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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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Optical Data Center Implementation within Data Center Networks Context Based on Mininet

Optical Data Center Implementation within Data Center Networ...

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electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Esraa Ali Abdulmahdi Adnan Hussein Ali Raed Khalid Ibrahim Ali Al-Askery Hayder Jasim Alhamdane Amal Ibrahim Mahmood Student at Dept. of Computer Engineering Techniques Electrical Engineering College Middle Technical University Baghdad Iraq Electrical Techniques Department Institute of Technology Middle Technical University Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Control and Automation Technical Engineering Electrical Engineering College Middle Technical University Baghdad Iraq College of Applied Arts Middle Technical University Baghdad Iraq Dept. of Computer Engineering Techniques Electrical Engineering College Middle Technical University Baghdad Iraq

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The technology of reconfigurable optical networks (RONs) is growing to be a promising solution to effectively cater to the rapidly increasing traffic generated by the digital society. Optical technology emerges as a viable alternative because to its capacity to offer a substantial bandwidth, little latency, and enhanced overall performance. Nevertheless, it is imperative to effectively manage the physical resources comprising a data center. This study provides an experimental demonstration of the RONs, which is enhanced by a control plane. The implementation relies on the monitoring of real-time network statistics. This enables the execution of various activities, including as network slice provisioning and reconfiguration, assignment of packet priority classes, and dynamic operations. These actions are performed to ensure the attainment of the desired Quality of Service level. The experimental results show the successfully implementation of RONs using Mininet Emulator and sFlow traffic monitoring technology. The results approve that optical datacenter has minimum delay with low OSNR even with long distance and fast switching time.

关键词： Data centers Optical switches Optical fiber networks Traffic control Real-time systems Service-oriented architecture Monitoring

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An Internet of Things (IoT) Assisted Forest Fire Detection Mechanism Using Hybrid Classification and Learning Model

An Internet of Things (IoT) Assisted Forest Fire Detection M...

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Innovative Computing, Intelligent Communication and Smart electrical Systems (ICSES), International Conference on

作者： Balachandran G Suresh Govindasamy Sankar R Vidhyasree V Neeraj Sunheriya H. Attar Department of ECE Jeppiaar Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Master of Computer Applications S.A. Engineering College Chennai Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Energy Engineer Zarqa University zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

For all the primary and secondary advantages they provide, forests are an indispensable part of humanity's natural resource arsenal. Forest fires and other natural catastrophes significantly affect both the rate of global warming and the viability of Earth's biota. Consequently, studying how to automatically detect forest fires is crucial for reducing the frequency and severity of such catastrophes. Determinants of fire prevention and suppression strategies can benefit from early detection as well. In this study, we use computer vision algorithms based on artificial intelligence to identify fire and smoke in photographs. This paper presents a new hybrid methodology called Enhanced Learning with Fire Classification Model (ELFCM). It uses learning in conjunction with classification norms to accurately report fire and smoke details. To test how well the proposed scheme works, it is cross-validated with the conventional Random Forest Classifier (RFC). According to the findings of the experiments, some of the unreported or incorrectly detected fires that ELFCM reported may be confirmed by using IoT devices. The fire department receives this recorded report for their records. In the end, we tested the fire classification findings by comparing our method's performance to that of other previously announced fire detection methods that employed commonly used performance matrices.

关键词： Deep learning Performance evaluation computer vision Forests Wildfires Computational modeling Classification algorithms Internet of Things Object recognition Random forests

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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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Stochastic analysis and cryptography as key pillars for micro-credentials skills for Bitcoin’s Basic Knowledge

Stochastic analysis and cryptography as key pillars for micr...

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Communications and Networking (BalkanCom), International Balkan Conference on

作者： Dimitrios Kiriakos Dimitrios Kotsifakos Parisa Ghodous Matevž Pustišek Paraskevi Kotsifakou Yannis Psaromiligkos Department of Business Administration The University of West Attica Attika Athens Greece Department of Informatics University of Piraeus Piraeus Greece Computer Science Department Professional Master CCI University of Lyon I Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia Department of Mathematics National and Kapodistrian University of Athens Athens

ISBN: (数字)9798350365955

ISBN: (纸本)9798350365962

The subject of this article is the development, study, and application of instructional models for the optimal design and control of an organized Bitcoin-related curriculum. The pedagogical observations of the analysis of this article are integrated into an active analytical teaching and learning perspective that combines the multiple knowledge bases of cryptocurrencies and includes elements of stochastic analysis and cryptography linked to micro-credentials. The present proposal as an applied teaching model is part of the perspective of the CHAISE course, which comprises learning modules. The CHAISE Blockchain teaching scenario accommodates the skills and knowledge needed for professional profiles, Blockchain Developers, Blockchain Architects, and Blockchain Managers. In addition to educational models for the optimal design and control of organized Bitcoin-related curricula, strategic visions for the potential and sustainability of monetary diversity are developed, studied, and implemented with emphasis on the technical part of the teaching plan and schedule. In this paper, we describe the methodological motivation of the curriculum for teaching the underlying Bitcoin technologies. The paper is interdisciplinary and education-oriented, and it addresses the institutional framework for blockchain establishment and inclusion in education structures.

关键词： Training Knowledge engineering Schedules Education Knowledge based systems Stochastic processes Bitcoin

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Evaluation of Cracking Susceptibility of Long-Term Aged Asphalt Binders Modified with Recycled High-Density Polyethylene and Recycled Polypropylene Plastics

SSRN

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SSRN 2024年

作者： Oyelere, Abeeb Wu, Shenghua Hsiao, Kuang-Ting Kang, Min-Wook Dizbay-Onat, Melike Cleary, John Venkiteshwaran, Kaushik Wang, Jinhui Department of Civil Coastal and Environmental Engineering University of South Alabama 150 Student Services Drive MobileAL36688 United States Department of Civil Coastal and Environmental Engineering University of South Alabama 150 Student Services Drive Shelby Hall 3116 MobileAL36688 United States Department of Mechanical Aerospace and Biomedical Engineering University of South Alabama 150 Student Services Drive Shelby Hall 3130 MobileAL36688 United States Department of Civil Coastal and Environmental Engineering University of South Alabama 150 Student Services Drive Shelby Hall 3142 MobileAL36688 United States Department of Mechanical Aerospace and Biomedical Engineering University of South Alabama 150 Student Services Drive Shelby Hall 3105 MobileAL36688 United States Department of Electrical and Computer Engineering University of South Alabama 150 Student Services Drive Shelby Hall 4127 MobileAL36688 United States

The use of recycled plastics as modifiers for asphalt binder have gained attention in recent years. Studies have confirmed the enhancement in the rutting resistance of asphalt binder with the addition of these types of plastics, however there are controversies with regards to their long-term cracking performance. The long-term cracking performance of asphalt binder is directly influenced by their rheological properties. This study evaluated the fatigue performance of recycled High-Density Polyethylene (rHDPE) and recycled Polypropylene (rPP) modified asphalt binders using several rheological indices including the Superpave intermediate-temperature PG (G*sin δ), fatigue life (Nf), complex modulus (G*), phase angle (δ), the Glover-Rowe (G-R) parameter, the crossover temperature (Tδ =45o), the rheological index (R-value), the peak shear stress, failure strain and the yield energy, as well as a newly developed monotonic cracking index (MCI). Additionally, the influence of the process of mixing plastic with asphalt at high temperature and shear rate on the cracking performance was also investigated. Results indicated that long term cracking performance were not consistent across all rheological indices. While Nf indicated improved cracking performance in asphalt binder with the addition of rHDPE and rPP in asphalt binders, G*sin δ, G-R parameter, R-value, Tδ =45o, and MCI indicated susceptibility to cracking with the addition of these recycled plastics. © 2024, The Authors. All rights reserved.

关键词： Ductile fracture

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Enhanced Prognostics and Health Management in Automated Driving Systems: Using Graph Neural Networks to Recognize Operational Contexts

Enhanced Prognostics and Health Management in Automated Driv...

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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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Experimental Analysis of an Efficient Dental Carries Prediction System Based on Improved Convolutional Neural Network (iCNN) Principle

Experimental Analysis of an Efficient Dental Carries Predict...

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Innovative Computing, Intelligent Communication and Smart electrical Systems (ICSES), International Conference on

作者： Ranjith S Vanitha Lakshmi M Sujatha V Suresh Govindasamy Pallavi Giri Khaled Al-Qawasmi Department of ECE Jeppiaar Engineering College Chennai Department of ECE. SOE Presidency University Bengaluru Department of Master of Computer Applications S.A. Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Civil engineering Laxminarayan Innovation Technological University Nagpur India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Dental caries is a kind of infectious illness that weakens the structure of teeth, leading to cavities. Because of the discomfort and expense of treatment, studies on dental caries have focused on early diagnosis, as it is one of the most common oral health problems. Due to the high costs and lengthy research times associated with oral healthcare, artificial intelligence has recently been employed to create models that can forecast the likelihood of dental caries. The agony and expensive price of therapy have prompted this investigation toward early detection. Models that may forecast the possibility of dental caries have recently been developed using artificial intelligence. This is in response to the high expenditures and significant time commitments connected with medical research in oral healthcare. In this study, a new method for efficiently identifying dental caries is presented using an Improved Convolutional Neural Network (iCNN). To test how well the suggested scheme works, it is cross-validated with the standard method called Support Vector Machine (SVM). This paper's suggested method, iCNN, demonstrates that dental practitioners may greatly benefit from deep learning to aid in the early diagnosis and treatment of dental caries.

关键词： Support vector machines Deep learning Neural networks Teeth Predictive models Dentistry Convolutional neural networks Artificial intelligence Standards Diseases

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Edge Computing Enabled Smart Warehouse Management System for Food Processing Industries

Edge Computing Enabled Smart Warehouse Management System for...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Gowrishankar V Veena P Ponmurugan P Annapoorani B T Vijayakumar P Siva Ramkumar M Department of Electronics and Communication Engineering Velalar College of Engineering and Technology Erode India Department of Electrical and Electronics Engineering K S R Institute for Engineering and Technology Tiruchengode India Department of Research Meenakshi Academy of Higher Education and Research Chennai Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Master of Computer Applications Kongu Engineering College Perundurai Tamil Nadu Department of Electrical and Electronics Engineering Faculty of Engineering Karpagam Academy of Higher Education Coimbatore India

Food safety is a primary concern across all global platforms, and food storage is a crucial and essential part of management. This clarifies that food warehousing should be fully automated or has some management system. Good food storage plays a vital role in food security, which is affected by food waste and food loss. As a result, it is critical to identify and remove microbial contamination before it spreads to other items. To end that, we propose an edge computing-enabled intelligent warehouse system with an ESP32 microcontroller and micropython. The food commodities studied in the other papers were fish, dairy, fresh food products, crop-field cucumber, seafood, wine, and pre-packaged food. The various gas sensors are used to detect microbial growth, food decay, and shelf life. Humidity and temperature sensors are employed to monitor the warehouse environment, along with RFID technology to track food items. Multiple nodes accessible via Telegram and dashboard notifications will be put in the building to enable law enforcement collaboration. These nodes will exchange information on the building's structure. Compared to the traditional warehouse management system, the proposed edge computing-enabled IoT warehouse shelves reduce waste and contamination in fruit and vegetable chambers. Designs are experimented with different sets of fruits and vegetables. Continuous live monitoring and an alert system demonstrate the effectiveness of the proposed innovative framework compared with an existing management system.

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Design and Implementation of Wireless Identification and Sensing Platform in Structure Monitoring 5

Design and Implementation of Wireless Identification and Sen...

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5th International Conference on Machine Vision and Information Technology, CMVIT 2021

作者： Qiu, Xunchao Yuan, Jianqing Hou, Lijun Gao, Wei Xi, Wangyuan Yan, Wei Department of Computer Engineering Harbin Finance University Harbin150040 China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin150040 China Student Affairs Department of Party Committee Northeast Forestry University Harbin150040 China

In the process of structure monitoring, active wireless sensor devices are often used. In order to replace the energy limitation bottleneck of the power supply unit, the Wireless Identification and Sensing Platform (WISP) with real-time data transmission function is adopted to realize the collection of structural vibration acceleration data. Firstly, the various functional modules of the WISP with three-axis acceleration are introduced. Then, through software programming of the low-power MSPF430F2132 microprocessor on it, the drive and control of the external three-axis acceleration sensor ADXL330 is realized. Finally, it was tested through comparative experiments. The research results show that the acceleration data monitored by the WISP is accurate, effective and reliable. Compared with the active wireless Narada unit, the similarity is more than 83%. Compared with the OpenSEES system, the accuracy of the structure frequency monitored by the WISP is 99.07%. Therefore, it is feasible and reliable to use the WISPs to perform multi-point deployment control in structural dynamic monitoring. © Published under licence by IOP Publishing Ltd.

关键词： Structural dynamics

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