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24th International Arab Conference on Information Technology, ACIT 2023

作者： Aziz, Danial Izzat Anwar Bin Abdul Yusoff, Marina Ibrahim, Nurain Alazaidah, Raed College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Malaysia Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798350384307

Agriculture is critical to ensuring and securing food supply for human consumption. Paddy crops are an agricultural practice that produces one of the highest food yields in agriculture. Paddy diseases must now be detected and diagnosed as soon as possible rather than later. Farmers currently use the naked eye to observe suspected infected paddies. The suspected infected paddy will then be sampled and sent to the laboratory for further examination. These methods are highly inefficient, time-consuming, and error prone. Most paddy farmers lack the knowledge to make an informed and accurate diagnosis of which paddies are infected by which diseases. This paper explores using deep learning algorithms to detect and classify various paddy diseases automatically. This method will take an image of paddy leaves and classify it based on the disease category indicated by the idea. The PaddyDoctor dataset from the Kaggle website was used to train and test the deep-learning models. Convolutional Neural Network (CNN) and Transfer Learning models, including MobileNet, MobileNetv2, and InceptionV3, have been constructed. The results of an experiment involving different dataset split ratios, learning rates, and batch sizes. CNN with MobileNetV2 outperformed CNN with MobileNetV1 and CNN with InceptionV3 by 98.05 percent, 80.08 percent, and 83.98 percent, respectively. © 2023 IEEE.

关键词： Agriculture Convolutional Neural Network Deep Learning Paddy Disease

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Metaverse: A Bibliometric Analysis by Internet Crawler and Big Data Technology 6

Metaverse: A Bibliometric Analysis by Internet Crawler and B...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Lim, Tek Yong Luo, Zhiqiang Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia Foshan University School of Industrial Design and Ceramic Art Guangdong China

ISBN: (纸本)9798350323535

Digital platforms have been widely adopted during Covid19 pandemic, but individuals and organizations have encountered various challenges with these digital platforms. Metaverse is a new Internet evolution that addresses the challenges of supporting humans in seamlessly moving between digital spaces and the physical world. Many bibliometric studies have been published on Metaverse, but previous findings on this topic are inconsistent and contradictory. Therefore, we explored trends in Metaverse research through bibliometric analysis. An online search was conducted on 16 February 2023. A total of 706 journal documents were obtained from the Scopus database. The results revealed that China was the most productive country with three China productive institutions, two China productive authors, and four China funding sponsors. This bibliometric analysis results confirmed that the rate of Metaverse research has sharply increased. The top nine most cited documents were classified as secondary research. Taking a socio-technical design approach, the Metaverse research areas identified by these review articles can be grouped into four main levels of computing: mechanical, informational, psychological, and social. The limitations of this bibliometric study were also identified. © 2023 IEEE.

关键词： Big data

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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Paddy Plant Stress Identification Using Few-Shot Learning Framework 14

Paddy Plant Stress Identification Using Few-Shot Learning Fr...

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14th International Conference on Computer and Knowledge Engineering, ICCKE 2024

作者： Moung, Ervin Gubin Sufian, Maisarah Mohd Farzamnia, Ali Naiidu, Pavindrah Naidu Narayanasamy Liaw, Valentino Angeline, Lorita Faculty of Computing and Informatics University Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield West YorkshireHD1 3DH United Kingdom Faculty of Engineering Universiti Malaysisa Sabah Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798331511272

The efficient identification of stress in paddy plant leaves is paramount for optimizing agricultural resources and ensuring robust crop yields in smart agriculture practices. This research explores the potential of few-shot learning (FSL) to address the inherent challenges posed by limited training data in stress identification. Three distinct FSL approaches - Siamese network, Matching network, and Model-Agnostic Meta Learning (MAML) - are evaluated for their accuracy in stress detection. The study introduces the importance of stress detection in smart agriculture and the challenges of limited training data. It then covers the methodology in five stages: dataset description, data pre-processing, FSL implementation, accuracy evaluation, and reporting. Among the FSL models, the Matching Network stands out with an impressive accuracy of 86% for 6-way 1-shot learning. This surpasses the performance of a Convolutional Neural Network (CNN) tested with a larger shot size (270-shots), which achieved an accuracy of 81%. These comparative results underscore the potential of FSL techniques in achieving precise stress identification, even when working with limited data. The objective of this research is to contribute valuable insights towards enhancing the efficiency of stress prediction in paddy leaves, thereby fostering healthier and more productive paddy crop production in smart agriculture. The findings presented here aim to inform the development of effective stress detection systems and advance the field of precision agriculture. ©2024 IEEE.

关键词： Zero-shot learning

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The AdaBoost Approach Tuned by Firefly Metaheuristics for Fraud Detection

The AdaBoost Approach Tuned by Firefly Metaheuristics for Fr...

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2022 IEEE World Conference on Applied Intelligence and computing, AIC 2022

作者： Petrovic, Aleksandar Bacanin, Nebojsa Zivkovic, Miodrag Marjanovic, Marina Antonijevic, Milos Strumberger, Ivana Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Technical Faculty Belgrade Serbia

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

The use of powerful classifiers is broad and the problem of fraud detection tends to benefit from similar solutions as well. The problem in the digital age cannot be disregarded as the number of cases is worrisome. The use of machine learning has been beneficial to many real-world problems, as the classification ability of such solutions is high. Furthermore, these solutions are not without shortcomings, and possibilities of hybrid methods are explored for the reasons of further enhancements. Therefore, in the research proposed in this manuscript, the adaptive boosting algorithm is optimized by the firefly metaheuristics and validated against the imbalanced credit card fraud detection dataset. Moreover, the synthetic minority over-sampling technique is applied for addressing the class imbalance. According to experimental findings, the proposed method shows substantially better performance than other state-of-the-art machine learning models for tackling the same problem in terms of standard classification metrics. © 2022 IEEE.

关键词： Optimization

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Features Based Reconstruction Technique for Torn Documents

Features Based Reconstruction Technique for Torn Documents

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2023 International Conference on Green Energy, computing and Intelligent Technology, GEn-CITy 2023

作者： Saleh, Marwan D. Salih, N.D. Isa, Wan Noorshahida Mohd Electronics and Computer Science University of Southampton Malaysia Johor Malaysia Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia

ISBN: (纸本)9781839539596

The reconstruction of torn documents that hold sensitive information or crucial historical data is of great significance in many domains. An efficient technique for hand-torn document restoration is presented in this paper. Pre-processing, feature extraction, and matching three main phases of this technique. The reconstruction is carried out by matching pairwise the feature vectors of the fragments. Using a database consists of 120 torn pages taken from books, newspapers and magazines, the performance of this technique has been measured using simple but effective criteria called error rate. The algorithm was able to accurately reconstruct 91% of the documents, with the remaining being partially reconstructed. © The Institution of Engineering & Technology 2023.

关键词： Image reconstruction

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Enhancement of Prototype Driving Simulator Using Available AI-Based Game Technology 8th

Enhancement of Prototype Driving Simulator Using Available ...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Cheng, Yun-Quan Mansor, Sarina Chin, Ji-Jian Karim, Hezerul Abdul Kar-Weng, Ban Faculty of Engineering Multimedia University Selangor Cyberjaya Malaysia Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Faculty of Science and Engineering University of Plymouth Plymouth United Kingdom

ISBN: (纸本)9789819930906

Simulators—games that simulate the real world in a virtual environment, such as racing simulators, have been widely studied and documented. Their uses could, however, be further expanded into the field of driving education. The motivations behind this study are to dive into the trend of immersive learning and exploit artificial intelligence (AI)-based game technology to benefit driving education. The preliminary work shows that the use of a driving simulator as a teaching tool for driving education has improved the passing rate of driving training. This paper proposes several enhancements to the prototype driving simulator, by utilizing rudimentary AIs to simulate basic traffic for certain scenarios. The driving simulator is developed in Unity and is paired with a driving rig consisting of a steering wheel and pedals. The project currently is a functioning driving simulator that can accept both controller and steering wheel inputs. For prototype enhancement, three scenario-based tracks and a mock-up town with a traffic light system and AI traffic were added. The prototype now includes five circuit tracks and two on-the-road tracks based on the Standard Examination syllabus, three scenario-based tracks, and a mock-up town with AI traffic, together with an automated test mode for the syllabus tracks. The free practice mode will be available for all tracks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Virtual reality

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Performance of Arithmetic Optimization Algorithm for ELM Tuning Applied to IoT security 30

Performance of Arithmetic Optimization Algorithm for ELM Tun...

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30th Telecommunications Forum, TELFOR 2022

作者： Milutinovic, Nemanja Gajevic, Masa Krstovic, Jelena Petrovic, Aleksandar Bacanin, Nebojsa Antonijevic, Milos Singidunum University Technical Faculty Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

In this paper, recently proposed arithmetic optimization algorithm for adopted for extreme learning machine tuning with the goal of improving security internet of things systems. One of the greatest challenges with extreme learning machine is finding initial values of weights and biases and determining satisfying number of neurons in the hidden layer for every particular task. To tackle this challenge, which is NP-hard by nature, the arithmetic optimization algorithm was employed. The proposed method was evaluated against ToN IoT Windows 10 dataset for internet of things security for binary classification, and compared to other extreme learning structures evolved by other state-of-the-art metaheuristics under the same experimental conditions. According to experimental findings, arithmetic optimization algorithm is a promising method for tuning extreme learning machine for this particular challenge. © 2022 IEEE.

关键词： Optimization

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of Computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Review of AI Technologies for Enhancing the Lives of Visually Impaired Individuals: Applications, Outcomes, and Future Directions

Review of AI Technologies for Enhancing the Lives of Visuall...

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2024 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Deriba, Fitsum Gizachew Wubineh, Betelhem Zewdu Teka, Mengistu Zenebe University of Eastern Finland School of Computing Yliopistokatu 2 JoensuuFI-80100 Finland Wroclaw University of Science and Technology Faculty of Information and Communication Technology Wroclaw Poland Wachemo University School of Computing and Informatics Hosaena Ethiopia

ISBN: (纸本)9798331527792

Imagine an environment where individuals with disabilities enjoy a significantly improved quality of life, not only because of the support they receive but also due to advancements in artificial intelligence (AI). While existing studies focus on exploring specific aspects of AI technologies for visually impaired (VI) individuals, there is limited research exploring the outcomes of designed and implemented AI technologies on these individuals. This study examines the current state of AI technologies in supporting VI individuals. We systematically reviewed 263 articles published between 2008 and 2023, of which 33 met the inclusion criteria for our study. This rigorous selection process ensured that only the relevant studies conducted with AI technology for VI were included in our analysis. The results identified several applications developed for various purposes, predominantly 45% focused on AI technology for independent navigation, followed by technology that addresses daily activities issues (21%). Our findings indicate that limited AI technologies have been developed for educational purposes and use in finance and e-commerce. The outcomes of using AI technology demonstrate improvements in navigation and mobility, as well as enhanced independence and quality of life. While our study identifies gaps in existing AI technology for VI, it also contributes to various stakeholders, including developers, users, and researchers, by providing a comprehensive understanding of the current state of AI technology for individuals with VI. © 2024 IEEE.

关键词： Assistive technology

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