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Conservative-Progressive Collaborative Learning for Semi-Supervised Semantic Segmentation

IEEE Transactions on Image Processing 2023年 32卷 6183-6194页

作者： Fan, Siqi Zhu, Fenghua Feng, Zunlei Lv, Yisheng Song, Mingli Wang, Fei-Yue Institute of Automation State Key Laboratory of Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China Tsinghua University Beijing100190 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China

Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by the high-quality labels to ensure a more reliable supervision, while the other network reserves differences via union supervision and is supervised by all the pseudo labels to keep exploring with curiosity. Thus, the collaboration of conservative evolution and progressive exploration can be achieved. To reduce the influences of the suspicious pseudo labels, the loss is dynamic re-weighted according to the prediction confidence. Extensive experiments demonstrate that CPCL achieves state-of-the-art performance for semi-supervised semantic segmentation. © 1992-2012 IEEE.

关键词： Semantic Segmentation

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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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computer systems science & Engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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Maximising Influence Spread in Complex Networks by Utilising Community-Based Driver Nodes as Seeds 1

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9th Annual International Conference on Information Management and Big Data, SIMBig 2022

作者： Sadaf, Abida Mathieson, Luke Bródka, Piotr Musial, Katarzyna Complex Adaptive Systems Lab School of Computer Science University of Technology Sydney Sydney Australia Department of Artificial Intelligence Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9783031354458

ISBN: (纸本)9783031354441

Finding a small subset of influential nodes to maximise influence spread in a complex network is an active area of research. Different methods have been proposed in the past to identify a set of seed nodes that can help achieve a faster spread of influence in the network. This paper combines driver node selection methods from the field of network control, with the divide-and-conquer approach of using community structure to guide the selection of candidate seed nodes from the driver nodes of the communities. The use of driver nodes in communities as seed nodes is a comparatively new idea. We identify communities of synthetic (i.e., Random, Small-World and Scale-Free) networks as well as twenty-two real-world social networks. Driver nodes from those communities are then ranked according to a range of common centrality measures. We compare the influence spreading power of these seed sets to the results of selecting driver nodes at a global level. We show that in both synthetic and real networks, exploiting community structure enhances the power of the resulting seed sets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Complex networks

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FACE UNLOCK SYSTEM FOR VEHICLES 2

FACE UNLOCK SYSTEM FOR VEHICLES

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Navya, Soleti Suresh, Juttu Sangeetha, A. Praveen, G. Nabi, Shaik Abdul Kumar, Dilip Mlr Institute of Technology Department of Computer Science and Information Technology India Malla Reddy Engineering College and Management Sciences Department of Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Cse Dept Avniet Jntuh India Mlr Institute of Technology Dept. of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350370188

Currently, all vehicles are key operated. Our intention is therefore to develop an intelligent unlocking system based on facial recognition technology. In Today's era of automation and smart devices, privacy and security are important issues in information technology so to information system, so it is urgent to change system security measures. In previous systems, many vehicle doors had mechanical locks with a limited number of keys. To enhance security, we propose an intelligent vehicle door unlocking system based on facial recognition the system uses camera sensor to recognize faces and image matching algorithms to recognize authenticated faces. Only people with matching faces can open the door this system not only provides additional security but also useful for systems without physical keys. © 2024 IEEE.

关键词： Network security

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Enterprise Financial Fraud Detection and Audit Optimization Based on Deep Learning

Enterprise Financial Fraud Detection and Audit Optimization ...

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作者： Zhao, Peng Deng, Liquan Department of Financial Management Gingko College of Hospitality Management Chengdu China School of Computer Science Chengdu College of University of Electronic Science and Technology of China Chengdu China

Aiming at the problem of enterprise financial fraud detection, this study aims to improve the accuracy and efficiency of detection through deep learning (DL) technology, so as to optimize the audit process and provide intelligent financial management guidance for enterprises. To achieve this goal, an advanced financial fraud detection model based on DL is designed and verified. This research is not only devoted to automatic extraction and in-depth analysis of complex features in enterprise financial data through self-defined DL model architecture, but also enables the model to accurately learn the key patterns of identifying fraud through large-scale and high-dimensional financial data training. Furthermore, the DL model is integrated into the traditional audit process, which realizes the automation and intelligent upgrade of the process. The results show that the proposed DL model has achieved excellent performance improvement in financial fraud detection, and all the evaluation indexes are over 90%, which is obviously superior to the traditional methods. This study not only provides a new technical means to ensure the financial security of enterprises, but also enhances the value of audit work. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decentralized finance

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Reinforcement-Based Collision Avoidance Strategy for Autonomous Vehicles to Multiple Two-Wheelers at Un-Signalized Obstructed Intersections

Reinforcement-Based Collision Avoidance Strategy for Autonom...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Delei Qi, Liang Luan, Wenjing Yang, Ruiping Liu, Kun Shandong Vocational College of Culture and Art Shandong Dezhou251114 China The College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China

ISBN: (纸本)9781665410205

Two-Wheelers (TWs) such as bikes, e-bikes, and motorcycles often occupy lanes illegally and exceed speed limits, which leads to many traffic accidents. Therefore, we use deep reinforcement learning to design driving strategies for Autonomous Vehicles (AVs) to avoid collision with TWs and reduce injury of TW riders with irregular riding behaviors at un-signalized occluded intersections. First, the collision-avoidance behaviors of TWs are modeled, respectively. The state spaces integrate a safe avoidance range of AVs, a new position of AVs after taking deceleration and a steering angle, a predicted acceleration, and position, speed, and steering angle of AVs and other vehicles. At the same time, a reward function is designed based on the injury of TW riders and the driving safety and comfort of AVs. Secondly, a reinforcement learning model for autonomous driving strategy is constructed. Finally, Soft Actor-Critic is used to train the model, and the randomness policy is used to help AVs flexibly deal with the uncertain behaviors of TW riders and realize the balance between exploring unknown behaviors and using existing information. The simulation results show that compared with an autonomous emergency braking system, the injury of the riders using the driving strategy is reduced by 18.02% on average;compared with a risk-aware high-level decision strategy, the injury is reduced by 41.24% on average. © 2024 IEEE.

关键词： Steering

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Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

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Intelligent Automation & Soft Computing 2023年第5期36卷 1603-1612页

作者： Mahendrakumar Subramaniam V.Vedanarayanan Azath Mubarakali S.Sathiya Priya Department of Electronics and Communications Engineering Velalar College of Engineering and TechnologyErodeTamil NaduIndia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennaiTamil NaduIndia College of Computer Science King Khalid UniversityAbhaSaudi Arabia Department of Electronics and Communications

Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business *** of Service(QoS)has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors,information and ***-sor data gathering is an efﬁcient solution to collect information from spatially dis-seminated IoT *** Learning Mechanism to improve the QoS(RLMQ)and use a Mobile Sink(MS)to minimize the delay in the wireless IoT s proposed in this ***,we use machine learning concepts like Rein-forcement Learning(RL)to improve the QoS and energy efﬁciency in the Wire-less Sensor Network(WSN).The MS collects the data from the Cluster Head(CH),and the RL incentive values select *** incentives value is computed by the QoS parameters such as minimum energy utilization,minimum bandwidth utilization,minimum hop count,and minimum time *** MS is used to col-lect the data from CH,thus minimizing the network *** sleep and awake scheduling is used for minimizing the CH dead in the *** work is simu-lated,and the results show that the RLMQ scheme performs better than the base-line *** prove that RLMQ increased the residual energy,throughput and minimized the network delay in the WSN.

关键词： Wireless sensor network quality of service parameters Internet of things reinforcement learning mobile sink

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Exploring the Role of Internet of Things in Wireless Sensor Networks for Industry 4.0 Applications 1

Exploring the Role of Internet of Things in Wireless Sensor ...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Palarimath, Suresh Maran, Pyingkodi N.r, Wilfred Blessing Sujatha, T. Renuga, S. R.s, Chithra College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Ponjesly College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350365016

Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings. © 2024 IEEE.

关键词： Wireless sensor networks

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MedPlantBot: AI Chatbot Architectural Design Framework for Responsible Use of Medicinal Plants 3

MedPlantBot: AI Chatbot Architectural Design Framework for R...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Vera, Maria Concepcion S. Palaoag, Thelma D. College of Information and Communications Technology Catanduanes State University Catanduanes Philippines College of Information Technology and Computer Science University of the Cordilleras Baguio City Philippines

ISBN: (纸本)9798350343274

Medicinal plants become a viable substitute goto treatment for common ailments when overburdened with hospitalization expenses and low accessibility to the government's health care system. As support and advancement with Catanduanes' alternative healing, this paper presented an approach to integrating Artificial Intelligence (AI) with Chatbot technology to address queries on treatment forms and usage of various medicinal plants. Employing documentary analysis and AI Chatbot framework design for its mixed-methods approach, the MedPlantBot prototype was built using conversational AI Chatbot architecture, Google's DialogFlow platform, and Kommunicate, a web chat interactive platform for bot and human interaction (Bot + Human hybrid). User acceptance, consisting of experts, traditional healers, and users of medicinal plants, for the prototype, averaged as 'Strongly Agreed' in terms of its usability. Significantly, this study can serve as an educational platform for the community to learn how to use medicinal plants available in their area to treat ailments. Ultimately, this study brings potential in providing local folks with a cost-effective alternative as an initial healing practice to improve their health and wellness, thereby contributing to the attainment of the third Sustainable Development Goal of the United Nations - Good Health and Well-being. © 2023 IEEE.

关键词： Machine learning

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ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification

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Neural Computing and Applications 2025年第9期37卷 6583-6600页

作者： Swapno, S M Masfequier Rahman Nobel, S. M. Nuruzzaman Islam, Md Babul Bhattacharya, Pronaya Mattar, Ebrahim A. Department of CSE Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India Robotics - Cybernetic College of Engineering University of Bahrain Manama Bahrain

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. © The Author(s) 2025.

关键词： Fruits

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