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4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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The Impact of data Splitting Techniques on the Accuracy of Rumour Detection Models in Microblogging Platforms: A Comparative Analysis

The Impact of Data Splitting Techniques on the Accuracy of R...

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International Seminar on Research of information technology and Intelligent Systems (ISRITI)

作者： Rini Anggrainingsih Dewi Wisnu Wardani Hasan Dwi Cahyono Brilyan Hendrasuryawan Bambang Widoyono Haryono Setiadi Faculty of Information Technology and Data Science Sebelas Maret University Surakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The swift propagation of online misinformation present a significant challenge, primarily due to the lack of supervision to control it. Automated false information detection methods are crucial to mitigate the adverse effect of false information. Conventional techniques have depended heavily on manual text feature extraction that is both time-consuming and less effective. To overcome this issue, rather than relying on traditional feature extraction methods, we propose a new technique by leveraging sentence embeddings produced by different transformer models. Transformer models excel in capturing the contextual meaning of words, allowing us to represent each post as a vector based on its context. These vectors are then classified as rumors or non-rumors using different supervised learning techniques. Our work offers valuable contributions in two key domains: rumor detection on ‘X’ platform and the evaluation of text embeddings generated by various transformer models, with a focus on how different data splitting techniques influence model performance. By testing different splitting strategies, this study explores how these methods affect the models' ability to generalize and detect rumors accurately. The findings will shed light on the connection between data splitting techniques and model effectiveness, providing guidance for developing strong rumor detection systems in social media environments.

关键词： Training Accuracy Computational modeling Training data Transformers Feature extraction data models Vectors Fake news Overfitting

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An Integrated Edge Computing Framework for Enhancing Cyber-Physical System Security through Network Traffic Anomaly Detection in Industrial IoT

An Integrated Edge Computing Framework for Enhancing Cyber-P...

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2024 Global Congress on Emerging Technologies, GCET 2024

作者： Zhukabayeva, Tamara Ahmad, Zulfiqar Karabayev, Nurdaulet Abdildayeva, Assel L.N. Gumilyov Eurasian National University Faculty of Information Technology Astana Kazakhstan Hazara University Department of Computer Science and Information Technology Mansehra21300 Pakistan Al-Farabi Kazakh National University Department of Artificial Intelligence and Big Data Almaty Kazakhstan

ISBN: (纸本)9798331542603

With the expansion of the Industrial Internet of Things (IIoT), the protection of Cyber-Physical System (CPS) is worth consideration. This research presents an integrated edge computing framework that aims at enhancing security by improving network traffic anomaly detection. It is done based on three advanced anomaly detection algorithms, namely Principal Component Analysis (PCA), isolation forest, and autoencoder. PCA is applied to the data to bring it in a compressed form where one is able to detect the outliers by having the most important features of the data extracted. The isolation forest algorithm, which was applied with the contamination factor of 0, is depicted below: 1, is used for the detection of the anomalies in the traffic, and, in fact, it was able to categorize several threats. However, the autoencoder model relies on reconstruction loss to decide if something is wrong with the data, with the 95th percentile as the standard for identifying activities that are odd. Edge computing increases the effectiveness of a framework for real-time anomaly detection and mitigation. The objective of this research is to evaluate PCA, isolation forest, and autoencoder methods and the outcome on the feasibility of the methods to enhance the security of IIoT systems. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Measures of Organizational Process Performance in the Capability Maturity Model Integration 24

Measures of Organizational Process Performance in the Capabi...

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24th International Arab Conference on information technology, ACIT 2023

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kittaneh, Ahmed Ali, Ahmed Alsarhan, Ayoub Alkhalidy, Muhsen The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Software Engineering Jordan Isra University Faculty of Information Technology Dept. of Software Engineering Jordan Isra University Faculty of Information Technology Dept. of Artificial Intelligence and Data Science Jordan The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Cyber Security Jordan The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Information Technology Jordan

ISBN: (纸本)9798350384307

The Capability Maturity Model Integration (CMMI) is widely utilized by organizations to facilitate process improvement. Among its 22 process areas, this paper focuses on the Organizational Process Performance (OPP) area, which assesses the effectiveness of a process. Specifically, this study examines one goal (establishing performance baselines and models) and two practices (establishing quality and process performance objectives and selecting processes within OPP. To measure these practices, the Goal Questions Metrics (GQM) approach is employed. data was collected using a questionnaire, and the defined measures are validated quantitatively. 250 responses were used for analysis. The results obtained indicate that implementing the defined measures in companies would be beneficial for achieving high-quality processes in OPP and advancing the maturity level of CMMI. © 2023 IEEE.

关键词： Goal Questions Metrics (GQM) Software Quality, Organizational Process Performance, Capability Maturity Model Integration, CMMI

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Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4140-4155页

作者： Nhan Vo, Van Quoc Long, Nguyen Dang, Viet-Hung Dac Ho, Tu Tran, Hung Chatzinotas, Symeon Tran, Dinh-Hieu Sanguanpong, Surasak So-In, Chakchai Duy Tan University Faculty of Information Technology Da Nang550000 Viet Nam Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Department of Information Security and Communication Technology Trondheim7034 Norway National Economics University DATCOM Lab Faculty of Data Science and Artificial Intelligence College of Technology Hanoi12116 Viet Nam University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Esch-sur-Alzette4365 Luxembourg Kasetsart University Faculty of Engineering Department of Computer Engineering Bangkok10900 Thailand Khon Kaen University Applied Network Technology College of Computing Khon Kaen40002 Thailand

This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. In the second phase, a secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions. © 2020 IEEE.

关键词： Drones

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Optimal Structure Exploration of Bayesian Network Based on Dealing with the Route Choice Model 6

Optimal Structure Exploration of Bayesian Network Based on D...

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6th International Conference on Computer information science and Application technology, CISAT 2023

作者： Wang, Ziyi Zeng, Yufei Wang, Rui College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Computer Science and Technology Harbin Institute of Technology Yichun China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781510668546

Route selection in residents' travel is a key link in travel decision-making in transportation systems. In existing studies, discrete choice models are often used to describe residents' travel path choices. However， with today's highly developed big data, the traditional discrete choice model is too simple to represent residents' travel path choices, ignoring the connection between various factors. Therefore, a big data-driven approach is proposed to solve this problem in this paper. Driven by research data and with the introduction of the Bayesian network model, the paper adopts a hill-climbing search strategy to improve the search efficiency, uses a variety of scoring functions to train data, which optimizes the Bayesian network structure. The experimental results show that the model fully considers the causation between the independent variables, which is more logical and makes the construction of the resident travel route selection model more accurate and efficient. © 2023 SPIE.

关键词： Bayesian networks

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Image Fusion Using Wavelet Transformation and XGboost Algorithm

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Computers, Materials & Continua 2024年第4期79卷 801-817页

作者： Shahid Naseem Tariq Mahmood Amjad Rehman Khan Umer Farooq Samra Nawazish Faten S.Alamri Tanzila Saba Department of Information Sciences Division of S&TUniversity of EducationLahore54770Pakistan Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Recently,there have been several uses for digital image *** fusion has become a prominent application in the domain of imaging *** create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same *** fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual *** fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on *** research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet *** fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient *** precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain *** tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is *** use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter *** simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original *** study enhances the performance of the eXtreme Gradient Boosting(XGBoos

关键词： Image fusion max-min average CWT XGBoost DCT inclusive innovations spatial and frequency domain

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Development of a Mental Health Chatbot Using Large Language Models for Indonesian Undergraduates 11

Development of a Mental Health Chatbot Using Large Language ...

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11th International Conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Gumilang, Mukhamad Angga Karsanti, Hayuning Titi Linsiya, Ria Wiyatfi Yudanarko, Kimi Dandy Sari, Lisa Novita Saputra, Ariz Salsabila, Naela Zahwa Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia Research Center for Data Science and Information Science National Research and Innovation Agency Jakarta Indonesia Faculty of Psychology Universitas Muhammadiyah Jember Jember Indonesia Department of Information Technology Politeknik Negeri Jember Jember Indonesia Departement of Health Politeknik Negeri Jember Jember Indonesia

ISBN: (纸本)9798331542313

Mental health issues among university students are an escalating global problem, especially in Indonesia. Young adults, especially college students, are a vulnerable demographic due to academic pressures, social transitions, and the challenges of independent living. This paper describes the development of a mental health chatbot specifically for Indonesian university students, utilizing large language models (LLMs). The development encompassed three essential phases: establishing a knowledge base, refining the LLM with culturally pertinent Indonesian data, and assessing the system. The findings demonstrated notable enhancements in the chatbot's precision, with training accuracy rising from 0.6 to 0.85 and validation accuracy from 0.5 to 0.79 due to fine-tuning and regularization. User acceptability testing including 58 students resulted in an acceptance percentage of 87.38%, indicating substantial satisfaction with the chatbot's response and usefulness. This research highlights the efficacy of AI-driven interventions in addressing the mental health requirements of Indonesian university students. © 2024 IEEE.

关键词： Students

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Opticurve: an optimized informer-curvelet framework for enhanced hyperspectral image segmentation and classification

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International Journal of information technology (Singapore) 2025年 1-16页

作者： Shaw, Kailash Wou Onn, Choo Li, Baihua Department of Computer Science and Engineering (AIML) Vishwakarma Institute of Technology Maharashtra Pune 411037 India Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia Department of Computer Science Loughborough University Loughborough LE11 3TU United Kingdom

Hyperspectral image (HSI) classification is crucial for applications in climate action, land use analysis, disaster risk reduction, and informed decision-making, given the complex spatial and spectral variations inherent in HSI data. Traditional methods struggle with accurately capturing these variations, necessitating more advanced techniques. This work introduces an Optimized Linformer-Curvelet (OptiCurve) Framework that integrates CNN-based feature extraction, Curvelet Transform for spatial detail capture, and Linformer for efficient feature representation. By combining these techniques, the model enhances HSI segmentation and classification, supporting improved outcomes in critical areas like environmental monitoring and disaster response. The framework is validated on four standard HSI datasets-Indian Pines, Pavia University, Kennedy Space Center, and Houston University-showing significant performance improvements over existing methods. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Climate action Disaster risk reduction Feature extraction Hyperspectral image classification Land use Linformer-Curvelet transform

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A Resource-Efficient Federated Learning Framework for Intrusion Detection in IoMT Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Fahim-Ul-Islam, Md. Chakrabarty, Amitabha Alam, Md. Golam Rabiul Maidin, Siti Sarah Brac University Department of Computer Science and Engineering Bangladesh INTI International University Faculty of Data Science and Information Technology Nilai Malaysia

The emergence of medical sensors in Smart Healthcare Systems (SHS) has enabled the development of sophisticated Internet of Medical Things (IoMT) networks which is crucial for tracking vital physiological parameters in consumer electronics. These networks are progressively incorporated into consumer devices allowing users to monitor their health parameters effortlessly. Nonetheless, they encounter considerable security and privacy issues stemming from weaknesses in data transfer. Despite the potential of Intrusion Detection Systems (IDS), there is a critical need for a real-time, highly precise attack detection system optimized for the edge-centric Internet of Medical Things (IoMT) environment in consumer sensor devices. Therefore, we introduce the federated kolmogorov-arnold network (FedIoMT), an advanced federated learning framework that utilizes meta-learning along with an advanced clustering method to achieve robust model aggregation. Our FedIoMT acts as both an adaptive personalized updating mechanism and a shared global classifier. In addition, we propose a novel intrusion detection model that uses the kolmogorov-arnold convolutional network (KANConvNet) as its local classifier, which improves scalability and interpretability in the FL framework updates. Our FedIoMT model demonstrates superior performance compared to other existing FL-based methods, achieving validation accuracies of 99.38%, 99.23%, 99.26%, and 99.2% across four benchmark IoMT datasets under the IID setting. FedIoMT utilizes significantly more floating point operations per second (FLOP) counts up to 2.8 times more than other models and also excels in memory efficiency, consuming up to 93% less peak memory across devices compared to the alternatives. We thoroughly evaluated FedIoMT on embedded consumer devices, demonstrating its resource-efficient characteristics. © 2025 IEEE.

关键词： Network intrusion

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