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arXiv 2023年

作者： Yigit, Yagmur Bal, Bahadir Karameseoglu, Aytac Duong, Trung Q. Canberk, Berk The Department of Computer Engineering Faculty of Computer and Informatics Istanbul Technical University Turkey The Microsoft Denmark The BTS Group Istanbul Turkey School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom The School of Engineering and Built Environment Edinburgh Napier University United Kingdom The Department of Artificial Intelligence and Data Engineering Istanbul Technical University Turkey

Existing distributed denial of service attack (DDoS) solutions cannot handle highly aggregated data rates;thus, they are unsuitable for Internet service provider (ISP) core networks. This paper proposes a digital twin-enabled intelligent DDoS detection mechanism using an online learning method for autonomous systems. Our contributions are three-fold: we first design a DDoS detection architecture based on the digital twin for ISP core networks. We implemented a Yet Another Next Generation (YANG) model and an automated feature selection (AutoFS) module to handle core network data. We used an online learning approach to update the model instantly and efficiently, improve the learning model quickly, and ensure accurate predictions. Finally, we reveal that our proposed solution successfully detects DDoS attacks and updates the feature selection method and learning model with a true classification rate of ninety-seven percent. Our proposed solution can estimate the attack within approximately fifteen minutes after the DDoS attack starts. © 2023, CC BY.

关键词： Denial-of-service attack

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Smart Warehouse Solutions for Efficient Onion Buffer Stock Management System

Smart Warehouse Solutions for Efficient Onion Buffer Stock M...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Ramakrishnan Raman L. Ramalingam Kamal Kishorbhai Sutaria Manika Kamthan S. Sangeetha S. Murugan Symbiosis International (Deemed University) Symbiosis Institute of Business Management Pune & Pune Maharashtra India Department of Artificial Intelligence and Data Science Chennai Institute of Technology (Autonomous) Chennai Tamil Nadu India Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India Symbiosis Law School Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

This research study presents an innovative smart warehouse system for onion buffer stock management. The system includes an RFID reader, a central controlling unit, a displaying unit, a temperature sensor, a moisture sensor, a relay, a cooling fan, storage nodes, and a cloud server. The RFID reader automatically tracks onion crates or pallets in the warehouse, enabling real-time visibility and traceability. The central controlling unit coordinates activities and collects data from sensors. Onions are stored at appropriate temperatures and moisture levels via sensors. Warehouse workers can monitor this data in real-time on the displaying unit once the central controlling unit processes it. Maintaining temperature and humidity need the relay and cooling fan. Based on sensor data, the main controlling unit activates the cooling fan to avoid onion spoiling. Storage nodes in the warehouse store onion bags and pallets. These nodes have RFID tags or sensors for convenient tracking of stock. data analysis, storage, and integration depend on the cloud server. It allows advanced data analytics, forecasting, and decision-making. It also helps suppliers, distributors, and retailers communicate and distribute products efficiently. Real-time visibility, smart monitoring, and proactive management of storage conditions advance efficient operations, prevent spoiling, and improve onion supply chain efficiency.

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Dynamic colormap visualization integrated with Harris hawks optimization for enhanced lung CT segmentation and diagnostic precision

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Cluster Computing 2025年第6期28卷 1-22页

作者： Drogham, Osama Ryalat, Mohammad H. Al-Najdawi, Nijad Alkhawaldeh, Rami S. AlShaqsi, Jamil Al-Betar, Mohammed Azmi Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Al-Salt Jordan College of Computer and Information Science Prince Sultan University Riyadh Saudi Arabia Department of Computer Information Systems The University of Jordan Aqaba Jordan Information Systems Department Sultan Qaboos University Muscat Oman Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman United Arab Emirates

This study presents a novel method that utilizes Harris Hawks Optimization combined with dynamic colormap visualization to enhance the quality of lung CT scan segmentation. The Harris hawks optimization algorithm is a swarm-based method used to enhance multi-level thresholding for image segmentation, hence facilitating the identification of regions of interest (ROIs) in medical images. An analysis of different colormap schemes including Accent, Gray, Hot, Inferno and Jet, was conducted to improve the visualization of segmented images. The experimental results show the efficiency of the HHO algorithm from the segmentation accuracy perspective as compared to the conventional optimization techniques using publicly available datasets from the Cancer Imaging Archive. In particular, the average SSIM was above 98% while the Jaccard Index was more than 90%. The expert evaluation confirms earlier findings that using the HHO algorithm with the Inferno colormap, particularly with four or five thresholds, achieves optimal image clarity and diagnostic value for clinical purposes. In addition, the method provides a promising way to enhance diagnostic precision and treatment strategies for lung diseases, making it highly valuable for pulmonary healthcare, particularly in urgent scenarios such as pandemics.

关键词： Image segmentation

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Constrained Coverage of Unknown Environment Using Safe Reinforcement Learning

Constrained Coverage of Unknown Environment Using Safe Reinf...

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IEEE Conference on Decision and Control

作者： Yunlin Zhang Junjie You Lei Shi Jinliang Shao Wei Xing Zheng School of Automation Engineering University of Electronic Science and Technology of China Chengdu China School of Artificial Intelligence Henan University Zhengzhou China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

Achieving a connected, collision-free and time-efficient coverage in unknown environments is challenging for multi-agent systems. Particularly, agents with second-order dynamics are supposed to efficiently search and reach the optimal deployment positions over targets whose distribution is unknown, while preserving the distributed connectivity and avoiding collision. In this paper, a safe reinforcement learning based shield method is proposed for unknown environment exploration while correcting actions of agents for safety guarantee and avoiding invalid samples into policy updating. The shield is achieved distributively by a control barrier function and its validity is proved in theory. Moreover, policies of the optimal coverage are centrally learned via reward engineering and executed distributively. Numerical results show that the proposed approach not only achieves zero safety violations during training, but also speeds up the convergence of learning.

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An efficient face recognition attack method based on generative adversarial networks and cosine metrics 5

An efficient face recognition attack method based on generat...

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5th Asian Conference on artificial intelligence technology, ACAIT 2021

作者： Ding, Hu He, Shumeng Wu, Yanwen Jin, Yongli Gan, Lin Xu, Gaodi Yang, Houqun Hainan University School of Computer Science and Technology Hainan Haikou China Chongqing University School of Big Data and Software Engineering Chongqing China Anhui University School of Computer Science and Technology Anhui Hefei China Purdue University Elmore Family School of Computer and Electrical Engineering West Lafayette United States Hainan Association for Artificial Intelligence Hainan Haikou China

ISBN: (纸本)9781665426305

Deep neural networks are vulnerable to attacks on adversarial samples. These attacks are caused by adding small magnitude perturbations to the input samples, which may lead to misclassification of the deep neural network. Based on the study of the adversarial sample attack network model, we propose an attack sample based on the generative adversarial network HNUGAN, incorporating the cosine metric of disparity recognition, for the features of the face dataset, to construct an attack sample to attack the face recognition system. Using these adversarial samples can reduce the recognition accuracy of models such as GoogleNet and ResNet to a very low level, and thus complete the attack on the target model. Interestingly, we can obtain a batch of adversarial samples through adversarial training to expand the dataset and retrain the target model to improve its robustness and resistance to attacks. © 2021 IEEE.

关键词： Face recognition

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A transformer based multi task learning approach to multimodal hate speech detection

Natural Language Processing Journal

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Natural Language Processing Journal 2025年 11卷

作者： Prashant Kapil Asif Ekbal Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar India School of Artificial Intelligence and Data Science Indian Institute of Technology Jodhpur Rajasthan India

Online hate speech has become a major social issue in recent years, affecting both individuals and society as a whole. Memes are a multimodal kind of internet hate speech that is growing more common. Online memes are often entertaining and harmless. The seemingly innocent meme, on the other hand, transforms into a multimodal form of hate speech—a hateful meme—when specific types of text, graphics, or combinations of both are used. The spread of these harmful or undesirable memes has the potential to disrupt societal peace. Therefore, it is vital to limit inappropriate memes on social media. Multimodal hate speech identification is an inherently difficult and open question. It necessitates collaborative language, visual perception, and multimodal reasoning. This line of research has been progressed in this work by building a multi-task learning-based multimodal system for detecting hateful memes by training four hateful meme data sets concurrently. This MTL framework, which consists of Contrastive Language Image Pretraining (CLIP), UNiversal Image-TExt Representation Learning (UNITER), and BERT, was trained collaboratively to transfer common knowledge while simultaneously training four meme datasets. The results show that the recommended strategy outperforms unimodal and multimodal approaches on four multilingual benchmark datasets, with considerable AUC-ROC, accuracy, and F1-score. The ablation studies are undertaken to emphasise the impact of the sub-component in the MTL model. The confusion matrix is shown as quantitative analysis.

关键词： Multi-task learning UNITER CLIP AUC-ROC F1 score

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Investigating the Use of Virtual Private Networks on ATM Network Security

Investigating the Use of Virtual Private Networks on ATM Net...

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International Conference on Computing and Networking technology (ICCNT)

作者： Mohan Garg S Senthurya Jagtej Singh Anila Bajpai Nakul S. Sharma K. Yuvaraj Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement – UG Management JAIN (Deemed to be University) Bangalore Karnataka India Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Digital personal Networks have been increasingly utilized in ATM networks for ease of communication. This paper studies the use of VPNs over ATM networks, providing a synopsis of their features and advantages and discussing bible safety threats. The paper also explains VPNs, their sub-types, and how they may be implemented in ATM surroundings; observing takes place once they provide additional protection capabilities while used on ATM networks; we also talk about the fate of VPNs throughout ATM networks, precisely due to the risk posed by using Internet-based providers. These novel results are encouraging and indicate that VPNs may also offer a solution for securing ATM transactions and protecting network data from threat actors.

关键词： Telecommunication traffic Network security Virtual private networks Cryptography Protection

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GALB: Load Balancing Algorithm for CP-ABE Encryption Tasks in E-Health Environment 5

GALB: Load Balancing Algorithm for CP-ABE Encryption Tasks i...

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5th International Conference on Research in Computational intelligence and Communication Networks, ICRCICN 2020

作者： Taha, Mohammad Bany Chowdhury, Rasel American University of Madaba Data Science and Artificial Intelligence Department Madaba Jordan University of Quebec Software Engineering and Information Technology Department Montreal Canada

ISBN: (纸本)9781728188188

Security of personal data in the e-healthcare has always been challenging issue. The embedded and wearable devices used to collect these personal and critical data of the patients and users are sensitive in nature. Attribute-Based Encryption is believed to provide access control along with data security for distributed data among multiple parties. These resources limited devices do have the capabilities to secure the data while sending to the cloud but instead it increases the overhead and latency of running the encryption algorithm. On the top of if confidentiality is required, which will add more latency. In order to reduce latency and overhead, we propose a new load balancing algorithm that will distribute the data to nearby devices with available resources to encrypt the data and send it to the cloud. In this article, we are proposing a load balancing algorithm for EHealth system called (GALB). Our algorithm is based on Genetic Algorithm (GA). Our algorithm (GALB) distribute the tasks that received to the main gateway between the devices on E-health environment. The distribution strategy is based on the available resources in the devices, the distance between the gateway and the those devices, and the complexity of the task (size) and CP-ABE encryption policy length. In order to evaluate our algorithm performance, we compare the near optimal solution proposed by GALB with the optimal solution proposed by LP. © 2020 IEEE.

关键词： Cryptography

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Study of Structural Optimization on Mg-1ca Interference Screws

SSRN

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SSRN 2024年

作者： Shen, Zhenquan Zhou, Xiaochen Bian, Dong Zhao, Ming Li, Yafei Zheng, Yufeng Faculty of Artificial Intelligence in Education Central China Normal University Wuhan430079 China School of Materials Science and Engineering Peking University Beijing100871 China Department of Orthopedics Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Schlumberger Technology Corporation HoustonTX77054 United States School of Materials Science and Engineering Zhengzhou University Zhengzhou450001 China Department of Materials Science and Engineering China

Biodegradable magnesium alloys have attracted great interest as the matrix materials for the next generation of absorbable metallic interference screws. It is necessary to optimize the structure so that screws can work better in vivo since their degradation is highly related to structural design. In this paper, Optimal Latin Hypercube Sampling technique (OLHS) is used to sample in the design space. The pull-out strengths of sample points as optimization objective are calculated through a continuum damage model (CDM) implemented into a finite element analysis (FEA). Response surface model (RSM) and Kriging model are respectively used as surrogate model for this optimization problem to compare. Both of the surrogate models are optimized based on a sequential quadratic programming (SQP) strategy. It is found that the result of Kriging model is acceptable after FEA verification. Relationship between parameters, rationality and limitation of this method are discussed. In the end, a final design and some advice about interference screw design are provided. © 2024, The Authors. All rights reserved.

关键词： Finite element method

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An Improved Method for Fault Detection Electric Power System Protection using SVMCNN Model

An Improved Method for Fault Detection Electric Power System...

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Electronics, Computing, Communication and Control technology (ICECCC), International Conference on

作者： NMG Kumar S.Shanmuga Priya Vineetha Varghese Renuga K Jyoti Prasad Patra Neerav Nishant Dept. of EEE Mohan Babu University Sree Vidyanikethan Engineering College Rangam Pet Tirupathi India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Rathinam Technical campus Coimbatore Tamilnadu India Department of Mathematics St. Joseph's Institute of Technology OMR Chennai Tamilnadu India EE And EEE Krupajal Engineering College-KEC Puri Odisha India Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

Multiple factors outside our control can contribute to the unanticipated breakdown of electrical power networks. It is of the utmost importance to prevent unforeseen power system issues from affecting other components of the protective system. It is the job of the protective systems to identify and categorize potentially dangerous voltage and/or current lines. To isolate the malfunctioning line, utilize the protection relay to trigger the circuit breaker. An SVM-CNN architecture is presented in this study for the purpose of detecting power outages in transmission lines. When it comes to preprocessing, feature selection, and training models, the correct sequence of activities is crucial. For anomaly-based fault detection algorithms to accurately represent and understand the data, data preprocessing is essential. Feature selection makes the process more feasible by reducing the entire data volume and helping HIF detection home in on the most relevant features. The results were evident with an accuracy improvement reaching 95.70%.

关键词： Training Support vector machines Power transmission lines Fault detection Computational modeling data preprocessing Feature extraction

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