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4th International Conference on Advancements in Computing, ICAC 2022

作者： Jayawardena, Aparna Ganegoda, Kasuni Imbulana, Sakuni Gunapala, Gavin Kodagoda, Nuwan Jayasinghe, Thilini Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

This research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. The application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries;leaf disease identification;cover crop identification;and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology. © 2022 IEEE.

关键词： Rubber

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Numerical Modelling of The System of Doubly Nonlinear Diffusion Equations with Convective Transfer and Damping

Numerical Modelling of The System of Doubly Nonlinear Diffus...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Sadullaeva, Shakhlo Azimbaevna Anarova, Shahzoda Amanbayevna Khaydarov, Abdugaffor Fayzullayeva, Zarnigor Nazirova, Dilorom Mirkhodzhaeva, Nazhiba departament Intellegent systems Joint Belarusian-Uzbek Intersectoral Institute of Applied Technical Qualifications Tashkent Uzbekistan Department of Convergence of Digital Technologies Computer Engineering Faculty Tashkent University of Information Technologies Tashkent Uzbekistan Department of Applied Mathematics and Computer Analysis National University of Uzbekistan Tashkent Uzbekistan Department of Informatics Computer Engineering Faculty Tashkent University of Information Technologies Tashkent Uzbekistan Department of Applied Mathematics and Computer Analysis NUUz Tashkent Uzbekistan

ISBN: (纸本)9798400716928

Currently, studies of double nonlinear equations and systems of double nonlinear equations representing many processes found in nature are considered relevant and necessary. These equations and systems, as a rule, are nonlinear, degenerate, and have properties that are absent in linear theory and therefore make them quite difficult for both analytical and numerical analysis of the solution. For this reason, great attention is paid to the research of nonlinear mathematical models, the construction of efficient numerical solution schemes and algorithms, and the creation of software using the capabilities of computer technologies. In this article, the mathematical model of the diffusion process in a two-component nonlinear medium under the influence of time-dependent convective transfer and damping is studied. The calculation scheme, algorithm, and software for nonlinear problems were developed. Based on a study of the qualitative properties of a nonlinear mathematical model of a reaction-diffusion system, computer modeling of reaction-diffusion processes was carried out. The developed computational schemes, algorithms, and software packages for solving systems of parabolic equations with double nonlinearity provide high productivity in studying the theory and processes of numerical solutions of such problems. © 2024 ACM.

关键词： Nonlinear equations

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Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

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computers, Materials & Continua 2023年第1期74卷 141-152页

作者： Nidhi Agarwal M.Gokilavani S.Nagarajan S.Saranya Hadeel Alsolai Sami Dhahbi Amira Sayed Abdelaziz Department of Information Technology KIET Group of InstitutionsDelhi201206India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for WomenNew DelhiDelhi110006India Department of Computer Science and Engineering KL UniversityVaddeswaramAndhra Pradesh522502India Department of Electronics&Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial *** nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor *** goal of any NL scheme is to improve the localization accuracy and reduce the localization error *** this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for *** presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the *** addition,the IAOAB-NLS model is stimulated by the behaviour of *** IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the *** guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS *** resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

关键词： Aquila optimizer node localization WSN intelligent models unknown nodes anchor nodes

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques 21

Early Fault Detection in Rolling Element Bearings Using Sign...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Saeed, Ali Akram, M. Usman Khan, Muazzam A. Afsar, M. Belal Quaid-i-Azam University ICESCO Big Data Analytics and Edge Computing Department of Computer Science Islamabad Pakistan National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Otto-von-Guericke University Faculty of Electrical Engineering and Information Technology Maddeburg Germany

ISBN: (纸本)9798350378078

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-To-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method. © 2024 IEEE.

关键词： Signal modulation

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Smart Waste Segregation for Home Environment 3

Smart Waste Segregation for Home Environment

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3rd International Conference on Advanced Research in Computing, ICARC 2023

作者： Rijah, Uthuma Lebbe Muhammed Abeygunawardhana, Pradeep K. W. Sri Lanka Institute of Information Technology Faculty of Graduate Studies and Research Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350347371

The segregation of waste and recycling is essential for effective waste management. Due to the busy schedule most of the people do not have a time to separate their waste. However, there is a significant issue with the segregation of the collected garbage. The implementation of an intelligent trash-management architecture is essential for the removal or reduction of waste and the maintenance of a clean, corporate environment. An IoT-stationed smart waste management device is proposed in this study that uses sensor devices to identify rubbish in the dustbins. With the aid of sensors, the waste substances in it will be separated through IoT as soon as it is discovered. Sensors and the IoT module are connected through a microcontroller. To detect the presence of garbage, an ultrasonic sensor is used. All garbage entered will be caught by the web camera and processed by the machine learning module once it has been processed. Using the model, we can identify the many forms of waste, such as paper, plastic, and glass, which account for most of the garbage materials found in a home area. This aids in removing the trash from the trash can in the most efficient and effective manner possible. This research presents an IoT, and Machine Learning based completely intelligent trash segregation and management System that recognizes the dustbins' wastes using sensor systems. This project aims to develop an automated waste segregation system using a CNN algorithm that will capture waste images from a camera with object detection and classify waste materials such as paper, plastic, and glass so that the waste can be recycled appropriately. The proposed architecture with CNN gives an accuracy of 84.67%. This system will help in garbage disposal by categorizing it, contributing to a cleaner environment. © 2023 IEEE.

关键词： Cameras

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Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification

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Iran Journal of computer Science 2024年第1期8卷 185-215页

作者： Kia, Marjan Sadeghi, Soroush Safarpour, Homayoun Kamsari, Mohammadreza Jafarzadeh Ghoushchi, Saeid Ranjbarzadeh, Ramin Department of Business Information System Business Analysis (ISBA) Park University Parkville United States School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Software Engineering University of Szeged Szeged Hungary Faculty of Electrical Engineering Malek-Ashtar University of Technology(MUT) Esfahan Iran Faculty of Industrial Engineering Urmia University of Technology Urmia Iran School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland

This study presents a novel approach for brain MRI classification by integrating multiple state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet, AlexNet, and ResNet50, with an attention mechanism. The primary objective was to accurately detect the presence or absence of brain tumors—a critical challenge in medical diagnosis. The methodology was validated on a comprehensive dataset comprising 3264 brain MRI scans, systematically divided into training, validation, and testing subsets. Preprocessing techniques tailored for MRI data were employed to enhance input quality. Each model was initially developed and evaluated individually to establish a performance baseline before their integration. The proposed ensemble technique capitalized on the complementary strengths of these models to enhance classification accuracy and robustness. The inclusion of an attention mechanism further refined the models by emphasizing critical regions in the MRI scans, thereby boosting predictive performance. In addition, data augmentation techniques were applied to mitigate overfitting and increase dataset resilience. Performance metrics, including precision, accuracy, and recall, demonstrated significant improvements across all evaluations, particularly for the models incorporating the attention mechanism. These findings highlight the potential of combining advanced DL architectures with attention mechanisms as a promising strategy for precise and reliable brain tumor classification in MRI scans. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AlexNet Attention mechanism Brain tumor classification MobileNet ResNet50

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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ChatGPT: Applications, Opportunities, and Threats

ChatGPT: Applications, Opportunities, and Threats

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2023 systems and information engineering Design Symposium, SIEDS 2023

作者： Bahrini, Aram Khamoshifar, Mohammadsadra Abbasimehr, Hossein Riggs, Robert J. Esmaeili, Maryam Majdabadkohne, Rastin Mastali Pasehvar, Morteza University of Virginia Department of Systems and Information Engineering CharlottesvilleVA United States Iran University of Science and Technology School of Computer Engineering Tehran Iran Azarbaijan Shahid Madani University Faculty of Information Technology and Computer Engineering Tabriz Iran Alzahra University Faculty of Engineering Department of Industrial Engineering Tehran Iran The American University Faculty of Medical Sciences Department of Medicine Managua Nicaragua Islamic Azad University Faculty of Engineering Department of Industrial Engineering Tehran Iran

ISBN: (纸本)9798350300642

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations. © 2023 IEEE.

关键词： Deep learning

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Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakarta Passenger Prediction 3

Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakar...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Siswanto, Joko Hendry Rahardja, Untung Sembiring, Irwan Lisangan, Erick Alfons Hakim, Muhammad Iman Nur Wibowo, Feri Polytechnic of Road Transportation Safety Road Transportation System Engineering Tegal Indonesia Satya Wacana Christian University Faculty of Information Technology Indonesia University of Raharja Faculty of Science and Technology Tangerang Indonesia Satya Wacana Christian University Faculty of Computer Science Indonesia The University of Western Australia Computer Science and Software Engineering Perth Australia Polytechnic of Road Transportation Safety Automotive Engineering Technology Tegal Indonesia Muhammadiyah University Purwokerto Faculty of Engineering and Science Banyumas Indonesia

ISBN: (纸本)9798350367492

A crucial role in the BRT transportation system's planning, development, and operation is the prediction of passenger numbers. Using time-series data, it is necessary to develop careful prediction models, appropriate techniques, and an indepth understanding of the number of BRT Transjakarta passengers. A prediction model is sought based on comparing combined LSTM and BiLSTM experiments using three evaluation matrices and time. Historical data used from daily passenger data for 13 BRT Transjakarta corridors (1/01/2021-31/12/2023). The best prediction model was obtained from the BiLSTM-CNN combination with the lowest MSLE (0.0425), MAPE (0.1598), and SMAPE (15.9387) evaluation matrix values. However, it required a longer time (00:02:14). Predictions of passenger numbers on the 13 Transjakarta BRT corridors for the next 12 months can be made simultaneously by predicting fluctuations occurring simultaneously. The strongest positive correlation is in corridor 9-6, while the strongest negative correlation is in corridor 12-5. The prediction results must be understood by stakeholders, managers, and technopreneurs to develop and support appropriate strategies to increase the number of BRT passengers. © 2024 IEEE.

关键词： Prediction models

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