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共 10483 条记录，以下是81-90 订阅

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Automated Skin Disease Detection Using GoogLeNet and MobileNet to Enhance the Diagnostic Accuracy with Convolutional Neural Networks 2

Automated Skin Disease Detection Using GoogLeNet and MobileN...

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2nd international conference on Advances in Computation, communication and Information technology, ICAICCIT 2024

作者： Santhiya, S. Parvathi, M. Jayadharshini, P. Harish, S.V. Navin, E. Indrajith, N. Kongu Engineering College Erode India Department of Artificial Intelligence India Nandha Engineering College Erode India Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798331541217

Skin diseases affect human health through physical discomfort, emotional distress, and social stigma The high clinical variability and that the diagnostic procedures are subjective and may change over time make it difficult to detect accurately. This paper suggests applying CNN architectures, such as GoogLeNet and MobileNet, for automated *** stages involved include image gathering, preprocessing, selection and design of CNN models such as GoogLeNet and MobileNet, intensive training, evaluation, and deployment. The aim of this system is to help healthcare professionals make reliable diagnoses especially in areas where dermatologists are not accessible, thus allowing for more timely treatments. © 2024 IEEE.

关键词： Convolutional neural networks

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Leveraging Blockchain for Secure IoT Device communication in Smart Cities 2

Leveraging Blockchain for Secure IoT Device Communication in...

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2nd IEEE international conference on IoT, communication and Automation technology, ICICAT 2024

作者： Obaid, Saif Sharma, Ashish Balaji, S. Muthu Mangaiyarkarasi, V. Chythanya, Nagaraju Krishna Praveenkumar, C. The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GLA University Department of Computer Engineering & Applications Mathura India CMR College of Engineering & Technology Department of ECE India New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai600073 India GRIET Department of CSE Telangana Hyderabad India Sri Ramakrishna Engineering College Department of EEE Coimbatore India

ISBN: (纸本)9798350368109

In this paper we focus on utilising blockchain for IoT devices in smart cities to meet the major issues including security, scalability and energy consumption. A permissioned blockchain architecture with a light-weight consensus model is discussed for implementing a Decentralized and Secure IoT Internet. It encompasses data gathering from different IoT devices, design of the blockchain network and assessment of its performance in a simulated smart city context. Static analysis show that the proposed solution provides protection against various types of IoT attacks and has a similar performance to the traditional networks, with latencies and energy consumption only slightly increased. From the works done, the proposal that blockchain has the potential to offer security and efficiency in the IoT communications in future smart city applications emerges. © 2024 IEEE.

关键词： Blockchain Decentralization IoT Security Smart Cities

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Managing Economic Load Dispatch through Multi-swarm Statistical Particle Swarm Optimization 2

Managing Economic Load Dispatch through Multi-swarm Statisti...

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2nd international conference on Computational Intelligence, communication technology and Networking, CICTN 2025

作者： Skukla, Siddharth Pal, Sitaram Singh, Nagendra Department of Electrical & Electronics Engineering RNTU Bhopal India Department of EEE Trinity College of Engineering and Technology Telangana Karimnagar India

ISBN: (纸本)9798331530389

In power systems, the ELD is crucial for managing the minimum generation cost of electrical-generating units while fulfilling the constraints and maintaining the load demand. Traditional optimization techniques do not provide an optimum solution for nonlinear, large, and complex ELD data. This study proposed a multiswarm statistical particle swarm optimization algorithm for solving the ELD problem. This paper presents a multiswarm statistical particle swarm optimization algorithm to solve nonlinear and complex ELD problems. Multi-swarm statistical particle swarm optimization (MSPSO) is a new and strong type of PSO that uses multiple swarms and statistical adaptation mechanisms to improve swarm exploration and convergence in search spaces. This makes MSPSO a good way to solve difficult nonlinear ELD problems. This work considered two case studies of 10 and 15 generatinlg unit data to examine the performance of MSPSO in terms of minimization of cost, speed of convergence, and computation time. The results obtained by MSPSO for both case studies were compared with those of the other optimization techniques. The results shown by MSPSO are better than those of the others. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Diabetic Prediction with Ensemble Model and Feature Selection Using Information Gain Method 2

Diabetic Prediction with Ensemble Model and Feature Selectio...

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2nd international conference on Intelligent data communication Technologies and Internet of Things, IDCIoT 2024

作者： Roy, Shreya Priyadarshini Beulah.s, Angelin Kasat, Avani Pankajalakshmi, P. Sivagami, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350327533

Diabetes is a chronic medical condition characterized by high levels of blood glucose levels, there is no specific cure for it, there are only ways to control it. Predicting diabetes in its early stages is very crucial as it affects the overall health of the patients when not predicted early. Through this work an Ensemble method of Logistic Regression, SVM, Naive Bayes and Random Forest Classifiers has been implemented. The result obtained has thoroughly been analyzed and documented. Proposed ensemble model obtained an accuracy of 85%. © 2024 IEEE.

关键词： Logistic regression

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IoT-based Smart Water Consumption Monitoring System for Residential Complex 2

IoT-based Smart Water Consumption Monitoring System for Resi...

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2nd international conference on Information and communication technology, ICICT 2024

作者： Ahamed, Imtiaz Ali, Md Liakot Salehin, Nazmus Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka1205 Bangladesh

ISBN: (纸本)9798331508227

Freshwater scarcity is a pressing global matter right now and the importance of putting more focus on monitoring and conserving water resources is growing. This research presents the development of a cost-effective, scalable, easy to maintain IoT-based water consumption monitoring system for residential buildings using Long Range communication technology (LoRa). The key components of the system include IoT-enabled smart water meters installed in each residential unit with LoRa communication modules for long-range, low-power communication which enables data transmission over considerable distances with minimal energy consumption, and an open-source IoT analytics platform called ThingSpeak. The water sensors capture real-time data on water usage, which is then transmitted to ThingSpeak through a microcontroller and WiFi module using LoRa communication. Sample tests demonstrate the system’s effectiveness in measuring water usage and flow rate at a given time, offering real-time visualization and user engagement, making it an essential tool for promoting sustainable water management practices. ©2024 IEEE.

关键词： Cost effectiveness

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Review on Predicting the Landslide Susceptibility using Machine Learning Techniques 2

Review on Predicting the Landslide Susceptibility using Mach...

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2nd international conference on Computational Intelligence, communication technology and Networking, CICTN 2025

作者： Sinha, Abhishek Giri, Shivam Kumar Yadav, Himanshu Prasad, Sanjeev Kumar Mishra, Priya School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generaliz ability challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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Automated Lung Cancer Diagnosis Using Vision Transformers (ViT) on CT Scan data 2

Automated Lung Cancer Diagnosis Using Vision Transformers (V...

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2nd IEEE international conference on IoT, communication and Automation technology, ICICAT 2024

作者： Agarwal, Rohit Gayathri, B. Rao, Cherukupalli Mallikarjuna Alsalami, Zaid Monisha Jothi, R. Jaya Deepthi, K. GLA University Department of Computer Engineering & Applications Mathura India CMR College of Engineering & Technology Department of CSE India GRIET Bachupally Department of CSE Telangana Hyderabad India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of CSE Tamil Nadu Chennai600073 India Mohan Babu University Department of AIM Andhra Pradesh Tirupati India

ISBN: (纸本)9798350368109

In this study, it brings into focus, the use of Vision Transformers (ViTs) in the diagnostic of lung cancer with the aid of Computed Tomography (CT) scan data. Taking advantage of the self-attention properties of ViTs the presented model considers global dependencies in imaging data and as a result demonstrates higher diagnostic performance. The training and validation of the proposed model were done using the public dataset, namely the LIDC-IDRI dataset and the classification accuracy is 92%. 5%, a specificity of 88 and the overall accuracy was 91%. 2% for detecting tumours in tissues examined while having a sensitivity of 88. 8%. This shows that ViTs can be made as a substitute to the prior CNN-based models and can be used as a reliable and efficient diagnostic tool in the early stage of lung cancer. These findings may be helpful in making clinical decision and also for increasing survival rate among the patients of lung cancer. © 2024 IEEE.

关键词： Lung cancer

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A Patient-Centric Blockchain Framework for Transparent and Secure Medical data Sharing using Modified AES 2

A Patient-Centric Blockchain Framework for Transparent and S...

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2nd IEEE international conference on Integrated Intelligence and communication Systems, ICIICS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

Due to its decentralised, accessible, and secured structure, blockchain-a framework that has historically been viewed with suspicion-has developed into a groundbreaking breakthrough. Reliable automated scripting and static data records are also a part of this system. The intricacy and expertise required to operate with blockchain technology is one of its disadvantages. Machine Learning (ML) frameworks are the key fixing this problem. It has the potential to make Blockchain more user-friendly. In this research paper, we reviewed the current state of blockchain-based cloud computing, machine learning, and safe data exchange. In this study, we suggest using a blockchain technology to provide a safe space where health entities can share medical information transparently. A modified advanced encryption standard (MAES) based blockchain encryption model is developed by us. This method is based on engineering and AI principles, and its main objective is to design useful artefacts that improve their respective domains. © 2024 IEEE.

关键词： Electronic health record

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Transforming Healthcare: Harnessing the Power of Machine Learning for Disease Diagnosis and Management 2

Transforming Healthcare: Harnessing the Power of Machine Lea...

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2nd IEEE international conference on IoT, communication and Automation technology, ICICAT 2024

作者： Srivastava, Ashutosh Singh, Akash Singh, Prabhawa Srivastava, Divya Verma, Madhushi Garg, Sahil Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350368109

Recent advancements in AI and ML have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. AI and ML models are increasingly utilized at every step of life. Its special ability to handle large amounts of data and give swift easy solutions makes it more popular and loyal to humanity. So here we provide brief overview of AI and ML which includes supervised, unsupervised and reinforcement learning. Next, we discuss the pros and cons of ML application to healthcare such as system privacy, ethical concerns and provide suggestions to upgrade it more for the future use. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT-Enabled Intelligent Aquarium System for Real-Time Monitoring and Automated Control 2

IoT-Enabled Intelligent Aquarium System for Real-Time Monito...

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2nd IEEE international conference on IoT, communication and Automation technology, ICICAT 2024

作者： Kumar, Swapnil Pal, Abhinav Chauhan, R.K. Pandey, Prashant Mishra, Rajan Rajkiya Engineering College Department of Electronics Engineering Sonbhadra231206 India Madan Mohan Malviya University of Technology Department of Electronics and Communiaction Engineering Gorakhpur273010 India

ISBN: (纸本)9798350368109

This paper discusses the importance of proper setup and maintenance of fresh water aquarium tanks for to ensure their well-being. To help with this, the aquarium monitoring system, using IoT & AI is proposed. This system uses various sensors to control aquarium parameters like water quality, teemperature, level e.t.c. An intelligent fish feeding mechanism is implemented as an example, allowing for remote or automated fish feeding in percise. Various parameters as turbidity, DO, Temperature, pH are being monitored by help of proposed system. To maintain temperature in required range an system have been developed and implemented. Real time data is displayed using web page utilizing firebase system. A telegram based bot have been developed to get access to real time data. Additionally, analytic models, simulations, and experiments are conducted to investigate the effects of IoT message delays and loss on water condition control. © 2024 IEEE.

关键词： Water tanks

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