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7th International Conference on Mechanical, Manufacturing and Plant engineering, ICMMPE 2021

作者： MohdZubir, Mohd Nashrul Mahmud, Faisal Hakeem Azman, Azfar Yazed Ahmad, Mohd Obaidellah, Unaizah Abdullah, Zuraidah Adib Kadri, Nahrizul Ahmad, Norhafizan Ali, Mohammed A. H. Department of Mechanical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Faculty of Computer Science and Technology Universiti Malaya Kuala Lumpur Malaysia Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Educational Management Planning and Policy Faculty of Education Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9789811933066

The project highlights the work on establishing a remote laboratory system that can be operated via the web as part of the effort to support online distance learning programs. To achieve this objective, integration of the hardware, software, and network communication modules was performed, enabling real-time remote access and interaction between the user and the remote infrastructure. The entire system was locally developed which requires linking between a web application software and server software to ensure uninterrupted online communication during the remote activities. The physical interaction within the lab was realized via human interface machine and microcontroller which translate the command given by the user from the web into set of instructions to control and operate the lab. The current prototype has solved the persisting challenges in implementing web-based remote control systems namely data lagging and false data acquisition via the use of server systems and data transfer algorithms. This strategy ensures data integrity and reliability to be displayed to the user in real-time. A new robotic viewing system has been developed and incorporated within the laboratory infrastructure. This new feature allows more freedom in coordinating user viewing flexibility which is crucial in capturing and mimicking the user presence in the lab and during the experimental conduct. The above results provide welcoming perception on the impact of implementing remote labs as part of the continuous efforts to liberate the education platform and promote education without boundaries. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Application programs

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Weather Classification for Autonomous Vehicles under Adverse Conditions Using Multi-Level Knowledge Distillation

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computers, Materials & Continua 2024年第12期81卷 4327-4347页

作者： Parthasarathi Manivannan Palaniyappan Sathyaprakash Vaithiyashankar Jayakumar Jayakumar Chandrasekaran Bragadeesh Srinivasan Ananthanarayanan Md Shohel Sayeed School of Computing SASTRA Deemed to be UniversityThanjavur613401India School of Computer science&Engineering Presidency UniversityBengaluru560064India Dr.A.P.J.Abdul Kalam School of Engineering Garden City UniversityBengaluru560049India Independent Researcher OxnardCA 93036USA Faculty of Information Science and Technology Multimedia UniversityMelaka75450Malaysia

Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational ***,accurately classifying diverse and complex weather conditions remains a significant *** advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather *** challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in *** address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational ***,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student *** framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous *** Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational *** proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per *** results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning *** work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,of

关键词： EfficientNetV2B3 multi-level knowledge distillation RestNet50V2 weather classification

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Multi-gradient-direction based deep learning model for arecanut disease identification

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CAAI Transactions on Intelligence technology 2022年第2期7卷 156-166页

作者： S.B.Mallikarjuna Palaiahnakote Shivakumara Vijeta Khare M.Basavanna Umapada Pal B.Poornima Department of Computer Science and Engineering Bapuji Institute of Engineering and TechnologyDavanagereAffiliated to Visvesvaraya Theological UniversityBelagaviKarnatakaIndia Faculty of Computer Science and Information Technology University of MalayaKuala LumpurMalaysia Adani Institute of Infrastructure Engineering AhmedabadIndia Department of Computer Science Davanagere UniversityDavanagereKarnatakaIndia Computer Vision and Pattern Recognition Unit Indian Statistical InstituteKolkataWest BengalIndia

Arecanut disease identification is a challenging problem in the field of image *** this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and *** to the effect of the disease,there are chances of losing vital details in the *** enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced *** proposed method extracts arecanut as foreground from the enhanced images using Otsu ***,the features are extracted for foreground information for disease identification by exploring the ResNet *** advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut *** results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.

关键词： deep learning image analysis pattern recognition

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Application of Parallel Signal Processing Using Reverse Blockchain technology in Conditions of Network Instability 4

Application of Parallel Signal Processing Using Reverse Bloc...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Khilenko, Volodymyr Zinchenko, Andrii Stepanov, Oleksii Kotuliak, Ivan Ries, Michal Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine National University of Life and Environmental Sciences of Ukraine Kyiv Department of Information Technologies Ukraine Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350391183

The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signal processing algorithms to improve the speed and quality of control systems for autonomous dynamic objects using reverse blockchain technology. The use of reverse blockchain technology is due to the possibility of loss of control signals and, at the same time, the inadmissibility of loss of control in the event of changes in the conditions and parameters of the operation of the obj ect. The purpose of the work is to analyze the implementation of solutions for parallel information processing for various models of industrially produced equipment and to develop solutions to increase the efficiency of reverse blockchain technology when using parallel data processing. © 2024 IEEE.

关键词： blockchain dynamic objects and technological processes improving the quality of control systems instability of communication channels loss of information and control signals reverse blockchain technology

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Development of Low Resource Machine Learning Models for Child Cognitive Ability Assessments 4

Development of Low Resource Machine Learning Models for Chil...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Kahawanugoda, Adithya Gnanarathna, Kulanika Meegoda, Nilan Monarawila, Randika Samarasinghe, Pradeepa Lindamulage, Asiri Gawesha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Automated cognitive assessment tools are state-of-the-art in assessing cognition development. Due to the low availability of resources, building automated cognitive ability evaluation tools is challenging. This study focuses on developing machine learning models using a limited amount of data to assess Reasoning IQ, Knowledge IQ, Mental Chronometry and Attention-levels of Sinhala-speaking children between the age of 7 to 9 years. Our solution includes Sinhala speech recognition systems, image classification models, gaze estimation, blink count detection and facial expression recognition models to evaluate the above four cognitive ability measuring factors. Open domain speech recognition has been used to evaluate complex Sinhala child verbal responses and limited vocabulary responses were assessed using an end-to-end speech recognition system, respectively achieving 40.1% WER and 97.14% accuracy. Additionally, the image classification models for handwritten Sinhala letter recognition and two shape recognition models have gained 97%, 89% and 99% accuracy. The linear regression model for attention level evaluation that utilizes the inputs from a combination of eye-gaze estimation, facial expression recognition and blink rate detection models has gained 85% accuracy. © 2022 IEEE.

关键词： Image classification

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Detection of Ganoderma Boninense Diseases of Palm Oil Trees Using Machine Learning 13

Detection of Ganoderma Boninense Diseases of Palm Oil Trees ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Haw, Yu Hong Zhao, Zhen Hum, Yan Chai Chuah, Joon Huang Voon, Wingates Khairunniza-Bejo, Siti Husin, Nur Azuan Yee, Por Lip Lai, Khin Wee Universiti Malaya Faculty of Engineering Kuala Lumpur Malaysia Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering and Science Selangor Malaysia Universiti Putra Malaysia Faculty of Engineering Serdang Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350347319

About one-third of the world's vegetable oil and fat supply is made up of palm oil, of which 75% is consumed as food. Palm oil is a vital economic resource for nations like Malaysia. The Basal Stem Rot disease of oil palm trees is one of many obstacles to the production of palm oil. The infection is brought on by a fungus called Ganoderma Boninense, which colonizes trees. Early detection is difficult since the symptoms of infection are sometimes mild to non-existent. Terrestrial laser scanning was used to collect 88 photos of the oil palm tree's grey-distribution canopy. The photos gathered were pre-processed to enhance the performance of the deep learning model. To train and verify the effectiveness of disease detection, a deep learning model called convolution neural network is used. The performance of disease detection is trained and tested using a convolutional neural network deep learning model, which divides the data into two classes: the healthy class and the non-healthy class. The improved DenseNet121 model reports a Macro F1-score of 0.7983. The model could only separate the images into two classes rather than categorizing the images into distinct infection levels, which is a limitation of our work. In order to investigate the feasibility of early oil palm disease diagnosis, it is advised for future research to undertake multi-class or multi-level classification using deep learning. © 2023 IEEE.

关键词： Palm oil

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Deep Fake Detection Using computer Vision-Based Deep Neural Network with Pairwise Learning

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Intelligent Automation & Soft Computing 2023年第2期35卷 2449-2462页

作者： R.Saravana Ram M.Vinoth Kumar Tareq M.Al-shami Mehedi Masud Hanan Aljuaid Mohamed Abouhawwash Department of Electronics and Communication Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Mathematics Faculty of ScienceSana’a UniversitySana’a 13509Yemen Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning-based approaches are applied successfully in manyﬁelds such as deepFake identiﬁcation,big data analysis,voice recognition,and image *** is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artiﬁcial intelligence for political abuse,spreading false information,and *** artiﬁcial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and *** paper has analyzed the features related to the computer vision of digital content to determine its *** method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction *** the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning *** proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

关键词： Deep fake deep belief network fuzzy clustering feature extraction pairwise learning

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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computer Systems Science & engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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Ontology-Based Crime News Semantic Retrieval System

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computers, Materials & Continua 2023年第10期77卷 601-614页

作者： Fiaz Majeed Afzaal Ahmad Muhammad Awais Hassan Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Information Technology University of GujratGujrat50700Pakistan Department of Computer Science University of Engineering&TechnologyLahore54890Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Engineering ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular *** news exists on the Internet in unstructured formats such as books,websites,documents,and *** such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement ***-based information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant *** are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given ***,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the *** ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal *** semantic system retrieves information in view of the similarity of the semantics among indexed data and user *** this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two *** the first experiment,we implemented a keyword-based textual IR systemusing Apache *** the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL *** keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.

关键词： Web 3.0 crime ontology semantic web knowledge representation

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The Prediction of Health information Quality Perception Using Machine Learning and Deep Learning Techniques 11

The Prediction of Health Information Quality Perception Usin...

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11th International Conference on information and Communication technology, ICoICT 2023

作者： Baqraf, Yousef Keikhosrokiani, Pantea Universiti Sains Malaysia School of Computer Sciences Penang Minden11800 Malaysia University of Oulu Faculty of Information Technology and Electrical Engineering Faculty of Medicine Oulun YliopistoPL 8000 Finland

ISBN: (纸本)9798350321982

With the substantial rise in the number of people seeking health information online, manually evaluating their perceptions of the quality of health information has become increasingly difficult. These perceptions can impact the acceptance or rejection of the information. To address this issue, the study has employed deep learning and machine learning models to automatically identify consumer perceptions of health information. Furthermore, the study used a survey to collect data from 253 individuals to train the models, measuring the perception of 18 dimensions related to health information quality. This will help health information providers to provide personalized health information that aligns with individual preferences. The RandomForest and neural network model are found to have achieved the best performance among all the algorithms with an Accuracy of over 90% in all the quality dimensions. In sum, our findings show that automating the identification of consumer perception is feasible, which is an essential step toward providing online health information that matches consumer perception and increases the willingness to use it. © 2023 IEEE.

关键词： Deep learning

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