the privacy of data stored at the edge server also under threat. Authentication is a major challenge which is be addressed to provide security and privacy to IoT devices connected in the network. The existing works uses cryptographic algorithms to solve the authentication problems, but with increase in growth of quantum computers and quantum computing, breaking the complex cryptographic algorithm is not an issue. Hence one should adopt the quantum approach to provide authentication in edge network. The proposed work uses quantum key and context information of IoT devices to store in the edge server using blockchain, which proves to be more secure. The performance analysis shows that it is better with respect to registration, encryption, decryption and authentication of IoT devices. ©2024 IEEE.

关键词： Authentication Blockchain Cryptography Edge computing Quantum key

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Addressing Data Imbalance in machine learning: Challenges and Approaches

Addressing Data Imbalance in Machine Learning: Challenges an...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Meher Langote Nikita Zade Swapnil Gundewar Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

A key difficulty of dealing with imbalanced datasets is that machine learning models are trained to favor the majority class and therefore perform poorly when it comes to a minority class classification. In this paper, we consider the following methods of tackling the issue of the overrepresentation of a class; random oversampling, under-sampling, SMOTE, cost-sensitive learning, and ensemble methods. Comparing them, we assess the performance of each of these techniques within different classifiers. Techniques including SMOTE together with combined methods including Random Forests also show higher capacity in minority class predictions. However, problems such as overfitting and the increase in the computational load are not escapable yet. We briefly recap the strengths and weaknesses of each approach and propose potential refinements including a combination of both methods and dynamic resampling methods to serve as the next steps toward improving the model. Thereby, our results draw attention to the necessity of accurate methods for handling the class imbalance, which will further yield more accurate and fair machine learning solutions across a variety of applications.

关键词： Adaptation models Accuracy machine learning algorithms Computational modeling Ensemble learning Informatics Random forests Mobile computing Overfitting Load modeling

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SPEAR: Design and Implementation of an Advanced Virtual Assistant 4

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Jain, Garima Shukla, Amita Bairwa, Nitesh Kumar Chaudhary, Anamika Patel, Ashish Jain, Ankush Noida Institute of Engineering and Technology Department of Computer Science and Buisness Systems Greater Noida India Noida Institute of Engineering and Technology Department of Artificial Intelligence Greater Noida India Noida Institute of Engineering and Technology Department of Data Science Greater Noida India Dronacharya Group of Institutions Department of Artificial Intelligence and Machine Learning Greater Noida India Netaji Subhash University of Technology Department of Computer Science and Enginnering Delhi India

ISBN: (纸本)9798331540364

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services. © 2024 IEEE.

关键词： Speech recognition

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Meta Heuristic-Based Community Detection of Social Network Using Cuckoo with InfoMap Algorithm 1

引用

International Conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2022

作者： Devi, S. Rajalakshmi, M. Saranya, S. Shana, J. Department of Information Technology Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning Coimbatore Institute of Technology Coimbatore India

ISBN: (数字)9789811984976

ISBN: (纸本)9789811984969

Facebook and Twitter are one of the social networks for communicating our thoughts, feelings, opinions of current scenarios. By using this communication platform rapidly, people can share information. In the analysis of the social network, identifying such communities and sharing information are a challenging task. These fast shares of information create scalability, inaccurate, genuine issues. Many existing techniques can be used for the community detection of the social network. However, these techniques are ineffective and high time-consuming for quick detection. This paper proposed a novel metaheuristic technique-based community detection of the social network by using the hybrid of cuckoo search with InfoMap algorithm (CIMA). In this proposed work, CIMA is used for community detection. This proposed work increases the intracommunity of the social network and improves its performance in an accurate way by detecting the community of the social network. The accuracy rate for AKSHO got 48%, ant lion got 52%, lion optimization got 66%, fish swarm optimization algorithm got 78%, and our proposed work CIMA got 94%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Pneumonia Detection in Chest X-Rays Using Combined Approach of Deep learning 2

Pneumonia Detection in Chest X-Rays Using Combined Approach ...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Aditya Pai, H. Pareek, Piyush Kumar Guruprasad, M.S. Chandrappa, S. Bhargava, Krishna Mit Art Design and Technology University Mit School of Computing Pune India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Department of Computer Science and Engineering Dehradun India School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

A respiratory infection like pneumonia faces a serious health challenge across the globe, especially in the poor and underdeveloped countries. The pneumonia is affected by various factors like bacteria or viruses. The reason for the affection is due to higher level of pollution, poor air quality, excess of population, poor infrastructure etc. To reduce the threat from pneumonia, there is a need for early and accurate detection of the infection by using chest X-ray images as the most effective diagnostic method. The paper proposes an automated chest X-ray analysis system by using VGG16 architecture and a weighted average ensemble method to enhance diagnostic accuracy. By integrating deep transfer learning, the system overcomes data scarcity issues, achieving a remarkable 99% accuracy on a publicly available dataset. The research underscores the potential of automated systems in improving pneumonia diagnosis, highlighting the urgent need for advanced diagnostics in resource-limited settings. © 2024 IEEE.

关键词： Air quality

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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector machine 5

Enhancing the Prediction Accuracy of Brain Tumor Detection b...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Revathi, M. Kalaiarasi, P. Jeevitha, S. Akilandasowmya, G. St. Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India Kalasalingam University Department of Machine Learning Krishnankoil India Jerusalem College of Engineering Information Technology Chennai India Sri Sairam Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331518097

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector machine. The results shows 98% percentage of accuracy. © 2024 IEEE.

关键词： Support vector machines

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Tropical Cyclone Detection and Tracking Using YOLOv8 Algorithm 1

Tropical Cyclone Detection and Tracking Using YOLOv8 Algorit...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Rout, Saroja Kumar Kumar, Kottu Santosh Sahu, Ruchismita Barik, Shekharesh Pradhan, Samarendra Department of Information Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Hyderabad India Templecity Institute of Technology and Engineering Department of Computer Science & Engineering Bhubaneswar India DRIEMS University Department of Computer Science & Engineering Cuttack India Data scientist Jayapur Cuttack India

ISBN: (纸本)9798350361780

Coastal areas around the world face a great threat from tropical cyclones, which makes timely and accurate identification essential for efficient disaster response. An enhanced method for detecting tropical cyclones is proposed in this work using the YOLOv8 (You Only Look Once version 8) object identification algorithm. Using the real-time object recognition capabilities of YOLOv8, the methodology is designed to tackle the challenges associated with finding cyclonic structures in satellite data. To detect crucial features typical of tropical cyclones - like cloud formations, circular symmetry, and dynamic atmospheric fluctuations - the YOLOv8 architecture is improved. To ensure its adaptability to different climatic circumstances, the model is trained using a varied dataset of labeled satellite photos. The YOLOv8-based methodology is superior to existing methods in detecting tropical cyclones with accuracy and efficiency almost in real-time, as demonstrated by the experimental results. Because of the algorithm's excellent precision, recall, and computing economy, early warning systems and catastrophe preparedness can benefit greatly from it. This research contributes to the ongoing effort to develop detection approaches for increased resilience in vulnerable coastal areas, while also utilizing YOLOv8's capabilities to address the global threat posed by tropical cyclones. This study can help clarify the necessity of improving models and conducting better observations of Tropical Cyclones. © 2024 IEEE.

关键词： Object detection

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