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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Nawaz, Adil Saeed, Faisal Leung, Victor C.M. Li, Jianqiang Wang, Zhaoxia Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Children's Hospital Department of Gastroenterology China

ISBN: (纸本)9798350386226

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network's prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB's input regions with high mutual information with the network's output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets - CNMC, ISBI2016, and BreaKHis - demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains. © 2024 IEEE.

关键词： Diseases

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SOC Estimation of an LFP Battery using Extended Kalman Filter with Extracted Prameter

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Transactions of the Korean Institute of Electrical Engineers 2023年第11期72卷 1372-1379页

作者： Lim, Hee-Sung Lee, Sang-Hyuk Lee, Kyo-Beum Dept. of Electrical and Computer Engineering Ajou University Korea Republic of Dept. of Fuelcell System Perfomance Development K-Fuelcell Korea Republic of Dept. of Research Center IFTech Korea Republic of

This paper proposes a method to estimate SOC(State of charge) by extracting parameters for each circuit element of an LFP(LiFePO4) battery equivalent circuit model and combining the Extended Kalman Filter and coulomb counting method. A lithium battery cell is generally modeled using an equivalent circuit, and parameters for each circuit element of the battery model can be extracted using pulse charging and discharging curves. Those curves are collected experimentally to characterize battery performance at various operating points. Numerical optimization algorithms are repeatedly performed in computer simulation to minimize errors in battery models and experimental data. SOC is estimated using extracted parameters and the Extended Kalman Filter algorithm. Copyright ©The Korean Institute of Electrical Engineers.

关键词： Extended Kalman filters

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A Review of Fintech Management and Security Development 10

A Review of Fintech Management and Security Development

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Ritonga, Ali Akbar Zuhri Harahap, Syaiful Sooai, Adri Gabriel Nugroho, Aryo Samsir Prasetyo Adi, Puput Dani Universitas Labuhanbatu Faculty of Science and Technology Department of Informatics Management North Sumatera Labuhanbatu Indonesia Universitas Labuhanbatu Faculty of Science and Technology Department of Information System North Sumatera Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Universitas Narotama Faculty of Computer Science Department of Information System Surabaya Indonesia Universitas Labuhanbatu Faculty of Computer Science Department of Informatics Engineering North Sumatera Labuhanbatu Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026. © 2024 IEEE.

关键词： Ethereum

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3DE Digital Platform for Electrical Distribution Design of Pumped Storage Power Stations

3DE Digital Platform for Electrical Distribution Design of P...

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2024 Asia-Pacific Conference on Software engineering, Social Network Analysis and Intelligent Computing, SSAIC 2024

作者： Jing, Feng Li, Xizhe Yuan, Zhengyang Li, Bingjie Li, Zhijian Northwest Engineering Corporation Limited Shaanxi Xi'an710065 China Xi'an Antoine Computer Network System Integration Co. Ltd Shaanxi Xi'an710075 China

ISBN: (纸本)9798350393682

Pumped storage power plants have the advantages of environmental protection, fast response, and reusability, and their role has gradually been widely recognized and rapidly developed. In recent years, the rapid development of digital technology has led to an increasing demand for virtual reality. As an emerging technology, 3DE digital platform is gradually entering people's vision. This platform can bring people into a completely different experience by generating a realistic virtual world in real-time. This article designs the electrical distribution of pumped storage power plants through the 3DE digital platform. The testing of the hydraulic turbine model of a pumped storage power plant showed that the simulation results of the turbine terminal voltage and the generator active power showed a smaller difference in the first swing curve, which was basically consistent. Only slight differences were observed in the following few swings, indicating that the hydraulic turbine model had little impact on electrical quantities during transient analysis of the power system. At the same time, the 3DE digital platform can predict future energy demand and store electricity in pumped storage tanks to supply electricity during peak hours and improve the stability of the power grid. © 2024 IEEE.

关键词： Pumped storage power plants

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Automated Detection of Intracranial Hemorrhage using Convolutional Neural Networks 1

Automated Detection of Intracranial Hemorrhage using Convolu...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Chakraborty, Pritam Bandyopadhyay, Anjan Misra, Mukul Gupta, Palak Sardar, Tanvir Habib Pandey, Bishwajeet School of Computer Engineering KIIT University Bhubaneswar India Dept of CSE GITAM University Bengaluru India Department of Intelligent System and Cyber security Astana IT University Kazakhstan

ISBN: (纸本)9798350364378

Detecting intracranial hemorrhage promptly and accurately is vital for ensuring optimal patient outcomes, especially in areas with limited access to radiologists. This paper introduces an automated method employing advanced convolutional neural networks (CNNs) to identify intracranial hemorrhage from Computed Tomography (CT) scans. The methodology involves comprehensive preprocessing techniques tailored for CT scan analysis. Subsequently, we systematically evaluate various CNN models, including DenseNet, ResNext101, and EfficientNet. Remarkably, InceptionNet emerges as the most proficient model, achieving an accuracy rate of 84.03%. Our optimized DenseNet model demonstrates exceptional performance in accurately detecting intracranial hemorrhage and categorizing its subtype. By leveraging state-of-the-art machine learning techniques, our method facilitates swift and precise identification of hemorrhagic conditions from CT scans, empowering healthcare professionals to make informed decisions and provide timely interventions. This advancement holds significant promise for improving patient care, particularly in underserved regions where radiologist shortages persist. ©2024 IEEE |

关键词： Machine learning

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Acute Lymphoblastic Leukemia Detection Using DenseNet Model from Microscopic Blood Smear Images

Acute Lymphoblastic Leukemia Detection Using DenseNet Model ...

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International Conference on Data Science and Network engineering, ICDSNE 2023

作者： Prasath, Navamani Jasthi, Monika Saikia, Rabul Loganathan, Muralidaran Devi, Salam Shuleenda Department of Electronics and Communication Engineering NIT Meghalaya Meghalaya Shillong793003 India Department of Computer Science and Information System Engineering St. Joseph University Dae Es Salaam Tanzania United Republic of

ISBN: (纸本)9789819967544

Acute lymphoblastic leukemia (ALL) is a cancerous condition that affects the bone marrow and blood. It is a fast developing illness that, if not identified and treated as soon as possible, could be fatal. ALL is often identified by looking at blood and bone marrow smears under a microscope. Leukemia can be detected and classified using detailed cytochemical tests. However, these procedures are expensive, time-consuming, and dependent on the knowledge and skills of the specialists involved. Using image processing techniques that examine microscopic blood smear images to search for the leukemic cells, leukemia can be detected. These methods are simple, quick, cost-effective, and unaffected by the judgments of experts. The suggested study describes a computer-aided diagnosis method that uses deep convolutional neural networks (CNNs) that have already been trained to compare leukemia images to normal images. The public dataset ALL-IDB 2 was used for the proposed research. The study uses the pre-trained model DenseNet-201 for performing the classification. With the DenseNet201 pre-trained networks employed in the study for the ALL_IDB2 dataset, a classification accuracy of 94.6% is achieved. In all of the classifications carried out, optimization strategies such as cross-validation, fine-tuning, and real-time augmentation are also compared. Also use Pre-trained series models like ResNet-50, VGG-19, Inceptionv3, MobileNet-v2, Xceptionv3, and VGG-16 for performing the comparison. The experimental result gives an improvement in accuracy (17.76, 10.6, and 13.2%) in comparison to the other approaches namely, residual neural network, customized combined CNN, and conVNet neural network, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer aided diagnosis

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A Fruit and Vegetable Detection Based on Pytorch Machine Learning Frame Work Model 2

A Fruit and Vegetable Detection Based on Pytorch Machine Lea...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Priyadharshini, U. Vijayan, R. School of Advanced Science Vellore Institute of Information Technology Vellore India School of Computer Science Engineering and Information System Vellore Institute of Information Technology Vellore India

ISBN: (纸本)9798350328202

Today, Agricultures has become a significant element of people's life. Among them fruits and vegetables are a great thing for healthy lives. Classification of fruits and vegetable are difficult for normal person because of some same similarities like shape and color. Also, Fruits and Vegetables classification is an important process in industries applications as well as local market. Lots of similarities available between fruits like apples, pears and peaches, and that kinds of fruits are generally used in daily routine. As well as some vegetables are same in shape, so it will be difficult to differentiate in this type of situation for old persons. The previous research for solution of this type of problem for classification of fruits CNN is used but the problem in this research is when they use the self-made datasets then their accuracy decreases as compare to public datasets accuracy increases. To solve this type of problem, here produce a kind of algorithms in the paper to classify fruits and vegetable, using deep leaning technique such as VGG16 and DenseNet169 model have capability to extract the feature of images very deeply using various small filters so it provide a better result. On this premise, it was used to classify fruit kinds against a complex background and integrated with data improvement technology for future usage. It might be helpful for human for automatic differentiate the both categories. This project is implemented in the python programming language and the dataset used Fruits and Vegetables Image Recognition Dataset for the classification. Which will help to differentiate between Fruits and Vegetables with high accuracy rate. For the implementation of this project used Python programming and this implementation using the tool like Jupiter and Kaggle Notebook and used GPU for highspeed execution. © 2024 IEEE.

关键词： Fruits

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TESTING FACILITY LOCATION WITH CONSTRAINED QUEUE TIME PROBLEM: A CASE STUDY IN FLORIDA, USA

TESTING FACILITY LOCATION WITH CONSTRAINED QUEUE TIME PROBLE...

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2024 Winter Simulation Conference, WSC 2024

作者： Monteiro, Almir Antonio Schneider, Rafael de Almeida Guimarães, Vanessa González, Pedro Henrique System Engineering and Computer Science Program Federal University of Rio de Janeiro Brazil COPPEAD Graduate School of Business Federal University of Rio de Janeiro Brazil

ISBN: (纸本)9798331534202

This study addresses the Testing Facility Location with Constrained Queue Time Problem. This optimization problem focuses on determining the best places to deploy testing sites and their available testers for infectious diseases, while constraining the maximum time in the queue with a given probability. An integer programming model is introduced and applied to the three biggest counties, in terms of population, of Florida, United States. Moreover, the Monte Carlo method is used to evaluate the model’s output, aiming to check if the queueing time constraint is being satisfied. Through the experiments, a testing facility deployment plan can be determined for each county and further validated by the simulation. The results show that the solutions returned by the model behaved successfully when submitted to the Monte Carlo method, not exceeding the time in the queue in more than the predefined probability. © 2024 IEEE.

关键词： Monte Carlo methods

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Fault Tolerant Control for the Vibration Displacement system of a Continuous Casting Mold Based on a Fault Detection Observer 43

Fault Tolerant Control for the Vibration Displacement System...

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43rd Chinese Control Conference, CCC 2024

作者： Hao, Wenqi Fang, Yiming Liu, Le Liu, Xiaonan Yanshan University Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Qinhuangdao066004 China

ISBN: (纸本)9789887581581

In this paper, a fault detection observer and an adaptive sliding mode fault-tolerant controller are designed for the vibration displacement system of a continuous casting mold with actuator faults and load torque disturbances. Firstly, a mathematical model of a continuous casting mold vibration displacement system driven by a servo motor with actuator faults and load disturbance uncertainties are established. Secondly, a fault detection observer is designed to obtain information on actuator faults, load disturbances, and system state variables. Then, based on the information from the fault detection observer, an adaptive sliding mode fault-tolerant controller is designed, and the stability of the designed system is proved using Lyapunov stability theory. Finally, the effectiveness of the designed adaptive sliding mode fault-tolerant controller based on fault observer is verified through simulation. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Continuous casting

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Autonomous Emergency Landing on 3D Terrains: Approaches for Monocular Vision-based UAVs 8

Autonomous Emergency Landing on 3D Terrains: Approaches for ...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Du, Weiming Lin, Junmou Du, Binqing Borhan, Uddin Md. Li, Jianqiang Chen, Jie College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

With the increasing use of unmanned aerial vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs face numerous emergencies during missions, in these situations, the UAVs need to autonomously find a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. However, due to the limitations of computing power and sensors, it is challenging to achieve this goal. This research aims to explore the monocular emergency autonomous landing algorithm. Specifically, this work focuses on extracting depth and vision information. A topology information extractor is designed to convert image to graph and assess the connectivity of terrain. Additionally, a depth information extractor is designed to calculate the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph by depth information and evaluate the landing suitability by a heuristic strategy. To make the action decision, a hybrid decision network (DGN) based on 3D topology information is proposed. Finally, this work build a simulated scenario based on real scene to verify the efficiency of DGN. The results of the experiment show that DGN outperforms its counterparts in terms of action prediction accuracy and landing success rate. © 2023 IEEE.

关键词： Landing

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