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15th International Conference on Industrial Logistics, ICIL 2023

作者： Bencak, Primož Hercog, Darko Lerher, Tone University of Maribor Faculty of Logistics Mariborska cesta 7 Celje3000 Slovenia University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 Maribor2000 Slovenia University of Maribor Faculty of Mechanical Engineering Smetanova ulica 17 Maribor2000 Slovenia

ISBN: (纸本)9789537738891

Robotics has been gaining attention in intralogistics applications in recent years. Automation of intralogistics processes aims to cope with the rising trends of workforce deficiency, aging, and increasing demands that came with the rise of E-commerce. Many improvements aim at bin-picking applications since order-picking requires most contributions while adding little to the products' value. Robotic bin-pickers are showing promising results;however, they are still subject to many limitations. First, the vision system must correctly determine the object's location and orientation. Second, a correct robotic gripper must be chosen. Lastly, appropriate grasping points that lead to successful picking must be selected. In this paper, we explore the influencing parameters of object detection using a 3D vision system. Second, we analyze an actual bin-picking application to determine the most appropriate selection of the robotic gripper. Based on the experiments, we provide the guidelines for selecting the most appropriate robotic bin-picking configuration. © FSB, Zagreb, Croatia, 2023.

关键词： Object detection

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Deep Neural Network Based Real-Time Intrusion Detection System

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SN computer science 2022年第2期3卷 1-12页

作者： Thirimanne, Sharuka Promodya Jayawardana, Lasitha Yasakethu, Lasith Liyanaarachchi, Pushpika Hewage, Chaminda Faculty of Engineering Sri Lanka Technological Campus Padukka Sri Lanka Faculty of Engineering University of Peradeniya Peradeniya Sri Lanka Department of Computer Science Cardiff Metropolitan University Cardiff United Kingdom

In recent years, due to the rapid growth in network technology, numerous types of intrusions have been uncovered that differ from the existing ones, and the conventional firewalls with specific rule sets and policies are incapable of identifying those intrusions in real-time. Therefore, that demands the requirement of a real-time intrusion detection system (RT-IDS). The ultimate purpose of this research is to construct an RT-IDS capable of identifying intrusions by analysing the inbound and outbound network data in real-time. The proposed system consists of a deep neural network (DNN) trained using 28 features of the NSL-KDD dataset. In addition, it contains the machine learning (ML) pipeline with sequential components for categorical data encoding and feature scaling, which is used before transmitting the real-time data to the trained DNN model to make predictions. Moreover, a real-time feature extractor, which is a C++ program that sniffs data from the real-time network traffic and derives relevant data related to the features of the NSL-KDD dataset using the sniffed data, is deployed between the gateway router and the local area network (LAN). Together with the trained DNN model, the ML pipeline is hosted in a server that can be accessed via a representational state transfer application programming interface (REST API). The DNN has revealed outstanding testing performance results achieving 81%, 96%, 70% and 81% for accuracy, precision, recall and f1-score accordingly. This research comprises a comprehensive technical explanation concerning the implementation and functionality of the complete system. Moreover, leveraging the extensive explanations provided in this paper, advanced IDSs capable of identifying modern intrusions can be constructed. © The Author(s) 2022.

关键词： Deep neural network Machine learning Pipelines Real-time intrusion detection system

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Comparison between Single-Input and Multi-Input Classification with the Application of Canny Feature Extraction and Classification Algorithms on the Toraja Buffalo Dataset 19

Comparison between Single-Input and Multi-Input Classificati...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Manga, Abdul Rachman Nanda, Assyahrin Handayani, Anik Nur Herwanto, Heru Wahyu Asmara, Rosa Andrie Lantara, Dirgahayu Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia Department of Electrical Engineering Universitas Negeri Malang Malang Indonesia Information Technology Department State Polytechnic of Malang Malang Indonesia Faculty of Industrial Engineering Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

Data classification plays a crucial role in artificial intelligence, particularly in enhancing model accuracy. This study focuses on classifying Toraja buffalo, a livestock breed with significant cultural importance in South Sulawesi, Indonesia. While the Single Input Approach is commonly used for classification, it often fails to capture all the necessary attributes to effectively distinguish between racial traits. Therefore, this research aims to evaluate the effectiveness of a multi-input approach, which integrates multiple data inputs to improve classification performance compared to the Single Input method. We employed four classification techniques: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree, using both Single Input and Multi Input configurations. Model performance was assessed through Precision, Recall, F1 Score, and Accuracy metrics. The findings indicate that the Multi Input approach consistently outperformed the Single Input method. Notably, KNN achieved its best performance with Multi Input, recording an F1 Score of 0.7263 and an Accuracy of 0.7333, significantly surpassing the results obtained from Single Input. Similarly, SVM also demonstrated substantial performance enhancements with Multi Input. Overall, the study highlights the importance of incorporating a wider array of informative data to enhance the model's capability in accurately classifying specific categories, with KNN showing the most pronounced improvements. © 2025 IEEE.

关键词： Support vector machines

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Clickbait Detection in Indonesia Headline News Using BERT Ensemble Models

Clickbait Detection in Indonesia Headline News Using BERT En...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Muhammad Edo Syahputra Ade Putera Kemala Farrel Alexander Tjan Rudy Susanto School of Computer Science Computer Science Binus University Jakarta Indonesia Faculty of Engineering Computer Engineering Binus Universitty Jakarta Indonesia

This study explores the effectiveness of combining BERT (Bidirectional Encoder Representations from Transformers) with convolutional neural networks (CNN) and multilayer perceptrons (MLP) for a specific task. The results showcase promising performance of the BERT + CNN model, with precision, recall, and F1-score values of 0.91, 0.86, and 0.89, respectively. The BERT + MLP model exhibits consistent performance with an F1-score of 0.875. A comparative analysis against a previous study utilizing IndoBERT highlights the competitive edge of our BERT + CNN model, particularly in terms of recall and F1-score. Additionally, our proposed model demonstrates competitive performance against other state-of-the- art models such as RoBERTa and xlmRoBERTa. This study contributes valuable insights into the optimization of BERT -based models for specific tasks, emphasizing the efficacy of the BERT + CNN architecture.

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Myocardial infarction detection based on deep neural network on imbalanced data

Myocardial infarction detection based on deep neural network...

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作者： Hammad, Mohamed Alkinani, Monagi H. Gupta, B.B. Abd El-Latif, Ahmed A. Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom32511 Egypt Department of Computer Science and Artificial Intelligence College of Computer Sciences and Engineering University of Jeddah Jeddah Saudi Arabia Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Computer Science and Information Engineering Asia University Taichung City Taiwan Mathematics and Computer Science Department Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Myocardial infarction (MI) is an acute interruption of blood flow to the heart, which causes the heart to suffer from a deficiency of blood and ischemia, so the heart muscle is damaged, and cells can die and lose their function. Despite the low incidence of MI in the world, it is still a common disease-causing death. Therefore, detecting the MI signals early can reduce mortality. This paper presented a method based on a deep convolutional neural network (CNN) for the detection of MI automatically. The proposed CNN is an end-to-end model without requiring any stages of machine learning and requires only one stage to detect MI from the input signals. In the case of imbalanced data, we optimize our deep model with a new loss function named the focal loss to deal with this case by constituting the loss indirectly the focus in those difficult classes. The Physikalisch-Technische Bundesanstalt (PTB) dataset was employed in the validation to classify the signals to normal and MI. The performance of our technique alongside state-of-the-art in the area shows an increase in terms of average accuracy and F1 score. Results show that focal loss improves the detection accuracy by 9% for detecting MI signals. In summary, the proposed method achieved an overall accuracy, precision, F1 score, and recall of 98.84%, 98.31%, 97.92%, and 97.63, respectively using focal loss and overall accuracy of 89.72%, a precision of 88.52%, a recall of 81.11% and F1 score of 83.02% without using focal loss. Our method using focal loss is an effective tool to perform a fast and reliable MI diagnosis to assist the cardiologists in detecting MI early. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Convolutional neural networks

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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Service Migration for Delay-Sensitive IoT Applications in Edge Networks

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IEEE Transactions on Services Computing 2025年第3期18卷 1782-1797页

作者： Li, Xiaocui Zhou, Zhangbing Wang, Yasha Deng, Shuiguang Hung, Patrick C. K. School of Information Engineering Beijing100083 China Peking University National Research & Engineering Center of Software Engineering Beijing100871 China Peking University New Generation Information Technology Research Institute Tianjin300380 China School of Information Engineering Frontiers Science Center for Deep-Time Digital Earth Beijing100083 China Institut Polytechnique de Paris Télécom SudParis Palaiseau91120 France Zhejiang University College of Computer Science and Technology Hangzhou310012 China Ontario Tech University Faculty of Business and Information Technology Oshawa Canada

The proliferation of Internet of Things (IoT) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided by IoT devices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated as IoT services. Thereafter, IoT applications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguous IoT services. The status of computational resources in IoT devices may change continuously along with their occupancy and release by IoT services. Considering the resource-scarceness of IoT devices, when the workload of IoT devices increases due to more services to be processed, certain IoT devices may hardly have enough remaining resources to co-host more instances of certain IoT services prescribed by forthcoming IoT applications with strict constraints. As a result, the delay satisfaction of both on-running and forthcoming IoT applications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a rEsource-Efficient service Configuration (E2rC) mechanism, which aims to optimize the configuration of computational resources provided by IoT devices with respect to complex requirements prescribed by IoT applications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhanced Deep Reinforcement Learning (DRL) approach with a two-layer Q-network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our E2rC is more efficient than the state-of-art counterparts in satisfying delay constraints of IoT applications, while reducing the energy consumption and improving the resource utilization efficiency of IoT devices. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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The Utilization of Google Cloud in the Healthcare Industry 2

The Utilization of Google Cloud in the Healthcare Industry

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kapse, Rishikesh Gourshettiwar, Palash Faculty of Engineering and Technology Dept. of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Dept. of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, 'artificial intelligence,' and 'machine learning' applications in diagnostics, diagnostics, and personalized medicine. Data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare. © 2025 IEEE.

关键词： Theranostics

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Prioritizing Network-On-Chip Routers for Countermeasure Techniques against Flooding Denial-of-Service Attacks:A Fuzzy Multi-Criteria Decision-Making Approach

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computer Modeling in engineering & sciences 2025年第3期142卷 2661-2689页

作者： Ahmed Abbas Jasim Al-Hchaimi Yousif Raad Muhsen Wisam Hazim Gwad Entisar Soliman Alkayal Riyadh Rahef Nuiaa Al Ogaili Zaid Abdi Alkareem Alyasseri Alhamzah Alnoor Department of Electromechanical Systems Engineering Thi-Qar Technical CollegeSouthern Technical UniversityBasra61001Iraq Department of Civil Engineering Wasit UniversityAl Kut52001WasitIraq Technical Engineering College Al-Ayen UniversityThi-Qar64001Iraq Department of Artificial Intelligence Engineering College of EngineeringAlnoor UniversityNinavah41012Iraq Information Technology Department Faculty of Computing and Information Technology at Rabigh King Abdulaziz UniversityJeddah22230Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyWasit UniversityAl kut52001WasitIraq Information Technology Research and Development Center University of KufaNajaf54001Iraq College of Engineering University of Warith Al-AnbiyaaKarbala56001Iraq Management Department College of Business Administration(COBA)A’Sharqiyah University(ASU)Ibra400Oman

The implementation of Countermeasure Techniques(CTs)in the context of Network-On-Chip(NoC)based Multiprocessor System-On-Chip(MPSoC)routers against the Flooding Denial-of-Service Attack(F-DoSA)falls under Multi-Criteria Decision-Making(MCDM)due to the three main concerns,called:traffic variations,multiple evaluation criteria-based traffic features,and prioritization NoC routers as an *** this study,we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA ***,an MCDM approach is essential to address these emerging *** the recent MCDM approach has some issues,such as uncertainty,this study utilizes Fuzzy-Weighted Zero-Inconsistency(FWZIC)to estimate the criteria weight values and Fuzzy Decision by Opinion Score Method(FDOSM)for ranking the routers with fuzzy Single-valued Neutrosophic under names(SvN-FWZIC and SvN-FDOSM)to overcome the *** results obtained by using the SvN-FWZIC method indicate that the Max packet count has the highest importance among the evaluated criteria,with a weighted score of *** contrast,the Hop count is identified as the least significant criterion,with a weighted score of *** remaining criteria fall within a range of intermediate importance,with enqueue time scoring 0.1845,packet count decremented and traversal index scoring 0.1262,packet count incremented scoring 0.1124,and packet count index scoring *** terms of ranking,SvN-FDOSM has two approaches:individual and *** the individual and group ranking processes show that(Router 4)is the most effective router,while(Router 3)is the lowest router under *** sensitivity analysis provides a high stability in ranking among all 10 *** approach offers essential feedback in making proper decisions in the design of countermeasure techniques in the domain of NoC-based MPSoC.

关键词： NoC-based MPSoC security flooding DoS attack MCDM FDOSM FWZIC fuzzy set

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Network Management devices in an SDN environment 24

Network Management devices in an SDN environment

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24th International Conference on Control Systems and computer science, CSCS 2023

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Tapus, Nicolae Okumus, Ibrahim Taner Politehnica University of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Erbil Polytechnic University Soran Technical College Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey

ISBN: (纸本)9798350313390

Network optimization and continued availability depend on a number of capabilities that are part of network management. The Maintenance, operating and also offering a safeguarded interaction network is very complicated. It calls for the network operators to grapple with low-level vendor particular arrangements to execute the high degree network policies which are complicated. A method for monitoring networks with OpenFlow controller is presented in this paper in two separate functions for the same networks. Bandwidth utilization, Meter values, charts and statistics are provided by the method to extend controller monitoring capabilities. The method architecture and implementation will be introduced in order to present the feature set. Additionally, softswitches are used as a switch and Mininet to evaluate a virtualized network. This analysis shows whether Meters or ports value is better for network management. © 2023 IEEE.

关键词： Network management

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