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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Karpurasundharapondian, P. Selvi, M. Thangaramya, K. Santhosh Kumar, S.V.N. Kannan, A. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Security and secure routing are important design issues in the design of Wireless Sensor Networks (WSNs). Intrusion Detection systems (IDSs) are useful for securing the communication in WSNs. An IDS can be developed by using machine learning based approach. On the other hand, it can be developed using a deep learning classification algorithm. Optimization techniques including Particle Swarm Optimization (PSO) are useful for choosing optimal features from given dataset. This paper proposes an extended CNN algorithm by extending the existing CNN classifier with rules, SVM and PSO named Rule Extended CNN with SVM (RE-SVM-CNN) for developing this IDS which reduces the false alarm rate by enhanced detection accuracy. © 2024 IEEE.

关键词： Wireless sensor networks

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Predictive Model for Accident Severity

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2022年第1期49卷 110-124页

作者： Mostafa, Solwan M. Salem, Sameh A. Habashyis, Shahira M. Teaching Assistant and a Master Student of Computer and Systems Department Faculty of Engineering Helwan University Cairo11792 Egypt Professor of Computer and Systems Department Faculty of Engineering Helwan University Cairo11792 Egypt Associate Professor of Computer and Systems Department Faculty of Engineering Helwan University Cairo11792 Egypt

In the development of sustainable transportation, traffic safety is a significant matter, and predicting the severity of traffic accidents is still a critical problem in the traffic safety field. However, the utilized road traffic accidents (RTAs) datasets suffer from imbalance distribution. This problem leads to a decrease in classification performance, specifically in predicting the minority classes. This paper aims to treat the class imbalance problem through Synthetic Minority Over-sampling Technique (SMOTE), Support Vector Machine-SMOTE (SVM-SMOTE), Borderline-SMOTE(BL-SMOTE), and Adaptive Synthetic (ADASYN), along with proposing an accurate predictive model for accident severity. Different accident severity models are employed, namely Random Forest (RF), K-Nearest Neighbor (KNN), Naïve Bayes (NB) classifiers, Decision Tree (DT), and Extra Trees (ET). These models are tested using real-world datasets. Several evaluation metrics are used to evaluate the proposed model. Experimental results show that the proposed model significantly improves predicting both the minority and majority classes. These results indicate the robustness and reliability of the proposed predictive model in enhancing road traffic safety and management. © 2022. All Rights Reserved.

关键词： Support vector machines

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Importance of Realistic Considerations of Time Synchronization for TAS in TSN Networks

Importance of Realistic Considerations of Time Synchronizati...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Ghosh, Aviroop Kunz, Thomas Yousefi, Saleh Carleton University Department of Systems and Computer Engineering Ottawa Canada Urmia University Department of Computer Engineering Urmia Iran

ISBN: (纸本)9798350335590

Industry 4.0 applications require communication networks with deterministic and bounded latency. Ethernet, augmented with the IEEE Time-Sensitive Networking (TSN) standards, can meet these requirements. One of the TSN standards, Time Aware Shaper (TAS) is ideal for periodic time-sensitive traffic in wired TSN networks. However, it relies on network-wide synchronization to a common reference time. Existing solutions for developing TAS schedules assume perfect synchronization or consider a worst-case error. This study highlights the need for using network-derived time synchronization parameters and errors to optimize TAS schedules. It raises the importance of accurate time synchronization information and proposes a novel methodology for efficient band-width utilization while ensuring deadline requirements are met for time-sensitive traffic in realistic networks. The effectiveness of the proposed framework is validated by simulating a TSN network in OMNeT++ and comparing the devised TAS schedules with schedules which use existing assumptions. © 2023 IEEE.

关键词： Ethernet

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A Comprehensive Review on Deep Learning Techniques on Cyber Attacks on Cyber Physical systems

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SN computer Science 2024年第7期5卷 891页

作者： Sagar, Maloth Vanmathi, C. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore 632014 India

Cyber-Physical systems (CPSs), which integrate control, computing, and communication, are considered as next-generation intelligent systems.A major concern in CPS is to ensure security. If security is not ensured, then it leads to both property damage and *** CPS is closely associated with real life, it is a hot research area in present security research. Nevertheless, cyber security issues are also raised. More efforts are still required for understanding CPS security from a more comprehensive perspective even though there are several studies on CPS *** identify and detect cyber security in CPS, numerous methods have been applied. The majority of recently published studies focused on Machine Learning (ML) techniques, with little attention paid to the development of Deep Learning (DL). Hence, the analysis of cyber security on CPS utilizing several algorithms of DL techniques is systematically *** appropriate cyber security risk levels and their corresponding detection and mitigation processesare determined. This study could augment the adaptability of the system of CPS data. Also, it could increase the generalization of the models of security attacks in CPS. Using DL algorithms is strongly recommended by most authors as a means to identify cyber security issues in *** CNN-centric LSTM, Deep Multi-architectural Approach, and DL-Meta classifier attained the highest accuracy in the detection rate of cyber security attacks in CPS when analogized with the other algorithms in DL techniques. Hence, for several critical systems in CPSs, the result renders clear guidelines to apply other regularization on DL techniques. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CPS Cyber security Cyber-attack Cyber-attack detection Deep learning Risk analysis Security threats

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Detailed Study on Various Techniques for Detecting Railway Track Anomaly 2

Detailed Study on Various Techniques for Detecting Railway T...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Komarasamy, Dinesh Iyapparaja, M. Mohana Saranya, S. Cibi Chakravarthy, M. Fredolin Tibba, B. Gayathri, R. Kongu Engineering College Department of Computer Science and Engineering Erode Tamil Nadu Perundurai India School of Computer Science Engineering and Information Systems Vit Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

A vital part of today's transportation infrastructure is the railway system that facilitates the massive movement of people and products. With so many people and commodities being transported, the rail tracks could develop defects and flaws over time. The development of these defects might jeopardize the safety and integrity of the rails due to persistent wear and tear, exposure to harsh environmental factors and the enormous loads carried by trains. Hence, to prevent faults and to avoid accidents, it is essential to maintain track safety. Thus, researchers focused on different technologies with different methodologies for detecting the defect that occurred in rail tracks as early as possible. Therefore, this paper summarizes the available methods and technologies for detecting defects and faults in railway tracks. © 2024 IEEE.

关键词： Defects

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Smart Business with Intelligent Business Cards (SBIBC)

Smart Business with Intelligent Business Cards (SBIBC)

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Kirubakaran, N. Preethi, S.T. Parijatham, R. Pramithi, R. Chennai Institute of Technology Department of Computer Science and Business Systems Chennai India S.A. Engineering College Department of Computer Science and Engineering Chennai India Madha Institute Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

The research emphasizes the creation of a powerful and efficient system for the automaticextraction of contact information from physical calling cards through computer vision and information extraction techniques. This is achieved by integrating Tesseract Optical Character Recognition (OCR) with the YOLO (You Only Look Once) object detection algorithm, which acts as a dynamic combination for text and relevant elements identification and digitization from calling cards. The developed system uses client-server architecture to enable communication and data processing seamlessly. The server handles computationally intensive tasks such as object detection, OCR processing, and data validation while the client side has an interactive user interface that enables easy uploading of images and retrieval of structured contact information. The optimizations include this: YOLO is customized for detecting particular card elements (name, phone number, email) and enhancing the capabilities of Tesseract OCR to improve effectiveness for text extraction from varied card formats or fonts in the system. It is designed to be scalable and efficient, processing large datasets without affecting accuracy. This approach not only eases the digitization of calling card details into databases but also goes a long way in contributing to the domains of computer vision, object detection, and mechanized information extraction. It extends the scope to broad applications in automated data entry, customer relationship management (CRM), and digital archival. It takes forward for AI-driven sustaining technologies the integration of these techniques into practical solutions by overcoming the challenges of data extraction from real-world images. © 2024 IEEE.

关键词： Optical character recognition

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Design an optimal FOPD controller for electric vehicle speed tracking control 7

Design an optimal FOPD controller for electric vehicle speed...

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7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Abood, Layla Hattim Abood, May H. Action, Dalal Hammood University of Technology Department of Control and Systems Engineering Baghdad Iraq Al-Iraqia University Department of Computer Engineering Baghdad Iraq Mtu Edu Department of Computer Engineering Baghdad Iraq

ISBN: (纸本)9798331505103

Electric Vehicles (EVs) become very important issue and gained attention due to many reasons like its economic price, saving environment and more reliable. In this study, controlling speed for EV is utilized by tracking the desired speed using Fractional Order Proportional Derivative (FOPD) controller with a smart tuning algorithm named Sunflower Optimization (SFO) algorithm to satisfy robust and efficient controlled speed. The numerical analysis is obtained using Matlab program in two steps, controlling the system to reach the desired value in best and stable response then apply two undesired signals (disturbance signals) to test system robustness and finally compare the suggested controller with different conventional controllers(Normal PD controller-Optimal PD controller). © 2024 IEEE.

关键词： MATLAB

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On Detecting Malicious Code Injection by Monitoring Multi-Level Container Activities 14

On Detecting Malicious Code Injection by Monitoring Multi-Le...

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14th International Conference on Cloud Computing and Services Science, CLOSER 2024

作者： Bhuiyan, Md. Olid Hasan Das, Souvik Majumder, Shafayat Hossain Majumdar, Suryadipta Hossain, Md. Shohrab Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh Concordia Institute for Information Systems Engineering Concordia University Canada

ISBN: (纸本)9789897587016

In recent years, cloud-native applications have been widely hosted and managed in containerized environments due to their unique benefits, such as being lightweight, portable, and cost-efficient. Their growing popularity makes them a common subject of cyberthreats, as evidenced by recent attacks. Many of those attacks take place due to malicious code injection to breach systems and steal sensitive data from a containerized environment. However, existing solutions fail to classify malicious code injection attacks that impact multiple levels (e.g., application and orchestrator). In this paper, we fill in this gap and propose a multi-level monitoring-based approach where we monitor container activities at both the system call level and the container orchestrator (e.g., Kubernetes) level. Specifically, our approach can distinguish between the expected and unexpected behavior of a container from various system call characteristics (e.g., sequence, frequency, etc.) along with the activities through event logs at the orchestrator level to detect malicious code injection attacks. We implement our approach for Kubernetes, a major container orchestrator, and evaluate it against various attack paths outlined by the Cloud Native Computing Foundation (CNCF), an open-source foundation for cloud native computing. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Network security

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Detection of Cyberbullying in Arabic using Machine Learning and ChatGPT 5

Detection of Cyberbullying in Arabic using Machine Learning ...

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Sadek, Ahmed Khalil, Mahmoud I. Salama, Cherif Ain Shams University Computer and Systems Engineering Department Cairo Egypt Ain Shams University American University in Cairo Computer and Systems Engineering Department Cairo Egypt

ISBN: (纸本)9798350381030

Cyberbullying is one of the complicated problems in Arabic communities. Cyberbullying detection is the main key to control racism and hate speech over the Internet especially on social media. Detection of cyberbullying in Arabic is challenging due to the variety of expressions and different Arabic dialects. Many researches discussed this topic using only one dialect and achieved good results. In this paper, we consolidated datasets with multiple Arabic dialects from different social media platforms. Text pre-processing enhanced the performance of the used models. We classified the comments in the dataset using Naive Bayes model, AraBERT model and ChatGPT for comparison purposes. The experiments showed that AraBERT outperformed the other classifiers for our multi-dialect dataset showing its potential for effectively monitoring Arabic content over the Internet. © 2023 IEEE.

关键词： Sentiment analysis

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RT-YOSO: Revisiting YOSO for Real-time Panoptic Segmentation 5

RT-YOSO: Revisiting YOSO for Real-time Panoptic Segmentation

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Ammar, Abdallah Khalil, Mahmoud I. Salama, Cherif Ain Shams University Computer and Systems Engineering Department Cairo Egypt Ain Shams University American University in Cairo Computer and Systems Engineering Department Cairo Egypt

ISBN: (纸本)9798350381030

Panoptic segmentation, a crucial computer vision task for scene understanding, simultaneously combines semantic segmentation and instance segmentation to classify pixels and identify object instances in images. Despite significant progress in this field, real-time performance with deep learning methods has yet to reach its full potential. In this paper, we present a novel adaptation of the "You Only Segment Once"(YOSO) architecture, designed for real-time panoptic segmentation. Our primary contribution involves substituting YOSO's original ResNet50 backbone with the time-efficient STDC networks. Our "Real-time-YOSO"(RT-YOSO) model significantly improves real-time performance, demonstrating its compatibility with robotic systems and autonomous driving scenarios. Thorough experiments on the Cityscapes dataset confirm the robustness and accuracy of our model, achieving 59.2% and 51.2% PQ with corresponding frame rates of 18.5 and 27.8 FPS, respectively. This establishes the best accuracy-speed trade-off by maintaining comparable accuracy while delivering an average of 50% higher FPS. The source code is publicly available at https://***/AbdallahReda/RT-YOSO © 2023 IEEE.

关键词： Semantic Segmentation

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