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Digital Applications, Transformation & Economy (ICDATE), International Conference on

作者： Chrisjonpane F. Lazaga Gerhard P. Tan Marife A. Rosales Master of Science in Electronics Engineering with Specialization in Artificial Intelligence and Automation Graduate School Polytechnic University of the Philippines Manila Philippines Department of Electronics Engineering Graduate School Polytechnic University of the Philippines Manila Philippines De La Salle University Manila Philippines

This study provides help to the Department of Information and Communication Technology with statistical data and analysis on the ICT’s SDG plans that are ramping up in the Philippines. The researchers use special-purpose surveys to produce an Internal-External Matrix in determining DICT’s strategy in terms of strengths, weakness, opportunities, and threats in an environment where fiber optic connectivity is available. CART and ANN models are used for analyzing and predicting the individual’s behaviors in taking fiber optic internet as their selected internet mode. The IE Matrix showed that the current strategies benefit and produce results, but not enough to make their organization known to the respondents. The CART and ANN models both resulted in approximately 86% of prediction accuracy aligned that the CART model’s top 3 feature importance results are; Certification Platform, Internet in Public Sector, and Professional Status, while the ANN model showed similar on CART results having the latter; Age, Incentive Motivation, and Salary of an individual are deemed integral in the created predicting model. The researcher hopes that this study will provide insight and solutions in improving the strategy, plans and market penetration in the future where fiber optic internet is widely available in the Philippines.

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Optimal Extreme Learning Machine based Traffic Congestion Control System in Vehicular Network 6

Optimal Extreme Learning Machine based Traffic Congestion Co...

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6th International Conference on electronics, Communication and Aerospace Technology, ICECA 2022

作者： Bharti, Rajendra Kumar Suganthi, D. Abirami, S.K. Kumar, Relangi Anil Gayathri, B. Kayathri, S. Btkit Department of Cse Dwarahat Uttarakhand Almora India Saveetha College of Liberal Arts and Sciences Saveetha University Department of Computer Science Tamilnadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamilnadu Coimbatore India Aditya College of Engineering and Technology Department of Electronics and Communication Engineering Surampalem India Bishop Heber College Bharathidasan University Department of Computer Science Tamilnadu Trichy India M. Kumarasamy College of Engineering Master of Computer Applications Tamilnadu Karur India

ISBN: (纸本)9781665482714

Over the past decade, Smart cities have been advanced and minimizing traffic congestion become the main concern in the progression of smart cities. The rapid growth in the number of road vehicles has raised the number of road accidents and traffic congestion. To Solve this problem, Vehicular Network (VN) formulated several novel concepts which include traffic control, vehicular communications, and navigation. Machine Learning (ML) will be an effective technique for identifying hidden insights into ITS without being programmed clearly by learning from data. This article develops an Optimal Extreme Learning Machine based Traffic Congestion Control System (OELM-TCCS) in vehicular networks. The presented OELM-TCCS technique mainly focuses on the identification and recognition of traffic congestion in VANET. To perform this, the presented OELM-TCCS technique primarily designs a new ELM model to carry out the classification process. In addition, the presented OELM-TCCS technique executes the satin bowerbird optimization (SBO) algorithm for parameter tuning of the ELM method. To demonstrate the boosted performance of the OELM-TCCS model, a series of experiments were executed. The experimental outcomes indicate the betterment of the OELM-TCCS method over recent methods with maximum accuracy of 99.17%. © 2022 IEEE.

关键词： Traffic congestion

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Quantum K-Nearest Neighbors for Emotion Recognition Using Visual Art Based on Color and Texture Analysis

Quantum K-Nearest Neighbors for Emotion Recognition Using Vi...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Ammar H. Shnain P. Jaya Prakash Varanasi Srinivas S. Thanalakshmi S. Senthil Kumar Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Information Technology Sri Venkateswara College of Engineering Tirupathi India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Master of Business Administration Gojan School of Business and Technology Chennai India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The emotion recognition is the process of recognizing a person emotion in various ways that also expressed in arts such as paintings, drawings are the visual art. In traditional automated detection methods performs based on fixed rules and manual steps which affect to recognize the arts. To overcome this problem, Quantum K-Nearest Neighbors (QKNN) for used classify the emotions using visual art. The WikiArt and AffectNet are collection of images, artworks based on visual art that provide as input to the proposed method QKNN. The gray scale conversion and data augmentation is used for pre-processing for improve the quality of images. The Histogram of Oriented Gradients (HOG) is used for extract features of pre-processed data. The KNN is used for classify the emotions and improved QKNN is utilized for enhance the accuracy of the classification. The performance metrices are used to evaluate the overall results of metrics such as accuracy of 98.46% and 99.12% for WikiArt and AffectNet are compared with existing works of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) respectively.

关键词： Support vector machines Visualization Emotion recognition Art Accuracy Nearest neighbor methods Feature extraction Data mining Convolutional neural networks Painting

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Identification of Alterations in Surgical Wounds Through the Application of Artificial Intelligence in Digital Images

Identification of Alterations in Surgical Wounds Through the...

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Biomedical engineering and Bioengineering (CI-IB&BI), IEEE International Congress of

作者： Javier Armando Rodríguez Prada Álvaro Augusto Cote Flórez Amolfi Hernando Pineda Gómez Hernán Darío Vargas Cardona Research and Education Unit Instituto del Corazón de Bucaramanga Bucaramanga Colombia Engineering and Sciences (Master in Data Science) Pontificia Universidad Javeriana Cali Colombia Department of Electronics and Computer Science Pontificia Universidad Javeriana Cali Colombia

ISBN: (数字)9798331532352

ISBN: (纸本)9798331532369

Globally, surgical site infections are common complications that are both serious and costly. While telemedicine has enhanced the remote assessment of surgical wounds, it still faces limitations. This study introduces a convolutional neural network (CNN) model designed to automatically classify digital images of surgical wounds as either altered or unaltered. The study utilized a dataset of 4,262 segmented and expert-labeled images. The CNN model achieved an accuracy of 83.46%, a sensitivity of 81.54%, and an AUROC of 92.22%. Although the MobileNet model demonstrated acceptable performance, it was less effective in comparison. The findings s uggest t hat C NNs a re e ffective for classifying images of surgical wounds, with potential for further improvement using advanced techniques and a multidisciplinary expert panel.

关键词： Sensitivity Digital images Telemedicine Transfer learning Surgery Wounds Convolutional neural networks Optimization Monitoring Biomedical engineering

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Advanced Spatiotemporal Anomaly Detection in Photovoltaic Power Plants Using Temporal Graph Networks Integrated with Publicly Available Weather Data

Advanced Spatiotemporal Anomaly Detection in Photovoltaic Po...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： B.Ravali Reddy Neeta Awasthy Priyameet Kaur Keer Ginni Nijhawan Vajenti Mala Ali Albawi T Srinivas Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering GL Bajaj Group of Institutions Mathura U.P. India Master of Business Administration New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Civil Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

For purposes of performance analysis and minimization of maintenance expenses, it is vital to identify an effective way in which to monitor anomalies in photovoltaic (PV) power plants. This paper proposes a novel framework to address the problem of anomaly detection based on the inherent graph structure of PV plant configurations using Temporal Graph Networks (TGNs). TGNs are built to capture spatial and temporal dependencies of the PV systems which allows detecting discrepancies affecting the power production, which traditional NN might fail not to notice. The model uses external weather data collected from NOAA and tracks how external parameters influence the performance of photovoltaic panels and therefore electricity generation. Our methodology solely employs TGNs to analyse the intricate relationship in the PV plant data to improve the recognition of abnormality associated with the equipment and weather conditions. The integration of NOAA's open source weather data into the extent of analysis further raises sophistication by offering a variety of features and conditions affecting PV results. Our approach achieves high accuracy of anomaly detection and can be considered to be better compared with the baselines that do not take graph dependencies or time aspect into account. This advancement has important implications for enhancing the operation and maintenance recommendations in the PV power plants and hence the reliability of the satellite power systems. Allying our efforts to one highly specialized method, this research presents a new state of the art in leveraging centralized data for anomaly detection in renewable energy.

关键词： Photovoltaic systems Renewable energy sources Accuracy Surveillance Data models Maintenance Anomaly detection Unsupervised learning Meteorology National Oceanic and Atmospheric Administration

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Automatic COVID-19 Prediction with Comprehensible Machine Learning Models

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Open Public Health Journal 2024年 17卷

作者： Puttegowda, Kiran Sunil Kumar, D.S. Mallu, Sahana Vijay, C.P. Ravi, Vinayakumar Sushmitha, B.C. Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Karnataka 570002 India Department of Master of Computer Application Acharya Institute of Technology Karnataka 560107 India Department of Electronics and Communication Engineering ATME College of Engineering Karnataka 570028 India Department of Computer Science Engineering (AI&ML) Vidyavardhaka College of Engineering Karnataka 570002 India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar 34754 Saudi Arabia Department of Electronics and Communication Engineering ACS College of Engineering Bengaluru India

Background: The development of technology has a significant impact on every aspect of life, whether it is the medical industry or any other profession. The potential of artificial intelligence has been demonstrated in data processing and analysis is used to inform decisions in the field of health care. The most crucial action is the early detection of a life-threatening illness to stop its development and spread. Highly contagious COVID-19 is a disease that requires immediate attention as it has spread globally. There is a need for a technology that can be utilised to detect the virus because of how quickly it spreads. With the increased use of technology, we now have access to a wealth of COVID-19-related information that may be used to learn crucial details about the virus. Objective: The objective of the work is to develop comprehensible machine learning models for the automatic prediction of COVID-19. These models aim to accurately predict the likelihood of COVID-19 infection based on relevant input features, such as symptoms, demographics, and potential diagnostic tests or imaging results. Methods: In this work, we mainly aimed to assess how well different machine learning methods might predict COVID-19 situations. In order to do this, we thoroughly evaluated a variety of widely used classifiers in machine learning. Popular algorithms like the random forest, k-nearest neighbour, and logistic regression were all included in our analysis. Results: To assess the performance of our suggested algorithms using different machine learning techniques, we used an open-source dataset in the study. Our algorithms performed better than other models that are currently in use, which is noteworthy. The high degree of precision in predicting COVID-19 instances is demonstrated by our remarkable accuracy of 96.34%. We also obtained a good F1 score of 0.98 for our models, indicating the strength and efficiency of our method in obtaining metrics for both recall and precision. Concl

关键词： COVID-19 Diagnosis k-nearest neighbour Machine learning Neural networks Random forest prediction

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Intelligent Road Crack Detection and Analysis Based on Improved YOLOv8

Intelligent Road Crack Detection and Analysis Based on Impro...

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Advanced Algorithms and Control engineering (ICAACE), International Conference on

作者： Haomin Zuo Zhengyang Li Jiangchuan Gong Zhen Tian School of Electronics and Communication Engineering Sun Yat-Sen University Guangzhou China Master of Science in Computer Science DigiPen Institute of Technology Redmond WA USA Vocational and Technical College Washington University in St. Louis Hebei Normal University Shijiazhuang Hebei China James Watt School of Engineering University of Glasgow Glasgow UK

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

As urbanization speeds up and traffic flow increases, the issue of pavement distress is becoming increasingly pronounced, posing a severe threat to road safety and service life. Traditional methods of pothole detection rely on manual inspection, which is not only inefficient but also costly. This paper proposes an intelligent road crack detection and analysis system, based on the enhanced YOLOv8 deep learning framework. A target segmentation model has been developed through the training of 4029 images, capable of efficiently and accurately recognizing and segmenting crack regions in roads. The model also analyzes the segmented regions to precisely calculate the maximum and minimum widths of cracks and their exact locations. Experimental results indicate that the incorporation of ECA and CBAM attention mechanisms substantially enhances the model's detection accuracy and efficiency, offering a novel solution for road maintenance and safety monitoring.

关键词： Deep learning Training Image segmentation Attention mechanisms Accuracy Target recognition Urban areas Robustness Road safety Safety

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Self-Supervised Learning with Variational Autoencoders for Anomaly Detection in Autonomous Drone Fleets

Self-Supervised Learning with Variational Autoencoders for A...

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Smart Technologies, Communication and Robotics (STCR)

作者： Kirubhakaran M M.Sundarrajan Gowtham T D. Agila Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Master of Computer Application Excel Engineering College Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Agni College of Technology Chennai Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

However, Unmanned Aerial Vehicles (UAVs) are increasingly being employed for different applications such as surveillance, logistics, disaster response, and smart city management, and thereby, the requirement for robust detection of anomalies and path optimization techniques has arisen. Existing traditional UAV monitoring systems typically depend on anomaly detection based on a rule or an unsupervised clustering method, i.e., DBSCAN and One-Class SVM, balancing high false positives and slow response time, correction, and suboptimal trajectory. To tackle these issues, we develop an AI-powered UAV anomaly detection and adaptive navigation framework where Isolation forests and MDPs are used to predict UAV trajectory, and DQNs are used for adaptive navigation. Experimental results in a simulated smart city arena with a multi-UAV fleet show that our proposed framework achieves 94% accuracy, 92% precision, and 91% recall, which are much superior to the existing approaches, such as One Class SVM (87% accuracy) and DBSCAN (80% accuracy). Additionally, the system compensates for false positives by 30%, improves energy efficiency by 15%, and reduces UAV collision risks by 20%, with high compatibility with real-time UAV fleet management. In the future, the scalability and the adaptability of the UAV-based smart city operation will be furthered through federated learning-based decentralised UAV coordination, reinforcement learning for autonomous swarm intelligence, and hybrid optimisation techniques for improving energy efficiency.

关键词： Support vector machines Accuracy Smart cities Navigation Scalability Autonomous aerial vehicles Energy efficiency Trajectory Anomaly detection Optimization

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Development of an Electric Automation Control Model Using Artificial Intelligence

Development of an Electric Automation Control Model Using Ar...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： V. Srikanth P. Aswini V. Asha Khobragade Pithamber Rajeev Sobti Z. Salman School of Sciences and Computer Studies CMR University (SATELLITE CAMPUS-OMBR LAYOUT) Bengaluru India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Hyderabad India Department of Master of Computer Application New Horizon College of Engineering Bangalore India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad Telangana India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The design of an AI-based electrical computerization control system is implemented to efficiently address the challenges encountered by contemporary electrical engineering. A model of an AI-based control system for electrical automation is presented. To achieve optimal control settings, the control strategy makes use of an artificial intelligence system. Even in the presence of a twenty per cent load intervention and 2.1 Hz wavelength interference, the research demonstrates that the organisation has significant anti-interference capabilities, as shown by an acceptable fail rate of 0.02 for each at the system management level. Consequently, using an AI algorithm in automated electrification control may lead to a significant improvement in control reaction time, cost savings, and efficient construction.

关键词： Industries Automation Costs Interference Electrification Control systems Time factors Artificial intelligence Turbines Load modeling

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Block Chain Technology for Securing IoT Devices and Data

Block Chain Technology for Securing IoT Devices and Data

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： H. Mickle Aancy Shilpa Prashant Kodgire MaddulaRatna Mohitha Manimegalai V Vijay G Sunita Dhotre Department of Master of Business Administration Panimalar Engineering college Chennai Tamil Nadu India Department of Electronics and Telecommunication Engineering Maharashtra Institute of Technology Aurangabad Maharashtra India Department of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali Andhra Pradesh India Department of Electrical and Electronics Engineering Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of English PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India Department of Computer Engineering Bharati Vidyapeeth(Deemed to be University) College of Engineering Pune Maharashtra India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

IoT detector data can be covered and authenticated using blockchain armature to avoid data duplication. It also enables secure device data transfer. IoT can handle scalability and trust challenges with blockchain, keeping data private and secure. Blockchain technology improves authentication, fraud resistance, and decentralized data security. It can polarize device authentication and communication, barring a central authority. The processing power demanded for blockchain mining and the vulnerability of present blockchains to mining monopolization are obstacles. Hyundai has patronized IoT security blockchain startups. Blockchain technology could secure IoT networks and sensitive data. Blockchain and IoT give a strong answer to linked device security issues. Blockchain's decentralized and inflexible tally can secure IoT bias and their data, according to this abstract. Blockchain technology allows secure, peer- to- peer connectivity and data sharing, guarding IoT bias from manipulation and unauthorised access. Blockchain's cryptographic algorithms guarantee data integrity, while smart contracts automate device relations to misbehave with security rules. Blockchain's practical counteraccusations in IoT security could alter how we guard our decreasingly linked world, as this exploration shows.

关键词： Resistance Scalability Smart contracts Authentication Mathematics Blockchains Fraud

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