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Artificial Intelligence (AI) for Improving Performance at the Cutting Edge of Medical Imaging 5

Artificial Intelligence (AI) for Improving Performance at th...

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Nour, Samar M. Faculty of Engineering Dept. Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350381030

Artificial intelligence (AI) has achieved great strides in recent years, with applications in a variety of areas of study, including healthcare. Consequently, the integration of artificial intelligence (AI) and medical imaging has ushered in a new era in healthcare diagnosis and therapy. Artificial intelligence (AI) has shown impressive potential in enhancing accuracy, efficiency, and diagnostic performance across a range of medical imaging modalities by using the power of deep learning (DL), machine learning (ML), and computer vision. In this paper, we are trying to investigate the connection between artificial intelligence (AI) and medical imaging, concentrating on how AI-driven strategies are improving performance at the cutting edge of medical imaging technologies through the proposed architecture model. Furthermore, the paper also explores the limitations and opportunities that result from incorporating artificial intelligence (AI) into the use of medical imaging. The potential for artificial intelligence (AI) to transform image-guided therapies and its implications for personalized medicine are investigated. © 2023 IEEE.

关键词： Medical imaging

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Multifaceted Discrete Emotion Recognition from EEG Physiological Signals via Machine Learning Techniques 5

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Gupta, Amriti Nayyar, Kunal Chopra, Harshit Gahlan, Neha Sethia, Divyashikha Dept. of Software Engineering Delhi Technological University India Dept. of Electronics & Communication Engineering Delhi Technological University India Dept. of Computer Engineering Delhi Technological University India

ISBN: (纸本)9798350361155

Emotions, intricate mental states shaped by neurophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. The Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. This paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. This research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. The exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested. © 2024 IEEE.

关键词： Emotion Recognition

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Fringing Effects in a Capacitive Micromachined Ultrasonic Transducer with a SiC Actuation Layer Insulated by High Dielectric Material 3

Fringing Effects in a Capacitive Micromachined Ultrasonic Tr...

引用

3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Ahamed, Sk. Sohel Pal, Moumita Chakraborty, Pritam Neogi, Biswarup Maity, Niladri Das, Reshmi Future Institute of Engineering and Management Dept. of electronics and communication kolkata India JIS College of Engineering Dept. of electronics and communication Kalyani India Institute of Engineering and Management Dept. of computer science and application Kolkata India NITTTR Dept. of technical education Kolkata India Mizoram University Dept. of electronics and communication Mizoram India

ISBN: (纸本)9798350374643

Sealed capacitive micromachined ultrasonic transducer (CMUT) mostly consists of two parallel electrodes separated by a structural layer of silicon nitride (Si3N4) and a vacuum cavity. Biasing the transducer element generates electrostatic force of attraction where the lines in the field bulge at the boundaries of the layers contributing to fringing effects. These fringing capacitances result in overall increment of the device capacitance. To capture this effect a very simple model of Landau and Lifschitz is incorporated, where the analytical model outputs are well matched with commercially available PZFlex FEM simulated results. Because silicon carbide (SiC) has a higher Young's modulus and lower residual stress than Si3N4, it is used as a structural material in the CMUT. The improvement in device capacitance is approximately 13.1% through adjustments in the thickness of gaps and insulation. However, factoring in the relative dielectric constant and the electrode radius, this improvement reduces to around 7.9%. Additionally, introducing a high-k insulating layer enables the upper electrode to be structured on the lower face of the SiC layer. This modification leads to an augmentation in the device's total capacitance. This article also examines HfO2 materials of varied thicknesses. © 2024 IEEE.

关键词： Silicon nitride

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From Manual to Machine: How AI is Redefining Web Scraping for Superior Efficiency: A Literature Review 3

From Manual to Machine: How AI is Redefining Web Scraping fo...

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3rd International Conference on communication, Control, and Intelligent systems, CCIS 2024

作者： Ayuso, Enrique Dumfeh Brogya, Maame Serwah Kumar Ahlawat, Vinay Sain, Mangal Dongseo University Dept. of Computer Engineering Busan Korea Republic of Gla University Dept. of Electronics & Communication Mathura India

ISBN: (纸本)9798331528201

Web Scraping involves the use of bots for the purpose of extracting data from the online web. To extract such data, the web scraper must conduct at least 3 different steps, i.e., collect the necessary links from the web, extract data using the provided links, and save the information in a neatly organized CSV file or database. However, traditional methods to accomplish these steps have faced many difficulties, such as anti-scraping measures and website changes. To tackle these difficulties and to accomplish the steps in a more rapid yet advanced way, Artificial Intelligence (AI) can be utilized. As of mid-2024, the rapid development and improvement of AI has resulted in its integration into many businesses and educational institutions. The current state of AI is remarkably advanced that it can be integrated in about any field and sector available. In this paper, the researcher defined the term Web Scraping, Artificial Intelligence, Natural Language Processing, and Machine Learning, explained the use of AI in Web Scraping including its importance and potential, and the understanding of ethical web scraping practices. This literature review indicated that the integrated use of AI improves the performance of Web Scraping compared to traditional methods. © 2024 IEEE.

关键词： Adversarial machine learning

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

引用

5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

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Enacting Rural Empowerment: Implementing Cooperative Commerce in Communities using Machine Learning 2

Enacting Rural Empowerment: Implementing Cooperative Commerc...

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2nd IEEE International Conference on Networking and communications, ICNWC 2024

作者： Skandarsini, R.R. Sangavi, M.S. Harika, Ravitla Varun, C.M. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India

ISBN: (纸本)9798350365269

In order to revitalize rural economies through the empowerment of self-help organizations and the promotion of economic growth, this study offers a revolutionary cooperative commerce platform. The platform integrates cutting-edge features such as digital ledger and inventory management for sellers, demand forecasting, and dynamic pricing, thereby fostering optimized production and efficient marketplaces. Central to the platform's efficacy is the implementation of user segmentation, which categorizes users based on income and spending behaviors, enabling targeted marketing strategies and continuous application improvements to enhance user engagement and profitability. Leveraging machine learning algorithms, personalized user experiences are cultivated, elevating buyer satisfaction and overall engagement. Additionally, administrative tools provide valuable insights for informed decision-making. Preliminary results demonstrate improved financial management for self-help groups, heightened economic activities, and an enriched user-centric experience. Identified challenges and proposed future enhancements underscore the platform's commitment to adaptability and responsiveness to the evolving needs of rural communities, ensuring sustained relevance and impact. © 2024 IEEE.

关键词： Predictive analytics

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Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and Anomaly Detection for Space Agriculture

Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and An...

引用

2024 IEEE International Conference on communication, Computing and Signal Processing, IICCCS 2024

作者： Das, Rajdeep Sharma, Vaishnavi Kumari, Priyanka Raj, Abhishek Das, Sudipta De, Indrajit Institute of Engineering and Management Dept. of Computer Science and Business Systems Kolkata India

ISBN: (纸本)9798350390759

As humanity ventures further into space exploration, securing sustainable resources becomes paramount. The success of space farming depends not only on the ability to cultivate crops but also on the effective management of environmental conditions. Introducing Vyoma-ADS: an innovative system that integrates IoT technology with an adaptive Anomaly Detection System, specifically designed for space agriculture. Our study underscores the crucial role of environmental monitoring within space shuttles, where even minor fluctuations can have a significant impact on plant growth and productivity. To tackle these challenges, we designed a compact IoT-enabled system to monitor the moisture content in the soil, the temperature and humidity of the surrounding environment, and the ppm value of unwanted gases in the atmosphere. Through rigorous testing and analysis, we established optimal thresholds conducive to plant growth in the unique conditions of space. Recognizing the inherent variability and occasional anomalies in environmental data, we investigated three distinct machine learning models for anomaly detection: Isolation Forest, One-Class Support Vector Machines (OCSVM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Our findings reveal that DBSCAN outperforms other techniques in identifying anomalies amidst fluctuating data. Additionally, we detail our approach to data visualization using a SaaS cloud platform, which enhances the comprehensibility of the study and facilitates better decision-making processes. This paper demonstrates how the integration of IoT monitoring and advanced anomaly detection methods can significantly improve the reliability and efficiency of space agriculture. By leveraging these technologies, we aim to create a robust system capable of maintaining optimal growing conditions for plants in space. © 2024 IEEE.

关键词： Support vector machines

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Small Object Detection for UAVs Using Deep Learning Models on Edge Computing: A Comparative Analysis 5

Small Object Detection for UAVs Using Deep Learning Models o...

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5th International Conference on Circuits, Control, communication and Computing, I4C 2024

作者： Vasishta, M S Saathvik Reddy Amireddy, Abhinava Teja Shrivastava, Pranjal Raghuram, S. Talasila, Viswanath Shastry, Prasad N Ramaiah Institute of Technology Dept. of Electronics and Telecommunication Engineering Bangalore India Ramaiah Institue of Technology Dept. of Electronics and Communication Engineering Bangalore India Ramaiah Institute of Technology Dept. of Computer Science Engineering Bangalore India Ramaiah Institute of Technology Dept. of Electronics and Communication Engineering Bangalore India Bradley University Dept. of Electrical and Computer Engineering PeoriaIL United States

ISBN: (纸本)9798331528539

This paper presents a comparative analysis of DNN-models, including TensorFlow Lite, SSD-MobileNet, Mask R-CNN and YOLOv8, for the high-precision detection and classification of small objects in aerial search and rescue scenarios. The study features a proprietary dataset collected through drone flights over an extended period. The model performance was evaluated on compact computing platforms like Jetson Nano and Raspberry Pi, emphasizing detection accuracy and computational efficiency. Results reveal trade-offs between model complexity and real-time processing capabilities. The findings guide the selection of on-board computer models, thereby enhancing drone capabilities in critical operations © 2024 IEEE.

关键词： Drones

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Intelligent Doctor Chatbot 4

Intelligent Doctor Chatbot

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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Khoza, Mahlori Olaifa, Moses Owolawi, Pius Malele, Vusi Tshwane University of Technology Dept. of Computer Systems Engineering Tshwane South Africa

ISBN: (纸本)9798350387988

This study proposes a cutting edge Intelligent Doctor Chatbot that improves healthcare delivery and provides patients with easily accessible and customized health related information through the use of Natural language processing (NLP) and artificial intelligence (AI) technologies. These technologies are used by the chatbot to provide accurate medical advice based on symptom analysis and general health guidance in an interactive manner. The doctor chatbot addresses the growing demand for reliable healthcare information and assistance, particularly in situations where access to medical professionals is limited. By leveraging its advanced algorithms and medical knowledge base, the chatbot provides users with quick and accurate answers to their health-related questions. A key feature of the doctor chatbot is that users describe their symptoms and receive preliminary assessments on potential causes and suggested courses of action. It allows people to take an active role in their healthcare management and preventive measures. © 2024 IEEE.

关键词： Diagnosis

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Enhancing WSNs Lifetime Through GOA based Clustering and Routing 1

Enhancing WSNs Lifetime Through GOA based Clustering and Rou...

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1st IEEE International Conference on Advances in Computing, communication and Networking, ICAC2N 2024

作者： Kaushik, Neeraj Singhal, Amit Pandey, Ashish Monad University Dept. of Computer Science and Engineering UP Hapur India GCET Dept. of Electronics and Communication Engineering UP Greater Noida India

ISBN: (纸本)9798350356816

WSNs are regularly battery-powered and it is not continually feasible to recharge or replace them due to a variation of applications. Hence, in this work, a Grasshopper Optimization Algorithm based on routing and power restriction optimization has been proposed for improving the battery lifetime in wireless sensor network. The proposed approach aims to enhance the effective longevity of individual sensor nodes within the network by accomplishing energy-efficient clustering along with energy-aware routing. The outcomes of the suggested method are compared with the several existing optimization methods in WSNs. It is observed that the proposed Grasshopper Optimization Algorithm (GOA) based technique outperforms all the other existing methods in terms of lifetime achieved under various power constraints. For checking robustness of approach, two different locations of base station and various populations of sensing nodes and gateways are also considered. © 2024 IEEE.

关键词： Sensor nodes

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