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Unlocking the Potential of Blockchain Integration in Secured Framework of Mental Health 3

Unlocking the Potential of Blockchain Integration in Secured...

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3rd IEEE international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Swati, Shipra Mishra, Ritesh K. Kumar, Alok Ingle, Rajesh Bennett University School of Computer Science Engg. & Tech. Uttar Pradesh Greater Noida India National Institute of Technology Department of Electronics & Communication Engg. Bihar Patna India Indian Institute of Information Technology Department of Computer Science & Engg. Chhattisgarh Naya Raipur India

ISBN: (纸本)9798350354348

The assessment of mental health within modern healthcare systems has become increasingly complex, particularly when implemented on IoT-supported platforms. This paper explores the potential of a blockchain framework to utilize the trivial properties of EEG in order to enhance the applicability, efficiency and effectiveness of mental health assessment. Blockchain ensures secure and tamper-proof storage of mental health data, enables seamless data integration from diverse IoT sources, and fosters trust and transparency among participants. The decentralized and immutable nature of blockchain is expected to enhance security of EEG data, ensuring its integrity and interoperability. It also incentivizes data sharing, facilitates research collaboration, and reduces costs associated with data management and exchange. These technologies have the potential to revolutionize mental health care, leading to more precise and effective assessment techniques, personalized interventions, and improved outcomes for individuals seeking mental health support. Furthermore, the integration of AI and analytics on blockchain-integrated systems may support collaborative research, paving the way for more effective and innovative solutions to improve diagnostic accuracy. © 2024 IEEE.

关键词： Electronic health record

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Deep Dive into IIoT Intrusion Detection systems: A Review and Future Directions 3

Deep Dive into IIoT Intrusion Detection Systems: A Review an...

引用

3rd IEEE international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Pawar, Sushama Laxman Karyakarte, Mandar S. BRACT's Vishwakarma Institute of Information Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

The Industrial Internet of Things (IIoT) has quickly become a cutting-edge technology that can transform various sectors by digitalizing and connecting them, boosting business opportunities and global economic growth. Sectors like manufacturing, logistics, transportation, energy, and aviation are increasingly adopting IIoT. However, IIoT is vulnerable to cyber threats, requiring robust security measures. The numerous sensors in IIoT systems generate vast amounts of data, attracting cybercriminals. An Intrusion Detection System (IDS), which observe network traffic to detect unusual behavior, is crucial for securing IIoT applications. Recently, machine learning and deep learning techniques have shown potential in mitigating security threats and enhancing IDS performance. This paper reviews different deep learning-based IDS methods for IIoT, including techniques, datasets, and comparative analysis, and highlights existing limitations and future research directions. © 2024 IEEE.

关键词： Network intrusion

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Research on sliding mode coordinated control of yaw stability of distributed drive electric wheel vehicle 3

Research on sliding mode coordinated control of yaw stabilit...

引用

3rd international conference on Intelligent Mechanical and Human-Computer Interaction Technology, IHCIT 2024

作者： Liu, Qiusheng Wang, Jian University of the East Manila Philippines Jiangxi College of Applied Technology Guangdong Ganzhou China

ISBN: (纸本)9781510683105

In view of the characteristics of independently controllable torque of each wheel of a distributed drive electric wheel vehicle and rapid adjustment speed, this paper studies the stability control problem during its driving. Using vehicle dynamics theory, the vehicle stability evaluation parameters yaw angular velocity and center of mass slip angle are selected as control variables, and considering their error change rates, a yaw moment sliding mode controller is designed, using coordination to eliminate the coupling influence of the two control variables. The control strategy generates the yaw moment required to maintain the stable driving of the vehicle and effectively distributes it to the four drive wheels. The CarSim-Simulink simulation test platform was used to verify the designed control strategy by taking typical angular step test conditions as an example. The results show that the adopted sliding mode coordinated control strategy can significantly improve the stability and safety of distributed drive electric wheel vehicles. © 2024 SPIE.

关键词： Velocity control

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3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

3rd International Conference on Artificial Intelligence, Aut...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

ISBN: (纸本)9781510666573

The proceedings contain 125 papers. The topics discussed include: autonomous obstacle crossing method of substation inspection robot based on locust optimization algorithm;research on patrol platform of fixed wing UAV for ultra long distance transmission and distribution lines;towards distributed multi-target tracking using teams of mobile robots;multi-sensor data fusion for indoor drone positioning;research on unmanned aerial vehicles’ passive location model based on iteration and merging;research on decision fusion model for driving unmanned logistic vehicles based on complex road conditions;autonomous unsupervised image inpainting networks based on structural priors;research on stability identification algorithm of autonomous vehicle based on data driven;and research on steering-by-wire control method for new energy vehicles based on large data analysis.

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Secure Crowdfunding Using Blockchain 3

Secure Crowdfunding Using Blockchain

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3rd IEEE international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Borse, Yogita Khan, Hassan Bana, Ujjwal Upadhyaya, Smit Somaiya Vidyavihar University K. J. Somaiya College of Engineering Department of Information Technology Mumbai India

ISBN: (纸本)9798350354348

In the existing crowdfunding method there are several drawbacks. First is the contributor does not have control over the amount invested and the entire amount is given in the hands of project manager/ Campaign Manager. Secondly, the centralized nature of the system introduces intermediaries where they charge fees for providing the service which reduces the funds available for the project and increases the cost for contributors as well. To fulfill all the drawbacks crowdfunding using blockchain was introduced to increase the transparency and remove the intermediaries from the process and keep security of the investors' funds. But after going through the multiple implemented system we realized that there's some changes required to overcome the spending problem in the blockchain based crowdfunding system. The drawback which came to our notice was that to use funds from smart contracts the fundraiser creates a request but the problem arises here is whether the fundraiser is using the funds appropriately or not. Another problem which we noticed is that the fundraiser raise the funds for already existing project which is available in local market and they simply raise the funds and provide the project to fundraiser. We came up with the solution of providing proof of work of the project and before requesting funds for the project the fundraiser has to provide the proof of progress in the project or the client consent to withdraw the funds. For providing traceability the fundraiser will have to provide the address to check the spending of the funds withdrawn from the contract. © 2024 IEEE.

关键词： distributed ledger

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New Algorithms for distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 23

New Algorithms for Distributed Fair k-Center Clustering: Alm...

引用

23rd international conference on Autonomous Agents and Multiagent systems, AAMAS 2024

作者： Wu, Xiaoliang Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie United States Department of Computer Science and Engineering State University of New York at Buffalo Buffalo United States Hunan Provincial Key Lab on Bioinformatics Central South University Xiangjiang Laboratory Changsha China

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17 + ∊)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3 + ∊)- approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4 + ∊)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems. © 2024 international Foundation for Autonomous Agents and Multiagent systems.

关键词： Machine learning

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Cervical cell classification based on attention mechanism and multi-resolution feature fusion 3

Cervical cell classification based on attention mechanism an...

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3rd international conference on information Science, parallel and distributed systems, ISPDS 2022

作者： Yu, Jingya Wang, Guoyou Cheng, Shenghua School of Automation Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing Wuhan China Britton Chance Center School of Engineering Sciences Huazhong University of Science and Technology Moe Key Laboratory for Biomedical Photonics Wuhan China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

Liquid-based thin-layer cell smears are very important for the early screening and prevention of cervical cancer, and computer-aided diagnosis can reduce the workload of pathologists. The cell classification method based on deep learning can process data efficiently. However, most classification methods are based on a single resolution for recognition. When the resolution is low, the processing speed of the whole slide image is faster, but lack of picture details, which makes the identification inaccurate. When the resolution is high, it takes more time to process the whole slide image, but with more image detail. To this end, we propose a model based on Attention Mechanism and Multi-resolution Feature Fusion Module (AMFM), which combine the advantages of various resolutions to classify cervical cells. Experiments show that the accuracy is increased by 3.93% and the AUC is improved by 0.022 on the four-classification task of the cervical cell compared to the model based on a single resolution. © 2022 IEEE.

关键词： Computer aided diagnosis

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Hardware acceleration design and implementation of YOLOv3-tiny based on ZCU102 platform 3

Hardware acceleration design and implementation of YOLOv3-ti...

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3rd international conference on Electronic information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Zhao, Yixuan Shi, Tianjie Liu, Feiyang Tian, Jing Xi'an Aeronautics Computing Technique Research Institute Xi'an China

ISBN: (纸本)9781510680449

Convolutional Neural Networks (CNNs) have been widely applied in the field of image recognition. To meet the extensive computational demands of CNNs, GPUs or other intelligent computing hardware are commonly employed for data processing. FPGA supports parallel computing and is characterized by programmability, high performance, low energy consumption, and strong stability. This paper provides a detailed introduction to Xilinx's FPGA development tool software, Vitis AI, designed for artificial intelligence, along with the process of generating and implementing FPGA hardware logic for deep learning algorithms. The functionality and performance were verified through FPGA experimental platforms. We used a 16-bit fixed-point method to segment neural network tasks and preprocess data, thereby reducing hardware resource consumption. By employing the ZCU102 development platform to accelerate the YOLOv3-Tiny algorithm, we achieved target object detection and recognition. The power consumption was only 9.9% of GPU Quadro K6000, and compared with other YOLOv3-tiny implementations on ZCU102, the detection speed was increased by 5.7 times. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computation theory

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Device Threat Identification and Risk Assessment using Feature Analysis in Machine Learning 3

Device Threat Identification and Risk Assessment using Featu...

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3rd IEEE international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Panchal, Krish Desai, Mihir Itankar, Tejas Mangrulkar, Ramchandra Dwarkadas J. Sanghvi College of Engineering Information Technology Department Mumbai India

ISBN: (纸本)9798350354348

In response to the growing threat of malware in our digitally linked society, this research paper introduces a ground-breaking Malware Prediction System (MPS). Our system seeks to foresee and proactively detect malware in real-time using cutting-edge machine learning algorithms, serving as a critical defence mechanism against shifting cyber threats. The procedure comprises creating a user-friendly website that is accessible to both individuals and businesses. Users can rapidly upload files from their Windows computers, kicking off the feature analysis process. Based on the unique attributes acquired from the given files, the study estimates the likelihood of a malware attack using machine learning methods. Through a simple and intuitive interface, the system offers a smooth experience, allowing users to execute scans and obtain quick, actionable findings. Furthermore, the aim of the paper is to bring users with a tool that not only improves security but also contributes to cybersecurity awareness. By making the prediction process accessible and user-friendly, we aim to foster a proactive approach to malware prevention. The integration of Microsoft's extensive dataset ensures the model's robustness and adaptability to diverse cyber threats, making the MPS a valuable asset in the ongoing battle against malware. © 2024 IEEE.

关键词： Cyber attacks

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3rd international conference on information Science, parallel and distributed systems, ISPDS 2022

作者： Zhang, Yemao Jia, Wei Min, Hai Lei, Yingke Zhao, Yang Fan, Chunxiao School of Computer and Information Hefei University of Technology Hefei230601 China College of Electronic Countermeasures National University of Defense Technology Hefei230037 China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

Few-shot semantic segmentation aims to tackle the problem that segmenting unseen object class using only a few support images with the same object class. At present, most related methods focus on prototype learning or feature similarity. However, these few-shot segmentation methods do not make good use of high-level features to enhance the prediction results. In this paper, we propose a lightweight Similarity-Guided and Multi-layer Fusion Network (SMNet) with two modules including Similarity-Guided Module (SGM) and Multi-Layer Fusion Module (MLFM). Specifically, the SGM utilizes cosine similarities in multiple high-level feature layers to augment the features in middle-level from query and support image, and then augmented features are refined via a residual attention module. In order to enhance the diversity of features, we reformulate the refined features as a spatiotemporal sequence problem. Then, we introduce the MLFM, which combines two ConvLSTMs to obtain fused feature from different scales. Finally, the decoder takes fused features to obtain predicted mask. Experiment results demonstrate that our model can achieve superior or competitive performances in several datasets. © 2022 IEEE.

关键词： Semantic Segmentation

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