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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Madan, S. Shetty, Kishan Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept of Artificial Intelligence and Data Science Karnataka Bengaluru India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350300826

Due to its dense population, India frequently experiences traffic congestion, which puts lives in danger by trapping vehicles like ambulances, police cars, and fire trucks which run on road for emergency purpose. It becomes crucial to give these vehicles priority and allow for their easy passage. However, it becomes challenging or even impossible for traffic police to effectively handle such circumstances. Therefore, there is a requirement for an automated system that can locate emergency vehicles in high-traffic areas, alert the controller, or drive itself to direct other vehicles to make room. This study suggests an automated method for identifying emergency vehicles from CCTV footage that makes use of deep convolutional neural networks (CNN). The objective is to effectively identify and classify emergency vehicles in real-time, leveraging the power of advanced object detection techniques. With an accuracy of 91.73% and a loss of 0.2120, the suggested technique outperformed existing optimizers in accurately recognizing and classifying emergency vehicles. © 2023 IEEE.

关键词： Object detection

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Detection of Behavioral Patterns of Viral Hepatitis Using data Mining 4

Detection of Behavioral Patterns of Viral Hepatitis Using Da...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Shende, Vaishali Gajendra Jency, A. Pavithra, K. Jayasudha, T. Venkatesan, G. Sharma, Manish East Point College of Engineering and Technology Department of Computer Science Engineering Bangalore560049 India Rmk College of Engineering and Technology Department of Artificial Intelligence and Data Science Thiruvallur601206 India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai600123 India Kpr Institute of Engineering and Technology Department of English Coimbatore641407 India Saveetha Engineering College Department of Civil Engineering Chennai602105 India Graphic Era Deemed to Be University Department of Computer Science Engineering Dehradun248002 India

ISBN: (纸本)9798350300888

The data collected by the Information Systems related to Public Health are underutilized, mainly due to their large volume and their complexity. This work was applied to the Knowledge Discovery in databases technique based on the National Centre for Disease Control (NCDC), with the objective of tracing the epidemiological profile of viral hepatitis. Foram used the data referring to the corporation of Chennai in the year 2022, from two quais it was possible to compare the information obtained with the knowledge available in the specialized literature. Obtain a clear, simple and objective representation of the epidemiological profile of viral hepatitis through a decision tree and classification rules. © 2023 IEEE.

关键词： Decision trees

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Review on application progress of federated learning model and security hazard protection

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Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data *** data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data *** is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our *** the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied *** paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： data silos Machine learning Federated learning Privacy protection Learning framework

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Masking of Colposcopy Images- A New Approach

Masking of Colposcopy Images- A New Approach

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Chatterjee, Priyadarshini Das, Surajit Siddiqui, Shadab B V Raju Institute Of Technology Department Of Computer Science And Engineering Medak Telengana Narsapur India B V Raju Institute Of Technology Department Of Artificial Intelligence And Data Science Medak Telengana Narsapur India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350371567

Masking, in the context of image processing and computer vision, refers to the process of isolating specific regions or objects of interest within an image while suppressing or removing irrelevant information. It involves the creation of a mask, which is essentially a binary image that defines which pixels in the original image belong to the region of interest and which do not. The mask typically consists of black and white pixels, where white pixels represent the areas to be preserved or highlighted, and black pixels represent the areas to be ignored or masked out. This paper presents a novel approach aimed at enhancing the accuracy of masking algorithms for colposcopy images, a crucial component in the diagnosis of cervical cancer among women. While previous research has demonstrated promising outcomes in this domain, our study introduces a novel masking algorithm tailored specifically to improve segmentation accuracy. The colposcopy dataset utilized in this research is sourced from the International Agency for Research on Cancer in France. © 2024 IEEE.

关键词： Diseases

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Emotion Decoding of Speech Using NLP Analysis Approach

Emotion Decoding of Speech Using NLP Analysis Approach

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Bobbili, Charitha Dhushetti, Sri Varsha Harshith, Y. Shanmugapriyan, J. Hariharan, Shanmugasundaram Vaishnavi, Bandar Vardhaman College of Engineering Department of Artificial Intelligence and Data Science India Nsn College of Engineering and Technology Dept of Electrical and Electorics Engineering India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India

ISBN: (纸本)9798331527495

Emotion decoding of speech using NLP provides an integrated solution for emotion-driven applications. This project creates a comprehensive system for speech-to-text conversion, sentiment analysis, transcription accuracy evaluation, and senti- ment visualization. The system converts spoken audio into text using the SpeechRecognition library and the Google Web Speech API. To ensure reliability, transcription accuracy is assessed using the Levenshtein distance algorithm, which calculates the smallest number of edits necessary to determine the similarity between the transcribed text and the reference text. VADER (Valence Aware Dictionary and Sentiment Reasoner) from the NLTK library is used by the system for emotion analysis. It is a potent tool for assessing sentiment intensity and assigning sentiment scores to associated emotional states like positive, negative, or neutral. Actionable insights into the speech's emotional content are offered by this analysis. The system creates pie charts using Matplotlib to show sentiment distribution in order to improve interpretability. Users can quickly and naturally understand the emotion landscape thanks to this functionality. This system is well-suited for real-world uses like psychological analysis, where emotional states offer vital diagnostic information, and customer service, where comprehending client emotions is essential. It does this by integrating speech processing, transcription evaluation, sentiment analysis, and visualization into a coherent framework. © 2025 IEEE.

关键词： Emotion Recognition

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Optimizing Wireless Ad-Hoc Networks: A Holistic Approach for Sustainable Energy Efficiency 5

Optimizing Wireless Ad-Hoc Networks: A Holistic Approach for...

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5th International Conference on Information Management and Machine intelligence, ICIMMI 2023

作者： Vashishtha, Laxmikant Singh, Uday Pratap Saxena, Vivek Patel, Shubham Department of Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology Rajasthan Jaipur India Department of CSE Poornima Institute of Engineering & Technology Rajasthan Jaipur India Department of Computer Science Poornima College of Engineering Rajasthan Jaipur India

ISBN: (纸本)9798400709418

This research paper explores the challenges and opportunities in designing energy-efficient architectures for wireless ad-hoc networks. As the demand for wireless communication continues to grow, the need for energy-efficient solutions becomes imperative to extend the lifespan of battery-powered devices and reduce the environmental impact. The paper investigates various techniques, protocols, and algorithms aimed at optimizing energy consumption in wireless ad-hoc networks, with a focus on enhancing overall network performance. Ad Hoc Wireless Network Wireless Ad Hoc Networks are made up of wirelessly connected groups of mobile terminals that allow unrestricted mobility within a defined region. Given mobile terminals' short battery life, emphasizing energy conservation is critical for the long-term viability of mobile Ad Hoc wireless networks. Identifying energy-saving measures benefits network designers in constructing more efficient networks with longer service life cycles. The purpose of this research is to conduct a simulation analysis of the sleep/wake-up, energy-saving protocol MM3, which is based on the IEEE 802.11b standard. The MM3 protocol is intended to improve power savings and is compared to IEEE 802.11 power save mode. Furthermore, two levels of multilayer power-saving protocols are employed to build the Enhanced Ad-Hoc On-Demand Distance Vector (EAODV), a better ad-hoc routing method. © 2023 ACM.

关键词： Mobile ad hoc networks

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Marine Life Analysis Based on Ocean Water Level Rise and Climate Change Using Underwater Imaging Techniques

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 657-669页

作者： Mate, Anuja Dinesh Arulananth, T.S. Sathiya, T. Jyothi, B. Swarna Raja, A. Wasim Yalawar, Mrutyunjaya S. Computer Technology Sou.Venutai Chavan Polytechnic Pune India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad 500043 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem India Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Engineering College Hyderabad India

Forecasting sea levels is crucial for harbour operations and coastal structure design. The oceans make up two-thirds of Earth’s surface;therefore, historically, the marine economy has been extremely diversified as well as abundant. In order to explore as well as harness this difficult environment, prominent businesses and ecological groups are developing and deploying novel gadgets backed by state-of-the-art marine mechatronics solutions. This research proposes a novel method of marine life monitoring with ocean water level rise analysis based on climate changes by underwater imaging using machine learning techniques. Here, the input has been collected as underwater images as well as processed for noise removal and normalisation. Then, these images were segmented for monitoring marine life using U-Net graph cut fuzzy clustering with a spatial neural network. Then this segmented image has been classified using multilayer region-based Q-belief vector neural networks for the analysis of sea level rise analysis based on climate change training. Experimental analysis has been carried out for various marine life monitoring datasets and climate change datasets in terms of training accuracy, specificity, recall, AUC, and Kappa-coefficient. Proposed techniques achieved specificity 93%, training accuracy 91%, AUC 95%, recall 96%, and Kappa-coefficient 90%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate changes Machine learning techniques Marine life monitoring Q-belief vector Water level rise analysis

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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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An Early-Stage Colorectal Cancer Detection from Colonoscopy Images Using Enhanced Res-UNET 1

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8th International Conference on Computers, Management and Mathematical sciences, ICCM 2022

作者： Mahanty, Mohan Vamsi, Bandi Dasari, Madhavi Appaji, Setti Vidya Sagar Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Computer science and Engineering Department Sridevi Women’s Engineering College Hyderabad India Baba Institute of Technology and Sciences Visakhapatanam India

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

The segmentation of the polyps during colonoscopy is one of the most crucial factors for successful colon cancer diagnosis. Due to the wide variety of sizes and shapes of these polyps, this process can be challenging. The introduction of deep learning models in biomedical image analysis has shown a great impact on disease detection and segmentation. Most of the deep learning models concentrated on larger size polyps only, smaller size polyps may not segmented accurately during the procedure and can cause cancer in 5–7 years. This can have a significant impact on the early-stage detection of colon cancer. This chapter proposes an enhanced Res-UNET-based deep learning–based model that can improve the performance of segmentation on small-size polyps. We evaluated the proposed model on ETIS-LaribPolypDB, CVC-ColonDB, CVC-ClinicDB, Kvasir-SEG colonoscopy image datasets. The results show that the proposed enhanced Res-UNET model achieves the top Dice segmentation accuracy and top Hausdorff distance over the recent state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image enhancement

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Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

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IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

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