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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Revathi, S. Raja, J. Mohanraj, M. Malathi, K. Mallala, Balasubbareddy Vidhya, R.G. B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Sri Eshwar college of engineering Department of Computer Science and Business Systems Coimbatore India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798331504403

This research analyses the complex dynamics of Cyber-Physical-Social systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. By exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems. © 2024 IEEE.

关键词： Cyber-Physical systems Cyber-Physical-Social systems (CPSS) Cybersecurity Internet of Things (IoT) Social Media

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Client Segmentation and Customization in E-Commerce: Applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sriprasadh, K. Palit, Shamik Pravallika, B. Manjunatha Lenka, Reena Singla, Atul Srm Institute Of Science And Technology Faculty Of Science And Humanities Department Of Computer Science And Applications Chennai Vadapalani India University Of Stirling Rak Campus Department Of Computing Science And Software Engineering United Arab Emirates Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India University Pune India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques. © 2024 IEEE.

关键词： Python

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Exploring Advancements in 6G Wireless Networks for the Future of High-Speed Connectivity 2

Exploring Advancements in 6G Wireless Networks for the Futur...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Suresh, J. Kumari, V Sheeja Pillai, N. Muthuvairavan Jayarin, P. Jesu David, D. Beulah Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India R.M.D Engineering College Department of Computer Science and Business Systems Tamil Nadu India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Information Technology ThandalamTamil Nadu Chennai India

ISBN: (纸本)9798350364699

High-speed connectivity technologies that aim to be faster and more secure have been explored so that there will be a change of the very nature of wireless communication. This article presents advances concerning the high-speed connection component of the sixth-generation wireless networks, which is simply denoted as 6G. Different performance parameters of 6G are compared with 4G (LTE) and 5G (5G) through multiplicity of research analyses done across disciplines. Our findings suggested that 6G networks are incredibly dynamic and will change how the internet is used. These SixG networks are due to accompany massive data transmission rates of 100 Gbps, five times higher than 5G's 20 Gbps. Latency also approaches same level as 6G where on average 1 millisecond is noticed compared to 5G's 10 and 4G's 50. Server speed and its response time in milliseconds will expose room for a number of real-time applications from only augmented reality imaging to the world of driverless automobiles and interactive games. Our study outlines the major consequences of the coming health shift. 4G allowed only for slow/limited improvements in healthcare whereas the 5G made telemedicine possible. On the other hand, 6G reportedly offers "precision remote surgery", allowing surgeons to be located remotely of a patient while still performing a high precision surgery. Attitude of people becomes completely unaware when they are briefed about concepts of latest generation known as 6G. Before studying about 6G, the number of those are eagerly delighted are 20%, having no idea on how it will be is 50%, while the other 30% are skeptics. Meanwhile, once people are better cognizant of 6G, fanatics raise up from 60 percent and sceptics drop till 10 percent. The significant shift is a reflection of 6G technology in the stakeholders' mind. © 2024 IEEE.

关键词： 5G mobile communication systems

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI 41

The Good, The Bad, and Why: Unveiling Emotions in Generative...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Cheng Wang, Jindong Zhang, Yixuan Zhu, Kaijie Wang, Xinyi Hou, Wenxin Lian, Jianxun Luo, Fang Yang, Qiang Xie, Xing Microsoft Research Beijing China CAS Institute of Software Beijing China Department of Computer Science William & Mary WilliamsburgVA United States School of Psychology Beijing Normal University Beijing China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Emotion significantly affects our daily behaviors and interactions. Although recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions and why. This paper aims to address this gap by incorporating psychological theories to gain a holistic understanding of emotions in generative AI models. Specifically, we propose three approaches: 1) EmotionPrompt to enhance the performance of the AI model, 2) EmotionAttack to impair the performance of the AI model, and 3) EmotionDecode to explain the effects of emotional stimuli, both benign and malignant. Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. More importantly, EmotionDecode reveals that AI models can comprehend emotional stimuli similar to the dopamine mechanism in the human brain. Our work heralds a novel avenue for exploring psychology to enhance our understanding of generative AI models, thus boosting the research and development of human-AI collaboration and mitigating potential risks. Copyright 2024 by the author(s)

关键词： Generative adversarial networks

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CNN-LSTM: Development of Offline Signature Authentication 1

CNN-LSTM: Development of Offline Signature Authentication

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Ganapathy, Revathy Lingeshwaran, D. Suthanthiradevi, P. Harish Kumar, M.V. Vels Insitute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350359756

Offline Signature Authentication is a critical task in the field of document authentication, and its accuracy is essential for ensuring security while transactions. This research proposes two approaches: Initially Pre-trained CNN models are used to extract features from signature images, which are then combined with handcrafted features such as HOG and some other geometric features of signature. Such combined features are passed to bidirectional LSTM model in which drop out layer undergoes classification which differentiate real and forgery signature. The proposed system has potential applications in document authentication and security, subsequently combination of CNN models and additional features provides more comprehensive representation of signature images resulting in improved accuracy. Three signature datasets are utilized namely GDPS, CEDAR, and BHSig-Bengali each with varying signature styles and image quality. Our experimental outcomes reveal that Bidirectional Convolutional LSTM along with handcraft features attained maximum accuracy in offline signature verification system. © 2023 IEEE.

关键词： Long short-term memory

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Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

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computer systems science & engineering 2023年第9期46卷 3847-3864页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana murthy Ravuri Daniel E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenangMalaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science University of Central AsiaNarynKyrgyzstan Faculty of Computers and Information South Valley UniversityQenaEgypt

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is *** are the major reason behind crop quality degradation and diminished crop ***,accurate pest detection is essential to guarantee safety and crop *** identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological ***,some progress has been made in agriculture by employing machine learning(ML)to classify and detect *** study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)*** presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image *** neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the *** last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification *** simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

关键词： Sustainable agriculture crop monitoring pest management insect classification computer vision

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Occupancy estimation in smart buildings using predictive modeling in imbalanced domains

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10917-10929页

作者： Guo, Jiaxun Amayri, Manar Najar, Fatma Fan, Wentao Bouguila, Nizar Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G1T7 Canada Grenoble Institute of Technology Grenoble France The Department of Computer Science and Technology Huaqiao University Xiamen China

This paper introduces a novel approach for occupancy estimation in smart buildings. In particular, we focus on the challenging yet common situation where the amount of training data is small and imbalanced (i.e. the classes are not approximately equally represented). Our model is based on two parts namely predictive modelling, performed via the inverted Dirichlet mixture (IDMM), and an over-sampling approach that we propose. The first part, in which the main goal is to tackle the small training data problem, concerns the calculation of the predictive distribution of the IDMM by marginalizing over its parameters, with their posterior distributions, which are estimated by a Bayesian variational inference approach that we develop. Based on over-sampling, the second part can be viewed as a complement to tackling the imbalanced domains problem. Extensive experiments and simulations involving synthetic data as well as real data extracted from smart buildings sensors show the merits of our statistical framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Intelligent buildings

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RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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Enhancing Cybersecurity in the Digital Age with Machine Learning for Threat Detection and Prevention

Enhancing Cybersecurity in the Digital Age with Machine Lear...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Jayarin, P.Jesu David, D. Beulah Jenifa Kumari, V. Sheeja Pillai, N. Muthuvairavan Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Information Technology Thandalam Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India R.M.D Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur India

ISBN: (纸本)9798350388602

Information security cannot be underemphasized today especially with the increased use of Information Communication Technology which requires improved threat identification techniques. In this study, we propose a novel methodology comprising four variants - CyberShield, SentinelGuard, ThreatHunter, and SecureNet - and evaluate their performance against existing approaches in terms of key performance metrics: which are efficiency, precision, recall, F1-score and AUC. In all variants above experiments that we had demonstrated, there is rather more distinctive feature towards threat identifiers guaranteed other than what is presently offered. Interestingly, CyberShield with +3.8% and ThreatHunter with +3.8%, both the highest, are more accurate in terms of classifications of cyper-attacks than other algorithms. Finally, all proposed variants are ranked higher than the current approaches in terms of recall with possible variation between 4.2% and 2.2 for TP rates, meaning these approaches have the potential to minimize false negatives. In addition, the advance variants actually cause slight increase in Precision and F1-Score but with aids to make Precision equivalent to Recall, the achieved performance is relatively higher. What this would do is expose the advantages of the new proposed variants in terms of the degree of influence human behavior and the rate of risk of cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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