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Efficient Approach Towards Detection and Classification for Image Forgery by Using machine Learning Technique 3

Efficient Approach Towards Detection and Classification for ...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Hussein, Abbas Hameed Abdul Kavitha, R. Kumar, Bura Vijay Issa, Ali Ashoor Al-Attabi, Kassem College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of ECE Tiruchengode India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq

ISBN: (纸本)9798350317022

Copy-move forgery (CMF) is one of the most malicious forgery attacks often manipulated to spread misinformation and influence security. In the paper, utilized machine Learning techniques specifically, a Multiclass Support Vector machine (MSVM)-based Particle Swarm Optimization (PSO) algorithm was utilized for image forgery. PSO is an optimization algorithm in which a group of particles representing individual solutions moves through a search space to find the best solution. The objective function that the PSO algorithm seeks to minimize or maximize is typically related to the MSVM's performance on a validation dataset. The MICC-F200 dataset was used to verify the uniqueness of CMF and evaluate the model's image classification performance. It's important to note that MSVM based PSO can become computationally expensive and memory-intensive when dealing with a large number of classes. They are also sensitive to noisy or mislabeled data points, which can potentially lead to suboptimal performance. The results obtained from this study reveal that the MSVM-based PSO model achieves a high accuracy of 0.9936 on the MICC-F200 dataset, outperforming other existing models such as VGG16 and Mobile Net, thereby ensuring accurate image classification. © 2023 IEEE.

关键词： Copy-Move Forgery Gray Scale Technique machine Learning Particle Swarm Optimization and Support Vector machine

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Dimensional Emotion recognition Based on ConvLSTM Network with Double Stream Convolution Feature 3

Dimensional Emotion Recognition Based on ConvLSTM Network wi...

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3rd international conference on Artificial intelligence and Computer Engineering, ICAICE 2022

作者： Qi, Mei Shi, Yun-Chang Zheng, Zhen Liu, Ze-Fen School of Information and Construction Engineering Anhui Open University 3 JiuHuashan Road Anhui Hefei230022 China

ISBN: (纸本)9781510663473

Academic emotions are closely related to the achievement of online learning goals, for the current problem that discrete emotion recognition cannot describe the continuous dimensional academic emotion changes, this paper proposes a dimensional emotion recognition method based on a dual-streams convolutional feature ConvLSTM network. Firstly, static convolution features of single frame images in video sequences are extracted, and dynamic convolution features are extracted from video sequences, secondly, the concatenated double stream convolution features are analyzed by ConvLSTM network to extract the sequence features containing spatio-temporal information;finally, the generated feature passes through two full connection layers and outputs the predicted value of Valence-Arousal. The average of the Concordance Correlation Coefficient (CCC) on the public data set AVEC2015 reached 0.198. The experiment proved that the CCC correlation coefficient index of the proposed method on the AVEC2015 data set increased by 2.64%~6.28% compared with the baseline method, which can effectively identify dimensional emotions, providing a method support for dimensional emotion recognition. © 2023 SPIE.

关键词： Convolution

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An Asynchronous Federated Learning Algorithm Based on a Backup Update of Model Version Parameters 3

An Asynchronous Federated Learning Algorithm Based on a Back...

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3rd international conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Sun, Yu Li, Hui Shen, Ying Xie, Jianfei Zhao, Yanan Gao, Xin Si, Nong Beijing University of Technology Faculty of Information Beijing100024 China School of Mechanical and Electrical Engineering Beijing Polytechnic Beijing100176 China North China Institute of Science & Technology Department of Internet of Things Engineering Hebei065201 China Inspur Group Co. Ltd. Digital Government Business Department Shandong Jinan250101 China

ISBN: (纸本)9798350382426

Federated Learning (FL) is a practical approach to alleviate the problem of 'Data Island' pain caused by privacy leakage in artificial intelligence (AI) security research. It enables multiple users to train a shared machine learning model cooperatively without uploading private data sets. However, in the process of FL model training, effectively coordinating the communication between participants and clients is the key to improving model training performance. The existing FL algorithms cannot balance model performance and accuracy stability. This paper proposes a novel FL algorithm based on a backup update of model version parameters (FedAvu). Adding a model version to help the server and client execute different training processes can improve the training efficiency and ensure the stability of the training accuracy. Experimental test results show that the proposed method can achieve better training efficiency on MNIST and CIFAR-10 data sets than existing algorithms. © 2023 IEEE.

关键词： Artificial intelligence Asynchronous update Data leakage Federated learning machine learning Privacy protection

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Sustainability and Resilience in the MICS SPOKE8 Project: The Role of the Digital Twin 3

Sustainability and Resilience in the MICS SPOKE8 Project: Th...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial intelligence and Neural Engineering, MetroXRAINE 2024

作者： Berti, Nicola Arman, Ala Esmaili, Parisa Zeynivand, Mohsen Battini, Daria Bianchini, Devis Cristaldi, Loredana De Giuli, Laura Boca Galeazzo, Ambra Gruosso, Giambattista Bella, Alessio La Leotta, Francesco Martiri, Luca Masero, Eva Mecella, Massimo Plebani, Pierluigi Rocco, Paolo Salmaso, Luigi Scattolini, Riccardo Susto, Gian Antonio Tanca, Letizia University of Padua Padua Italy Sapienza Università di Roma Roma Italy Politecnico di Milano Milan Italy University of Brescia Brescia Italy

ISBN: (纸本)9798350378009

The concept of the Digital Twin (DT) has risen to prominence in parallel with the ongoing digitization initiatives across industries and the rise of Industry 4.0. This innovative approach harnesses a diverse array of cutting-edge technologies, including Artificial intelligence (AI), Big Data analytics, and the Internet of Things (Io'T), to smoothly merge virtual representations with their physical counterparts. Through this integration, organizations can conduct in-depth analysis and make real-time decisions, all backed by data. This transformative fusion of digital and physical fields not only enhances operational efficiency but also opens up new avenues for innovation and value creation within various sectors. © 2024 IEEE.

关键词： Artificial intelligence Big data Digital twin Industry 4.0 & 5.0 machine Learning (ML)

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Proceedings of 2022 3rd international Symposium on Artificial intelligence for Medical Sciences, ISAIMS 2022

Proceedings of 2022 3rd International Symposium on Artificia...

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3rd international Symposium on Artificial intelligence for Medical Sciences, ISAIMS 2022

ISBN: (纸本)9781450398442

The proceedings contain 105 papers. The topics discussed include: the effect of hg content in water bodies for precision medicine of Hg disease;the spatial migration pattern of Hg content for the prediction of disease during a long time;Cryptophycin’s clinical use in xenograft pancreatic tumor cell treatments based on computer science;standardization of clinical terminology based on hybrid recall and Ernie;interaction process between changes in terms of time and space of pH value and the growth of marine organisms in the Jiaozhou Bay;clinical research progress of acupuncture in the treatment of sciatica;research on identification of abnormal foot arch in school-age children based on foot pressure analysis;intelligent diagnosis of vascular anomalies with deep learning;the value of brain-computer interface in stroke upper rehabilitation;and research on abnormal behavior recognition of the elderly based on spatial-temporal feature fusion.

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Multi-Sense Attention Network (MSANet): Enhanced Human Activity recognition Using Deep Learning Architectures with Self-Attention Mechanisms 24

Multi-Sense Attention Network (MSANet): Enhanced Human Activ...

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Proceedings of the 3rd international conference on Computing Advancements

作者： Hashibul Ahsan Shoaib Arifa Eva Mst. Moushumi Khatun Adit Ishraq Sabiha Firdaus Dr. M. Firoz Mridha St. Francis College Brooklyn USA American International University Bangladesh (AIUB) Dhaka Bangladesh Advanced Machine Intelligence Research Lab Dhaka Bangladesh Bangladesh University of Business and Technology Dhaka Bangladesh AIUB Dhaka Bangladesh

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A Hexagonal Fractal Antenna with Switchable Radiation pattern for Network Enabled On-Body Wireless Communications 3

A Hexagonal Fractal Antenna with Switchable Radiation Patter...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Bhanu Satpathy, Rudra Ramesh, G.P. Alzubaidi, Laith H. Chanti, Yerrolla Hussein, A.H.A. Sai Theja, Badrireddy Chennai Department of Electronics and Communication Engineering India The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal Telangana India College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

This research presents a novel approach to enhance network communication through the development of a Hexagonal Fractal Antenna (HFA). The exponential growth in wireless communication demands innovative antenna solutions that can cater to evolving connectivity needs. This study explores the application of fractal geometry, specifically hexagonal patterns, to antenna design, offering a compact, multiband, and wideband solution that addresses the challenges of modern network communication exhibits superior performance characteristics, making it ideal for diverse wireless applications. Its compact form factor enables deployment in both urban and rural settings, where space constraints are prevalent. These results were compared with existing methods namely such as Omnidirectional Dual Polarized Low- Profile Textile Antenna (ODLPTA), RF- Harvesting Tightly Coupled Antenna (RF-TCRA) and Dual Polarized Embroidered Textile Armband Antenna (DP-ETTA). When compared to existing methods the suggested method achieved better performance in terms of Network efficiency of 98.4 %, Gain of 1.8% and Peak of 92.3 respectively. © 2023 IEEE.

关键词： Antenna Gain Hexagonal Fractal Antenna Network Efficiency Radiation pattern and Wireless Communications

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Exploring ASR Models in Low-Resource Languages: Use-Case the Macedonian Language 1

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4th international conference on Deep Learning Theory and Applications (DeLTA)

作者： Bogdanoski, Konstantin Mishev, Kostadin Simjanoska, Monika Trajanov, Dimitar Ss Cyril & Methodius Univ Fac Comp Sci & Engn Rugjer Boshkovikj 16 Skopje North Macedonia iReason LLC 3rd Macedonian Brigade 37 Skopje North Macedonia

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

We explore the use of Wav2Vec 2.0, NeMo, and ESPNet models trained on a dataset in Macedonian language for the development of Automatic Speech recognition (ASR) models for low-resource languages. The study aims to evaluate the performance of recent state-of-the-art models for speech recognition in low-resource languages, such as Macedonian, where there are limited resources available for training or fine-tuning. The paper presents a methodology used for data collection and preprocessing, as well as the details of the three architectures used in the study. The study evaluates the performance of each model using WER and CER metrics and provides a comparative analysis of the results. The findings of the research showed that Wav2Vec 2.0 outperformed the other models for the Macedonian language with a WER of 0.21, and CER of 0.09, however, NeMo and ESPNet models are still good candidates for creating ASR tools for low-resource languages such as Macedonian. The research presented provides insights into the effectiveness of different models for ASR in low-resource languages and highlights the potentials for using these models to develop ASR tools for other languages in the future. These findings have significant implications for the development of ASR tools for other low-resource languages in the future, and can potentially improve accessibility to speech recognition technology for individuals and communities who speak these languages.

关键词： machine learning Deep learning Automatic speech recognition Inference metric ASR pipeline Speech-to-text Wav2Vec2 ESPNet NeMo Natural language processing Macedonian language Low-resource languages Word error rate WER Character error rate CER

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Objective Depression Detection Using EEG and Eye Movement Signals Induced by Oil Paintings

Objective Depression Detection Using EEG and Eye Movement Si...

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45th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Liu, Luyu Peng, Dan Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong Univ Ctr Brain Like Comp & Machine Intelligence Dept Comp Sci & Engn Key LabShanghai Educ Commiss Intelligent Interac 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Med RuiJin Mihoyo Lab Clin Neurosci Ctr 197 Ruijin 2nd Rd Shanghai 200020 Peoples R China

ISBN: (纸本)9798350324471

Depression is a mental disorder characterized by persistent sadness and loss of interest, which has become one of the leading causes of disability worldwide. There are currently no objective diagnostic standards for depression in clinical practice. Previous studies have shown that depression causes both brain abnormalities and behavioral disorders. In this study, both electroencephalography (EEG) and eye movement signals were used to objectively detect depression. By presenting 40 carefully selected oil paintings-20 positive and 20 negative-as stimuli, we were able to successfully evoke emotions in 48 depressed patients (DPs) and 40 healthy controls (HCs) from three centers. We then used Transformer, a deep learning model, to conduct emotion recognition and depression detection. The experimental results demonstrate that: a) Transformer achieves the best accuracies of 89.21% and 92.19% in emotion recognition and depression detection, respectively;b) The HC group has higher accuracies than the DP group in emotion recognition for both subject-dependent and subject-independent experiments;c) The neural pattern differences do exist between DPs and HCs, and we find the consistent asymmetry of the neural patterns in DPs;d) For depression detection, using single oil painting achieves the best accuracies, and using negative oil paintings has higher accuracies than using positive oil paintings. These findings suggest that EEG and eye movement signals induced by oil paintings can be used to objectively identify depression.

关键词： Emotion recognition

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NeuroPCA: Enhancing Alzheimer's disorder Disease Detection through Optimized Feature Reduction and machine Learning 3

NeuroPCA: Enhancing Alzheimer's disorder Disease Detection t...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Sagili, Sreenivas Reddy Chidambaranathan, Senthilnathan Nallametti, Nagarjuna Bodele, Harana M. Raja, Laxmi Gayathri, P.G. Rochester Institute of Technology Department of Computer Science United States Virtusa Corporation 20 Corporate Pl South Piscataway United States Cvr College of Engineering Department of Csit Telangana Hyderabad India J D College of Engineering & Management Department of Electronics and Telecommunications Nagpur India Coimbatore India Kongunadu College of Engineering and Technology Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350369083

Alzheimer's (memory loss disorder) disorder Disease (AD) poses a significant diagnostic challenge due to its complex etiology and subtle onset of clinical symptoms. The Early and accurate detection will be a crucial for effective management and intervention. The integration of machine Learning-Driven (ML) techniques based on neuro imaging and clinical data may offers a promising avenue for enhancing of AD detection. However, the highest dimensionality of such data could be impede the efficacy and accuracy of ML models due to curse of dimensionality and over fitting. This research will introduces the "NeuroPCA System,"a novelist diagnostic framework by employing Principal Components based Analysis (PCA) for features reduction, tailored specifically for Alzheimer's (memory loss disorder) disorder Disease detection. The NeuroPCA System will systematically reduces the dimensionality of input health data (e.g., MRI, PET scans, and genetic information) while by preserving the most informative variance components crucial for distinguish between AD patients and healthy controls. The proposed methodology may encompasses preprocessing of the multi-modal dataset to normalized feature scales, followed by the application of PCA to extracts the principal components that capturing of the maximum variance in the dataset. The reduced feature set will then utilized to train and validate a suite of machine Learning-Driven models like Supportive Vector machines (SVM) for classify subjects into AD and non-AD groups. The effectiveness of the NeuroPCA System may evaluated depends on classification accuracy, sensitivity, specificity, and computational efficiency, by using a cross- validated dataset of diagnosed AD patients and a control group. The NeuroPCA System also demonstrates a significant improvement in model performance and computational efficiency, by highlighting the potential of PCA for feature reduction in the context of Alzheimer's (memory loss disorder) disorder Disease detect

关键词： Support vector machines

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