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Aluminium-Metal oxide (Al2O3) Nanofluid Cooling Technique: Enhancing the Efficiency of Crystalline Silicon Cell Panels

Aluminium-Metal oxide (Al2O3) Nanofluid Cooling Technique: E...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kukreti, Sanjeev Dahiwade, Mandakini Shnain, Ammar Hameed Dixit, Krishna Kant Upadhyay, Shrikant Ramya, R. Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Basic Engineering Indira College of Engineering and Management Pune India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Saveetha school of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350355338

Solar power is rapidly becoming more popular due to its enormous potential as a long-term energy solution. The efficiency of a photovoltaic panel decreases with increasing temperature. The power output of the PV module decreases as the temperature rises unless the heat is removed. Module cooling has the potential to increase the efficiency of photovoltaic panels. This system uses an aluminium-metal oxide (Al2O3) nanofluid for cooling purposes. By keeping a constant temperature, a solar energy conversion system that combines photovoltaic (PV) and aluminium-metal oxide (Al2O3) nanofluid (Aluminium-metal oxide (Al2O3) nanofluid) modules can achieve greater efficiency. Investigations into the nano fluidity of aluminium-metal oxide (Al2O3) have led to the development of a model demonstrating the potential efficacy of PV-Aluminium-metal oxide (Al2O3) combo modules. The research shows that a cooling system made of aluminium-metal oxide (Al2O3) nanofluids can increase the efficiency of electric conversion in a photovoltaic array. A photovoltaic module's output power drops with increasing temperature if the heat is not dissipated. © 2024 IEEE.

关键词： Solar power generation

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Scheduling in Cloud of Things: An Overview 3

Scheduling in Cloud of Things: An Overview

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Hussein Obaid, Abdulrahman Mohammed Ali Abdo Mohammed, Awadh Pani, Santosh K. Alsabry, Ayman Ali Qasem, Hamzah 21 September University of Medical and Applied Sciences Department of Medical Information Technology Sana'a Yemen 21 September University of Medical and Applied Sciences Department of Bioedical Engineering Technology Sana'a Yemen School of Computer Engineering Kiit University Bhubaneswar India International University of Technology Twintech Department of Computer Science Sana'a Yemen International University of Technology Twintech 21 September University of Medical and Applied Sciences sana'a Yemen

ISBN: (纸本)9798350305333

Computing, Internet, digital devices, smart devices, and other technologies were leading to a new terminology known as cloud of things (CoT). Cloud of Things is a powerful technology used to analyze and store massive data from thousands of distributed devices (smartphones, vehicles, home appliances, sensors, etc.). Further, Cloud of Things integrated two concepts, Internet of Things and Cloud Computing. There are many issues in cloud computing that have been widely studied, such as scheduling. On the other hand, a few issues have been studied in Cloud of Things. The objective of this paper is to discuss and classify the scheduling issues in Cloud of Things. Researchers have developed algorithms for different environments such as heterogeneous/homogeneous clusters, cloud computing, and large-scale community grids. By reviewing and considering many of these articles, we provided an overview for reuse these state-of-the-art algorithms in Cloud of Things environments. In addition, this paper provides researchers with ideas and platform for finding open research and exploring Cloud of Things issues. © 2023 IEEE.

关键词： Scheduling algorithms

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Advancing Underwater Communication with NBO-MFO Hybrid Routing in UWSNs

Advancing Underwater Communication with NBO-MFO Hybrid Routi...

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International Conference on Communications and Signal Processing

作者： S.A. Kalaiselvan M.Lenin Kumar M. Amanullah T.R. Mani Chigurupati Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Electronics and Communications Engineering Vignan Institute of Technology and Science Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering TKR College of Engineering and Technology Telangana India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In this research provides a solution for optimizing routing path selection in Underwater Wireless Sensor Networks (UWSNs). The algorithm combines Namib Beetle Optimization (NBO) and Moth-Flame Optimization (MFO), integrating them into the ILEACH methodology for efficient routing path selection. The algorithm operates in an iterative manner, commencing with NBO’s feature selection process, evaluating fitness through average classification error and feature selection ratio. Simultaneously, MFO mimics moth behavior, updating positions via a logarithmic spiral function based on flame locations. The mutual learning phase facilitates information exchange between NBO and MFO, enhancing their collective intelligence and refining the routing path selection process. ILEACH’s contribution lies in its fitness aggregation and selection methods. It amalgamates various criteria, including energy levels, distances, and node conditions, into a comprehensive fitness metric. Subsequently, ILEACH selectively identifies optimal routing paths aligned with energy-efficient criteria and node statuses, ensuring efficient data transmission and energy conservation within UWSNs. This integrated approach fosters a comprehensive strategy for routing path selection in UWSNs, ensuring sustainability and efficiency in data transmission within challenging underwater environments.

关键词： Wireless sensor networks Machine learning algorithms Scalability Signal processing algorithms Routing Feature extraction Energy efficiency

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A comprehensive study on the microstructure of plasma spraying coatings after electron beam remelting 14

A comprehensive study on the microstructure of plasma sprayi...

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14th International Conference on Electron Beam Technologies, EBT 2022

作者： Wȩglowski, M.St. Śliwiński, P. Dymek, S. Kalemba-Rec, I. Kapuściński, M. Wrona, A. Kustra, K. Lukasiewicz - Institute of Welding Gliwice Poland Lukasiewicz - Institute of Non-Ferrous Metals Gliwice Poland AGH University of Science and Technology Faculty of Metals Engineering and Industrial Computer Science Krakow Poland

One of the most popular thermal spraying technology is atmospheric plasma spraying (APS). However, it should be noted, that numerous imperfections in the APS surface layers can be occurred. The porosity, microcracks as well as lamellar microstructure occurred. Moreover, the adhesion of the coating is limited. However, the reduction of porosity and other imperfections by remelting process can be eliminated. The laser or electron beams for remelting are the most popular technologies. Mainly, power of the beam and travelling speed influence on the thickness and the final properties of the remelted coatings. In the paper, the electron beam (EB) process for remelting of plasma spraying surface layers in relation to microstructure is presented. The Ni20%Cr + 30%Re APS coating on stainless steel substrate 316Ti grade by electron beam was remelted. The effect of remelting process on microstructure was presented. The light microscopy (LM) as well as scanning electron microscopy (SEM) and energy disperse spectroscopy (EDS) for microstructure analysis of APS coatings after EB remelting were used. The results show that the APS coatings subjected EB remelted becomes much denser. Moreover, the porosity was reduced and the chemical composition of the coatings after remelting became homogeneous. © Published under licence by IOP Publishing Ltd.

关键词： Remelting

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Developing Thailand Wildfire Prevention Decision Support System 43

Developing Thailand Wildfire Prevention Decision Support Sys...

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43rd Asian Conference on Remote Sensing, ACRS 2022

作者： Buntub, Jirapast Punyabukkana, Proadpran Juntakut, Pongpun Pratanwanich, Ploy N. Defense Engineering and Technology Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Civil Engineering Academic Division Chulachomklao Royal Military Academy Nakhon Nayok26000 Thailand Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok10330 Thailand

This study presents the development of Thailand Wildfire Prevention Decision Support System (ThaiWDSS) with AI-based community detection. The system assists authorities in wildfire incident management and control with focus on the residential areas. The features include the ability to predict wildfire spread, detect community areas in danger, assess damages, and simulate firebreaks. We utilized Rothermel fire spread equation and the opensource Geographic Resources Analysis Support System (GRASS GIS) while retrieving the most recent hotspot and Digital Elevation Model (DEM) from NASA’s Fire Information for Resource Management System (FIRMS). We fetch wind data from external data providers, extract land cover data relevant to Thailand from Copernicus Global Land Cover Layers (CGLS) to calculate fuel models specific to Thailand based on 13 standard fuel behavior models from Landscape Fire and Resource Management Planning Tools (LANDFIRE). The users can observe the areas where fire may damage in various time frames. It also detects any community buildings in danger by a deep learning model to detect structures in the satellite images. We are able to identify temples and schools from *** because temples and schools are usually set up as shelters when it is necessary to evacuate people from the affected areas. One of the most important features of the ThaiWDSS is the capability to allow the authorities to simulate firebreaks and visualize the risks of fire spread prediction in real time. This assessment is an essence of the planning process before they make the final decision to manage such incidents. We allowed the users to experiment our proposed system using two case studies from actual fire incidents in Thailand. Preliminary comments from experts and authorities from the Royal Thai Army Air Defense Command suggest that ThaiWDSS will be a great asset during the operation as well as in the training process. © 43rd Asian Conference on Remote Sensing, ACRS 2

关键词： Forecasting

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Enhancing Literary Analysis through Artificial Intelligence and Machine Learning: Insights from 'The Alchemist' 2

Enhancing Literary Analysis through Artificial Intelligence ...

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2nd IEEE International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2024

作者： Vinodini, S. Adithya Pothan Raj, V. Sathyabama Institute of Science and Technology Faculty of Arts Dept. of English Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Dept. of CSE Chennai India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354461

The rapid growth of digital literature has necessitated the advancement of analytical tools to extract valuable insights from extensive textual data. This paper investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in literary analysis, presenting innovative methods to understand and interpret complex narratives and themes in literary works. Traditional literary analysis relies on human interpretation, which, while insightful, is often subjective and labor-intensive. The advent of digital humanities has sparked interest in computational methods to enhance traditional literary analysis. AI and ML, especially through Natural Language Processing (NLP), offer powerful solutions to analyze literary themes. Techniques such as sentiment analysis, topic modeling, and named entity recognition can dissect thematic elements, emotional tones, and character dynamics in texts. For example, sentiment analysis reveals underlying themes related to mood and character psychology, while topic modeling uncovers hidden thematic structures. Machine learning algorithms, through supervised and unsupervised learning, can recognize and classify themes across different literary works. Despite challenges like the complexity of human language and contextual nuances, AI and ML significantly enhance the speed and scale of literary analysis, uncovering subtle themes often overlooked by human readers. The Flair tool, developed by Zalando Research, exemplifies a robust NLP library that enhances literary analysis through pre-trained models for tasks such as Named Entity Recognition (NER), sentiment analysis, text classification, and Part-of-Speech (POS) tagging. Applying Flair to Paulo Coelho's "The Alchemist"demonstrates its potential to analyze genres, themes, and stylistic categories, showcasing the transformative capabilities of AI and ML in the digital humanities. © 2024 IEEE.

关键词： Natural language processing systems

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Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 IEEE.

关键词： Distributed ledger

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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AKQ: A Hybrid Quantum-Classical Image Encryption System

AKQ: A Hybrid Quantum-Classical Image Encryption System

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2023 RIVF International Conference on Computing and Communication Technologies, RIVF 2023

作者： Nguyen, Tran Khanh Thi Thanh Truc, Nguyen Hai, Vu Tuan University of Information Technology VNU-HCM Ho Faculty of Computer Science Chi Minh city Viet Nam University of Information Technology VNU-HCM Ho Faculty of Software Engineering Chi Minh city Viet Nam

ISBN: (纸本)9798350315844

Image encryption is an essential field between computer vision and security which aims to protect the image before future attacks. Nowadays, it is threatened by quantum computers, which can break some classical cryptography. The encryption system has been upgraded from quantum key exchange protocol to post-quantum key-establishment mechanisms, but they are not explicitly designed for the image encryption task. In this paper, we propose a hybrid quantum-classical theoretical framework and its instance AKQ system which is more secure and suitable in the post-quantum era. © 2023 IEEE.

关键词： Quantum computers

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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