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检索条件"任意字段=International Conference on Advances in Intelligent Control and Computer Engineering"

共 47494 条记录，以下是131-140 订阅

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A comprehensive review of recent advances and challenges in artificial intelligence and machine learning 4th

A comprehensive review of recent advances and challenges in ...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Singla, Ishdeep Hans, Aarti Smiksha Diya Swami, Harsh Kumar Ranjan, Rahul Computer Science and Engineering Chandigarh University Mohali Gharuan India

ISBN: (纸本)9781032911571

Modern advances in Artificial Intelligence (AI) and Machine Learning (ML) have transformed many businesses and elements of everyday life. Machine learning and deep learning support disease diagnosis, drug development, and patient risk assessment. This study examines how healthcare organizations evaluate enormous data sets using machine learning. This paper examines pros and downsides of current methods and research obstacles. Our goal is to educate healthcare professionals and government organizations on machine learning-based big data analytics in intelligent healthcare. AIML-powered chatbots enable governments educate and serve residents. They assist people get permits, answer questions, and use government services. Assistants help students make appointments, answer inquiries, and offer course material. Lead generation, product recommendations, and marketing efforts using AIML chatbots may boost revenue. AIML helps companies make data-driven decisions, find patterns, and recognize trends using data analysis and predictive analytics. Machine Learning studies how computers learn and improve without programming. Machine learning (ML) is a very captivating technology that has garnered significant interest over the course of technological advancements. As indicated by its nomenclature, this attribute endows the computer with a capacity that enhances its resemblance to human behaviour: the ability to acquire knowledge. © 2025 the Author(s).

关键词： Adversarial machine learning

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Spectrum Sensing Methodology Using Artificial Intelligence for Cognitive Radio Network 1

Spectrum Sensing Methodology Using Artificial Intelligence f...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Bhavani, M. Monica Balamurugan, P. Sankaran, Vikram Nattamai Majumder, Sohom Rakshitha, L.P. Nandagopal, K. SRM University Data Science & Business Systems Department Tamil Nadu Chennai India Institute: Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mathematics Tamil Nadu Chennai India Department of Industry Experts AtlantaGA United States JIS College of Engineering Department of Business Administration West Bengal Kalyani Kolkata India Brindavan Group of Institutions Department of Mathematics Karnataka Bangalore India Mohan Babu University Department of Mathematics Andhrapradesh Tirupati India

ISBN: (纸本)9798331518578

Spectrum sensing is an essential part of cognitive radio networks (CRNs) that enables efficient utilisation of the radio frequency spectrum and dynamic spectrum access. Conventional spectrum sensing technologies, such as energy detection, matched filtering, and cyclostationary features recognition, are often computationally complex, noise-sensitive, and unable to detect signals at low signal-to-noise ratio (SNR) levels. Artificial intelligence (AI)-based approaches have become a viable substitute to deal with these issues. CRNs can provide more precise, flexible, and effective spectrum sensing owing to AI approaches. In intricate and dynamic situations, these algorithms can identify primary users, predict spectrum usage, and categorise spectrum accessibility. Major AI-driven techniques for spectrum sensing are reviewed in this work, with an emphasis on supervised, unsupervised, and reinforcement learning techniques. The suggested approach supports the creation of intelligent, independent CRNs for next-generation wireless networks by improving spectrum efficacy, cutting down on sensing time, and facilitating real-time decision-making. © 2025 IEEE.

关键词： Signal to noise ratio

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A Hybrid Approach for Cardiovascular Disease Risk Evaluation 6

A Hybrid Approach for Cardiovascular Disease Risk Evaluation

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sesadri, U. Jumlesha, Shaik Raju, S. Hrushikesava Adinarayna, S. Choudhury, Nabanita Jadala, Vijaya Chandra Department of CSE Vardhaman College of Engineering Telangana Hyderabad India AP Tirupati517501 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur522502 India Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam531162 India Faculty of Computer Technology Assam down town University Assam Guwahati India School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Hanumakonda Telangana Warangal506371 India

ISBN: (纸本)9798331522667

Nowadays, the risk estimators are to be applied based on the population characteristics of their country, which is termed as race attribute. There were specific tools to determine the risk of cardiovascular disease. Among many diseases, the mortality rate of the people is increasing due to a lack of quality lifestyle. In order to provide a guide to the individual based on their specific characteristics and lifestyle, would classify the risk into specific categories like severe (High), Moderate, and normal(Low) with pre-defined threshold constraints. The risk assessment estimators alert the individual to control certain modifiable attributes irrespective of their non-modifiable elements. The integration of random forests and intelligent rule mining would generate the CVD risk prediction. Efficiency as well as interpretability are important in this competitive hybrid model. The ultimate benefits assured are extending the lives of the patients by knowing the importance of daily work, habits, and their control food, presenting a powerful opportunity by practicing better personalization monitoring and improved patient care, and improved informed decisions for immediate action. The early prevention initiation by identifying the risk severity optimizes follow-up of better practices and treatment styles, resulting in improved decision making for better health care of the individual. © 2025 IEEE.

关键词： Accuracy Cardio-vascular disease Efficiency Hybridization Modifiable attributes Prediction Risk estimator

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EchoNet Dynamic: Deep Learning-driven Advancements in Dynamic Heart Function Analysis through Echocardiography 3

EchoNet Dynamic: Deep Learning-driven Advancements in Dynami...

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3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vidhya, K. Nagarajan, B. Jenefa, A. Catherine Joy, R. Cherian, Minu Franz Kingstein, N. School of CST Karunya Institute of Tech. and Sci Coimbatore India Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Division of ECE Karunya Institute of Tech. and Sci Coimbatore India Computer science and engineering department Amal Jyothi college of engineering kanjirapally Kottayam India

ISBN: (纸本)9798331527549

advances in echocardiography have significantly improved the diagnostic processes for heart function analysis. However, traditional imaging techniques often struggle with the dynamic complexities of cardiac activities. Existing methods fail to capture detailed variations in cardiac function over time, leading to suboptimal clinical decision-making and patient outcomes. Traditional echocardiography relies heavily on manual interpretation, which can be subjective and varies significantly between observers. This research presents EchoNet Dynamic, a novel method for improving echocardiographic data analysis and interpretation based on deep learning and the Transformer model. The EchoNet Dynamic dataset used in this study comprises 15,000 B-mode echocardiographic images from 750 patients, divided into training, validation, and testing sets for comprehensive model evaluation. The Transformer model demonstrated remarkable effectiveness, achieving an accuracy of 98.5%, an AUC of 0.992, and an F1 score of 0.972, surpassing traditional diagnostic tools by a significant margin. The EchoNet Dynamic model demonstrates a promising enhancement in detecting and diagnosing cardiac functions, offering substantial improvements over conventional echocardiographic methods and setting a new benchmark in cardiac healthcare. © 2025 IEEE.

关键词： Medical imaging

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Developing a Blockchain-Based AI-IoT Platform for Industrial Automation and control Systems 1

Developing a Blockchain-Based AI-IoT Platform for Industrial...

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1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dalal, Aryendra Sareddy, Mohan Reddy Santharam, Vinoth Thoguluva Bhattacharjee, Partha Pratim Barboza-Sanchez, Luis Jesus Sahoo, Deepali Rani Middle Georgia State University GA United States Orasys LLC TX United States Software Development Engineer 5 Comcast Chennai India National Institute of Technology Mizoram India Jr. Centenario 156 Lima La Molina15026 Peru Symbiosis Law School Noida India Pune India

ISBN: (纸本)9798331518578

Blockchain, artificial intelligence (AI), and the internet of things (IoT) come together to offer a transformative opportunity to increase automation, effectiveness, and security across a variety of industries. Blockchain ensures decentralised trust, data integrity, and safe transactions, while AI enables analytics in real time, predictive insights, and intelligent decision-making. IoT enables smooth communication among the digital and physical worlds by connecting and gathering enormous volumes of data from smart devices. By combining these innovations, a synergistic ecosystem is produced in which AI safely analyses data provided by the Internet of Things and stores it on blockchain networks, guaranteeing transparency and immutability. Smart cities, healthcare, supply chain management, and autonomous systems are just a few of the many fields where this incorporation can be used to promote innovation and operational resilience. To fully utilise this trinity, however, issues like flexibility, compatibility, and privacy concerns need to be resolved. This study examines the advantages, drawbacks, and potential avenues for further research of the synergy amongst blockchain, AI, and the IoT. © 2025 IEEE.

关键词： Blockchain

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Development of Real-Time Oxygen and Temperature Monitoring System with CI/CD Pipeline Integration and HMI 1

Development of Real-Time Oxygen and Temperature Monitoring S...

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1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kolte, Gaurav Amar Gunnal, Aditya Kolhare, Nilima R. COEP Technological University Dept. of Electronics and Telecommunication Engineering Pune411005 India COEP Technological University Dept. of Instrumentation and Control Engineering Pune411005 India

ISBN: (纸本)9798331518578

The steel manufacturing industry stands as the backbone of global infrastructure development, providing the raw materials essential for constructing everything from skyscrapers to automobiles. Central to this industry's efficiency is the performance of its furnaces, where the transformation of raw materials into high-quality steel occurs. Steel furnace environments require precise monitoring of parameters like temperature and oxygen levels to ensure efficiency, safety, and compliance with environmental standards. This paper presents a novel monitoring device designed to address these needs while incorporating modern software development practices. The device offers real-time monitoring and graph visualization of temperature and oxygen levels, surpassing conventional methods. It introduces the integration of Git, Continuous Integration/Continuous Deployment (CI/CD) pipelines, Real-Time Operating Systems (RTOS) and diagnostic tools, enhancing reliability and facilitating seamless updates. This amalgamation of hardware and software engineering replaces outdated monitoring systems. Through its multifaceted capabilities, this research contributes significantly to the evolution of smart, interconnected industrial systems, promising improved performance and safety in steel furnace operations. The NXP LPC 55s69 micro-controller unit is used for the computation of oxygen and temperature from a zirconia-based oxygen sensor and thermocouple using nernst-einstein equation. For integration of the device with a PLC control we have used RS232 and RS485 communication protocols. © 2025 IEEE.

关键词： Thermocouples

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Target Identification and Detection Using Hyperpectral Signature Transformation 13

Target Identification and Detection Using Hyperpectral Signa...

引用

13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Zhang, Nian Wilson, Oshin Thompson, Lara A. Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC United States Department of Mechanical Engineering BME University of the District of Columbia WashingtonDC20008 United States

ISBN: (纸本)9798331516147

Hyperspectral imaging has emerged as a powerful technique for target detection and material identification across various fields, from remote sensing to environmental monitoring. This paper proposes a spectral signature matching method for detecting a known target (i.e., sea water) in hyperspectral images. By analyzing pixel-by-pixel spectral characteristics, spectral matching algorithm computes the similarity between the spectral signature of each pixel and that of known target materials. The proposed method employs the Jasper Ridge hyperspectral data from an urban landscape to demonstrate the algorithm’s effectiveness, with results revealing high accuracy in target detection even in heterogeneous environments where various land cover types (like water, vegetation, urban structures, soil, and rocks) are intermixed within the same area. This highlights the algorithm’s robustness in discerning similar spectral features. The experimental results demonstrate that the Jeffries-Matusita Spectral Angle Mapper (JMSAM) outperformed the Spectral Angle Mapper (SAM) and the Normalized Spectral Similarity Score (NS3) algorithms, by achieving the lowest MSE of 0.056933. © 2025 IEEE.

关键词： Remote sensing

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

This research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. The system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. These data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. The machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. The system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. The solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. This integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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K-shape based Spatio-temporal graph attention network for traffic flow prediction 4

K-shape based Spatio-temporal graph attention network for tr...

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2024 4th international conference on intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Zhang, Jin Yang, Yimin Wu, Xiaoheng Wang, Honggang School of Computer and Information Engineering Henan University Kaifeng475001 China Academy for Advanced Interdisciplinary Studies Kaifeng475001 China Urban Mobility Institute Tongji University Shanghai201804 China

ISBN: (纸本)9781510686304

Traffic flow prediction is a crucial component of intelligent transportation systems, providing scientific support for traffic management and urban planning. However, current studies are most focused on short-term prediction, with long-term prediction remaining a challenge. In addition, to control the dynamic spatio-temporal characteristics of urban traffic effectively is still difficult for traditional methods. To address these challenges, this paper proposes a K-shape-based spatiotemporal graph attention network model (KSTGAT) for long-term traffic flow prediction. The model employs a multi-head attention mechanism to extract long-term temporal features and replaces the traditional linear mappings in the attention mechanism with GRU to better capture nonlinear dynamic changes. For spatial feature extraction, K-shape is used to cluster the input sequences, and a composite adjacency matrix is constructed based on the clustering results and predefined adjacency matrix. This is followed by a graph attention network to extract dynamic spatial features. Additionally, the model also incorporates the periodic characteristics of traffic flow data. Experimental results demonstrate that KSTGAT outperforms state-of-the-art models, proving its effectiveness in long-term prediction. © 2025 SPIE.

关键词： Urban transportation

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A Transfer Learning based Deep Learning Framework for Lung Disease Detection Utilizing CXR Images

A Transfer Learning based Deep Learning Framework for Lung D...

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2025 international conference on intelligent control, Computing and Communications, IC3 2025

作者： Kansal, Kajal Kansal, Kanika School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Abes Engineering College Department of Computer Science and Engineering - Data Science Uttar Pradesh Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527495

The classification of various lung diseases with the help of chest X-ray images is one of the significant and challenging issues in medical imaging applications. This work proposes a transfer learning-based deep neural network architecture to classify lung diseases using a publicly available dataset. The proposed DenseNet201 model uses the interconnected structure to optimize feature transmission, minimize computation redundancy, and improve the gradient flow. The proposed model achieved a better classification accuracy of 98.34% compared to the other existing methods. These results show that DenseNet201 effectively learns discriminative features from the input medical image data. The proposed framework is suitable for large-scale implementation of lung disease classification. It helps develop more efficient computer-aided diagnostic systems, assisting clinicians in decision-making. © 2025 IEEE.

关键词： Lung cancer

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