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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Haruna, Ahmed Abba Badi, Ibrahim Ahmed Muhammad, L.J. Abuobieda, Albaraa Altamimi, Abdulaziz University of Hafr Al Batin College of Computer Science and Engineering Department of Computer Science Hafr Al Batin Saudi Arabia Libyan Academy Mechanical Engineering Department Misurata Libya Federal University of Kashere Computer Science Department Gombe Nigeria

ISBN: (纸本)9781665472753

Deep learning techniques haven been the efficient artificial intelligence techniques that is being used for developing models for autonomous feature extraction, classification and segmentation of the apple leaves diseases sand other plant diseases. However, most deep learning systems, such as Convolutional neural networks (CNN) and Long Short-term Memory (LSTM), necessitate a huge quantity of training data and are plagued by issues such as inflating gradients, overfitting, and class imbalance, among others. In this work, CNN-LSTM deep learning algorithm was proposed to address the challenges facing CNN and LSTM respectively. This work used the CNN-LSTM deep learning algorithm to develop a learning model for classification of foliar disease of apple leaves. In order to create learning classification models based on accuracy, specificity, sensitivity, and AUC standard performance evaluation techniques, both CNN and LSTM algorithms were employed. CNN-LSTM happened to be the best model in terms of accuracy, specificity, sensitivity, and AUC standard performance evaluation methodologies, with 98.00%,95.00%, 96.00parcent, and 94.00% respectively. Hence, the model has shown a significant improvement as compared to other models for being able to correctly classified apple leaves into affected leaves with disease/s and non-affected leaves with disease/s with 98.00% accuracy. Likewise, for the ability to classify the apple leaves affected with disease/s, correctly CNN-LSTM also outperformed other models with 95.00parcent. Moreover, CNN-LSTM based model also has shown a significant improvement as compared other models, for being able to correctly classified apple leaves that were not affected with disease/s with 96.00%. © 2023 IEEE.

关键词： Long short-term memory

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Slag and Porosity Defective Region Identification in Welding Images Using computer Vision Techniques

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Defect and Diffusion Forum 2023年 428卷 143-148页

作者： Rajalakshmi, Ratnavel Sivasankaran, S. Shinow, Abhinav Basil Abhimannan, Giridharan Boopathy, C. School of Computer Science & Engineering Vellore Institute of Technology Chennai India HCL Technologies Ltd Chennai India School of Mechanical Engineering Vellore Institute of Technology Chennai India ACB Technologies Chennai India

The process of welding is prone to many defects and these defects can cause the formation of many defective regions. It is necessary to identify the regions of defects as these may cause problems and breakages. In this work, we have proposed a method to detect and identify the defects that are commonly seen in seam welds. Manually identifying the detects is not only error prone and time consuming, most of the defects are not visible to the human eyes. In recent days, X-ray images of weld seam are used for this purpose. In this paper we have applied computer vision techniques and proposed an image processing pipeline to generate a binary segmentation of the image to identify the regions of slag and porosity defect seen in weld seams. From the experimental results on the publicly available dataset, GDX-ray images, it could be observed that, there is a significant improvement in detecting various defects with the proposed approach. © 2023 Trans Tech Publications Ltd, Switzerland.

关键词： Defects

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BPREC: A Bayesian Personalized Recommendation Based on User Preferences for Mining 21

BPREC: A Bayesian Personalized Recommendation Based on User ...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Qiu, Laixiang Feng, Jianping Zhou, Wang College of Computer Science Chongqing College of Mobile Communication Chongqing401420 China Mechanical and Electrica Engineering College Hainan Vocational University of Science and Technology Hainan571126 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798331519254

Faced with overwhelming product information, users often struggle to make choices, impacting their shopping experience and time. To address this, recommendation systems provide precise suggestions that streamline decision-making. The BPRec model, based on user preferences, enhances recommendation relevance and quality. Experiments on realworld datasets show that BPRec significantly improves recommendation effectiveness, thus enhancing the shopping experience. © 2024 IEEE.

关键词： Matrix factorization

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Recent Advances in Single-Atom Catalysts for Photoelectrocatalytic Water Splitting

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Carbon Energy 2025年第4期7卷 114-142页

作者： Jiao Yang Xiaoyang Zheng Syed Shoaib Ahmad Shah Chao Wang Xueyao Li Zhishuo Yan Lishan Peng Key Laboratory of Rare Earths Ganjiang Innovation Academy Chinese Academy of Sciences Institute of Applied Physics and Materials Engineering University of Macau Department of Mechanical Engineering Graduate School of Engineering The University of Tokyo Department of Chemistry School of Natural Sciences National University of Sciences and Technology Department of Chemistry University of Connecticut School of Rare Earths University of Science and Technology of China Department of Electrical and Computer Engineering North Dakota State University

Hydrogen is a highly promising energy carrier because of its renewable and clean qualities. Among the different methods for H2production, photoelectrocatalysis（PEC） water splitting has garnered significant interest, thanks to the abundant and perennial solar energy. Single-atom catalysts（SACs）, which feature well-distributed atoms anchored on supports, have gained great attention in PEC water splitting for their unique advantages in overcoming the limitations of conventional PEC ***, we comprehensively review SAC-incorporated photoelectrocatalysts for efficient PEC water splitting. We begin by highlighting the benefits of SACs in improving charge transfer, catalytic selectivity, and catalytic activity, which address the limitations of conventional PEC reactions. Next, we provide a comprehensive overview of established synthetic techniques for optimizing the properties of SACs, along with modern characterization methods to confirm their unique structures. Finally, we discuss the challenges and future directions in basic research and advancements, providing insights and guidance for this developing field.

关键词： hydrogen production photoelectrocatalysis single-atom catalysts water splitting

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Neural Network-based Synthesis for Digital Sound Processing

Neural Network-based Synthesis for Digital Sound Processing

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Yadav, Sourav Dixit, Shivagya Chakka, Siddhi Rohan Illinois Institute of Technology Computer Science ChicagoIL United States Srm Institute of Science and Technology Computer Science Ghaziabad India Osmania University Mechanical Engineering Hyderabad India

ISBN: (纸本)9798350306293

The allure of synthesizers built upon analog circuits lies in their individuality and idiosyncratic traits, which have captivated enthusiasts since the 1960s. In contrast, the digital age ushered in sound modules that generate a clean and almost predictable sound, setting them apart from their analog counterparts. Venturing into the expansive realm of neural networks, this research delves into the field of machine learning and the significant attention it has garnered in recent years. The widespread application of these technologies now permeates our daily lives. Exploring the intersection of creativity and machine learning, researchers have investigated how these same techniques can be harnessed for expressive purposes. In the domain of digital sound processing, this paper serves as a proof of concept, demonstrating how machine learning methods can synthesize sound and infuse it with an analog essence. © 2023 IEEE.

关键词： Long short-term memory

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Optimization of a Sustainable Wheel for Enhanced Vehicle Maneuverability and Power Generation 11th

Optimization of a Sustainable Wheel for Enhanced Vehicle Man...

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11th International Conference on Creative Technology, ICCT 2023

作者： Rayed, Ashraf Mahmud Haque, Ariful Rony, Mrinmoy Roy Esakki, Balasubramanian Rahman, Mizanur Shoma, Shamsun Nahar Department of Mechanical Engineering Chittagong University of Engineering and Technology Raozan Chattogram4349 Bangladesh Chennai India Department of Computer Science and Engineering BGC Trust University Bangladesh Chandanaish Bangladesh

ISBN: (纸本)9783031591631

The research optimizes vehicle wheel rims to create sustainable power and reduce weight. The study employs aluminum alloy AZ80 to lighten wheel rims while retaining safety. The project aims to improve vehicle agility and power generation while reducing pollution and energy use. The research method focuses on improving the efficiency of a vehicle's tire rim to generate renewable energy. The study employs aluminum alloy AZ80 to reduce wheel rim weight. Topological optimization and structural research utilizing the finite element approach show a 15% mass decrease while retaining a safety factor 3. The study’s findings indicate that reducing the weight of the wheel rim using the aluminum alloy AZ80 can result in energy savings and pollution reduction. The research provides insights into boosting wheel stiffness, rigidity, mechanical strength, and fatigue life and suggests a viable route for improving sustainable transportation. This research proves that power may be generated most efficiently from a vehicle's wheel rim. More study is required to realize this method's potential and create superior variants entirely. The research has massive consequences for optimizing automobiles, particularly in Bangladesh, and provides insights for improving wheel stiffness, rigidity, mechanical strength, and fatigue life. The study focuses on generating sustainable electricity utilizing an inverter mechanism and a belt-pulley system within the wheel hub, which provides a viable route for the growth of sustainable transportation. Using the aluminum alloy AZ80 to reduce the weight of the wheel rim can also result in energy savings and pollution reduction. The research's topological optimization methods and usage of AZ80 aluminum alloy to reduce wheel rim weight are unique. Structural analysis utilizing the finite element approach and mass optimization shows a 15% mass reduction while keeping a safety factor of 3. The research can improve wheel stiffness, rigidity, mechanical strength, a

关键词： Finite element method

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A Systematic Investigation of Cyber Harassment Intention Behaviors and Its Impacts on Social Media Platforms

A Systematic Investigation of Cyber Harassment Intention Beh...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Abarna, S. Sheeba, J.I. Devaneyan, S. Pradeep Puducherry Technological University Department of Computer Science and Engineering India Sri Venkateshwaraa College of Engineering and Technology Department of Mechanical Engineering Puducherry India

ISBN: (纸本)9798350348217

Several technologies have been able to fundamentally affect our lives in just a few decades by integrating themselves into our society. News and opinion are now mostly shared on social media. There are both positive and negative aspects to this. Nowadays, practically everyone has a smartphone or other internet-accessing gadget. Modern technology becomes available to kids at progressively younger ages. The majority of them use social media, which contributes to the rise of cyber harassment and the negative aspects of the internet. Due to the ease of access to these platforms, cyberbullying is prevalent on social media. Moreover, when dealing with others, the perpetrators have the ability to remain anonymous. Cyberbullying statistics for 2022 highlight the alarming incidence of harassment in the virtual world, and they are disturbing. According to these data, Cyberbullying sufferers are more than twice as likely to self-harm or entertain suicidal thoughts as a coping mechanism. Anxiety and sadness might develop in certain victims of cyberbullying. Psychologists believe that social media has contributed to an increase in stress and depression over the last decade. On the other side, the most typical targets for online bullies are young children, women, and individuals with non-traditional sexual orientations. Students are often unable to concentrate on their studies because they are afraid of the trolls. As a result, this Research Essential focuses on Cyber harassment statistics and facts in order to raise awareness among parents, students, and authorities worldwide and reveal alarming facts about virtual harassment, the intention of the harasser, its impact, the relationship between victim and harasser, and its many different shapes and forms. © 2023 IEEE.

关键词： Social networking (online)

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Scenarios of AI in Smart Grid Systems

Scenarios of AI in Smart Grid Systems

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

作者： Kumar, Sanjay Upadhyay, Rimpi Haripriya Department of Mechanical Engineering Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India

ISBN: (纸本)9798350355338

The power system exhibits traits of openness, ambiguity, and complexity due to the implementation of electricity market reform and the utilization of renewable energy and power demand response in various circumstances. The development and implementation of intelligent power grid systems have emerged as a prevailing phenomenon. The utilization of artificial intelligence (AI) within the context of smart grid infrastructure offers robust technological assistance for the development of digital power networks. This research presents a novel approach that uses Long Short-Term Memory (LSTM) to forecast an institutional building's energy use. The suggested approach makes use of weather forecasts to estimate daily energy consumption ahead of time. The Meteorological Department provides weather forecasts, which allow the model to represent the complex link between energy usage and meteorological conditions. The performance of the LSTM model is compared with Support Vector Regression (SVR) and Gaussian Process Regression (GPR) in order to evaluate its efficacy. © 2024 IEEE.

关键词： AI infrastructure Artificial Intelligence energy generation Smart grid systems technology

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Role of Machine Learning in Sustainable Manufacturing Practices: An Outline

Role of Machine Learning in Sustainable Manufacturing Practi...

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International Conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2023

作者： Kaur, Rupinder Kumar, Raman Aggarwal, Himanshu Department of Computer Science and Engineering Punjabi University Punjab Patiala147002 India Department of Mechanical and Production Engineering Guru Nanak Dev Engineering College Punjab Ludhiana141006 India

ISBN: (纸本)9789819967735

The present research examines the potential of machine learning (ML) to improve sustainability and lessen harmful environmental effects across various industries. The paper discusses ML’s uses in sustainable manufacturing practices (SMP), such as supply chain management, waste management, energy efficiency, equipment maintenance, water quality management, and other fields. The investigations show how ML approaches may be used to forecast equipment failure, lower carbon footprints, optimize resource use, and improve water quality. The study highlights the benefits of utilizing ML to increase sustainability, including real-time data, simplified processes, lower costs, and a sustainable future while admitting the difficulties and limits of ML techniques. The article ends by recommending enterprises and the industrial sector explore the prospect of using ML to attain their goals for sustainability. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy efficiency

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