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NetFeatures-Transformer: Enhancing Brain Network Features to Classify Alzheimer's Disease 22

NetFeatures-Transformer: Enhancing Brain Network Features to...

引用

22nd IEEE/WIC international conference on Web Intelligence and Intelligent Agent technology (WI-IAT)

作者： Cao, Yu Kuai, Hongzhi Zhong, Ning Wang, Bixin Fan, Fengmei Yan, Jianzhou Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Maebashi Inst Technol Fac Engn Maebashi Gumma Japan Peking Univ Huilongguan Beijing Huilongguan Hosp Clin Med Sch Beijing Peoples R China

ISBN: (纸本)9798350309188

The convergence of AI, big data, and magnetic resonance imaging (MRI) has unlocked transformative potential in the field of neurological disease. For example, the resting-state fMRI technique is widely applied to identify functional alterations in brain and perform classification tasks for Alzheimer's Disease (AD). However, the high network complexity and difficult-to-interpret features of these models make it challenging to translate them into clinical applications. To overcome these limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing from resting-state fMRI. In this work, a NetFeatures-Transformer is proposed to perform functional brain network classification through the integration of graph-based evidence, realizing interpretable discrimination of healthy controls (HC) and AD. The key contributions include: combining a transformer encoder with network nodal features to enhance classification performance, capturing different aspects of the brain network's topology. Additionally, the model utilizes the attention scores from trained transformer encoders to enhance interpretability, revealing the significant brain connections that have impact on the classification tasks at the same time.

关键词： NetFeatures-Transformer Evidence Combination-Fusion computing (ECFC) Brain Network Analysis Alzheimer's Disease (AD)

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Cybersecurity and Electoral Processes. An Analysis of Block Chain Enabled Biometric Voter System and Risk Control in Kenya's 2022 Electoral Process and the United States Election System Infrastructure. 14

Cybersecurity and Electoral Processes. An Analysis of Block ...

引用

14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Irungu, John Girma, Anteneh University of the District of Columbia Department of Computer Science United States

ISBN: (纸本)9798350313277

Electoral Processes Infrastructure Security and Integrity has been an emerging challenge due to the verifiability disputes that arise in competitive elections. This is despite investments in Secure and electoral technology that meets the Confidentiality, Integrity and Availability (CIA triad) of information security. To ensure electoral infrastructure efficiency, information security and voters data confidentiality, governments have invested in Blockchain modeled technology, Biometric Voter Registration and Authentication as well as secure electronic voting mechanisms. This paper studies the Information Security issues raised in the Data Collection, Results Transmission Servers (RTS), Management in the Kenyan electoral process of 2022 where Biometric Voter Registration (BVR) technology was used to authenticate the voter registration and a hybrid System used in voting and transmitting results. Comparatively, this paper also investigates the United States electoral voting platforms, and documented attempts to interfere with the elections Cyber Security Infrastructure through Malware Attacks and Spearphishing on State and Local Networks. The demonstrates the shows the cost and benefit of traditional data centers against block chain secured BVR and virtualized platform. To enhance electoral integrity and security, the findings recommend a proposed change in the Information Management policies and protocols in the administration of electoral hardware and software assets as well as unrestricted access for Results Transmission Audit. © 2023 IEEE.

关键词： Biometric Voter Registration (BVR) Blockchain CIA triad Cloud computing

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The Simulation Study on the Effect of Transmission Error in Split computing Approach 14

The Simulation Study on the Effect of Transmission Error in ...

引用

14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Lee, Jaewook Ko, Keunsoo Ko, Haneul Pukyong National University The Department of Information and Communication Engineering Busan Korea Republic of Catholic University of Korea The Department of Artificial Intelligence Bucheon Korea Republic of Kyung Hee University The Department of Electronic Engineering Yongin Korea Republic of

ISBN: (纸本)9798350313277

The split computation service has gained wide attention to reduce the artificial intelligence-based service completion time. In a split computation service, the mobile device and edge server need to exchange the intermediate data to cooperatively process the AI-based service (i.e., process the deep model in AI-based service). In this case, the performance of split computation (e.g., classification accuracy) is degraded due to the transmission error. However, most of the works did not consider the transmission error effect of the split computation service. In this paper, we conduct various simulations to analyze the effect of transmission error on the split computation service. From the simulation results, we find some general trends that the later deep layer and convolution layer have better error-tolerant capability. © 2023 IEEE.

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Federated Learning with U-Net for Brain Tumor Segmentation: Impact of Client Numbers and Data Distribution 15

Federated Learning with U-Net for Brain Tumor Segmentation: ...

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15th international conference on Information and Communication technology convergence, ICTC 2024

作者： Le, Thu Thuy Pham, Nhat Truong Tran, Phuong-Nam Dang, Duc Ngoc Minh Fpt University AiTA Lab Dept. of Computing Fundamental Ho Chi Minh City Viet Nam Sungkyunkwan University Dept. of Integrative Biotechnology Suwon Korea Republic of Kyung Hee University Dept. of Computer Science and Engineering Gyeonggi-do Yongin-si Korea Republic of

ISBN: (纸本)9798350364637

Brain tumor segmentation plays a crucial role in diagnosis and treatment planning. However, sharing patient data for training deep learning models raises privacy concerns. This paper proposes a federated learning (FL) approach utilizing a U-Net architecture for the segmentation of brain tumors. We evaluate the performance of federated U-Net models under various data distribution and varying numbers of clients. Specifically, we compare the effectiveness of two FL methods: Federated Averaging (FedAvg) and Federated Stochastic Gradient Descent (FedSGD). Through experiments conducted on the BraTS dataset, we observe that as the number of clients increases, the overall performance of the models tends to decrease. Moreover, we find that skewed data distribution often outperforms equal data division. Additionally, we consistently observe that FedAvg yields superior results compared to FedSGD. Our proposed approach enables hospitals to train models on local data collaboratively without directly sharing sensitive information. This preserves patient privacy while ensuring accurate tumor segmentation. The results of our study underscore the significance of strategic data distribution in FL environments and provide valuable insights for optimizing FL strategies in medical imaging applications. © 2024 IEEE.

关键词： Image segmentation

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Speech Recognition Paradigms: A Comparative Evaluation of SpeechBrain, Whisper and Wav2Vec2 Models 9

Speech Recognition Paradigms: A Comparative Evaluation of Sp...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Yerramreddy, Dhanvanth Reddy Marasani, Jayasurya Gowtham, Ponnuru Sathwik Venkata Harshit, Guduru Anjali Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India Amrita Vishwa Vidyapeetham School of Engineering Dept. of Electronics & Communication Engineering Amritapuri India

ISBN: (纸本)9798350394474

Speech recognition plays a pivotal role in the realm of natural language processing that deals in converting the language into the written text, providing human-computer interaction and enables us to use it widely for applications starting with voice assistants and delving upto the transcription services. Due to the complexity present in performing the task of speech recognition has led to the development of various models to enhance accuracy and efficiency. Our project mainly delves into three prominent speech recognition models that are Whisper, Wav2Vec2 and Speechbrain each of them representing distinct approaches of transcribing spoken language. The significance of the models used lies in their potential to perform better for realtime applications by improving the accuracy and reliability of speech recognition. To evaluate the best model that performs effectively, an array of metrics are used including levenshtein distance and it's similarity percentage, jaccard similarity along with semantic similarity that provides an additional layer of evaluation describing the model's understanding of contextual meaning in spoken language. Out of the three models, Speech Brain model outperformed all the other models through the calculation of Word Error Rate (WER), Character Error rate (CER) and BLEU score. The results have shown the model's efficiency in converting spoken language(audio files) into precise and contextually relevant text. The results shows that these models contribute to the field of speech recognition highlighting the strengths of each approach and among them considering speechbrain as an ideal solution for accurate and meaningful transcription. © 2024 IEEE.

关键词： Speech recognition

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Power Loss Minimization in DG allocation using Artificial Intelligent computing Techniques 7

Power Loss Minimization in DG allocation using Artificial In...

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7th IEEE international conference for convergence in technology, I2CT 2022

作者： Dugaya, Neeti Bhongade, Sandeep Chaturvedi, Krishna Teerth University Institute of Technology Department of Electrical Engineering Bhopal India Shri G S Institute of Technology Indore Department of Electrical Engineering Indore India

ISBN: (纸本)9781665421683

In this work, evolutionary computation techniques like particle swarm optimization (PSO) and genetic algorithm (GA) are used to access the optimal DG allocation for minimization of power losses to facilitate the extensive integration of DG into distribution systems. The impact of DG integration is accessed to analyse the losses and performance of the system. Soft computing technique PSO is exhibited to be suitable for execution at any type of power system with the specified system bus data and line data. GA and PSO artificial intelligence computational techniques have been modelled and tested on the typical IEEE 30-bus system. Both of the artificial intelligence computational techniques with same control parameters having upper and lower limits have been tested, compared and analysed on the standard IEEE 30-bus test system. The results validate that the appropriate allocation and size of DG are the significant factors that affect the accommodation of the growing levels of DG in distribution networks. © 2022 IEEE.

关键词： Genetic algorithms

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Underwater Object Detection Based on DN-DETR

Underwater Object Detection Based on DN-DETR

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2023 IEEE international conference on Real-Time computing and Robotics, RCAR 2023

作者： Mai, Ke Cheng, Wen Wang, Jingnan Liu, Shilan Wang, Yiru Yi, Zhengkun Wu, Xinyu China Shenzhen Institute of Advanced Technology Cas China

ISBN: (纸本)9798350327182

We present a method for underwater object detection with DN-DETR (DeNoising-DEtection TRansformer) and DN-Deformable-DETR. Both DN-DETR and DN-Deformable-DETR are DETR-like detectors that introduce a denoising acceleration training method. Denoising is capable of accelerating the convergence rate and reducing the training instability of the native DETR detector. In addition, DETR has a strong ability on learning global image context and performs better in the detection of large and small targets. The results show that DN-Deformable-DETR has better average precision (+3.7AP ) and better performance on large targets (+5.1AP L) and small targets (+11.5AP S) than Faster R-CNN. Compared with DN-Deformable-DETR, DN-DETR obtained the optimal value in a single metric (+0.8AP 50). © 2023 IEEE.

关键词： Object detection

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Research on Information technology in the Factory Production of Edible Fungi 6

Research on Information Technology in the Factory Production...

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6th international conference on Intelligent computing and Control Systems, ICICCS 2022

作者： Yao, Li Huang, Min Wang, JiangRui Guangdong Polytechnic of Science and Trade College of Food and Bioengineering Guangzhou510430 China

ISBN: (纸本)9781665410359

Among them, the research and application of information technology is the only way to realize the fully automatic industrial production of edible fungi. Find the point of convergence between information technology and the common development of the edible fungus industry. Through the use of information technology, the edible fungus industry can be significantly improved in production, sales and management. When analyzing the application of information technology in the edible fungi industry, this research study mainly start from the status quo of the development of domestic edible fungi information technology, and deeply discuss the key points of the application of information technology in the edible fungi industry to promote the development of the industry. © 2022 IEEE.

关键词： Big data

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Distribution Network Theoretical Line Loss Analysis Based on BP Neural Network Algorithm 3

Distribution Network Theoretical Line Loss Analysis Based on...

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3rd international conference on Consumer Electronics and Computer Engineering, ICCECE 2023

作者： Ye, Xin Lv, Zhiming Jiangxi University of Science and Technology School of Information Engineering Ganzhou Jiangxi China State Grid Ganzhou Company GanZhou JiangXi China Jiangxi Ganzhou Institute of Technology Department of Information Engineering Jiangxi Ganzhou China

ISBN: (纸本)9798350331578

In order to use accurate and easy-To-use line loss calculation method to calculate the theoretical line loss of distribution network, this paper proposes the use of BP neural network algorithm. Aiming at the disadvantage that it is easy to converge to the local optimal solution, the optimization method is combined with multi-Algorithm. Finally, 68 distribution line data sets are used to demonstrate the optimization method, and the hybrid algorithm of multi-Algorithm superiority fusion is confirmed to have the highest accuracy and convergence speed. © 2023 IEEE.

关键词： Neural networks

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Distortion Analysis and Compensation for Structure Dynamics of Ground Robot in Hybrid Simulation

Distortion Analysis and Compensation for Structure Dynamics ...

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2023 IEEE international conference on Real-Time computing and Robotics, RCAR 2023

作者： Li, Dongjin Qi, Chenkun Hu, Yan Wang, Wenlong Gao, Feng Shanghai Jiao Tong University School of Mechanical Engineering Shanghai200240 China Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing100094 China

ISBN: (纸本)9798350327182

The hybrid simulation system can simulate the six-degree-of-freedom motion of the complicated space docking process without need for real spacecraft. The ground robot is a part of the motion simulator of the hybrid simulation system. To reproduce the docking process accurately, the ground robot requires very high structure stiffness, which is difficult to be achieved in practice. The limited structure dynamics of the ground robot brings simulation distortion. In this paper, the divergence and convergence distortion caused by the structure dynamics of the ground robot is studied. The stability analysis for the hybrid simulation system is carried out. A distortion compensation method for the structure dynamics of the ground robot is proposed and verified. An application of the compensation method on a docking simulation is given. © 2023 IEEE.

关键词： Degrees of freedom (mechanics)

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