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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Codrean, Marius Novac, Mihaela Cornelia Codrean, Mihaela Gordan, Cornelia Emilia Novac, Ovidiu Constantin Bujdoso, Gyongyi Slovac, Francisc University of Oradea Department of Electrical Engineering Electrical Engineering and Information Technology Faculty Oradea Romania National Geothermal Research Center Technology Transfer Center NGRC-TTC University of Oradea Oradea Romania University of Oradea Department of Electronics and Telecommunications Electrical Engineering and Information Technology Faculty Oradea Romania University of Oradea Department of Computers and Information Technology Electrical Engineering and Information Technology Faculty Oradea Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary

ISBN: (纸本)9798350321388

The aim of this work is to develop numerical procedures for analyzing edge hardening using induction heating by turbulent currents. The section through the steel bar, whose lower edge needs to be hardened, is presented. For the inductor adapted to the piece configuration, the 2D model is assumed. The surface treatment of the piece must be differentiated: in the tip area, the heating must be greater than in the rest of the surface. Only the tip area needs to be subjected to hardening, while the rest of the piece must remain elastic to ensure the desired mechanical strength. The main purpose of this work is to propose inductor models for achieving differentiated heating of the blade. © 2023 IEEE.

关键词： Induction heating

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Enhanced Congestion Control Model Based on Message Prioritization and Scheduling Mechanism in Vehicle-to-Infrastructure (V2I) 3

Enhanced Congestion Control Model Based on Message Prioritiz...

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3rd International Conference on Emerging Electrical Energy, Electronics and Computing Technologies 2021, ICE4CT 2021

作者： Ismail, Noraini Hossain, Mohammad Asif Md.noor, Rafidah Wahab, Ainuddin Wahid Abdul Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Darul Iman Kuala Nerus21030 Malaysia

Vehicular Ad Hoc Networks (VANETs) is an origination of Mobile Ad-Hoc Network (MANET), where road vehicles will distribute messages and provide safety alerts to notice hazardous circumstances to the drivers. One of its greatest challenges is ensuring Quality of Service (QoS) owing to channel sharing, high traffic and topology changes in VANETs. These challenges cause of degradation of network performance. Congestion control should be properly considered to improve the network performance and transmission message over VANETs. However, there seem significant limitations to most of the other current congestion control mechanisms. In this paper, an enhanced congestion control model based on prioritization and scheduling-based strategy is proposed. This strategy assigns priorities of the safety messages and non-safety messages based of the message type and network condition. Further, safety message can be classified into event-driven messages and beacon messages. At that point, all messages transmit into the appropriate queue based on their priority and schedule all messages in each queue. However, when new message enqueue, reschedule in each queue may cause additional unwanted delay to be processed cause of inefficient scheduling technique. Thus, an improved dynamic scheduling algorithm also proposed to schedule the messages in this circumstance. © Published under licence by IOP Publishing Ltd.

关键词： Vehicular ad hoc networks

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Selection of a Simple 5G Handover Algorithm: Preparation for Named Data Networking

Selection of a Simple 5G Handover Algorithm: Preparation for...

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International Conference on Wireless and Telematics (ICWT)

作者： Ade Nurhayati Syaiful Ahdan Tutun Juhana Nana Rachmana Syambas School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Applied Science Telkom University Jakarta Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

Named Data Networking as an alternative network for 5G network traffic is required to be able to provide better performance compared to other networks such as internet protocol networks. In NDN wireless, it is known that there is producer mobility and consumer mobility, so optimal mobility management is needed, including the handover process. In this paper the author proposes a simple RSSI-based 5G handover algorithm with distance as a reference RSSI value. This algorithm is a preparation for the handover algorithm on the NDN wireless network. By using the assumed distance value MS to eNB as the basic value for calculating RSSI, handover determination is determined by comparing the RSSI value to the RSSI threshold value. With the scenario of different numbers of users and cluster sizes, from the handover process involving 8 eNB. It can be calculated how big the handover probability is for each user. From the simulation results, it can be concluded that the more users and the larger the cluster size, the greater the handover probability value. This is linear, with the wider the cluster, the probability of getting an RSSI signal from the original eNB becomes smaller, making it possible to get an RSSI signal from neighboring eNB, so that a handover occurs.

关键词： 5G mobile communication Wireless networks Simulation Clustering algorithms Telecommunication traffic Handover Probability

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Facial Palsy Detection Through Changes in Facial Muscle Functionality Using CNN Algorithm

Facial Palsy Detection Through Changes in Facial Muscle Func...

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International Conference of computer and informatics engineering (IC2IE)

作者： Chindy Aulia Sari Fitra Abdurrachman Bachtiar Lailil Muflikhah Aris Widayati Informatics Engineering Department Faculty of Computer Science University of Brawijaya Malang Indonesia Faculty of Medicine University of Brawijaya Malang Indonesia

Facial palsy is a common condition in the medical field that results from facial nerve disorders, leading to weakened facial motor functions. Detecting facial palsy typically involves observing the functional ability of facial muscles, a process currently performed manually by healthcare professionals relying on visual cues. However, manual detection is prone to errors, this needs for early and automated facial palsy detection. Overall, our research aims to improve the detection of facial palsy by leveraging deep learning techniques. By automating the detection process, we hope to provide a more accurate and efficient approach for early identification of this condition. In this research, we aim to develop a facial palsy detection system using the Convolutional Neural Network (CNN) algorithm. We utilize three datasets, namely CK+, JAFFE, and YouTube Facial Palsy, which are organized into normal and dysfunctional classes. The dataset is divided into training and testing data in an 80:20 ratio, and preprocessing techniques such as image resizing and grayscale conversion are applied. To find the optimal parameters for our model, we employ k-fold cross validation during the training process. The evaluation results show that the best model achieved optimal performance with the following parameters: 3 hidden layers, a fully connected layer (fc)-1 with a hidden size of 128, fc-2 with a hidden size of 16, and fc-3 with a hidden size of 32. The optimizer used was Adam, with a learning rate of 0.001, a batch size of 64, and trained for 10 epochs. This best model attained an average training accuracy of 0.958, testing accuracy of 1, recall of 1, and precision of 0.99.

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Factors Influencing Knowledge Sharing in Prison: A Review and AHP Analysis

Factors Influencing Knowledge Sharing in Prison: A Review an...

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International Conference of computer and informatics engineering (IC2IE)

作者： Ejo Imandeka Achmad Nizar Hidayanto Heru Suhartanto Jan Pidanic Faculty of Computer Science University of Indonesia Depok Indonesia Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice Czech Republic

Knowledge sharing has been recognized as the most critical factor in successful knowledge management. Knowledge sharing in prison is a complicated topic that frequently faces resource restrictions, stigma and isolation, communication hurdles, and other security concerns. Moreover, resources for knowledge sharing in a secure environment are still few. This research investigates the critical factors for knowledge sharing in prisons. This research uses the Kitchenham approach with five reputable publishers from 2018 to 2023. There are 1284 articles in the initial phase and 25 in the final phase. Critical factors were ranked using Analytical Hierarchy Process (AHP) with the Triple-helix approach to the representatives of academics, policymakers, and the community in the Indonesian context. The critical knowledge-sharing factors are leadership support, organizational culture, organizational structure, incentives, and individual beliefs. However, acknowledging the critical factors can aid in improving the knowledge-sharing system in prison and changing the criminal justice system.

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Mechanism for Enhancement of Functionality in Deep Echo State Network by Optimizing Leaking Rate 3

Mechanism for Enhancement of Functionality in Deep Echo Stat...

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3rd International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

作者： Inoue, Shuichi Nobukawa, Sou Nishimura, Haruhiko Watanabe, Eiji Isokawa, Teijiro Graduate School of Information and Computer Science Chiba Institute of Technology Narashino Japan Yamato University Faculty of Informatics Osaka Japan National Institute for Basic Biology Okazaki Japan Graduate School of Engineering University of Hyogo Himeji Japan

ISBN: (纸本)9798350300604

Deep echo state network (Deep-ESN) model consists of multiple reservoir layers, that can respond to layer-specific different time-scales. This dynamical characteristic leads to enhance performance of ESN. However, neither the design guidelines for the hyperparameters of the network and individual neurons nor the mechanism to produce the diverse dynamical response have been clarified. In this study, we proposed an approach to generate the dynamical responses with different time-scales in each layer by adjusting the leaking rate of neurons. Through the evaluations time-series prediction task for different leaking rates, multiscale entropy analysis for each reservoir layer, and cross-correlation between adjacent layers, we found that when the leaking rate is set to low, the layer-specific dynamics with different time-scales are generated, as well as a mechanism whereby the signal propagates to subsequent layers with a delay, i.e., queue characteristic. These characteristics produce a high memory capacity. Consequently, diverse responses with delay leads to an enhancement of Deep-ESN functionality. © 2023 IEEE.

关键词： Deep learning

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Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

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computers, Materials & Continua 2023年第3期74卷 5607-5623页

作者： Abdallah Elhigazi Abdallah Mosab Hamdan Shukor Abd Razak Fuad A.Ghalib Muzaffar Hamzah Suleman Khan Siddiq Ahmed Babikir Ali Mutaz H.H.Khairi Sayeed Salih Faculty of Engineering Future UniversityKhartoum 10553Sudan Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Information Assurance and Security Research School of ComputingUniversiti Teknologi MalaysiaJohor81310Malaysia School of Psychology and Computer Science University of Central LancashirePreston PR12HEUK Deaptement of Management Information Systems College of Administrative ScienceApplied Science UniversityAl Eker623Bahrain Deaptement of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh11461Saudi Arabia

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 *** to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate ***,the security design of the wireless structure is vulnerable to versatile *** example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false *** security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world *** paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks *** proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the *** scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in *** proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related *** experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

关键词： 802.11 media access control(MAC) wireless local area network(WLAN) artificial neural network denial-of-service(DoS)

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Intracranial Hemorrhage Classification using CBAM Attention Module and Convolutional Neural Networks 13

Intracranial Hemorrhage Classification using CBAM Attention ...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Rahimi, Parnian Naderan, Marjan Jamshidnezhad, Amir Rafie, Shahram Shahid Chamran University of Ahvaz Faculty of Engineering Department of Computer Engineering Ahvaz Iran Ahvaz Jundishapur University of Medical Sciences School of Allied Medical Sciences Department of Health Informatics Ahvaz Iran Ahvaz Jundishapur University of Medical Sciences Department of Neurology Ahvaz Iran

ISBN: (纸本)9798350330151

Intra-Cerebral Hemorrhage (ICH) is a life-threatening condition characterized by internal bleeding in the brain. The conventional methods for detecting and diagnosing ICH are often time-consuming and complex. However, recent advancements in deep learning techniques have revealed encouraging outcomes by enhancing the performance and efficiency of ICH detection and classification. In this study, we present the CBAM-NeXt model, a fusion of the Convolutional Block Attention Module (CBAM) and the ResNeXt-101 architecture. The CBAM module incorporates both channel and spatial attention mechanisms, making the model selectively emphasize more informative parts of input images. Furthermore, we employ the Extreme Gradient Boosting (XGBoost) algorithm to categorize the extracted features. Our proposed method was trained and evaluated on the RSNA Intracranial Hemorrhage CT challenge (2019) dataset. The results illustrate that our proposed method obtained the average F1 score and average accuracy of 70% and 97.9%, respectively. Our results outperform contemporary baselines in the RSNA challenge and benchmark. © 2023 IEEE.

关键词： Convolutional neural networks

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Development of A Chatbot for the Online Application Telegram Chat with An Approach to the Emotion Classification Text Using the Indobert-Lite Method 4

Development of A Chatbot for the Online Application Telegram...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Hulliyah, Khodijah Rayyan, Faishal Bakar, Normi Sham Awang Abu Syarif Hidayatullah State Islamic University Jakarta Faculty of Science and Technology Department of Informatics Engineering Jakarta Indonesia KICT International Computer Science Department Gombak Malaysia

ISBN: (纸本)9781665453950

The increasing preference for text-based communication on online chat applications has caused the number of social interactions to increase rapidly. However, text-based communication usually results in misunderstandings resulting from the absence of feeling intonation and emotions in the text. This study aims to create a chatbot that can detect emotions text to be entered into online chat applications. This study used a pre-trained model specifically trained from a collection of Indonesian-language datasets, namely IndoBERT-lite. The dataset used to train the model is a collection of Indonesian tweets totaling 4,403 which have been labeled with 5 classes of emotions, namely love, happy, anger, sadness, and fear. The hyperparameters used in this study to train the model were 5 epochs, batch size 16, learning rate 0.000003, and adam optimizer. Based on the test results with the parameters already mentioned, the accuracy, F1 score, recall, and precision values were obtained in the training set of 89%, 89%, 89%, and 90%, while the validation set obtained 70%, 71%, 70%, and 72%. © 2022 IEEE.

关键词： Social networking (online)

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Recommending Public Transit Route: Ant-Colony Optimization or Dijkstra?

Recommending Public Transit Route: Ant-Colony Optimization o...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Abdul Jabbar Aryo Pinandito Informatics Engineering Department Faculty of Computer Science Universitas Brawijaya Malang Indonesia Information System Department Faculty of Computer Science Universitas Brawijaya Malang Indonesia

Advances in algorithmic studies have been made recently, especially in the area of finding the shortest path. Despite taking a greedy approach, the Dijkstra algorithm is well recognized for its efficacy and has established itself as the industry standard for identifying the shortest path in a graph. Conversely, scientists have embraced the Ant-Colony Optimization (ACO) approach, which draws inspiration from the behavior of ants. This study examines the two methods and determines if, given equivalent resources, ACO can generate Dijkstra-like recommendations. Research data is subjected to both algorithms, and the outcomes are presented visually. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. Memory consumption, computation time, initial memory allocation, and final memory allocation are evaluation parameters. ACO is more efficient than Dijkstra in terms of execution time and number of pathways traversed, according to the study's repeated evaluation methodology. This iterative assessment procedure is carried out to address the possibility of single measurements resulting from the JavaScript Garbage Collector. The study's findings imply that a larger memory may be necessary for ACO to deliver the desired outcomes. These results could also be impacted by each algorithm's distinct qualities.

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