2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)最新文献_第10页

Design of English Speech Comprehensive Training System for Smart Phone 智能手机英语语音综合训练系统的设计

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10099573

Xiuhui Hao

引用次数: 0

Design of COVID19 Detection based on Relative Eccentric Feature Selection using Deep Vectorized Regressive Neural Network for Corona Virus 基于相对偏心特征选择的冠状病毒深度矢量回归神经网络检测设计

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10099777

Saket Mishra, A. Mantri

{"title":"Design of COVID19 Detection based on Relative Eccentric Feature Selection using Deep Vectorized Regressive Neural Network for Corona Virus","authors":"Saket Mishra, A. Mantri","doi":"10.1109/ICICACS57338.2023.10099777","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099777","url":null,"abstract":"The world has seen various diseases in different variants, numerous pandemics in the twentieth century like COVID-19. Fly infections are the fundamental driver of contaminations. COVID-19 declared a global pandemic with major impacts on economies and societies around the world. The diagnosis of COVID19 or non-COVID-19 cases early detection at the correct separation early stages of disease are one of the main concerns of the current coronavirus pandemic. At present, accurate detection of coronavirus disease usually takes a long time and is prone to human error. To address this problem, the proposed Deep learning and Design of COVID19 detection based on Relative Eccentric Feature Selection (REFS) Using Deep Vectorized Regressive Neural Network (DVRNN) for corona virus the early detection of the COVID19 virus. Initially collects the COVID19 sample test dataset, then the raw dataset trained into preliminary process is used to remove unwanted noise. After that preliminary processed dataset trained into the feature selection process is done to identify the best features of COVID19 using Ensemble recursive selection. Further, the proposed DVRNN algorithm is done to classify the accurate detection of coronavirus. The proposed model would be useful for the Timely and accurate identification of various stages of coronavirus. Therefore, it can detect the accurate results of COVID19 effectively and accomplish good performance compared with previous methods.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122939290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated Scheduling and Control System of Microgrid Based on Dynamic Programming Algorithm 基于动态规划算法的微电网综合调度控制系统

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10099706

Bin Chen, Jiang Jiang, Yan-qing Shao

{"title":"Integrated Scheduling and Control System of Microgrid Based on Dynamic Programming Algorithm","authors":"Bin Chen, Jiang Jiang, Yan-qing Shao","doi":"10.1109/ICICACS57338.2023.10099706","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099706","url":null,"abstract":"With the continuous development of the Internet, the technical requirements for microgrids are also constantly improving. Today, micropower applications are mainly found in industrial and commercial areas, urban areas and remote areas. With the rapid development of computers and telecommunication equipment, microgrids are also integrated into the fields of computers, control technology, algorithm strategies, etc., and gradually change to the direction of automation. Microgrid technology is becoming a new force in network systems to make up for the deficiencies of traditional networks. Therefore, the development of microgrid is very important to the development of power grid. This paper aims to study the integrated dispatch and control system of microgrid based on dynamic programming algorithm. On the basis of analyzing the comparison between dynamic programming algorithm and other algorithms and the requirements of the integrated system of regulation, the integrated dispatching and control system of microgrid is analyzed. In order to verify the advantages of the proposed algorithm, this paper compares the dynamic programming algorithm with the traditional algorithms GA and PSO. The results show that the dynamic programming algorithm can directly calculate the optimal value of multiple data blocks, which speeds up the convergence process, thus greatly reducing the iteration cycle of the algorithm, and can better meet the needs of automatic power generation instruction cycle.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128013938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design System of Plant Decorative Ceramic Pattern Based on Multi-Objective Genetic Algorithm 基于多目标遗传算法的植物装饰陶瓷图案设计系统

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100079

Fan Shuai

引用次数: 1

Development of Student Biochemical Index Monitoring System Based on K-means Cluster Analysis 基于k -均值聚类分析的学生生化指标监测系统开发

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100254

Chunxin Wang

引用次数: 0

An Automated Sleep Stage Classification for Healthcare Monitoring by using Single Channel EEG Signal 基于单通道脑电图信号的健康监测睡眠阶段自动分类

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100136

Sasi Kumar Gurumoorthy, D. T. Chethana, M. Ramesh, B.P Upendra Roy, C. Sapna Kumari

{"title":"An Automated Sleep Stage Classification for Healthcare Monitoring by using Single Channel EEG Signal","authors":"Sasi Kumar Gurumoorthy, D. T. Chethana, M. Ramesh, B.P Upendra Roy, C. Sapna Kumari","doi":"10.1109/ICICACS57338.2023.10100136","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10100136","url":null,"abstract":"It is well-established that biomedical signals convey crucial data regarding the functioning of living systems. The physiological and clinical information included in these signals can be improved with adequate processing. Modern qualitative and quantitative analyses of physiological systems and events rely on digital signal processing and pattern recognition methods. Analysis and interpretation of a medical practitioner's signal carry the weight of the analyst's knowledge and expertise, yet such analysis is inherently subjective. If done logically, computer analysis of biomedical information might provide credibility to the expert's interpretation by providing an objective second opinion. Furthermore, it allows for enhanced diagnosis and online monitoring of critically ill patients. The current research intends to develop effective methods for utilizing sleep-monitoring health gadgets. When it comes to handling complex classification or pattern recognition issues, the Support Vector Machine (SVM) is the instrument of choice. In this article, we focus on using support vector machines (SVMs) to identify and categorize apnea. When compared to other methods of categorization, such as sophisticated statistical approaches, SVM performed better. Both an adaptive classification model and a novel approach to merging the decisions of ensemble-based classification models are proposed in the work. The current method relies on an ensemble classifier system and a huge number of features, making it both effective and trustworthy.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129039290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi Path TCP Over Network Coding for Wireless Network 无线网络的多路径TCP Over Network编码

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10099667

K. Priya, Makeshvar S, Madhan Rai S, C. Kalaivani, Manchala Tejesh, Mohammed Hasan M

{"title":"Multi Path TCP Over Network Coding for Wireless Network","authors":"K. Priya, Makeshvar S, Madhan Rai S, C. Kalaivani, Manchala Tejesh, Mohammed Hasan M","doi":"10.1109/ICICACS57338.2023.10099667","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099667","url":null,"abstract":"When hardware and software are combined in item, you get a wireless sensor network (WSN). In WSN, both low and high-end devices (such as sensors and servers, respectively) are online. WSN's RPL routing protocol was developed so that even in weak and lossy connections, data may be collected efficiently and reliably. The RPL (Routing Protocol for Low-Power and Lossy Networks) network builds and maintains its topology using objective functions like hop count or ETX (Expected Transmission Count). Constructions like the Destination Oriented Directed Acyclic Graph (DODAG) that rely on the Rank attribute inevitably led to issues like greedy parent election, thundering heads, and network instability. When a Software Defined Network (SDN) is used with a WSN, the routers are managed by a controller server node. The network is dynamic since it contains both mobile and power- limited nodes. The term “routing” refers to the process of determining the best path between two points. In this paper, a software-defined- networking-based Energy Harvest routing approach is provided. The proposed approach focuses the link's capacity and the path's available bandwidth into a single, manageable entity. The SDN controller node can reconfigure the paths on the fly. As a result of a decrease in conjunction- related packet loss, the packet delivery ratio improves. The data- driven design solution that facilitates nimble and versatile system organisation through a collaborative link between DDS and SDN. NS2 simulator is used to do simulations of the proposed method in a variety of settings.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116738156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning based Brain Tumor Detection with Internet of Things 基于深度学习的物联网脑肿瘤检测

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100253

Vooradi Sandya, Veeresh Baligeri, Bechoo Lal, Vishwanath Petli, P. S

{"title":"Deep Learning based Brain Tumor Detection with Internet of Things","authors":"Vooradi Sandya, Veeresh Baligeri, Bechoo Lal, Vishwanath Petli, P. S","doi":"10.1109/ICICACS57338.2023.10100253","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10100253","url":null,"abstract":"Classification, preprocessing, feature extraction, and segmentation are all parts of the planned study that will be utilized to categories and detect brain tumor pictures. Magnetic resonance imaging (MRI) gives direct information about anatomical structures as well as possibly abnormal tissues where patients are being watched by physicians, making brain tumor identification not as much simpler for clinical diagnosis. This suggested system utilizes a machine learning strategy to identify, and categories brain tumors known as gliomas. Kirsch's edge detected pixels are used to identify the edges of the boundaries. Using this improved brain scan, the ridge let transform is used to extract the ridge let multi-resolution coefficients. As an added step, the ridge let converted coefficients are used to create features, which are then improved with the help of the CANFES classifier. Evaluation factors like as sensitivity, specificity, and accuracy are applied to the results in the context of tumor detection. Both the old approach and the suggested methodology are implemented in simulation using a programming environment like MATLAB, and the results of these simulations are compared to demonstrate the efficacy of the proposed algorithm. The suggested tumor detection approaches employing Co-Active Adaptive Neuro-Fuzzy Expert System Classifier have an accuracy of 98.73%, which offers iv accurate detection of the tumor, and so should be regarded as superior to the current traditional procedures.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"483 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115272064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Detection of QRS Complexes in ECG Signal 心电信号中QRS复合物的检测

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100203

Revanth N, M. Anto Bennet

引用次数: 0

Quantum Machine Learning in Healthcare: Developments and Challenges 医疗保健中的量子机器学习:发展与挑战

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100075

Sita Rani, Piyush Kumar Pareek, Jaskiran Kaur, Meetali Chauhan, P. Bhambri

{"title":"Quantum Machine Learning in Healthcare: Developments and Challenges","authors":"Sita Rani, Piyush Kumar Pareek, Jaskiran Kaur, Meetali Chauhan, P. Bhambri","doi":"10.1109/ICICACS57338.2023.10100075","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10100075","url":null,"abstract":"Machine learning is playing a very significant role to process voluminous data and its classification in a variety of domains. Due to better performance and rapid development in the last decade, quantum computing is also benefiting many areas. With the amalgamation of these two technologies, a new domain for processing big data more efficiently and accurately has evolved, known as quantum machine learning. Healthcare is one of the prominent domains where a huge volume of data is produced from several processes. Efficient processing of healthcare data and records is very important to facilitate many biological and medical processes to provide better treatments to patients. The fundamental aim of this paper is to present a state-of-the-art review of quantum computing concepts, quantum machine learning framework, and the various applications of quantum machine learning in the domain of healthcare. The comparison of QML healthcare models with ML-based healthcare applications is discussed in this work. The authors also present the various challenges faced in the deployment of quantum machine learning algorithms in the domain of healthcare and possible future research directions.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"492 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116194732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0