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Information and Communication technology for Development for Africa (ICT4DA), International Conference on

作者： Mebit Kefale Getahun Yirga Yayeh Munaye Wasyihun Admass Habtamu Shiferaw Asmare Faculty of Computing Mekdela Amba University Mekdela Ethiopia College of Computing Injibara University Injibara Ethiopia School of Software Engineering University of Science and Technology of China Hefei China College of Computing Mekdela Amba University Mekdela Ethiopia

ISBN: (数字)9798331527792

ISBN: (纸本)9798331527808

This study addresses the need for parsing tools in Ge’ez natural language processing (NLP) systems. Parsing breaks down sentences into their component parts (words and phrases) and assigns grammatical roles. This research provides the basis for more complex NLP tasks by designing a parser specifically for declarative Ge’ez sentences. The parser utilizes context-free grammar (CFG) rules, a lexicon, and the Earley parsing algorithm, known for its effectiveness with context-free languages. The researchers evaluated the parser’s accuracy on a test set of 355 sentences, achieving an impressive 96.6% accuracy after applying error correction techniques. This work lays the way for further development of Ge’ez text parsers. Future advancements could involve expanding the parser’s capabilities to handle more intricate sentence structures and enrich its understanding of the language. By refining this tool, researchers can unlock the potential of Ge’ez for deeper analysis and broader NLP applications.

关键词： Accuracy Refining Africa Natural language processing Information and communication technology Grammar Error correction

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How often should one update control and estimation:review of networked triggering techniques

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Science China(Information Sciences) 2020年第5期63卷 6-23页

作者： Zhiyong CHEN Qing-Long HAN Yamin YAN Zheng-Guang WU School of Electrical Engineering and Computing University of Newcastle School of Software and Electrical Engineering Swinburne University of Technology Institute of Cyber-Systems and Control Zhejiang University

Management of resources is a constant topic in industrial systems. How to use minimum communication resources is of particular interest for control and estimation of networked systems. It raises the question for researchers in the field: how often should one update control and estimation? One of the most intelligent approaches is to trigger updates by events. In the literature, event-triggered control and estimation have been widely studied in the last decade. On one hand, events should be triggered sufficiently frequent to maintain system performance; on the other hand, the possibility of Zeno behavior caused by infinite frequency should be avoided. This review aims at revisiting some existing triggering techniques in a unified formulation, separated from system dynamics and control and estimation strategies. It brings readers better understanding of triggering mechanisms, the underlying technical challenges, and some promising future research topics.

关键词： event-triggered control event-triggered estimation networked systems Zeno behavior

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Enhancing Blockchain-Based Access Control in Cloud computing with Machine Learning Techniques 2

Enhancing Blockchain-Based Access Control in Cloud Computing...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Suji Prasad, S.J. Ramprasad, S.R. Sivaraman, P. Reddy, Karumuru Venkat Battula, Bhanu Prakash Manikandan, R. Kongu Engineering College Dept. E&I Engineering TN Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Dept. of CSE India Bannari Amman Institute of Technology Dept. of EEE TN Erode India St. Martin's Engineering College Dept. of IT Telangana Secunderabad India KKR & KSR Institute of Technology and Sciences Dept. of CSD AP Guntur India Panimalar Engineering College Dept. of ECE TN Chennai India

ISBN: (纸本)9798331534950

As cloud computing continues to expand, the importance of robust access control mechanisms to ensure security and privacy is paramount. Traditional access control systems often struggle with scalability, adaptability, and security vulnerabilities. This paper proposes a novel integration of blockchain technology with machine learning techniques to address these challenges. By harnessing the decentralized and immutable nature of blockchain and the predictive power of machine learning, our framework enhances the security and efficiency of access control systems in cloud environments. We developed a model that uses neural networks to automate and optimize access decisions and deployed it on a simulated cloud platform. Our results demonstrate significant improvements in threat detection and system adaptability compared to conventional methods. This research not only strengthens access control in cloud computing but also sets a foundational approach for future security enhancements in the field. © 2024 IEEE.

关键词： Cloud platforms

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Ensemble Machine Learning Method for Photovoltaic Power Forecasting

Ensemble Machine Learning Method for Photovoltaic Power Fore...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qingguo Zhou Xiaorun Tang Qingquan Lv Ziyuan Li Jun Shen Jinqiang Wang Binbin Yong School of Information Science and Engineering Lanzhou University Lanzhou China School of Computing and Information Technology University of Wollongong Wollongong Australia

Photovoltaic power prediction plays an extremely important role in the construction of smart power grid and power grid security protection. In order to solve the problem of unstable power generation and even damages to the power grid caused by the ever changing irradiance and meteorological conditions, this paper leverages the traditional time series prediction modeling method to the machine learning approaches, such as recurrent neural network (RNN), convolutional neural network (CNN) and decision tree (DT), and uses the ensemble machine learning method to improve the final prediction accuracy, by training and testing on the radiation and meteorological data collected from a photovoltaic power station in Gansu Province of China, which enjoys the best solar resources in the country. The experimental results show that the ensemble model achieves the highest prediction accuracy, and its root mean square error(RMSE) is 0.4477. This is of great significance to the power generation evaluation and dispatching of photovoltaic power station.

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

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IEEE Network 2025年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab Nanyang Technological University College of Computing and Data Science Singapore Guangdong University of Technology School of Automation Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia National University of Singapore College of Design and Engineering Department of Electrical and Computer Engineering Singapore

The rapid development of generative artificial intelligence (AI) technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Skin Cancer Diagnosis Using High-Performance Deep Learning Architectures 3rd

Skin Cancer Diagnosis Using High-Performance Deep Learning A...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Bindhu, A. Ahilan, A. Vallisree, S. Maria Jesi, P. Muthu Kumar, B. Marriwala, Nikhil Kumar Sabr, Aznul Qalid Md Department of Computer Science Infant Jesus College of Arts and Science for Women Tamil Nadu Kanyakumari District Mulagumoodu India Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Electronics Engineering MIT Campus Anna University Tamil Nadu Chennai India Department of Computer Science and Engineering Loyola Institute of Technology and Science Loyola Nagar KK District Thovalai India School of Computing and Information Technology REVA University Karnataka Kattigenahalli560064 India Department of Electronics and Communication Engineering University Institute of Engineering and Technology Kurukshetra University Kurukshetra Thanesar India Department of Artificial Intelligence FCSIT University in Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9789819986453

Skin cancer is one of the dangerous diseases which causes human death. It is originated by the abnormal growth of melanocytic cells. The malignant Skin cancer is also referred by another name as melanoma. The key source for the computer aided diagnosis is Dermoscopy image which acquires the skin cancer. Fortunately, a high percentage of skin cancers can be cured if they are found early. Automatic early detection approaches are being used as quick and practical skin cancer screening due to the expense and morbidity of this biopsy procedure. The existing methods of melanoma detection meet-up the demerits such as less accuracy, false segmentation and more time consumption. To overcome these issues, the proposed High performance Deep Learning Architecture method for classifying the skin cancer. It involves three types of techniques are used for comparison study are MobileNet, ResNeXt101, and Multi-Model Ensemble. The performance metrics used for the comparison are accuracy, precision, and recall. The comparison of the accuracy value for MobileNet, ResNeXt101 and Multi-model ensemble techniques are 97.2%, 84.2% and 86% respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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LookCursorAI: Machine Learning-Enhanced Eye-Powered Interaction 3

LookCursorAI: Machine Learning-Enhanced Eye-Powered Interact...

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3rd IEEE World Conference on Applied Intelligence and computing, AIC 2024

作者： Saranya, V.S. Naidu, U Ganesh Kumar, ParvathananiRajendra Elamathi, E. Jayavarapukarthik Ajithsundaram Srm Institute of Science and Technology Department of Computing Technologies India B v Raju Institute of Technology Department of Csbs Telanagana Medak Narsapur India Vijayawada Agiripalli India K Ramakrishnan College of Engineering Department of Aids Tamilnadu Trichy India Koneru Lakshmaiah Education Foundation Department of Cse India Amrita School of Business Ernakulam India

ISBN: (纸本)9798350384598

This project presents the development and implementation of a hands-free computer interaction system leveraging facial recognition technology. Aimed at enhancing accessibility for individuals with physical disabilities, the system enables users to control their computers exclusively through facial expressions and movements. By harnessing real-time detection and tracking of facial landmarks using sophisticated algorithms, the system interprets a diverse range of facial gestures, from subtle cues like blinking to more pronounced actions such as mouth movements. Through customizable thresholds and intuitive interfaces, users can seamlessly navigate interfaces, manipulate content, and communicate with others without the need for traditional input devices. The system's robust performance and user-friendly design underscore its potential to revolutionize the way individuals with disabilities engage with computing devices, fostering independence and empowerment in the digital realm. This project not only exemplifies a paradigm shift in human-computer interaction but also embodies a commitment to inclusivity and accessibility in technology design. © 2024 IEEE.

关键词： Gesture recognition

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A Comparison of LSTM and GRU for Bengali Speech-to-Text Transformation 1st

A Comparison of LSTM and GRU for Bengali Speech-to-Text T...

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1st International Conference on Advances in computing Research, ACR’23

作者： Jahan, Nusrat Sultana, Zakia Chowdhury, Fahim Ahmed, Sajjad Parvez, Mohammad Zavid Barua, Prabal Datta Chakraborty, Subrata Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Information Technology APIC Adelaide Australia Information Technology Torrens University Adelaide Australia Peter Faber Business School Australian Catholic University Blacktown Australia School of Computing Mathematics and Engineering Charles Sturt University Bathurst Australia University of Southern Queensland Toowoomba Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England Armidale Australia Cogninet Australia Pty Ltd Surry Hills Australia

ISBN: (纸本)9783031337420

This paper represents an approach to speech-to-text conversion in the Bengali language. In this area, we have found most of the methodologies were focused on other languages rather than Bengali. We started with a novel dataset of 56 unique words from 160 individual subjects was prepared. Then in this paper, we illustrate the approach to increasing accuracy in a speech-to-text over the Bengali language where initially we started with Gated Recurrent Unit(GRU) and Long short-term memory (LSTM) algorithms. During further observation, we found that the output of the GRU failed to give any stable output. So, we moved completely to the LSTM algorithm where we achieved 90% accuracy on an unexplored dataset. Voices of several demographic populations and noises were used to validate the model. In the testing phase, we tried a variety of classes based on their length, complexity, noise, and gender variant. Moreover, we expect that this research will help to develop a real-time Bengali speak-to-text recognition model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Evaluation Method of Ship Equipment Applicability Based on Agent Attention Transformer

Evaluation Method of Ship Equipment Applicability Based on A...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Tianyu Gao Yongjiang Li Lingling Feng Jingmeng Zhao Xiaopeng Fan School of Electronics and Information Engineering Harbin Institute of Technology Harbin China China Institute of Marine Technology and Economy Beijing China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

This paper proposes a maritime shipborne equipment applicability assessment method based on agent attention Transformer to address the limitations of traditional assessment methods. As the application of artificial intelligence in the military field continues to expand, the assessment of new equipment applicability has become particularly crucial. This paper constructs a meshed index system that follows principles of comprehensiveness, hierarchy, and objectivity, dividing equipment applicability into four dimensions: reliability, maintainability, supportability, and availability, and further subdividing them into specific third-level indicators. Based on the Agent attention Transformer, the agent attention mechanism optimizes feature vectors and combines them with the triplet center-cross entropy loss function, effectively enhancing classification effects and recognition rates. Simulation experiments have verified the effectiveness of the proposed method, achieving an accuracy rate as high as 99.54%, and further confirming the model's superior performance in ablation experiments. The application of this method provides important data support for maritime cluster collaborative tasks and has significant military application value.

关键词： Attention mechanisms Collaboration Transformers Vectors Entropy Indexes Reliability Marine vehicles Artificial intelligence Military equipment

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Design of a Robot for carrying out research on hybrid robot's mobility: case of a mecanum wheel-legged robot

Design of a Robot for carrying out research on hybrid robot'...

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International Conference on Electronics, Communication and computing Technologies (ICECCT)

作者： Kouame Yann Olivier A. Rajashekhar C. Biradar R. Karthik Geeta D. Devanagavi School of Electronics and Communication engineering REVA University Bangalore India School of Computing and Information Technology REVA University Bangalore India

The advancement of technology favors the creation of intelligent machines capable of executing complex tasks. Automated machines that can be teleoperated or autonomous open up a vast pool of possibilities, among which substituting humans in some instances where their safety is endangered, which is one of the significant advantages. Having the ability to substitute humans sets some requirements like object manipulation, mobility, and intelligence. Various mechanisms are employed to provide mobility to the machines considering the mobility aspect. The most common mobile robots are quadruped robots because of their advantages. Over the years, many ideas have been proposed for quadruped robot designs to address specific problems. An improvement of quadruped robots is hybrid wheel-legged robots, on which researches are still ongoing-this paper attempt to address the robots' mobility problem with a mecanum wheel-legged robot design proposal. The paper discusses the design concerning three aspects: mechanical, electronic, and software. The targeted goal is to provide a robot platform that can be used to conduct research on wheel-legged robot mobility.

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