Python's popularity as an interpreted language, particularly among scientists and engineers, is due to its ease of use and flexibility, despite certain performance limitations. Enhancing Python for parallel progra...
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This paper proposes an active fault diagnosis method to enforce the diagnosability of discrete event systems using labeled Petri nets by constructing a diagnostic supervisor. For a non-diagnosable net model, its diagn...
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Developing countries face significant challenges in healthcare, education, technological advancement, and farming, with agriculture playing an important role in economic growth. Ensuring adequate food production is es...
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Developing countries face significant challenges in healthcare, education, technological advancement, and farming, with agriculture playing an important role in economic growth. Ensuring adequate food production is essential for citizens' survival and economic stability. Early detection of plant disease is critical for increasing agricultural yields through efficient control methods. As a result, autonomous plant disease detection models are vital for reducing labor-intensive tasks. Machine learning and deep learning models have recently been introduced to automate disease identification in plants by detecting symptoms on their leaves. This study reviews various publications that use machine learning and deep learning approaches to detect plant and crop diseases. This review provides (i) an automated plant disease detection system, (ii) an automated wheat leaf rust detection system, (iii) publicly available plant disease datasets, (iv) an overview of machine learning and deep learning methods for plant disease detection, (v) remote sensing technology in plant disease detection, and (vi) future trends and research directions. This review is contributing to the literature by establishing a solid foundation for the development of more valuable machine learning and deep learning methods for plant disease detection. This review study critically examines various problems, including model generalization, dataset diversity, and computing factors, to assist prospective researchers in developing robust and scalable solutions. Additionally, this review presented a detailed overview of the limitations, challenges, and recent advancements in plant disease detection, especially in wheat leaf rust detection and remote sensing technology. Furthermore, current challenges and future directions in wheat disease detection are thoroughly discussed. This study provides a fundamental guide for the development of AI-driven algorithms, providing strategies to enhance production, profitabili
The rapid advancement of deepfake technology has introduced significant challenges and opportunities across various domains. This article proposes a robust deepfake detection pipeline utilising a combination of attent...
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作者:
Cai, XiaojuanZhang, HaiboAhmed, Chuadhry MujeebKoide, HiroshiKyushu University
Faculty of Information Science and Electrical Engineering Department of Information Science and Technology Fukuoka819-0395 Japan Kyushu Institute of Technology
Faculty of Computer Science and Systems Engineering Department of Artificial Intelligence Fukuoka Iizuka820-8502 Japan Newcastle University
School of Computing Secure and Resilient Systems Group Newcastle upon TyneNE1 7RU United Kingdom Kyushu University
Section of Cyber Security for Information Systems Research Institute for Information Technology Fukuoka819-0395 Japan
Advanced Persistent Threats (APTs) involve attackers maintaining a long-term presence on victim systems, leading to the stealthy exfiltration of sensitive data during network transfers. Despite existing methods to det...
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The primary function of communication through the Internet is to ensure data security. Internet of Things (IoT) devices have evolved to include embedded systems and sensors capable of connecting, gathering, and sendin...
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Bias studies on multilingual models confirm the presence of gender-related stereotypes in masked models processing languages with high NLP resources. We expand on this line of research by introducing Filipino CrowS-Pa...
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The Master of Science, major in Computing and informationsystems (CIS) is a graduate program offered jointly by the graduate faculties of the Department of computer Science in the School of Engineering and the Depart...
ISBN:
(纸本)9781450373258
The Master of Science, major in Computing and informationsystems (CIS) is a graduate program offered jointly by the graduate faculties of the Department of computer Science in the School of Engineering and the Department of computer Management and informationsystems in the School of Business. We describe the nature of the program and emphasizes the advantages of a joint degree program that spans academic units. We also touch on problems that may be encountered and how they can be overcome. The successes realized and situations encountered by this merger may benefit other institutions that face similar resource constraints.
Mixers offer a strong balance between resource efficiency and accuracy which makes them promising feature extractors for loop closure detection (LCD). This paper evaluates the performance of Mixers in LCD by comparing...
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Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated...
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