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2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Aqid, Bukhori Muhammad Chacko, Jeeta Ann Jacobsen, Hans Arno Department of Computer Science University of Indonesia Jakarta Indonesia Department of Informatics Technical University of Munich Munich Germany Electrical and Computer Engineering University of Toronto Toronto Canada

ISBN: (纸本)9798350343069

This paper explores the use of reinforcement learning and various machine learning techniques to optimize the configurations of Hyperledger Fabric v2 Channels and Orderers. Our goal is to increase the average throughput and success rate. To achieve this, we train multiple optimization agents using different algorithms, such as Q-learning and Deep Q Network. These agents interact with a Hyperledger Fabric v2 instance in a single host environment, using FabCar as the smart contract and Hyperledger Caliper as the benchmark platform. Based on the data collected from our reinforcement learning agents, we observed that the agents tend to choose larger batch sizes to optimize their results. Our training data also suggests that it is possible to achieve an average throughput of more than 98% for send rates up to 200 TPS. Our prediction results demonstrate that, even with the limitation of training time, reinforcement learning agents can increase the average throughput by around 59% and the average success rate by around 18%, while machine learning models can increase the average throughput by around 45% and the average success rate by around 8%, compared to the baseline results provided by the default configuration. © 2023 IEEE.

关键词： Reinforcement learning

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Tailoring Fano resonance characteristics for GSS4T1-based reconfigurable photonics

Tailoring Fano resonance characteristics for GSS4T1-based re...

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2024 Frontiers in Optics, FiO 2024

作者： Foteinopoulou, Stavroula Tsitsas, Nikolaos L. Department of Electrical and Computer Engineering University of New Mexico NM87131-0001 United States School of Informatics Aristotle University of Thessaloniki Thessaloniki54124 Greece

We present a GSS4T1-based metagrating designed to exhibit a homogeneous-optical-medium response, when GSS4T1 amorphous, or a judicious Fano response, when GSS4T1 crystalline, which enables a negative beam steering tha... 详细信息

关键词： Photonics

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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Urban Safety Improvement 22

Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Ur...

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22nd IEEE International Symposium on Intelligent Systems and informatics, SISY 2024

作者： Glucina, Matko Zigulic, Nikola Frank, Domagoj Lorencin, Ivan Matika, Dario Istrian University of Applied Sciences Department of Engineering Pula Croatia University of Rijeka Faculty of Engineering Rijeka Croatia University North Koprivnica Department of Computer Science and Informatics Croatia Juraj Dobrila University of Pula Faculty of Informatics Pula Croatia University North Department of Electrical Engineering Varazdin Croatia

ISBN: (纸本)9798350385601

Individual safety in urban and city centers is one of the fundamental rights of every person. Today's innovations open up various possibilities for improving the quality of life and safety, but if they are not applied, they carry the danger of an additional increase in crime or greater insecurity in urban centers. This investigation aims to apply insights into the anticipation of illegal actions by various criminal groups that use illegal means, such as weapons, to get property that does not belong to them. In this research, computer vision and advanced strategies such as YOLOv10 and YOLOv9 are used to compare their properties and performance for improving security in urban and populated areas. The best results were achieved by YOLOv9m with a high percentage of mean average precision in the amount of 0.923, while the worst performance was shown by YOLOv10n with a mean average precision of 0.902. Precision and recall for the YOLOv9 and YOLOv10 models were also taken into account and the results are as follows: 0.984.. 0.993.. 0.979.. and 0.996. © 2024 IEEE.

关键词： Neural networks

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A hybrid deep learning approach for phenotype prediction from clinical notes

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4503-4513页

作者： Khalafi, Sahar Ghadiri, Nasser Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Identifying patient cohorts from clinical notes in secondary use of electronic health records is a fundamental task in clinical information management. The patient cohort identification process requires identifying the patient phenotypes. However, the growing number of clinical notes makes it difficult to analyze the data manually. Therefore, automatically extracting clinical concepts is crucial to identify patient phenotypes correctly. This paper’s proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a Convolutional Neural Network (CNN) for identifying patients’ phenotypes in clinical notes. Furthermore, an extra CNN layer is run parallel to the hybrid proposed model to extract more features related to each phenotype. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding’s performance in identifying patient phenotypes. We also measured the effect of adding additional data cleaning steps on discharge reports to identify patient phenotypes using deep learning models. Results demonstrated the proposed hybrid model extracts more features than existing methods of patient phenotype extraction and provides a better F1-score. We show that complementing the proposed hybrid model with an extra CNN in identifying different phenotypes improves the F1 scores. In addition, eliminating punctuation, numbers, and stop words in discharge reports before training hybrid models increased model performance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Embeddings

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Effect of CLAHE-based Enhancement on Bean Leaf Disease Classification through Explainable AI 12

Effect of CLAHE-based Enhancement on Bean Leaf Disease Class...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Noviandy, Teuku Rizky Maulana, Aga Khowarizmi, Faris Muchtar, Kahlil Universitas Syiah Kuala Artificial Intelligence Department of Informatics Aceh Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Aceh Indonesia

ISBN: (纸本)9798350340181

Global agriculture faces a major threat from plant diseases, particularly those affecting bean leaves, resulting in significant crop yield losses and impacting farmers' livelihoods and food production. To overcome the limitations of manual visual inspection methods, researchers have adopted computer vision and deep learning techniques for automated disease detection. This study improves the accuracy of classifying bean leaf diseases using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the EfficientNetB0 architecture. Explainable AI technique, specifically Grad-CAM, is employed to analyze predicted images for transparency and interpretability. The proposed approach outperforms models trained on original images, achieving higher accuracy, precision, recall, and F1 score. CLAHE proves to be effective in optimizing CNN-based models for plant disease detection. Grad-CAM analysis reveals the model's focus on disease-related features, enhancing understanding of its behavior. This research contributes to the development of robust automated systems for plant disease detection, benefiting farmers and agricultural practitioners in decision-making. © 2023 IEEE.

关键词： Deep learning

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Causal Effects of Environmental Exposures and Biological Traits on the Difference between Phenotypic and Chronological Ages

Causal Effects of Environmental Exposures and Biological Tra...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Pala, Daniele Xie, Yuezhi Xu, Jia Zhang, Yuqin Shen, Li University of Pavia Department of Computer Electrical and Biomedical Engineering Pavia Italy University of Pennsylvania Department of Biostatistics Epidemiology and Informatics Philadelphia United States

ISBN: (纸本)9798350337488

Aging is a physiological process associated with numerous cardiovascular, degenerative and neurological conditions. The current demographic changes in the world, characterized by an increasing average age especially in Europe and North America, are causing an increment in the prevalence of such conditions, leading to a public health problem as more resources are required to manage treatments. Recent research has defined a new score named Phenotypic Age, i.e. an adjusted age that takes into account the current health status considering a series of biomarkers, that can be useful to provide a more precise estimation of the probability of developing aging-related conditions. Prevention of such conditions can be performed more efficiently by studying the mechanisms that lead to an increased phenotypic age rather than attempting to treat a patient that has already a high health risk. In this work, we combine pairwise association techniques and mediation analysis to define a strategy to investigate the inner causal mechanisms that lead from specific environmental exposures to an increasing gap between phenotypic and chronological age, considering the influence of biological variables. Four environmental exposures and 11 biological traits have been identified in the NHANES dataset, and each trait has been tested as a mediation variable for each exposure. Almost all mediations reported significant indirect effects with specific results that provide new insights into the causal mechanisms that lead to a deranged aging rate. © 2023 IEEE.

关键词： Aging Exposome Mediation Analysis Phenotypic Age Public Health

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Embedded Coded Relay System for Molecular Communications

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computers, Materials & Continua 2022年第8期72卷 2729-2748页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Farid Shawki Imran Khan Ki-Il Kim Department of Electrical Engineering Faculty of EngineeringMinia UniversityMiniaEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)Norway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Electrical Engineering University of Engineering&TechnologyPeshawar814Pakistan Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Korea

With the emergence of the COVID-19 pandemic,the World Health Organization(WHO)has urged scientists and industrialists to exploremodern information and communication technology(ICT)as a means to reduce or even eliminate *** World Health Organization recently reported that the virus may infect the organism through any organ in the living body,such as the respiratory,the immunity,the nervous,the digestive,or the cardiovascular *** the abovementioned goal,we envision an implanted nanosystem embedded in the intra living-body *** main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or to implement a targeted drug delivery system(i.e.,delivery of the therapeutic drug to the diseased tissue or targeted cell).The communication among the nanomachines is accomplished via communication-based molecular *** control/interconnection of the nanosystem is accomplished through the utilization of Internet of bio-nano things(IoBNT).The proposed nanosystem is designed to employ a coded relay nanomachine disciplined by the decode and forward(DF)principle to ensure reliable drug delivery to the targeted ***,both the sensitivity of the drug dose and the phenomenon of drug molecules loss before delivery to the target cell site in long-distance due to the molecules diffusion process are taken into *** this paper,a coded relay NM with conventional coding techniques such as RS and Turbo codes is selected to achieve minimum bit error rate(BER)performance and high signal-to-noise ratio(SNR),while the detection process is based on maximum likelihood(ML)probability and minimum error probability(MEP).The performance analysis of the proposed scheme is evaluated in terms of channel capacity and bit error rate by varying system parameters such as relay position,number of released molecules,relay and receiver *** results are validated through simulation and demonstrate that the proposed scheme can

关键词： Molecular communication nanonetwork internet of bio-nano things coded relay scheme coding

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Adaptive Vehicle Headlights Direction System based on Road Lane Detection

Adaptive Vehicle Headlights Direction System based on Road L...

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2023 International Conference on informatics engineering, Science and Technology, INCITEST 2023

作者： Nizar, Taufiq Nuzwir Hartono, Rodi Jatmiko, Didit Andri Universitas Komputer Indonesia Department of Computer Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Electrical Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350317862

Almost half of the vehicle accident fatalities occur at night. Driving at night is riskier. Even the most familiar roads can look distant in the dark. It becomes difficult to see other vehicles, pedestrians, and other objects. One of the problems is the lack of light, especially at road bends, because the vehicle headlights are always straight. The purpose of the research is to design the adaptive vehicle headlights direction that is always facing the road even if it is on the curving road. There are several processes to implement the system are detecting roads, processing detected road conditions and controlling the direction of the headlights. To detect the road using the camera sensor and image processing based the road lane and recognizing it using Hough Transform algorithm. the results of road detection are processed to drive the servo motor which adjust the direction of the lights. The implemented system works with the success rate 76.63% with accuracy error rate 1.28 degrees for the left bend and 80% with accuracy error rate 1.45 degrees to the right bend. © 2023 IEEE.

关键词： Headlights

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Deploying YOLOV10 for Affordable Real-Time Handgun Detection 22

Deploying YOLOV10 for Affordable Real-Time Handgun Detection

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22nd IEEE International Symposium on Intelligent Systems and informatics, SISY 2024

作者： Zigulic, Nikola Glucina, Matko Frank, Domagoj Lorencin, Ivan Sverko, Zoran Matika, Dario University of Rijeka Faculty of Engineering Rijeka Croatia Istrian University of Applied Sciences Department of Engineering Pula Croatia University North Department of Computer Science and Informatics Koprivnica Croatia Juraj Dobrila University of Pula Faculty of Informatics Pula Croatia University North Department of Electrical engineering Varaždin Croatia

ISBN: (纸本)9798350385601

The development of artificial intelligence is one of the most significant technological innovations that contributes to humanity with its characteristics and facilitates, secures, and improves everyday life. However the challenge arises when the programmer-engineer learns the algorithm of artificial intelligence to perform the proposed task, that is, the challenge lies in the issue of available hardware resources. Artificial intelligence algorithms perform many human-impossible tasks, such as detection and counting, i.e. calculating the interrelationships of individual objects, segmentation of tumors and other malignant diseases, i.e. tissues, classification of specific states of classes of a scene, and many other similar technologies. This research paper examines the possibility of implementing the You Only Look Once algorithm of the tenth generation on certain devices such as an affordable Raspberry Pi, and will discuss the advantages and disadvantages of changes in detection parameters, i.e. inferences to the applied model. In addition, a mock-up of the device will be shown, which will serve to provide timely information about criminals and suspicious persons who possess firearms in different situations, such as normal weather conditions in a populated place or in shops where petty robberies are frequent. The testing will be done using recorded videos in real-time scenarios. Finally, real-time inference or detection in real-time will be tested and the actions that Raspberry will perform will be simulated. The results indicate that the optimal model achieved a precision of 0.938, recall of 9.863, mAP50 of 0.91, and mAP50-95 of 0.739. This was achieved using an image size of 640, IOU threshold of 0.7, confidence threshold of 0.6, and training for 600 iterations with the Stochastic Gradient Descent optimizer, without augmentations, and employing the ONNX inference format. © 2024 IEEE.

关键词： Stochastic systems

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