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Classification Framework for Autoimmune Liver Disease using Machine Learning and Deep Learning Techniques 5

Classification Framework for Autoimmune Liver Disease using ...

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5th technology Innovation Management and Engineering Science international conference, TIMES-iCON 2024

作者： George, E. Ben Rosline, G. Jeba Varghese, Abraham Rajesh, Gnana D. Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Al Mussanah Oman

ISBN: (纸本)9798350362602

—Autoimmune diseases affect people of all ages, genders, and ethnic backgrounds, making it one of the most widespread health issues we face today. These disorders occur when the immune system mistakenly targets healthy cells within the body and causing a wide range of potentially life-threatening diseases. There are over 80 recognized autoimmune disorders, including type-1 diabetes, rheumatoid arthritis, Graves' disease, alopecia, celiac disease, and autoimmune liver disease(AiLD). Autoimmune liver disease is one such chronic disorder characterized by liver inflammation and damage. If not detected on time and untreated within the given time, it might lead to liver cirrhosis or liver cancer. Identifying and treating autoimmune liver disease is a complex and time consuming process which involves symptom analysis, laboratory tests, and clinical assessments. Detecting the disease can be more challenging because many symptoms of autoimmune liver diseases overlap with those of other autoimmune conditions. As a result, there is a need for an optimized, accurate, efficient, and cost-effective methodology to identify the disease without extensive and invasive procedures such as organ transplants. This paper introduces an innovative approach that employs the power of supervised machine learning and deep learning techniques to predict the onset of autoimmune liver diseases in advance. The machine learning model is constructed using a comprehensive dataset comprising the complete medical history of patients, physical examination records, histopathology and genetic data. Along with the machine learning model, the deep learning model is also developed for liver MRI images of various patients to make accurate prediction. This system provides a second opinion for medical professionals that may avoid the need for extensive and invasive medical procedures, including biopsies, and ultimately improve patient care and outcomes. ©2024 IEEE.

关键词： Autoimmune Liver Disease Classification Deep Learning Machine Learning

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A reliable edge server placement strategy based on DDPG in the Internet of Vehicles 22

A reliable edge server placement strategy based on DDPG in t...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Zhou, Zhou Han, Yonggui Shojafar, Mohammad Wang, Zhongsheng Abawajy, Jemal Changsha Univ Sch Comp Sci & Engn Changsha Peoples R China Xian Technol Univ Sch Comp Sci & Engn Xian Peoples R China Univ Surrey Inst Commun Syst ICS 5G 6GIC Guildford Surrey England Deakin Univ Fac Sci Geelong Vic Australia

ISBN: (纸本)9798350381993;9798350382006

In the Internet of Vehicles, low service delay and fast response are two essential factors to ensure the safety and smooth operation of vehicle networking. As core technologies, the 5G and edge computing networks play a fundamental role in reducing the pressure on the backbone network of vehicle networking and decreasing the service exchange delay. The previous edge server placement strategy can not be directly applied to deploying vehicle networking services, resulting in the degradation of system performance and user quality of experience. To solve the above problem, we propose a reliable edge server deployment algorithm called CFD based on Deep Deterministic Policy Gradient (DDPG). Firstly, the Canopy algorithm is used to cluster the location information of roadside units, and the initial cluster number is obtained. Then, the fuzzy C clustering algorithm (FCM) is leveraged to remove the "noise" and acquire the roadside units' initial division and priority matrix. Finally, based on the DDPG algorithm, the optimal division of roadside units is obtained, and the cluster center is utilized as the deployment location of the edge server. Many experiments have been conducted, and the results show that, compared with the benchmark algorithm, the CFD algorithm improves the load balancing degree by 25%.

关键词： Internet of Vehicles reliable edge computing reliable edge server deployment resource optimization Deep Deterministic Policy Gradient (DDPG)

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Application of greedy algorithm and multi scale optimization algorithm in dynamic grid computing 6

Application of greedy algorithm and multi scale optimization...

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2023 IEEE 6th international conference on information Systems and Computer Aided Education, ICISCAE 2023

作者： Xi, Bei'er Duan, Qinglin Dalian University of Technology Faculty of Vehicle Engineering and Mechanics Dalian China

ISBN: (纸本)9798350313444

Aiming at the high computational complexity of the traditional radial basis function (RBF), which is difficult to be applied effectively in large-scale computation, a method using greedy algorithm and multi-scale optimization algorithm is proposed to optimize the RBF. Based on RBF calculation method, this paper explores the application of greedy algorithm and multi-scale optimization algorithm in dynamic grid computing. Greedy algorithm improves computing efficiency and accuracy by selecting the best node, while multi-scale optimization algorithm improves computing speed and accuracy by dividing the grid into different scales. The effectiveness of greedy algorithm and multi-scale optimization algorithm to improve the efficiency and accuracy of dynamic grid computing is proved by an example. © 2023 IEEE.

关键词： Dynamic grid computing Greedy algorithm Multi-scale optimization algorithm Radial basis function

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The specific application of the fuzzy comprehensive evaluation method with subjective and objective weighting 4

The specific application of the fuzzy comprehensive evaluati...

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4rth international conference on applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2024

作者： Liu, Hangyu Chen, Liang Cheng, Tengfei Shi, Haoliang Li, Xin School of Mathematics and Information Science Guangxi University Guangxi530004 China School of Mathematics and Information Science Center for Applied Mathematics of Guimngxi Guangxi University Guangxi530004 China School of Computer and Electronic Information Guangxi University Guangxi530004 China School of Journalism and Communication Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510681637

Fuzzy comprehensive evaluation is a method based on fuzzy mathematics that applies the principle of fuzzy relationship synthesis to quantitatively assess factors with unclear boundaries and difficult quantification through comprehensive evaluation. As this method gradually improves and becomes more widely applied, it becomes capable of handling problems involving a large number of complex phenomena and interactions among various factors, yielding satisfactory results. To demonstrate the practical feasibility of this method, we applied it to a specific problem concerning lamprey, resulting in conclusions consistent with natural phenomena, thus validating the rationality of fuzzy comprehensive evaluation. Furthermore, based on our conclusion, feasible solutions are provided to address the problem of lampreys in the five major lakes. © 2024 SPIE.

关键词： Fuzzy set theory

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Multi objective Ant Colony Optimization Technique for Task Scheduling in Cloud computing 3

Multi objective Ant Colony Optimization Technique for Task S...

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3rd international conference on applied Artificial Intelligence and computing, ICAAIC 2024

作者： Hemanth, S.V. Kirubha, D. Reddy, Sheri Ramchandra Chelladurai, T. Soundari, A. Gnana Amirthayogam, G. Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India Rajarajeswari College of Engineering Department of Computer Science and Engineering Karnataka Bangalore560074 India Telangana Hyderabad501401 India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Thandalam600124 India Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350375190

Cloud computing (CC) is a powerful paradigm for accessing IT services over the internet, allowing multiple consumers to share resources. Therefore, it requires task scheduling, allocating, and gradually releasing resources. Hence, efficient Task Scheduling (TS) and Resource Allocation (RA) in cloud systems is crucial to optimize resource usage and ensure timely completion. Assigning computing tasks in a cloud environment is a multifaceted process affected by numerous factors, including network bandwidth availability, completion time, and cost considerations. Traditional scheduling models based on heuristic algorithms have limitations in complex in cloud environments. To tackle these issues, we introduce Multi objective Ant Colony Optimization (MACO) and Adaptive Sequence Dynamic Allocation (ASDA) techniques for TS and RA in the cloud environment. Initially, the tasks submitted globally are divided into long and short queues. After that, the MACO algorithm schedules the task based on queues. Subsequently, the proposed ASDA algorithm efficiently allocates the most appropriate Virtual Machine (VM). If a VM is already occupied, the proposed method promptly chooses the next available VM based on the progress of the work completion process. According to the experimental results, the proposed algorithm achieves significant cost savings compared to other algorithms. © 2024 IEEE.

关键词： Virtual machine

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Transformative enhancement of predictive models via fourier transformer-based denoising for non-intrusive load monitoring 4

Transformative enhancement of predictive models via fourier ...

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4rth international conference on applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2024

作者： Zhu, Lin Jiang, Lei He, Xin Jin, Jesse S. Zheng, Yu Henan University The School of Software Kaifeng475004 China Tianjin University The Intelligent Computing Division Tianjin300072 China Changsha University of Science and Technology The School of Electrical and Information Engineering Changsha410004 China

ISBN: (纸本)9781510681637

Energy disaggregation is a pivotal technique employed in Non-Intrusive Load Monitoring (NILM) to accurately estimate the power consumption of individual appliances in households. Recent advancements in trasformer models, particularly in domains like Natural Language Processing (NLP), have underscored their potential for enhancing classification performance. In this paper, we propose a novel algorithm for energy decomposition using an adapted transformer-based architecture. Our approach integrates the rapid Fourier transform with a multi-head attention mechanism to efficiently transform time-series feature data into frequency domain features, followed by noise reduction achieved by multiplying the data with a trainable matrix. The DFT-NILM model, also known as the denoising Fourier transformer, has been evaluated on publicly available electrical data. The evaluation shows that the DFT-NILM model outperforms existing state-of-the-art methods in terms of performance. © 2024 SPIE.

关键词： Noise abatement

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Spiking Neural Networks for Signal Classification with Digital and Analog Neuromorphic Systems: A Comparative Study

Spiking Neural Networks for Signal Classification with Digit...

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international Joint conference on Neural Networks (IJCNN)

作者： Happek, Leon Huang, Jen-Tse Gener, Alejandro Garcia Leupers, Rainer Galicia, Melvin Collins Aerosp Cork Ireland Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9798350359329;9798350359312

Spiking Neural Networks (SNNs) have suitable properties for realizing efficient processing at the edge, while having several similarities to brain-inspired computing that can be applied in neuromorphic computing systems. On the other hand, signal processing for wireless communication is an essential application that is critical at the edge. Signal modulation detection is a task that requires reliable accuracy in noisy environments, such as commercial and defense avionics applications. This task can be performed in a neuromorphic system running an SNN classification model using two different technologies: digital or analog. We present a comparative study between a digital manycore-based and an analog memristor-based neuromorphic implementation. We use Intel's LAVA and NeuroPack opensource frameworks to implement them and compare them in terms of accuracy. The results show that the digital technology achieves relevant accuracies, while the analog technology requires a more elaborate portability model to achieve comparable results.

关键词： Spiking Neural Networks Signal Classification Signal Modulation Digital Neuromorphic computing Analog Neuromorphic computing Loihi2 Memristors

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A Framework for BMC Firmware Vulnerability Analysis and Exploitation 17

A Framework for BMC Firmware Vulnerability Analysis and Expl...

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IEEE Congress on Cybermatics: 17th IEEE international conference on Internet of Things, iThings 2024, 20th IEEE international conference on Green computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2024, 10th IEEE international conference on Smart Data, SmartData 2024

作者： Wang, Jiapeng Zheng, Zhihan Qiu, Kefan Tan, Yu-An Liang, Chen Li, Wenjuan Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Beijing Information Science & Technology University School of Cyberspace Science and Technology Beijing China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong

ISBN: (纸本)9798350351637

The Out-of-Band management system for server is a specialized controller used for monitoring and managing the status of servers. Server BMC(Baseboard Management Controller) firmware has higher privileges, and conducting firmware vulnerability analysis and exploitation based on the characteristics of the BMC firmware operating environment has become an important research direction at present. To improve the effectiveness and ease of BMC firmware detection, non-intrusive methods of firmware code retrieval need to be investigated. We propose a technique called lateral extension to transfer control from the BMC to the server. It accesses high-value targets in the server environment, including RAM, code, and files. The BMC controller is emulated as a master/slave device over the PCI bus of the server. Arbitrary read/write access to the hard drive, memory, and UEFI firmware of the server is accomplished by starting Direct Memory Access (DMA) operations. © 2024 IEEE.

关键词： Firmware

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Research on Wireless Communication Optimization Based on Edge Computation 4

Research on Wireless Communication Optimization Based on Edg...

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4th international conference on Electronic information Engineering and Computer technology, EIECT 2024

作者： Wen, Yuhan Yuan, Nanbo Zhang, Weiya Xiamen University Malaysia Sepang Malaysia School of Electrical Engineering Yancheng Institute of Technology Yancheng China

ISBN: (纸本)9798331528850

The exponential growth of the number of Internet of Things (IoT) devices is placing significant strain on traditional wireless communication networks, particularly in terms of bandwidth, latency, and energy consumption. As a distributed computing architecture, edge computing enables the consolidation of computing resources at the user's edge, thereby alleviating the burden on the central server and reducing data transmission latency. This paper introduces a multi-level edge computing architecture, implements dynamic resource scheduling and collaborative computing strategies among edge nodes, and employs distributed computing methods to reasonably allocate computing tasks to different nodes, thereby addressing complex network scenarios. Additionally, the model incorporates a resource allocation algorithm based on deep reinforcement learning, which enables adaptive adjustments to bandwidth, computing resources, and communication paths. This results in optimal resource scheduling under varying network loads, markedly enhancing transmission efficiency and overall system performance in edge computing environments. In the experimental phase, the proposed model was validated using a simulation platform. The results demonstrated that the proposed model exhibited notable enhancements in data transmission delay, energy consumption, and throughput compared to the traditional method. © 2024 IEEE.

关键词： Mobile edge computing

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An Improved Multi-Speaker Tibetan Speaker Diarization Based on EDA-EEND 7

An Improved Multi-Speaker Tibetan Speaker Diarization Based ...

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7th international conference on Computer information Science and Application technology, CISAT 2024

作者： Gan, Zhenye Ma, Feiyue Northwest Normal University College of Physics and Electronic Engineering Lanzhou China Engineering Research Center of Gansu Province for Intelligent Information Technology and Application Lanzhou China

ISBN: (纸本)9798350375107

Speaker diarization research is the process of segmenting the audio of different speakers and clustering the audio of the same speaker in an audio or video task, which is commonly referred to as accomplishing the task of 'who is talking when', the current researches on speaker diarization mainly consist of multi-module systems relying on traditional clustering and end-to-end clustering. Due to the limitations of model structure and linear transformation, the above two methods can only be limited to deal with a fixed number of speakers, this paper combines attractor computing based on LSTM coding and decoding and EDA-EEND based on the self-attention mechanism of SA-EEND, in order to avoid the problem of mismatch between the simulation of the end-to-end speaker diarization and the real dataset, it also introduces of the PIT loss function creates a unique Tibetan language dataset. Tibetan dataset, the experimental results show that speaker diarization can not only be applied in small languages but also flexibly expand the number of two speakers to multiple speakers, and the effect of 45.3% in multiple speakers can be comparable to that of two speakers. © 2024 IEEE.

关键词： Decoding

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