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

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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Multiclass boosting: simple and intuitive weak learning criteria 23

Multiclass boosting: simple and intuitive weak learning crit...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Nataly Brukhim Amit Daniely Yishay Mansour Shay Moran Department of Computer Science Princeton University Department of Computer Science Hebrew University Google Research Department of Computer Science Tel Aviv University Google Research Faculty of Mathematics Faculty of Computer Science Faculty of Data and Decision Sciences Technion Google Research

We study a generalization of boosting to the multiclass setting. We introduce a weak learning condition for multiclass classification that captures the original notion of weak learnability as being "slightly better than random guessing". We give a simple and efficient boosting algorithm, that does not require realizability assumptions and its sample and oracle complexity bounds are independent of the number of *** addition, we utilize our new boosting technique in several theoretical applications within the context of List PAC Learning. First, we establish an equivalence to weak PAC learning. Furthermore, we present a new result on boosting for list learners, as well as provide a novel proof for the characterization of multiclass PAC learning and List PAC learning. Notably, our technique gives rise to a simplified analysis, and also implies an improved error bound for large list sizes, compared to previous results.

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Detection of Diffusion Model-Generated Faces by Assessing Smoothness and Noise Tolerance

Detection of Diffusion Model-Generated Faces by Assessing Sm...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Baoping Liu Bo Liu Ming Ding Tianqing Zhu School of Computer Science University of Technology Sydney NSW Australia Data61 CSIRO NSW Australia Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

The fast growth of artificial intelligence (AI) raises much concern about the misinformation brought by AI-generated content (AIGC), especially Deepfake techniques that generate fake human faces. The recent development of Diffusion Models (DMs) moves another critical step forward to generate high-resolution and realistic human faces, which has become a challenge for existing Deepfake detectors. In this paper, we propose a DM-generated image detector by looking into the generation pipeline of DMs and the details of DM-generated images. The detector is based on the observation that DM-generated human faces show over-smooth textures and do not contain details as real human faces. Through a comprehensive analysis of DM-generated faces in spatial and frequency domains, we noticed that the over-smoothness improves the tolerance of Gaussian noise since excessive smoothness mitigates some of the impact of noise. Inspired by the observations, we propose a Deepfake detector capable of recognizing challenging DM-generated faces. We mainly propose the Noise Residual Unit (NRD) in our framework to collect the frequency response of images to Gaussian noise as distinctive features for classification. In detail, for an input face image, we add Gaussian noise to it and get the noise-degraded image. Then, the NRU generates the Noise Residual Image (NRI) by calculating the residual of the high-pass-filtered original image and the high-pass-filtered degraded image. The NRI indicates the high-frequency impact brought by the Gaussian noise and, therefore, suggests the tolerance of the original image to noise degradation. The original image and NRI are encoded and fused to obtain the joint representation, which is then fed to a classifier to predict the binary label. We conducted comprehensive experiments to evaluate the effectiveness of the proposed detector. The results indicate that our proposed detector achieves state-of-the-art detection performance on DM-generated faces and generali

关键词： Deepfakes Analytical models Gaussian noise Frequency-domain analysis Pipelines Detectors Frequency response

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A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System 12

A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System

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12th International Electrical Engineering Congress, iEECON 2024

作者： Rakprayoon, Panjawee Ounsiri, Akechai Chongyoa, Ratchanon Bangkok University Faculty Of Engineering Dept. Ai Engineering And Data Science Pathum Thani Thailand Dna Robotics And Automation System Company Limited Pathum Thani Thailand Bangkok University Faculty Of Engineering Dept. Computer And Robotics Engineering Pathum Thani Thailand

ISBN: (纸本)9798350383591

For an AI tic-tac-toe manipulator application, a real-time vision-based approach is proposed. The technique employs the RealSense camera to capture color and depth images. Combining object detection and image processing techniques to identify X's and O's positions on the game board. However, we recognize that errors can occur during object movement when using only 2D images. To solve this problem, a calibration method between RealSense and the manipulator based on the point-to-plane ICP algorithm is presented. This method provides the 3D object position in the robot coordinate, resulting in increasing the accuracy from 86.25% to 97.25%. Our experiment shows that this technique is highly effective and can accurately identify position objects. © 2024 IEEE.

关键词： Object detection

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Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning

arXiv

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arXiv 2024年

作者： Robaa, Mahmoud Balat, Mazen Awaad, Rewaa Omar, Esraa Aly, Salah A. CS & IT Dept. Egypt-Japanese University of Science & Tech. Alexandria Egypt Computer Science Faculty of Science Fayoum University Fayoum Egypt Faculty of Computing & Data Science Badya University Giza Egypt

This study introduces an explainable AI (XAI) framework for the detection of dyslexia through handwriting analysis, achieving an impressive test precision of 99.65%. The framework integrates transfer learning and transformer-based models, identifying handwriting features associated with dyslexia while ensuring transparency in decision-making via Grad-CAM visualizations. Its adaptability to different languages and writing systems underscores its potential for global applicability. By surpassing the classification accuracy of state-of-the-art methods, this approach demonstrates the reliability of handwriting analysis as a diagnostic tool. The findings emphasize the framework’s ability to support early detection, build stakeholder trust, and enable personalized educational strategies. © 2024, CC BY.

关键词： Federated learning

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An Efficient Model based on Machine Learning Algorithms for Virtual Machines Classification in Cloud Computing Environment 4

An Efficient Model based on Machine Learning Algorithms for ...

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4th International Conference on Innovative Research in Applied science, Engineering and Technology, IRASET 2024

作者： Amahrouch, Abdelhadi Bouhamidi, Mehdi Saadi, Youssef El Kafhali, Said Sultan Moulay Slimane University Faculty Of Sciences And Techniques Data Science For Sustainable Earth Laboratory B.P. 523 Beni Mellal23000 Morocco Polytechnic University Laboratory Of Industrial And Human Automation Mechanics And Computer Science Hauts-de-France France Hassan First University Of Settat Faculty Of Sciences And Techniques Computer Networks Modeling And Mobility Laboratory B.P. 539 Settat26000 Morocco

ISBN: (纸本)9798350309508

In cloud computing, virtual machines consolidation (VMC) techniques are commonly used to improve resource utilization and reduce energy consumption. Task scheduling in cloud systems is a crucial aspect of VMC as it involves mapping clients' requirements to the appropriate computing resources such as Virtual Machines (VMs) or Physical Machines (PMs). The cloud provider must ensure that tasks are executed efficiently using the available shared computing resources while maintaining the quality of service (QoS) and minimizing the carbon footprint. Therefore, a good VM migration based on the customer's needs and IT resource capacity is required to maintain the best performance of the cloud system. In this work, we introduce an approach that leverage machine learning-based algorithms for VMs classification based on their latency sensitivity to facilitate subsequent migration into a suitable PM for better VMC. These algorithms categorize VMs into two groups: potentially inter-active machines (exhibiting periodic behavior on a daily scale) and latency-insensitive machines (for example, batch workloads, development, and test workloads). Our model demonstrated robust performance, achieving an accuracy of around 83%, thus establishing itself as the most proficient classifier in this study. © 2024 IEEE.

关键词： Virtual machine

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Granular-Ball Computing Based Fuzzy Twin Support Vector Machine for Pattern Classification

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IEEE Transactions on Fuzzy Systems 2025年

作者： Lang, Guangming Zhao, Lixi Miao, Duoqian Ding, Weiping Changsha University of Science and Technology School of Mathematics and Statistics Changsha410114 China Tongji University School of Computer Science and Technology Shanghai201804 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

The twin support vector machine (TWSVM) classifier and its fuzzy variant (FTSVM) have received considerable attention due to their low computational complexity. However, their performance often deteriorates when the input data is affected by noise. To overcome this limitation, this study leverages the robustness of granular-ball computing (GBC) against noise to develop more effective classification models by integrating GBC with TWSVM and FTSVM. First, we introduce the granular-ball twin support vector machine (GBTWSVM) classifier, which incorporates GBC with the TWSVM framework. By replacing traditional point-wise inputs with granular-ball representations, we derive a pair of non-parallel hyperplanes for the GBTWSVM classifier by solving a quadratic programming problem. Afterwards, we develop the granular-ball fuzzy twin support vector machine (GBFTSVM) classifier, where the membership and non-membership functions of granular-balls are defined using Pythagorean fuzzy sets (PFS), enabling a more nuanced differentiation of the contributions of granular-balls from distinct regions within the input space. By incorporating these functions into the FTSVM framework, we derive a pair of non-parallel hyperplanes for the GBFTSVM classifier through the solution of a quadratic programming problem. Finally, we present algorithms for the GBTWSVM and GBFTSVM classifiers and evaluate their performance on 21 benchmark datasets. Experimental results demonstrate the superior scalability, computational efficiency, and robustness of the proposed classifiers in pattern recognition, highlighting their potential as advanced tools for noise-tolerant classification. © 1993-2012 IEEE.

关键词： Membership functions

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Dual-Aspect Noise-Based Regularization for Multi-Modal Relation Extraction in Media Posts

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1324-1336页

作者： Kai Sun Bin Shi Samuel Mensah Wenjian Liu Bo Dong School of Computer Science and Technology and Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China School of Computer Science and Technology The University of Sheffield Sheffield U.K. Faculty of Data Science City University of Macau Macau China School of Continuing Education and Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

Multi-Modal Relation Extraction (MMRE) plays a key role in various multimedia applications including, recommendation and information retrieval systems. MMRE aims to extract the semantic relation between entities by leveraging context from a text-image pair. By utilizing context from images, the challenge of learning from noisy images in MMRE emerges as a research problem by itself. For instance, subtle variations in similar images can act as noise and potentially impact the predictions made by MMRE models. To tackle this problem, current work utilizes attention mechanisms to fuse relevant text and image features or devise data augmentation techniques (e.g., via generative models) to improve generalization. However, the current performance still remains unsatisfactory. In an effort to improve upon the performance, we propose a Dual-Aspect Noise-based Regularization framework that encompasses two techniques: 1) noise removal through an adaptive gating mechanism, 2) fighting noise with noise to improve feature stability in the learning process. We find that combining these techniques encourages the model to focus on more relevant image features for MMRE. We carry out extensive experiments and demonstrate that our proposed model is further enhanced by exploring data augmentation techniques. This additional improvement leads the model to achieve state-of-the-art performance on the widely-used Multi-modal Neural Relation Extraction (MNRE) dataset, and show its effectiveness and generalizability on the Multi-Modal Named Entity Recognition task.

关键词： Feature extraction Noise data mining Noise measurement Social networking (online) Adaptation models Transformers Training Predictive models data models

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A Trichotomy for Transductive Online Learning

arXiv

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arXiv 2023年

作者： Hanneke, Steve Moran, Shay Shafer, Jonathan Department of Computer Science Purdue University United States Faculty of Mathematics Faculty of Computer Science Faculty of Data and Decision Sciences Technion Israel Institute of Technology Computer Science Division UC Berkeley United States

We present new upper and lower bounds on the number of learner mistakes in the ‘transductive’ online learning setting of Ben-David, Kushilevitz and Mansour (1997). This setting is similar to standard online learning, except that the adversary fixes a sequence of instances x1, . . ., xn to be labeled at the start of the game, and this sequence is known to the learner. Qualitatively, we prove a trichotomy, stating that the minimal number of mistakes made by the learner as n grows can take only one of precisely three possible values: n, Θ (log(n)), or Θ(1). Furthermore, this behavior is determined by a combination of the VC dimension and the Littlestone dimension. Quantitatively, we show a variety of bounds relating the number of mistakes to well-known combinatorial dimensions. In particular, we improve the known lower bound on the constant in the Θ(1) case from Ω (plog(d) ) to Ω(log(d)) where d is the Littlestone dimension. Finally, we extend our results to cover multiclass classification and the agnostic setting. © 2023, CC BY.

关键词： E-learning

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PROMPTING LABEL EFFICIENCY IN FEDERATED GRAPH LEARNING VIA PERSONALIZED SEMI-SUPERVISION 49

PROMPTING LABEL EFFICIENCY IN FEDERATED GRAPH LEARNING VIA P...

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2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024

作者： Mao, Qinghua Lin, Xi Su, Xiu Li, Gaolei Chen, Lixing Li, Jianhua Shanghai Jiao Tong University China Big Data Institute Central South University China School of Computer Science Faculty of Engineering The University of Sydney Australia Shanghai Key Laboratory of Integrated Administration Technologies for Information Security China

ISBN: (纸本)9798350344851

Federated graph learning (FGL) enables the collaborative training of graph neural networks (GNNs) in a distributed manner. A critical challenge in FGL is label deficiency, which becomes more intricate due to non-IID decentralized data. Existing methods have focused on extracting knowledge from abundant unlabeled data, leaving few-shot labeled data unexplored. To this end, we propose ConFGL, a novel FGL framework to enhance label efficiency in federated learning with non-IID subgraphs. We formulate a semi-supervised objective to harness both unlabeled and labeled data, where self-supervised learning is achieved via a graph contrastive module. Additionally, a personalized federated learning (FL) strategy is adopted to concurrently train a global model and an individual model, which helps alleviate the representation disparities encoded by local models. Extensive experiments on four node-level datasets under non-IID settings have shown that ConFGL can consistently provide an average of 4.10% accuracy gains over supervised FL personalization methods while maintaining a higher GPU throughput. © 2024 IEEE

关键词： Graph neural networks

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