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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

共 11178 条记录，以下是81-90 订阅

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Classification of Poikilocytosis Abnormality Using Ensemble Deep Learning Technique 3rd

Classification of Poikilocytosis Abnormality Using Ensemble...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Dhar, Prasenjit Singh, Mohit Kumar Ahmed, Tanveer Suganya, Devi K. Medical Imaging Laboratory Computer Science and Engineering Department National Institute of Technology Silchar Assam India

ISBN: (纸本)9789819768011

Red blood cells (RBCs) also known as erythrocytes are an essential component of the human body that play several important roles. RBCs transport oxygen (O2) from the lungs to the body’s tissues, remove carbon dioxide (CO2), regulate pH balance, support the immune system, and provide diagnostic information. The abnormal shapes of RBCs (signified as poikilocytosis) are unable to carry O2 and CO2, leading to decreased O2 delivery to the tissues and an increased workload on the heart and lungs. Anemia, thalassemia, and other blood-related illnesses affect the body for insufficient replenishment (oxygen, protein, nutrients). Hematologists take more time to examine RBC shapes manually using a microscope. In this study ensemble deep learning technique was introduced to examine poikilocytosis abnormality accurately and efficiently. The proposed ensemble model outperforms state-of-the-art approaches with an excellent accuracy and F1-score of 98.77%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Multi-document Question Answering Using Transformers, LANGCHAIN 3rd

Multi-document Question Answering Using Transformers, LANGCH...

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Reddy, Likith Sai Vinta, Surendra Reddy School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819743582

We integrated our lives with advanced technologies like gpt, cloud technology but still we are facing problems with retrieving answers and data with the questions we have, especially when there are huge numbers of data documents. The college has a huge number of student data, it’s difficult for them to choose the best candidates for placements by checking the data manually and we have faced many problems in the Covid era to extract and retrieve the data from a huge number of multiple documents so there is need of model which solves the problem of retrieving answers and data from the large data documents. So here we propose the model which retrieves answers from the given data documents using HugginfFace LLM, LANGCHAIN and FAIRSEQ. LANGCHAIN is the framework for developing end-to-end applications for LLMs. FAIRSEQ is a deep learning model used for TEXT-TO-SPEECH Conversion. The FAIRSEQ model helps to train custom models for translation, summarization, language models, and other text generation tasks. This model can enhance semantic understanding. These models can capture complicated relationships and contextual cues, leading to more accurate and comprehensive answers where users can have easy handling of the model and provide comprehensive answers for given complex questions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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IoT-Based Secure Healthcare Framework Using Blockchain technology with Nature-Inspired Optimization Algorithms 3rd

IoT-Based Secure Healthcare Framework Using Blockchain Techn...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Tripathy, Ramamani Satyanarayana Murty, P.T. Maram, Balajee Garg, Ankit Daniya, T. Santhosh Kumar, B. Computer Science and Engineering Chitkara University Himachal Pradesh India Shri Vishnu Engineering College for Women Bhimavaram India AIT-Department of Computer Science and Engineering Chandigarh University Mohali GharuanPunjab140055 India GMR Institute of Technology Rajam532127 India Telangana Ibrahimpatnam501506 India

ISBN: (纸本)9789819730896

This study introduces a novel method for resolving security and privacy issues in IoT-based healthcare systems by combining blockchain technology with optimization algorithms that are inspired by natural processes. Concerns concerning the danger of private patient data have been raised by the healthcare industry's explosive growth of IoT applications. We provide a comprehensive framework that makes use of the security capabilities built into blockchain to guarantee data integrity, authentication, and access control in order to reduce these risks. In order to improve system performance and resource allocation, we also develop optimization algorithms that are inspired by nature. The architecture of the framework includes smart contracts, communication protocols, and secure data flow to automate business processes within the healthcare ecosystem. Our method improves security while maximizing resource utilization by fusing blockchain's tamper-proof record with the adaptability of nature-inspired algorithms. The effectiveness of the suggested framework is assessed using simulations or real-world testing and benchmarked against current techniques. In the end, this research helps build a robust and effective healthcare framework that protects patient information, respects privacy, and improves the overall effectiveness of IoT-enabled healthcare systems. Additionally, the system's effectiveness and scalability can be improved by including optimization techniques that draw inspiration from nature. In order to create a safe and effective healthcare environment, this research paper offers a comprehensive framework that blends IoT, blockchain, and nature-inspired optimization algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs 3rd

Comparison of Different CIC Filter Architectures on the Basi...

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Dwivedi, Pushpendra Panchore, Meena Raikwal, Pushpa Agarwal, Akash Department of Electronics and Communication Engineering Bhagalpur College of Engineering Bihar Bhagalpur813210 India Department of Electronics and Communication Engineering National Institute of Technology Bihar Patna800005 India Department of Electronics and Communication Engineering PDPM Indian Institute of Information Technology Design and Manufacturing Madhya Pradesh Jabalpur482005 India

ISBN: (纸本)9789819743582

The most appropriate filter for the first stage of any decimation filter with a large value of decimation factor is a Cascaded Integrator Comb (CIC) filter. CIC Decimation Filter is characterized by a drop in its Passband gain and very less attenuation in its stopband. So, to improve the frequency response of CIC Decimation Filter for compensation of Passband Droop and Stopband Attenuation, various CIC filter architectures have been developed. This paper discusses various CIC filter architectures that are used for Sigma-Delta based Analog-to-Digital Converter (ADC) applications. Also, their performance is compared by defining a new parameter called as Noise factor. The filter architecture consisting of cascaded CIC, sine, and cosine filters performs best in terms of Noise factor for all the values of Decimation ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wiener filtering

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An Optimal Feature Selection-Based Approach to Predict Cervical Cancer Using Machine Learning 3rd

An Optimal Feature Selection-Based Approach to Predict Cervi...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Al Mamun, Abdullah Uddin, Khandaker Mohammad Mohi Chakrabarti, Anamika Nur-A-Alam, Md. Mahbubur Rahman, Md. Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering University of Business and Technology Dhaka Bangladesh

ISBN: (纸本)9789819739363

Cervical cancer has been one of the leading causes of female early death in recent years. In developing countries, cervical cancer occurs in more than 85% of cases. Cervical cancer has been associated with several risk factors. For forecasting the prognosis of cervical cancer patients, we created a prediction model in this study based on early screening and risk trends in individual health records. In this paper, we analyze the risk variables for cervical cancer using ML classification methods. The study’s dataset is very unbalanced and includes missing values. Consequently, the ROS approach, a sampling method was used. The effectiveness of class imbalance was demonstrated by comparing the suggested model’s accuracy, sensitivity, and specificity. The RF, GB, and MLP machine learning models perform better with 99.07, 98.57, and 98.51% accuracy when using the Random Oversampling technique, and XGBoost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Light Weighted YOLO Vehicle Tracker for Toll Gate Using Centroid Algorithm 3rd

Light Weighted YOLO Vehicle Tracker for Toll Gate Using Cen...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Singh, Pankaj Pratap Prasad, Shitala Kalita, Gyanjyoti Das, Jahnu Mahanta, Pallab Jyoti Department of Computer Science and Engineering CIT Kokrajhar Assam India School of Mathematics and Computer Science IIT Goa Goa India

ISBN: (纸本)9789819730896

This paper focuses on vehicle detection at highway toll gates. The study involves the detection of vehicles within a custom dataset, which comprises four vehicle classes: car, truck, bus, and motorcycle. The YOLOv4 model is utilized for training and is complemented by the implementation of the Centroid tracking algorithm. In the proposed vehicle track dataset, each class comprises 1500 annotated images for training and 300 images in the testset. These images are sourced from the Google Open Images V6 dataset, and the OIDv4 toolkit is employed to scrape dataset images. The primary objective of this study is to train a vehicle detection model and evaluate the effectiveness of various tracking algorithms, with a specific focus on the centroid tracker for real-time application at the toll gates. Importantly, this model is designed to operate efficiently on conventional and cost-effective GPUs and demonstrates a commendable level of precision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image annotation

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Research on Electromagnetic Immunity Hardware-In-The-Loop Simulation Test of ADAS in Intelligent Vehicle 3rd

Research on Electromagnetic Immunity Hardware-In-The-Loop Si...

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3rd international conference on Information Control, electrical engineering, and Rail Transit, ICEERT 2023

作者： Jia, Jia Zhou, Shiying Cui, Maoyuan Zhang, Dongbo Jia, Jin Beijing Jiaotong University Electronic and Information Engineering College Beijing100044 China China Faw Group CO. LTD Intelligent Internet Connection Development Institute Changchun130000 China Chongqing University of Technology Key Laboratory of Advanced Manufacturing Technology for Automobile Parts Ministry of Education Chongqing404100 China

ISBN: (纸本)9789819788903

The EMC testing of the ADAS of intelligent vehicles is greatly affected by weather, road conditions, and other factors in actual road testing. Therefore, this article conducts EMC simulation testing research on the ADAS system of intelligent vehicles based on a semi-anechoic chamber. This article conducts a joint simulation of typical ADAS operating conditions using PreScan and Simulink. By simulating typical test conditions of the ACC system, the effectiveness and reliability of the condition in real road scenarios were evaluated, and the vehicle hardware-in-the-loop test was conducted. The test showed the ACC activation results of the vehicle, indicating that the testing method proposed in this article is feasible and provides a certain reference value for actual road testing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware-in-the-loop simulation

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd international conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Research on IoT Service Slicing Algorithm Based on Deep Q Network for Railway 5G Core Network Side 3rd

Research on IoT Service Slicing Algorithm Based on Deep Q Ne...

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3rd international conference on Information Control, electrical engineering, and Rail Transit, ICEERT 2023

作者： Zhao, Yifan Zou, Jinbai Hu, Chuanbo Shanghai Institute of Technology Shanghai201418 China

ISBN: (纸本)9789819788903

5G technology has ushered in explosive development since the release of the relevant standards, and various industries have seized the opportunity to carry out digital transformation, the railway industry has also grasped the cutting-edge technology of communication, proposed the construction of railway-specific 5G system (5G for Railway, 5G-R). In order to fully utilize the best performance of 5G-R IoT and reduce its construction and maintenance costs, it is of great significance to carry out the research on 5G-R IoT service network slicing. This paper designs a 5G-R core network slicing model based on SDN (Software Defined Network) and NFV (Network Functions Virtualization) technologies, optimizes the model based on DQN (Deep Q Network) algorithm, and proposes a slicing algorithm for IoT services on the 5G-R core network side. Simulation results show that the algorithm proposed in this paper has feasibility and the average resource utilization is 28.4% higher than the average resource utilization of the comparison algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G mobile communication systems

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Deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable Machine Learning Framework 3rd

Deciphering Diabetes: Illuminating Prognostic Insights with ...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rony, Mohammad Abu Tareq Islam, Mohammad Shariful Saha, Pritom Johora, Fatama Tuz Ahammad, Mejbah Alam, Shah Md Nazmul Rahman, Md Saifur Nag, Anindya Department of Statistics Noakhali Science and Technology University Noakhali3814 Bangladesh Computer Science and Telecommunication Engineering Noakhali Science and Technology University NoakhaliNoakhali-3814 Bangladesh Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering University of Chittagong Chittagong Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University Tempe United States Strategic Management University UniversityoftheCumberlands Williamsburg United States Computer Science and Engineering Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9789819783540

Diabetes is a chronic disease that impacts millions of people globally. Early detection and management of diabetes can help mitigate potential complications and enhance patient outcomes. Machine learning (ML) algorithms have become more prevalent for predicting diabetes in recent years. The research paper proposed a machine learning-based approach for predicting diabetes utilizing the Diabetes Health indicator dataset and compared the comprehensive performance of multiple ML algorithms. The study used the LIME (Local Interpretable Model-Agnostic Explanations) package to provide interpretable predictions and acquire insights into the factors contributing to the model’s predictions. Although the ML models were interpretable, they also delivered the highest accuracy 86% demonstrated by the XGBoost classifier in prediction. The LIME produces local explanations for individual predictions, emphasizing the most significant features contributing to the prediction. The study found that, for the XGB model, BMI has a negative influence, while Genhlth positively impacts the outcome. Moreover, Age and Income have a positive impact on the outcome of diabetes. The findings of this research can potentially improve patient outcomes and alleviate the global burden of diabetes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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