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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Jeng-Shyang Pan Yingying Wang Shu-Chuan Chu Ning Zhong College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Management Chaoyang University of Technology Taiwan Department of Life Science and Informatics Maebashi Institute of Technology Maebashi Japan Faculty of Information Technology Beijing University of Technology Beijing China

Gannet optimization algorithm (GOA) is a meta-heuristic algorithm based on habits of gannet proposed by Zhang et al. In this paper, we propose a Gannet optimization algorithm using parallel strategy (PGOA). Since the GOA algorithm has the risk of easily falling into local optimality, the use of the parallel strategy can largely avoid falling into local optimality. Therefore, we use the parallel strategy to improve the GOA algorithm, which greatly improves the performance and efficiency of the algorithm. The improved algorithm is applied to image segmentation, and the processed images are evaluated using PSNR, SSIM, and FSIM as evaluation metrics. The experimental results show that the improved GOA algorithm can achieve higher quality image segmentation compared to other algorithms on image segmentation,

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Reliability-considered Multi-platoon's Groupcasting using the Resource Sharing Method

arXiv

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arXiv 2024年

作者： Huang, Chung-Ming Wu, Yen-Hung Dao, Duy-Tuan Dept. of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Faculty of Electronics and Telecommunications Engineering The University of Danang-University of Science and Technology Viet Nam

In the context of 5G platoon communications, the Platoon Leader Vehicle (PLV) employs groupcasting to transmit control messages to Platoon Member Vehicles (PMVs). Due to the restricted transmission power for groupcasting, it may need to pick one PMV as the Platoon Relay Vehicle (PRV) to be responsible for re-groupcasting messages of PLVs. To optimize the usage of limited spectrum resources, resource sharing can adopted to enhance spectrum efficiency within the platoon. This study proposes a resource allocation method, which is called Resource Sharing for Platoon Groupcasting (RSPG), for platoon’s groupcasting based on transmission reliability. RSPG utilizes the tripartite matching to assign a subchannel to either a PLV or PRV that shares the assigned subchannel with the corresponding individual entity (IE), which does not belong to any platoon. The simulation results shown that the proposed method has the better performance in terms of the QoS satisfaction rate of IEs, the number of allocated subchannels for platoons, and spectral efficiency. © 2024, CC BY.

关键词： 5G mobile communication systems

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2D-DOA for a Monostatic ULA EMVS-MIMO Radar Based on RC-PARAFAC

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IEEE Sensors Journal 2025年

作者： Wang, Jianlong Shi, Junpeng Wen, Fangqing China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hvdroelectric Engineering College of Computer and Information Technology Yichang443002 China National University of Defense Technology College of Electronic Countermeasure Hefei230037 China City University of Macau College of Computer and Information Technology China Three Gorges University Faculty of Data Science 999078 China

The electromagnetic vector sensor (EMVS) embedded multiple-input multiple-output (MIMO) radar is an emerging technology capable of two-dimensional (2D) direction of arrival (DOA) estimation. In this paper, we propose a low-complexity algorithm for monostatic uniform linear array (ULA) EMVS-MIMO radar to rapidly estimate 2D-DOA. First, we decompose the received signal into a third-order tensor using parallel factor analysis (PARAFAC). Next, we eliminate data redundancy through a reduced complexity (RC) process. Then, we use the least squares method to extract ambiguous elevation angle information from the spatial response matrix and apply the vector cross-product (VCP) technique to the electromagnetic vector to achieve unambiguous 2D-DOA estimation. Finally, we combine these estimates to achieve high-resolution, unambiguous elevation angle estimation. The proposed algorithm is suitable for large-scale and sparse EMVS-MIMO radar systems. © 2001-2012 IEEE.

关键词： Direction of arrival

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Investigating End-to-End Delay in Underwater High Speed Communications

Investigating End-to-End Delay in Underwater High Speed Comm...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sharma, Yogesh K. Pareek, Ravi Kant Spoorthi, B. Das, Neeraj Sood, Gourav Kalpana, B. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India

ISBN: (纸本)9798350329773

The studies paintings ambitions to analyze the cease-to-cease postpone in underwater high-velocity communications using numerous conversation techniques. It's far primarily based on the development of a communique machine able to reliably and efficaciously transmitting information over an extended-range underwater channel. The proposed device is based totally on several novel strategies, including acoustic time reversal, linear frequency modulated waveforms, and a combination of time-area equalization with frequency-domain optimization. All these will be accomplished to maximize transmitter efficiency whilst preserving data integrity. Further, the empirical consequences can be analyzed to determine the highest quality conversation parameters and power-put off alternate-off to be able to be proven and evaluated the use of semi-analytical strategies. The investigations will offer records approximately the impact of channel impairments, stochastic noise, and their outcomes on the system's overall performance. Eventually, overall performance evaluation may be completed to compare the gadget performance with the prevailing protocols and standards. © 2023 IEEE.

关键词： Stochastic systems

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Feature-Aware Malicious Output Detection and Mitigation

arXiv

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arXiv 2025年

作者： Dong, Weilong Li, Peiguang Tian, Yu Zeng, Xinyi Li, Fengdi Wang, Sirui Tianjin University China Meituan Group China Dept. of Computer Science and Technology Institute for AI Tsinghua University China Aerospace Information Research Institute Chinese Academy of Sciences China Faculty of Information Technology Monash University Australia

The rapid advancement of large language models (LLMs) has brought significant benefits to various domains while introducing substantial risks. Despite being fine-tuned through reinforcement learning, LLMs lack the capability to discern malicious content, limiting their defense against jailbreak. To address these safety concerns, we propose a feature-aware method for harmful response rejection (FMM), which detects the presence of malicious features within the model’s feature space and adaptively adjusts the model’s rejection mechanism. By employing a simple discriminator, we detect potential malicious traits during the decoding phase. Upon detecting features indicative of toxic tokens, FMM regenerates the current token. By employing activation patching, an additional rejection vector is incorporated during the subsequent token generation, steering the model towards a refusal response. Experimental results demonstrate the effectiveness of our approach across multiple language models and diverse attack techniques, while crucially maintaining the models’ standard generation capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Malay SMS Spam Detection Tool Using Keyword Filtering Technique 1

Malay SMS Spam Detection Tool Using Keyword Filtering Techni...

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1st International Recent Trends in technology, Engineering and Computing Conference, IRTTEC 2020

作者： Hanif, Ellya Izatty Foozy, Cik Feresa Mohd Hamid, Isredza Rahmi A. Mohamed, Rozlini Yusof, Munirah Mohd Aziz, Ruhaya Ab. Shamala, Palaniappan Faculty of Computer Science and Information Technology University Tun Hussein Onn Malaysia Johor Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Johor Malaysia

Message Service (SMS) Spam is one form of mobile device attack that can affect mobile user's security and privacy. This study developed a Malay SMS Spam detection framework specifically for Malay language. The paper discusses about the spam word detection using keywords filtering technique and security question to enable authorized user reset password when forgot the password. The development of the detection tool is done using the Object Oriented Methodology (OOM). There are four phases in OOM which is Requirement Phase, Analysis Phase, Design Phase and Coding Phase. This detection tool is then further analyzed to evaluate the functionality. This project also compares the existing tool to know the lack of current existing tool. Most of the existing spam tools are capable of identifying English text-based messages but there are close to none to filter the Malay language text-based SMS spam. It is expected that the developed tool is able to detect the SMS spam by using keywords filtering technique in Malay language. © Published under licence by IOP Publishing Ltd.

关键词： Authentication

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Evaluation of Metaverse Tools Based on Privacy Model Using Fuzzy MCDM Approach 1

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3rd International Multi-Disciplinary Conference - Theme: Integrated sciences and Technologies, IMDC-IST 2024

作者： Husin, Nor Azura Abdulsaeed, Ali A. Muhsen, Yousif Raad Zaidan, Ali Shakir Alnoor, Alhamzah Al-mawla, Zahraa Raad Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Malaysia Civil Department College of Engineering Wasit University Kut Iraq School of Management Universiti Sains Malaysia Pulau Pinang11800 Malaysia Management Technical College Southern Technical University Basrah Iraq Department of Education University of Al-Mustansiriyah Baghdad10001 Iraq

ISBN: (数字)9783031517167

ISBN: (纸本)9783031517150

The issue of uncertainty in decision modelling for control engineering tools that support industrial cyber-physical metaverse smart manufacturing systems (ICPMSMSs) can be classified as a multi-criteria decision-making challenge for three primary reasons. Firstly, there are multiple components in the industry cyber-physical system (ICPS) that need to be taken into account. Secondly, these components vary in terms of their importance. Lastly, there is a variation in the available data. Despite substantial efforts, neither of the control engineering tools developed for ICPMSMSs has been capable of completely satisfying all ICPS aspects. As a result, choosing the best possible tool to support ICPMSMSs is a difficult process. Even though studies have evaluated control engineering tools, there are still unresolved issues regarding information ambiguity and uncertainty. In order to overcome these issues, this research improves the fuzzy weighted with zero inconsistency (FWZIC) method by adding Hexagonal Fuzzy Numbers to the process of assigning weights to the ICPS’s multiple components. Subsequently, the established Hexagonal-FWZIC method will be combined with the additive ratio assessments (ARAS) method to handle the rank in modelling control engineering tools. According to the results of the Hexagonal-FWZIC analysis, cybersecurity is the most influential factor, while the digital twin component has the least. The ARAS findings indicate that, of the ten tools studied, tools_1, tool_3, and tool_4 are the best three tools. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cyber Physical System

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Intelligent Detection System for Multi-Step Cyber-Attack Based on Machine Learning

Intelligent Detection System for Multi-Step Cyber-Attack Bas...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Khattab M. Ali Alheeti Abdulkareem Alzahrani Omar Hammad Jasim Duaa Al-Dosary Hamsa M. Ahmed Muzhir Shaban Al-Ani Computer Networking Systems Department University of Anbar Ramadi Iraq Faculty of Computer Science and Information Technology Al Baha University Al Baha Saudi Arabia Human Resources Department Ramadi Iraq College of Computer Sciences and Information Technology University of Anbar Anbar Iraq Department of Information Technology University of Human Development College of Science and Technology Sulaymaniyah Iraq

Cyber-attacks involve stifling processes and activities, conciliating data, or restricting data access by carefully modifying computer systems and networks with malware. There has been a significant increase in these types of attacks over time. Due to the rise in complexity and structure, advanced defensive methods are needed. In the face of growing security threats, traditional methods of identifying cyber-attacks are ineffective. In this paper, the intelligent of intrusion a detection system is suggested. Moreover, the suggested system attempts to evaluate the capability of the k-nearest neighbour's algorithm (KNN) in terms of distinguishing between authentic and tampered data. A reliable dataset named the Multi-Step Cyber-Attack Dataset (MSCAD) is utilized to determine the behavior Among the new sorts of attacks. Moreover, 60% of the dataset was utilized for training the model, and a remaining 40% was used for testing. Evaluation metrics like accuracy, precision, recall, and F1 score are used. Experiments suggest that the proposed system-based KNN could enhance detection performance. Moreover, the suggested approach increases detection accuracy while minimizing false alarms.

关键词： Measurement Training Intrusion detection Machine learning Malware Complexity theory Reliability

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Integrating Machine Learning and OBIA for Vegetation Classification in Archived Grayscale Aerial Imagery

Integrating Machine Learning and OBIA for Vegetation Classif...

引用

International Conference on information technology (INCIT)

作者： Kumpee Teeravech Phummipat Ounban Wira Srimala Taweesak Samma Kraisri Sawangsri Kittakorn Viriyasatr Warakorn Luangluewut Chamnan Kumsap Faculty of Computer Science and Information Technology Rambhai Barni Rajabhat University Chanthaburi Thailand Department of Special Investigation Ministry of Justice Nonthaburi Thailand Defence Technology Institute Nonthaburi Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

This study explores the use of machine learning models to classify water, vegetation, and non-vegetation land cover types in archived grayscale aerial imagery. The input images are segmented using a superpixel algorithm, and the resulting segments are mapped to expert-provided reference data. The region-based and patch-based approaches are evaluated using artificial neural networks and convolutional neural networks, respectively. The region-based method achieves an average accuracy of 0.83, while the patch-based method reaches 0.79. Although the patch-based method shows slightly lower overall accuracy, it significantly improves recall rates, particularly for the water and non-vegetation classes.

关键词： Image segmentation Accuracy Machine learning algorithms Vegetation mapping Land surface Machine learning Gray-scale Classification algorithms Convolutional neural networks information technology

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Enhancing Vegetable Quality Prediction with Fuzzy Interference System 2

Enhancing Vegetable Quality Prediction with Fuzzy Interferen...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Al-Rajab, Murad Asif, Muhammad Chuhan, Saad Hussain Mustafa, Muzzamil Ilyas, Amna Kamran, Rukshanda Ahmad Abazeed, Ashraf Riad Abu Saima, Mahmoud Geeta, Sai Abu Dhabi University Computer Science and IT College of Engineering United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Universiti Malaysia Faculty of Compter Science and Information Technology Sarawak94300 Malaysia ADVETI Abu Dhabi Vocational Education and Training Institute United Arab Emirates United Arab Emirates University College of Science United Arab Emirates Institute of Management Technology Dubai Head QAIE and Accreditation United Arab Emirates

ISBN: (纸本)9798350335644

We are living in this hurly burly world of Tumult and Turmoil where many problems arises who have not any exact solution/answer like we have health and nutrition problems people facing difficulties to select best vegetable for their health and if they select best vegetable for themselves they don't know about the quality of the vegetable which they have selects. In the vegetable processing industry, some manufacturers add extra ingredients to prolong the shelf life and maintain the quality of their products. However, excessive amounts of these ingredients can have negative health implications such as palpitations, headaches, allergies, and even cancer. Therefore, it is crucial to implement a system to assess the quality of vegetables being used, which can provide consumers and patients with information regarding their quality content. This system is particularly beneficial for addressing human-related issues, especially in determining the percentage of quality. Various methods have been established to achieve optimal solutions in response to rapidly changing living conditions. This paper proposes the development of a fuzzy-based system that takes inputs such as season, time, and condition to detect the quality of vegetables. The output of this system is determined using Kappa statistics. © 2023 IEEE.

关键词： Vegetables

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