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3rd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Countering Deepfakes using an Improved Advanced CNN and its Ensemble with Pretrained Models

Countering Deepfakes using an Improved Advanced CNN and its ...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Abeer Mathur Moulik Tejpal Kshitiz Bhargava Krishnaraj Natarajan Manvendra Singh Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false propaganda. To tackle this issue, this paper introduces a sophisticated DeepFake detection model designed to identify and mitigate the increase of these deceptive images. The model architecture integrates an ensemble approach, combining the strengths of two pre-trained Convolutional Neural Network (CNN) models—MobileNet and Xception—with a novel CNN architecture, the Advanced CNN (ACNN). This rigorous validation process enabled the model to achieve a high accuracy rate of 97.89% in detecting DeepFakes. The successful implementation of this ensemble CNN approach demonstrates its effectiveness in distinguishing between real and fabricated imagery with high precision. This research makes a substantial contribution to the field of digital image forensics, offering a reliable tool for stakeholders across various sectors to identify and counteract the spread of DeepFake images online.

关键词： Training Deepfakes Solid modeling Accuracy Forensics Solids Convolutional neural networks Stakeholders Standards Testing

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Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

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Computer systems Science & engineering 2022年第5期41卷 479-492页

作者： A.S.Anakath R.Kannadasan Niju P.Joseph P.Boominathan G.R.Sreekanth School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science CHRIST(Deemed to be University)Bengaluru560029India Department of Information Security School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information *** use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing *** cloud is nowadays highly affected by internal threats of the *** applications such as banking,hospital,and business are more likely affected by real user *** intruder is presented as a user and set as a member of the *** becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or *** major issue in today's technological development is identifying the insider threat in the cloud *** data are lost,compromising cloud users is *** and security are not ensured,and then,the usage of the cloud is not *** solutions are available for the external security of the cloud ***,insider or internal threats need to be *** this research work,we focus on a solution for identifying an insider attack using the artificial intelligence *** insider attack is possible by using nodes of weak users’*** will log in using a weak user id,connect to a network,and pretend to be a trusted ***,they can easily attack and hack information as an insider,and identifying them is very *** types of attacks need intelligent solutions.A machine learning approach is widely used for security *** date,the existing lags can classify the attackers *** information hijacking process is very absurd,which motivates young researchers to provide a solution for internal *** our proposed work,we track the attackers using a user interaction behavior pattern and deep learning *** usage of mouse movements and clicks and keystrokes of the real user is stored in a *** deep belief neural

关键词： Cloud computing security insider attack network security privacy user interaction behavior deep belief neural network

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A Comparative Analysis of Multi-agent Simulation Platforms for Energy and Mobility Management 20th

A Comparative Analysis of Multi-agent Simulation Platforms ...

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Proceedings of the 20th European Conference on Multi-Agent systems, EUMAS 2023

作者： Ali, Aliyu Tanko Leucker, Martin Schuldei, Andreas Stellbrink, Leonard Sachenbacher, Martin Institute for Software Engineering and Programming Languages University of Lübeck Ratzeburger Allee 160 Lübeck Germany Institute for Multimedia and Interactive Systems University of Lübeck Ratzeburger Allee 160 Lübeck Germany

ISBN: (纸本)9783031432637

Effective energy and mobility management benefits from multi-agent simulations (MAS) to model complex interactions among various agent types. Selecting the optimal MAS platform to implement and simulate these interactions is vital for achieving accurate results, scalability to realistic problem sizes, and efficient computational performance. This paper investigates the energy and mobility domain and identifies key parameters such as the number and complexity of agents, parallel computing power, CPU requirements etc., for developing MAS in the context of this domain. It then presents a comprehensive evaluation of various MAS development platforms. Using a multi-level selection and elimination approach, we narrowed down our evaluation to two final candidates. We then implemented key aspects of our model in both platforms to compare them in terms of practical relevance. Our findings reveal that the *** platform outperforms the Mesa platform in terms of runtime performance, has a smaller memory footprint for large numbers of agents, and offers scalability, making it the most suitable choice for developing MAS for integrated energy and mobility models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Scalability

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Harnessing the Power of AI with Deep Learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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Continuous American Sign Language Recognition Using Computer Vision And Deep Learning Technologies

Continuous American Sign Language Recognition Using Computer...

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2022 IEEE Region 10 Symposium, TENSYMP 2022

作者： Senanayaka, S.A.M.A.S. Perera, R.A.D.B.S. Rankothge, W. Usgalhewa, S.S. Hettihewa, H.D. Abeygunawardhana, P.K.W. Sri Lanka Institute of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9781665466585

ISBN: (纸本)9781665466585

Sign language is a non-verbal communication method used to communicate between hard of hearing or deaf and ordinary people. Automatic Sign language detection is a complex computer vision problem due to the diversity of modern sign languages and variations in gesture positions, hand and finger form, and body part placements. This research paper aims to conduct a systematic experimental evaluation of computer vision-based approaches for sign language recognition. The present research focuses on mapping non-segmented video streams to glosses to gain insights into sign language recognition. The proposed machine learning model consists of Recurrent Neural Network (RNN) layers such as Long Short-Term Memory (LSTM). The model is implemented using current deep learning frameworks such as Google TensorFlow and Keras API. © 2022 IEEE.

关键词： Deep neural networks

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Deepfake Detection: Emerging Techniques and Evolving Challenges

Deepfake Detection: Emerging Techniques and Evolving Challen...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Krishnaraj Natarajan Abeer Mathur Kshitiz Bhargava Manvendra Singh Moulik Tejpal Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most applications for these techniques or tools are in the fields of entertainment and education, some individuals with unlawful intent have also been benefited from them. These individuals use such techniques for various purposes, including the spread of misleading information and unnecessary propaganda, the incitement of political instability, hate and unrest, as well as for purposes of torture and blackmail. These high-quality and convincing manipulated images, audio, or videos are commonly referred to as ‘Deepfakes’.Since then, various solutions to the problems raised by Deepfakes have been proposed in academic studies. This literature review contains relevant publications that offered a variety of approaches to give an updated summary of the research activities in different types of Deepfake attacks, their detection, and countermeasures. It also assesses the effectiveness of the detection capabilities of different techniques with various datasets and algorithms applied in Deepfake detection, while also outlining the various benefits and drawbacks of various methodologies.

关键词： Deep learning Deepfakes Smart contracts Microwave communication Media Forgery Blockchains Internet of Things Systematic literature review Microwave imaging

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Price Estimation of Used Cars Using Machine Learning Algorithms 3rd

Price Estimation of Used Cars Using Machine Learning Algorit...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Valarmathi, B. Gupta, N. Srinivasa Santhi, K. Chellatamilan, T. Kavitha, A. Raahil, Armaan Padmavathy, N. Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031289743

In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precise car prices. Despite the fact that there are websites offering this service, they could not employ the most precise prediction system. It is also possible to predict a used car's true market value using a variety of models and techniques. It's important to understand their genuine market value before buying or selling. Both buyers and sellers will be benefitted from these accurate predictions. Support Vector regression, Random Forest regression, and CatBoost regression techniques are used in the proposed system. In the existing method [13], mean absolute error for decision tree regression was 0.6711, which was the least among other algorithms like Linear regression, Lasso regression, Ridge regression, Bayesian Ridge regression, and etc., they used. In the proposed system, mean absolute error (MAE) for Support Vector regression, CatBoost regression and Random Forest regression techniques are 0.1459, 0.1371 and 0.1284 respectively. The prices of second hand/used cars are predicted using the CatBoost regression, Support Vector regression, and Random Forest regression techniques. The accuracy of these algorithms are 86.28%, 85.40% and 87.16%. Among these three algorithms, Random Forest regression gives the least MAE of 0.1317 and the highest accuracy of 87.16%. © 2023, ICST institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

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Computer systems Science & engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

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Utilizing Organizational Patterns to Build High-Performing Remote Teams 28

Utilizing Organizational Patterns to Build High-Performing R...

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Toldy, Kristián Khail, Waheedullah Sulaiman Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Helmholtz-Zentrum Berlin für Materialien Energie Gmbh Germany

ISBN: (纸本)9798400700408

In recent years, remote work has become a more popular option for companies across various industries. While remote work provides numerous benefits, such as flexibility and increased work-life balance, it also presents unique challenges that can hinder team collaboration, productivity, and overall success. In order to overcome these obstacles, it is essential to adopt effective organizational patterns that can help build high-performing remote teams. Building a high-performing remote team requires a set of strategies with some key focus points to be kept in mind such as communication, trust, goals, collaboration, and performance. For all of these, there have been an extensive research and publication of observed patterns to which we have also contributed with a new set of these four patterns: Use Common Language, Discuss Publicly, Lights, Camera, Action!, and Praise in Public, Correct in Private. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Computer programming

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