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automation of Depression Prediction Using DTI-MRI Images and EMG Signals 3

Automation of Depression Prediction Using DTI-MRI Images and...

引用

international conference on Advances in computing, communication and Applied Informatics (ACCAI)

作者： Snekha, S. Sanjiv, S. Sanjana, M. Ramya, J. St Josephs Coll Engn Artificial Intelligence & Data Sci Chennai Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

According to World Health Organisation (WHO), Depression is a leading cause of disability worldwide with almost percent of the population affected, including 5.0 percent of adults. There are nearly 280 million people in the world who suffer from depression. Major depression disorder (MDD) is the greatest cause of disability and morbidity, post-COVID-19. It is known to significantly affect the quality of life and create a socioeconomic burden. The solution to this problem includes diagnosing depression before even the onset of the disease. There exist many cases studies have been conducted in this area of research which includes clinical trials on using DTI-MRI Images and MRI Images to detect depression in mood-affected and non-affected individuals. It was proven to be successful that depression can be diagnosed from DTI-MRI and MRI Images. This served as an innovation towards this research of automating depression prediction from DTI-MRI Images using Machine Learning and thus saving lives.

关键词： World Health Organization Depression Major Depression Disorder COVID-19 Socioeconomic disease DTI- MRI Machine Learning

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Systems of Intelligent Information Processing Using Fuzzy Logic 27

Systems of Intelligent Information Processing Using Fuzzy Lo...

引用

27th international conference on Soft computing and Measurements, SCM 2024

作者： Kozlova, Liudmila P. Kozlova, Olga A. Saint Petersburg Electrotechnical University 'LETI' Faculty of Industrial Automation and Electrical Engineering St. Petersburg Russia The Bonch-Bruevich St. Petersburg State University of Communication Faculty of Information Systems and Technologies St. Petersburg Russia

ISBN: (纸本)9798350363708

One of the main units of work of any system is information. That is why methods of its processing are important for the full functioning of the entire process. The article discusses the scope of intelligent information processing algorithms, the complexity and features of such systems, and proposes a fuzzy logic method for achieving high results. © 2024 IEEE.

关键词： Fuzzy logic

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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific computing

Octopus: Experiences with a Hybrid Event-Driven Architecture...

引用

2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Pan, Haochen Chard, Ryan Zhou, Sicheng Kamatar, Alok Vescovi, Rafael Hayot-Sasson, Valerie Bauer, Andre Gonthier, Maxime Chard, Kyle Foster, Ian Southern University of Science and Technology Department of Computer Science China Illinois Institute of Technology Department of Computer Science ChicagoIL United States University of Chicago Department of Computer Science ChicagoIL United States Argonne National Laboratory Data Science and Learning Division LemontIL United States

ISBN: (纸本)9798350355543

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus' ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients. © 2024 IEEE.

关键词： Event-driven architecture research automation scientific computing

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Recaptured Image Forensics Using Transfer Learning 7

Recaptured Image Forensics Using Transfer Learning

引用

2023 7th international conference On computing, communication, control And automation, ICCUBEA 2023

作者： Singh, Medha Singh Singha, Nitin Muniraj, Manigandan National Institute of Technology Electronics and Communication Engineering Delhi Delhi India School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350304268

Recaptured Image detection is a field of security forensics that deals with detecting the originally captured image from its reimaged counterpart. The different algorithms that have been proposed to differentiate the original and recaptured images work with great accuracy on a large dataset and high-definition images. But the challenge remains for small-scale and small- size images as the existing algorithms required customized hand engineering whilst dealing with different datasets. Our proposed system is a dual learning model, which takes into account the enormous research for computer vision and applies concept of transfer learning for recaptured images detection. The ensemble of 6 pre-trained networks, namely AlexNet, Res Net, InceptionNet, GoogleNet, SqueezeNet, and MobileNet, has been compared on same dataset. It will allow institutional level detection of image spoofing and the recaptured image forgery for small-scale images with considerable accuracy without requiring specialized preprocessing on their end. We have achieved the highest accuracy of 90 percent through our model containing Transfer Learned MobileNet and SVM classifier. As per the latest resources, this is the first instance of applying transfer learning for recaptured image detection. © 2023 IEEE.

关键词： Pre-trained networks Recaptured image forensics SVM Classifier Transfer Learning

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Optimizing Smart Home Systems: Utilizing Blockchain and Edge computing 5

Optimizing Smart Home Systems: Utilizing Blockchain and Edge...

引用

5th international conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Xu, Rong School of Information and Architectural Engineering Anhui Open University Anhui Hefei230022 China

ISBN: (纸本)9798350379945

The evolution of smart home systems into the Internet of Things (IoT) era has revolutionized the traditional home, offering greater convenience and automation. However, with these benefits come significant security concerns, particularly around data privacy and centralized vulnerabilities. Current smart home models rely heavily on centralized servers, exposing them to single points of failure and potential breaches that undermine user trust and system reliability. In addition, interoperability challenges between devices and opaque data management further complicate these issues. To address these challenges, this study proposes a novel smart home architecture that integrates blockchain and edge computing technologies. Blockchain provides a decentralized approach to data management, ensuring transparency, security, and user control over personal data. By distributing data across nodes and employing advanced cryptographic techniques, blockchain enhances resilience against cyber-attacks and unauthorized access. The proposed methodology includes the design of a robust smart home gateway using advanced micro-controller units and enhanced Wi-Fi modules. This gateway orchestrates device communication and user interaction, enhancing system flexibility and scalability. Experimental evaluations confirm the effectiveness of the proposed system, demonstrating reduced storage overhead for blockchain nodes and optimized communication protocols. The results highlight the feasibility and benefits of decentralized, edge-enabled smart home solutions in improving user experience and system reliability. © 2024 IEEE.

关键词： Smart homes

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Design and Optimization of Sensing-communication Integration System in Industrial automation Based on FDA and PA 25

Design and Optimization of Sensing-Communication Integration...

引用

25th IEEE international conference on Industrial Technology, ICIT 2024

作者： Feng, Freddy Y.P. Mi, De Guan, Mingxiang Sun, Lichao Tang, Hui Liu, Chang Xiao, Pei Lu, Chen Shenzhen Institute Of Information Technology Shenzhen China College Of Computing Birmingham City University Birmingham United Kingdom School Of Engineering Lancaster University Lancaster United Kingdom Extend Robotics Limited Reading United Kingdom Institute For Communication Systems University Of Surrey Guildford United Kingdom

ISBN: (纸本)9798350340266

Industrial automation is a rapidly evolving field that aims to improve efficiency, productivity, and safety in various verticals. The integration of sensing and communication technologies has become crucial in this automation process. Re-cent advancements in Frequency Diverse Array (FDA) antennas have shown promising performance advantages over traditional phased array antennas in certain aspects, e.g., the FDA-based radar can focus its beam in both angle and range domains simultaneously. In this paper, we propose a novel architecture for sensing-communication integration in the context of industrial automation, where FDA antennas are employed as the radar sensing array, while phased array antennas are utilized for communication purposes. The integration of FDA antennas brings benefits such as more accurate object detection, improved spatial resolution, and efficient data transmission. We present a comprehensive analysis of the proposed architecture, including design considerations, system modeling, and performance evalu-ation. The results demonstrate the potential of FDA antennas in enhancing the sensing-communication integration in industrial automation, leading to improved efficiency, productivity, and safety in industrial environments. © 2024 IEEE.

关键词： Integration

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Detection and Mitigation of DDoS Attacks in Network Traffic Using Machine Learning Techniques 2

Detection and Mitigation of DDoS Attacks in Network Traffic ...

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2nd international conference on Advancements in Electrical, Electronics, communication, computing and automation, ICAECA 2023

作者： Uma, Maheswari V. Vishnukumar, M. Meganathan, P. Shyamsunder, C.M. Kumaraguru College of Technology Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350306811

Distributed Denial of Service (DDoS) attacks accelerate to a critical risk to online services, requiring advanced detection mechanisms. In this paper, a machine learning-based approach is proposed to detect the DDoS attacks using LSTM, SVM, and logistic regression models trained on a dataset of DDoS attacks published by Bennet University. This paper shows that the proposed models achieve high accuracy and low false positive rates, outperforming traditional methods. Furthermore, the accuracy of LSTM, SVM, and logistic regression models are compared and is observed that LSTM achieves the highest accuracy, followed by SVM and logistic regression. As a part of the evaluation process, the best model for the proposed network has been trained and selected which detects and mitigates the network attack. LSTM obtained a powerful veracity score of 99.04%, while SVM and Logistic Regression completed veracity scores of 97.4% and 83.93%, individually. Therefore, LSTM is an active model to label SDN localized DDoS attacks. Additionally, this paper demonstrates the potential of machine learning algorithms in detecting DDoS attacks and offers insights for developing more effective DDoS security systems. © 2023 IEEE.

关键词： Denial-of-service attack

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Cyber Security in IOT based Electronics & communication Systems using AI & ML techniques - A comprehensive Review of the Design Concepts for Implementation in Digital World with Applications 15

Cyber Security in IOT based Electronics & Communication Syst...

引用

15th international conference on Advances in computing, control, and Telecommunication Technologies, ACT 2024

作者： Laasya, P. Sai Pavithra, G. Manjunath, T.C. Dept. of Electronics & Communication Engg Dayananda Sagar College of Engineering Shavigemalleshwara Hills Kumaraswamy Layout Karnataka Bangalore111 India Dept. of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331300579

Artificial Intelligence (AI) & Machine Learning (ML) stands as a robust technology empowering cybersecurity team with the automation of repetitive tasks, expediting threat detection and response, and enhancing the precision of actions to fortify security against a myriad of threats and cyberattacks. This article conducts a meticulous systematic literature review, analyzing the diverse use cases of AI in cybersecurity. Out of 2395 studies examined, 236 were identified as primary sources. Employing a thematic analysis approach and aligning with the NIST cybersecurity framework, this article categorizes the identified AI use cases. The resulting classification framework offers readers a comprehensive understanding of AI's potential to enhance cybersecurity across various contexts. The review also unveils future research prospects in burgeoning cybersecurity application domains, advanced AI methodologies, data representation techniques, and the establishment of novel infrastructures, pivotal for the successful integration of AI-based cybersecurity in the contemporary landscape marked by digital transformation and polycrisis. This research delves into the domain of cybersecurity within electronic communication systems, employing advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. The study aims to present comprehensive design concepts for the implementation of robust cybersecurity measures. With the ever-growing complexity and sophistication of cyber threats, the integration of AI and ML becomes imperative to fortify electronic communication systems against potential risks. The research explores innovative approaches, strategies, and technologies, offering a holistic view of how AI and ML can be effectively harnessed to enhance cybersecurity in electronic communication systems. The outcomes of this study provide valuable insights for the development and implementation of advanced cybersecurity measures in the realm of electronic communications. ©

关键词： Adversarial machine learning

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Deep Convolutional Neural Networks for Intelligent Emotional computing: A Multidimensional Analysis and Practical Application of Emotional Features in Digital Learning Interfaces 2

Deep Convolutional Neural Networks for Intelligent Emotional...

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2nd international conference on Artificial Intelligence and automation control, AIAC 2024

作者： Zhang, Naixin Leong, Waiyie Zhang, Tong Chengdu Jincheng College Inti International University Sichuan Chengdu China Inti International University Faculty of Engineering and Quantity Surveying Nilai Malaysia The Affiliated Hospital of Southwest Jiao Tong University The Third People's Hospital of Chengdu Department of Cardiology Chengdu China

ISBN: (纸本)9798331529147

This study explores an intelligent emotional computing method baed on deep convolutional neural networks (DCNNs) aimed at accurately identifying and analyzing learners' emotional states within digital learning interfaces. A multi-channel, multi-scale convolutional neural network structure is designed to effectively integrate multimodal emotional features such as facial expressions, physiological signals, behavioral data, and learning environments. Experimental validation on both self-built datasets and publicly available datasets demonstrates that the proposed model achieves superior performance in emotion recognition tasks, with an average accuracy rate of 85.2%, significantly outperforming traditional methods based on handcrafted features and shallow networks. © 2024 IEEE.

关键词： Contrastive Learning

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Fingerspelling Classification for Robot control 22

Fingerspelling Classification for Robot Control

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22nd IEEE international conference on Industrial Informatics, INDIN 2024

作者： McCready, Kevin Coleman, Sonya Kerr, Dermot Siddique, Nazmul Kerr, Emmett Aloimonos, Yiannis Fermüller, Cornelia School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom Department of Electronic and Mechanical Engineering Atlantic Technological University Co.Donegal Letterkenny Ireland Institute for Advanced Computer Studies University of Maryland College ParkMD United States

ISBN: (纸本)9798331527471

Improvements to human-robot interaction methods could increase the ease of use of robots in manufacturing environments. Many of these environments are noisy and therefore preclude the use of audio communication between humans or in human-robot interactions. Therefore, this paper proposes using a gesture based communication system for robot control. To that end, the VGG16 and VGG19 convolutional neural network (CNN) structures are used for gesture classification along with 3 datasets of American Sign Language (ASL) fingerspelling images. The model performance is evaluated, and modifications made to their parameters to improve performance, before applying them to robot control tasks. The results show that with parameter tuning, test accuracies of up to, 100% are achievable. © 2024 IEEE.

关键词： Convolutional neural networks

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