2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)最新文献

{"title":"Fuzzy Optics Enabled Antenna Model for Push-To-Talk Communication in Underwater Networks","authors":"Prakash Pandey, Prerna Mahajan, A. Raizada","doi":"10.1109/ICOCWC60930.2024.10470907","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470907","url":null,"abstract":"This paper explores a unique antenna model to allow push-totalk underwater communications. This antenna version uses fuzzy logic to adaptively control the transmission and reception parameters of the antenna. The russification of the antenna parameters reduces the complexity of antenna control, permitting even low-give-up underwater gadgets to transmit and acquire alerts successfully. This version uses binary water droplet-like antenna systems, which are adaptively tuned in response to various sign energy levels. The binary antenna structure can attain enormous gains with minimal cease-consumer setup time and value. Moreover, this antenna model allows for a quick reconfiguration to suit converting water conditions and tool features, allowing the antenna to keep top-quality overall performance for underwater conversation. Our assessment of this version in actual-global situations shows that push-to-communicate communique is feasible with reasonable hyperlink reliability.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"76 21","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529578","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}

{"title":"Exploring the Potential of Recurrent Neural Networks for Medical Image Segmentation","authors":"Aaditya Jain, Sanjeev Kumar Mandal, Monika Abrol","doi":"10.1109/ICOCWC60930.2024.10470661","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470661","url":null,"abstract":"Recurrent Neural Networks (RNNs) are a modern-day state-of-the-art algorithm that is brand new modern getting used for clinical picture segmentation. RNNs are, in particular, nicely applicable for this undertaking due to the fact they can be skilled to bear in mind patterns over long sequences brand new information. This enables them to perceive structural patterns in an image and carry out sophisticated segmentation obligations together with tumor or organ boundary identification. similarly, RNNs have the ability to contain earlier know-how from different pics and medical data, as well as contextual know-how from external resources such as electronic fitness information. This paper critiques the contemporary in RNNs for medical picture segmentation, outlining the key methods and programs contemporary RNNs inside the field. We discuss both the successes and demanding situations of trendy RNN-based procedures and provide destiny studies directions for the improvement of modern-day extra correct and efficient segmentation equipment.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"56 34","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529886","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}

{"title":"Assessing Optimal Hyper parameters of Deep Neural Networks on Cancers Datasets","authors":"Pankaj Kumar Goswami, A. Kannagi, Anubhav Sony","doi":"10.1109/ICOCWC60930.2024.10470543","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470543","url":null,"abstract":"This paper studies the most valuable hyperparameters of deep neural networks applied to most cancer datasets. We appoint a mixture of looking algorithms, including grid seeks, random search, and Bayesian optimization, to discover the best mixture of hyperparameters for deep neural networks. The overall performance of the one-of-a-kind algorithms is evaluated towards present most cancers datasets and as compared towards each other. Outcomes show that Bayesian optimization becomes the maximum green and correct technique for finding the most fulfilling hyperparameter for our goal deep neural networks. This research can provide precious insight to practitioners who layout and build deep-mastering models for most cancer datasets. Furthermore, it also helps to optimize the performance of the trained neural networks while applied to this specific trouble location. The painting aims to assess the most beneficial hyperparameters of deep neural networks (DNNs) on most cancer datasets. DNNs are increasingly employed within the class and analysis of cancer datasets due to their ability to capture complicated styles and hit upon relationships between relevant capabilities. However, the effectiveness of those models is somewhat affected by the layout and selection of hyperparameters, which govern their education and represent a critical factor in the model optimization manner. In this painting, we optimize the choice of hyperparameters for a DNN using a grid search approach for every dataset, one after the other. Primarily, we optimize several parameters, along with the number of layers, neurons in keeping with layer, activation functions, studying fee, range of epochs, batch size, and dropout charge. The performance of the optimized DNN version is then evaluated by studying its accuracy, AUROC, and precision while evaluating on a take-a-look-at the set. Consequences show that extensive improvements in overall performance may be performed while the most reliable hyperparameters are chosen.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"74 16","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529581","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}

{"title":"Distribution Network Reconstruction Based on Discrete Particle Swarm Algorithm Based on Quantum Coding","authors":"Ning Luo, Ludong Chen, Jinsen Liu, Pengcheng Zhang, Fei Zheng","doi":"10.1109/ICOCWC60930.2024.10470728","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470728","url":null,"abstract":"Quantum-coded discrete particle swarm optimization (QBPSO) is a new optimization method which combines quantum computation principle and traditional particle swarm optimization (PSO) algorithm. By introducing the probabilistic representation of qubits, the algorithm improves the population diversity and enhances the global search ability. In the distribution network reconfiguration problem, the algorithm can be used to find the optimal structure of the network in order to reduce the loss, balance the load, improve the reliability of power supply and so on.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"60 24","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529601","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}

{"title":"Implementing Information Security Models to Reduce Cyber Security Risks in Networked Applications","authors":"Ajay Rastogi, R. Raghavendra, Megha Pandeya","doi":"10.1109/ICOCWC60930.2024.10470822","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470822","url":null,"abstract":"This paper examines the need for imposing data safety models to reduce cyber security risks in networked packages. Security models enable groups to research the current kingdom in their networks and evaluate the associated security risks. The paper offers an overview of existing and developing fashions and evaluates their overall performance. It provides details of a safety model that has evolved to boom protection threat awareness in agencies., presenting a more standardized problem-on-hand method for analyzing and decreasing cyber safety risks. The paper concludes with a dialogue of the benefits and challenges of using protection fashions. With the ever-evolving international of networked applications and cyber threats., using security fashions have to be an essential and prioritized degree for groups to increase their cybersecurity posture and reduce the risk of network threats..","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"224 8","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529680","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}