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International Electrical engineering Congress (iEECON)

作者： Panjawee Rakprayoon Akechai Ounsiri Ratchanon Chongyoa Dept. AI Engineering and Data Science Faculty of Engineering Bangkok University Pathum Thani Thailand DNA ROBOTICS and Automation System Company Limited Pathum Thani Thailand Dept. Computer and Robotics Engineering Faculty of Engineering Bangkok University Pathum Thani Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

For an AI tic-tac-toe manipulator application, a real-time vision-based approach is proposed. The technique employs the RealSense camera to capture color and depth images. Combining object detection and image processing techniques to identify X's and O's positions on the game board. However, we recognize that errors can occur during object movement when using only 2D images. To solve this problem, a calibration method between RealSense and the manipulator based on the point-to-plane ICP algorithm is presented. This method provides the 3D object position in the robot coordinate, resulting in increasing the accuracy from 86.25% to 97.25%. Our experiment shows that this technique is highly effective and can accurately identify position objects.

关键词： Three-dimensional displays Robot kinematics Robot vision systems Games Object detection Manipulators Real-time systems

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Priority Based Cyber Incident Handling: Work in Progress

Priority Based Cyber Incident Handling: Work in Progress

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International Conference on Electrical engineering, computer science and Informatics (EECSI)

作者： Mulyati Deris Stiawan Wijang Widhiarso Rahmat Budiarto Department of Information System Multi Data Palembang University & Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Sriwijaya University Palembang Indonesia Department of Informatics Multi Data Palembang University Palembang Indonesia College of Computer Science & IT Albaha University Saudi Arabia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Effective and efficient cyber incident handling is crucial for maintaining the security of information systems and organizational data. This research aims to develop a priority-based cyber incident handling method by adopting the NIST 2.0 framework and utilizing machine learning classification algorithms. This method enables organizations to quickly and accurately identify and respond to incidents based on threat levels and impact, focusing resources on the most critical incidents. The research employs the Support Vector Machine (SVM) classification algorithm to determine incident priorities. The study is still developing for handling cyber incidents, followed by testing and evaluating the system prototype within organizations. Initial results indicate that the priority-based approach using the SVM classification algorithm achieves a True Positive (TP) Rate of 0.999 and a False Positive (FP) Rate of 0.008, with a correlation of 98.88%, resulting in excellent performance in identifying positive cases and a low error rate.

关键词： Support vector machines Error analysis Prototypes Focusing Organizations Machine learning NIST Classification algorithms Security Informatics

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Comparison of Biocode Based Machine Learning and Segmentation Model for Automated Prawn Size Prediction for Real Prawn Farm 5

Comparison of Biocode Based Machine Learning and Segmentatio...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Zariful, Muhammad Wafiq Abd Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Fakhrurroja, Hanif Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Basri, Aida Maryam Universiti Technologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei National Research and Innovation Agency Research Center for Smart Mechatronics Bandung Indonesia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Universiti Technologi Brunei School of Applied Science and Matematics Bandar Seri Begawan Brunei

ISBN: (纸本)9798350374407

The aquaculture industry has witnessed a significant surge in technological integration over the past decade, leveraging advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance production efficiency. Monitoring the growth of aquatic cultures, such as fishes and prawns, plays a pivotal role in optimizing production. However, traditional methods, notably in prawn aquaculture in Asia, still heavily rely on stress-inducing techniques like netting and manual measurements. This paper proposes a novel approach using YOLO-based detection algorithm with biocode reference point based on the ArUco design to predict prawn size based on the length and are dimension to analysed the improvement in predicting the size. The propose solution is compared extensively using statistical tests against segmentation model with and without fine-tuning. Initial results show that both YOLOv8 segmentation and YOLOv8 with Segment Anything Model (SAM) give similar results with the actual mean length of 15.7 cm and predicted mean lengths are 20.5 cm and 20.8 cm respectively. However, after fine-tuning the parameter, the accuracy of predicted results improved significantly with 15.8 cm for YOLOv8 segmentation and 16 cm for YOLOv8 with SAM. The mean absolute error of 0.93 cm for YOLOv8 segmentation and 1.10 cm for YOLOv8 with SAM, and the width of 0.48 cm and 0.78 cm respectively. © 2024 IEEE.

关键词： Deep learning

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Multilayer CVD graphene electrodes using a transfer-free process for the next generation of optically transparent and MRI-compatible neural interfaces

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Microsystems & Nanoengineering 2022年第5期8卷 183-196页

作者： Nasim Bakhshaee Babaroud Merlin Palmar Andrada Iulia Velea Chiara Coletti Sebastian Weingärtner Frans Vos Wouter A.Serdijn Sten Vollebregt Vasiliki Giagka Department of Microelectronics Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyMekelweg 4Delft 2628 CDThe Netherlands Technologies for Bioelectronics Group Department of System Integration and Interconnection TechnologiesFraunhofer Institute for Reliability and Micro-integration IZMGustav-Meyer-Allee 25Berlin 13355Germany Department of Imaging Physics Faculty of Applied ScienceDelft University of TechnologyLorentzweg 1Delft 2628 CJThe Netherlands Erasmus University Medical Center(Erasmus MC) dr.Molewaterplein 40Rotterdam 3015 GDThe Netherlands

Multimodal platforms combining electrical neural recording and stimulation,optogenetics,optical imaging,and magnetic resonance(MRI)imaging are emerging as a promising platform to enhance the depth of characterization in neuroscientific *** conductive,optically transparent,and MRI-compatible electrodes can optimally combine all *** as a suitable electrode candidate material can be grown via chemical vapor deposition(CVD)processes and sandwiched between transparent biocompatible ***,due to the high graphene growth temperature(≥900℃)and the presence of polymers,fabrication is commonly based on a manual transfer process of pre-grown graphene sheets,which causes reliability *** this paper,we present CVD-based multilayer graphene electrodes fabricated using a wafer-scale transfer-free process for use in optically transparent and MRI-compatible neural *** fabricated electrodes feature very low impedances which are comparable to those of noble metal electrodes of the same size and *** also exhibit the highest charge storage capacity(CSC)reported to date among all previously fabricated CVD graphene *** graphene electrodes did not reveal any photo-induced artifact during 10-Hz light pulse ***,we show here,for the first time,that CVD graphene electrodes do not cause any image artifact in a 3T MRI *** results demonstrate that multilayer graphene electrodes are excellent candidates for the next generation of neural interfaces and can substitute the standard conventional metal *** fabricated graphene electrodes enable multimodal neural recording,electrical and optogenetic stimulation,while allowing for optical imaging,as well as,artifact-free MRI studies.

关键词： transparent transfer process

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An Efficient Hybrid PAPR Reduction for 5G NOMA-FBMC Waveforms

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computers, Materials & Continua 2021年第12期69卷 2967-2981页

作者： Arun Kumar Sivabalan Ambigapathy Mehedi Masud Emad Sami Jaha Sumit Chakravarty Kanchan Sengar Department of Electronics and Communication Engineering JECRC UniversityJaipur303905India Center for System Design Chennai Institute of TechnologyIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Kennesaw State UniversityGAUSA

The article introduces Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC),known as hybrid waveform(NOMAFBMC),as two of the most deserving contenders for fifth-generation(5G)*** spectrum access and clampdown of spectrum outflow are unique characteristics of hybrid *** compare the spectral efficiency of Orthogonal Frequency Division Multiplexing(OFDM),FBMC,NOMA,and *** is seen that the hybrid waveform outperforms the existing *** to Average Power Ratio(PAPR)is regarded as a significant issue in multicarrier *** combination of Selective Mapping-Partial Transmit Sequence(SLM-PTS)is an effective way to minimize large peak power *** SLM,PTS,and SLM-PTS procedures are applied to the NOMA-FBMC *** hybrid structure is applied to the existing ***,the correlated factors like Bit Error Rate(BER)and Computational Overhead(CO)are studied and computed for these *** outcome of the work reveals that the NOMA-FBMC waveform coupled with the SLM-PTS algorithm offers superior performance as compared to the prevailing systems.

关键词： 5G NOMA-FBMC SLM-PTS PAPR BER OFDM

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Improving Brain MRI Image Segmentation Quality: A Hybrid Technique for Intensity Inhomogeneity Correction 34

Improving Brain MRI Image Segmentation Quality: A Hybrid Tec...

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34th Conference of Open Innovations Association FRUCT, FRUCT 2023

作者： Aziz, Samah Abdel Hawbani, Ammar Wang, Xingfu Abdelhamid, Talaat Maolood, Ismail Y. Alsamhi, Saeed Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Hefei23002 China Zagazig University Zagazig44519 Egypt Menoufia University Faculty of Electronic Engineering Menoufia Egypt -System Information Ministry of Higher Education and Scientific Research Kurdistan Region Erbil Iraq National University of Ireland Insight Centre for Data Analytics Galway Ireland

ISBN: (纸本)9789526524603

Intensity inhomogeneity is a significant issue in magnetic resonance imaging (MRI), where the presence of bias field causes distortions in pixel values, resulting in inconsistent and erroneous intensities across the image. This artifact not only hampers accurate diagnosis by radiologists but also negatively impacts the performance of computer-aided diagnosis algorithms, particularly in tasks like segmentation. In our proposed approach, we use a hybrid technique called KIFCM, which integrates K-means and Fuzzy C-means to enhance brain tumor segmentation. K-means provides computational efficiency, while Fuzzy C-means improves accuracy by detecting missed tumor cells. We employ a bias correction method based on the level set framework, removing noise with a median filter and applying the hybrid KIFCM technique for optimal segmentation. Our method effectively addresses intensity variation challenges, ensuring precise brain tumor region segmentation. We compare our results with DFCM and MFFLs, and the comparison shows the efficiency of our proposed method by highlighting the superior quality and accuracy of 81% achieved, requiring less computational time. Consequently, our results demonstrate KIFCM's potential to boost both accuracy and speed in MRI-based brain tumor detection through computer-aided diagnosis. © 2023 FRUCT Oy.

关键词： Magnetic resonance imaging

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Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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computer systems science & engineering 2023年第9期46卷 2755-2772页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Mohamed K.Nour Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent *** most commonly-utilized NLP task is the‘Text Classification’*** main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined *** ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the *** of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best *** current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this *** presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding *** Arabic text classification,the DHBM technique is *** technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification *** adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this *** experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC *** outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

关键词： Arabic corpus text classification machine learning deep learning dice optimization

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Evaluation of IT Governance with BAI Domain at Senior High School Using Cobit 5

Evaluation of IT Governance with BAI Domain at Senior High S...

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International Conference on Information Management and Technology (ICIMTech)

作者： Yulius Denny Prabowo Erick Fernando Jullend Gate Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Department of Information System Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Information System Faculty of Computer Science and Design Institut Teknologi dan Bisnis Kalbis Jakarta Indonesia

This study's objective is to evaluate the system's level of maturity by adopting the COBIT Framework 5. The COBIT Framework is one of the ISACA-issued frameworks that defines the concept of information technology governance. The BAI (Build, Acquire, and Implement) domain is the subject of this investigation. The BAI domain is one of the COBIT 5 Framework domains responsible for program and project management, description of needs, identification of solutions, building change availability and capacity, change acceptance and transition, knowledge, assets, and configuration. This study employs qualitative methods to perform literature reviews and question-and-answer sessions, and then collects quantitative analytical data through the formulation of statements or inquiries. There were 5 respondents who responded to the statement, and they implemented the system. The BAI domain of the CBT Examination system in Jakarta's Senior High School XYZ has been calculated and determined to be at level 4, indicating that the system is functioning appropriately. There are subdomains with a lower maturity level in BAI01, BAI02, BAI03, BAI04, BAI06, BAI07, and BAI08, specifically at a rate between 3.8 and 3.96. Additionally, BAI05, BAI09, and BAI10 contain subdomains with a higher level of maturity, with a value range of 4.07 to 4.15. All BAI subdomains culminate at level 4, the system's maturity level, with a value of 3.93. Level 4 indicates that the level of development for Managed and Measurable is fairly good. It can also provide recommendations for improvements, such as paying attention to project management methods before beginning implementation, being able to estimate the time required for the implementation process, and paying attention to the role of trained stakeholders.

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Artificial intelligence for sustainable internet research

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International Journal of computers and Applications 2022年第6期44卷 501-502页

作者： Haldorai, Anandakumar Sharif, Muhammad Ravana, Sri Devi Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering COMSATS University Islamabad Wah Cantt Pakistan Department of Information System Faculty of Computer Science & Information Technology University of Malaya Malaysia

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Deep Learning Algorithm for Room Temperature Detection Using Bioelectric Potential of Plant Data

SSRN

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SSRN 2024年

作者： Tahyudin, Imam Rozaq, Hasri Akbar Awal Nambo, Hidetaka Department of Information System Faculty of Computer Science Amikom Purwokerto University Jl. Letjendpol Soemarto Banyumas Purwanegara 53127 Indonesia Graduate School of Informatics Faculty of Computer Science Gazi Üniversitesi Ankara Yenimahalle Turkey Graduate School of NST Division of Electrical Engineering and Computer Science Kanazawa University Kakumamachi Ishikawa Kanazawa920-1192 Japan

Plants are living things that have many secrets in them. One that is very interesting is how plants can communicate with the surrounding environment through the activities of the plants themselves. Then, from this communication activity, plants produce electricity, which we can use to study further the behavior of these plants, commonly known as bioelectric potential. This study has aim to analyze the ability of bioelectric potential of plant to response a room temperature in different condition. This research is analyzed using a deep learning algorithm, namely Long-Short Term Memory (LSTM). The data was recording using a modified amplifier and the output is signal data. The signal obtained is an analog signal which is then converted into a digital signal. The data that has been obtained is then transformed first before being analyzed using the Wavelet Transform method. The algorithm will be evaluated between the baseline and hyperparameter tuning models. The result showed that hyperparameter tuning has the smallest RMSE. Therefore, it is better than baseline model. © 2024, The Authors. All rights reserved.

关键词： Wavelet transforms

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