2021 International Conference on Advances in Computing and Communications (ICACC)最新文献

Two Speed Modified Radix-4 Booth Multiplier With Different Adder Configurations 两种速度改进的基数-4展位乘法器与不同的加法器配置

2021 International Conference on Advances in Computing and Communications (ICACC) Pub Date : 2021-10-21 DOI: 10.1109/ICACC-202152719.2021.9708329

V. P. Reshma, V. R. Adersh

引用次数: 0

E-Quarantine: Remote Real Time Monitoring of COVID-19 Patients 电子隔离:远程实时监测COVID-19患者

2021 International Conference on Advances in Computing and Communications (ICACC) Pub Date : 2021-10-21 DOI: 10.1109/ICACC-202152719.2021.9708172

V. Vinod, Muhsin Ahamed Fazal, Tom Thomas, T. Jose, Ms.Amitha Mathew

引用次数: 0

Impact of Machine Learning Algorithms in Intrusion Detection Systems for Internet of Things 机器学习算法在物联网入侵检测系统中的影响

2021 International Conference on Advances in Computing and Communications (ICACC) Pub Date : 2021-10-21 DOI: 10.1109/ICACC-202152719.2021.9708404

Jinsi Jose, Deepa V. Jose, Karna Srinivasa Rao, Justin Janz

引用次数: 1

A Comparison on Instance Segmentation Models 实例分割模型的比较

2021 International Conference on Advances in Computing and Communications (ICACC) Pub Date : 2021-10-21 DOI: 10.1109/ICACC-202152719.2021.9708272

Nipun Anoob, Sanju Jacob Ebey, P. Praveen, Prasidh Prabudhan, P. Augustine

引用次数: 0

A Novel Forward Filter Feature Selection Algorithm Based on Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR) for Machine Learning 基于最大对偶交互和最大特征相关性(MDIMFR)的机器学习前向滤波特征选择算法

2021 International Conference on Advances in Computing and Communications (ICACC) Pub Date : 2021-10-21 DOI: 10.1109/ICACC-202152719.2021.9708300

M. Anitha, K. Sherly

{"title":"A Novel Forward Filter Feature Selection Algorithm Based on Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR) for Machine Learning","authors":"M. Anitha, K. Sherly","doi":"10.1109/ICACC-202152719.2021.9708300","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708300","url":null,"abstract":"In the last few decades, Feature selection is one of the most challenging and open problem to researchers. The rapid progress in computational techniques causes the generation and recording of data in huge size. Though there exists various feature ranking methods, the processing of data is still a challenging task due to its computational complexity. The filter method has many advantages over the wrapper method. The filter methods are classifier independent and have better computational efficiency. Here, a subset of features is selected based on a certain goal function. Most of these goal functions employs the principle of information theory. Most of the algorithms in earlier studies addressed two factors, that is, maximization of relevancy and minimization of redundancy without considering the interaction among the features. This paper developed a new forward filter feature selection algorithm based on mutual information known as Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR). This method considers all the three factors: relevance, redundancy, and feature interaction. This method is experimented on three datasets and compares the performance with existing methods. The results show that MDIMFR outperforms the existing competitive feature selection methods of recent studies: mRMR, JMIM and CMIM. MDIMFR also achieves good stability in average classification accuracy for a certain number of features, say k and above. Hence, these k features can be considered as an optimal feature set.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126498089","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

Generating Text to Realistic Image using Generative Adversarial Network 使用生成对抗网络生成文本到逼真图像

2021 International Conference on Advances in Computing and Communications (ICACC) Pub Date : 2021-10-21 DOI: 10.1109/ICACC-202152719.2021.9708376

K. A. Safa Hassan Ali, S. Chinchu Krishna

{"title":"Generating Text to Realistic Image using Generative Adversarial Network","authors":"K. A. Safa Hassan Ali, S. Chinchu Krishna","doi":"10.1109/ICACC-202152719.2021.9708376","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708376","url":null,"abstract":"Generative Adversarial Network (GANs) has become one of the most interesting ideas in the last years in Machine Learning. Generative Adversarial Network is a very exciting area and that’s why researchers are so excited about building generative models as they are set to vary what machines can do for humans. This paper proposes the generation of realistic images according to their semantics based on text description using a Knowledge Graph alongside Knowledge Guided Generative Adversarial Network (KG-GAN) that comes with the embeddings generated from the Knowledge Graph (KG) into GAN. The Knowledge Graph is made from the text description by making the machine understand from the Natural Language Processing (NLP) techniques. The Knowledge Graph produced from the text description is converted to its embeddings by utilizing a Graph Convolutional Networks (GCN) and is fed into the GAN for generating realistic images by training the generators and discriminators and also the performance is evaluated. The experimental study is completed on a Caltech-UCSD Birds 200-2011 (CUB-200-2011) dataset and results that the approach using the knowledge graph for image generation using GAN has performed well and with high accuracy in comparison to the other established techniques generated in the past years for text to image generation in GAN.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121171106","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

Learning to play Football using Distributional Reinforcement Learning and Depthwise separable convolution feature extraction 使用分布式强化学习和深度可分离卷积特征提取学习踢足球

2021 International Conference on Advances in Computing and Communications (ICACC) Pub Date : 2021-10-21 DOI: 10.1109/ICACC-202152719.2021.9708400

Aniruddha Datta, Swapnamoy Bhowmick, Kunal Kulkarni

{"title":"Learning to play Football using Distributional Reinforcement Learning and Depthwise separable convolution feature extraction","authors":"Aniruddha Datta, Swapnamoy Bhowmick, Kunal Kulkarni","doi":"10.1109/ICACC-202152719.2021.9708400","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708400","url":null,"abstract":"In recent years we have seen a huge surge in benchmark tasks for Deep Reinforcement learning algorithms and a tremendous growth in the field of reinforcement learning itself but oftentimes the stochasticity and real time decision making of real world strategic and competitive games and also the choice between a multitude of actions are not mirrored in the environments used for RL agents. To address this issue Google released Gfootball, a football game engine based environment which was popularized by Manchester City FC sponsoring a Kaggle competition but the majority methods revolved around Rule based RL agent, imitation learning, reward modifications etc. and the pure reinforcement learning included feature extractors which had parallel neural networks on costly hardware. We propose a much simpler method involving depthwise separable convolutions as the base feature extractor which yields competitive results across a lot of benchmarks in very few episodes compared to the original paper. We also used Quantile regression DQN due to highly stochastic nature of the environment to exploit the quantiles of the return distribution to improve performance.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132668365","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

2021 International Conference on Advances in Computing and Communications (ICACC) Pub Date : 2021-10-21 DOI: 10.1109/icacc-202152719.2021.9708208

引用次数: 0

Attention Span Detection for Online Lectures 在线讲座的注意力广度检测

2021 International Conference on Advances in Computing and Communications (ICACC) Pub Date : 2021-10-21 DOI: 10.1109/ICACC-202152719.2021.9708082

V. Karthikraj, Varsha A. Patil, Senthil Thanneermalai, Thirumalainambi Yadav

引用次数: 2

Design of a Fast Chase Algorithm based High Speed Turbo Product Code Decoder 基于快速追踪算法的高速Turbo产品译码器设计

2021 International Conference on Advances in Computing and Communications (ICACC) Pub Date : 2021-10-21 DOI: 10.1109/ICACC-202152719.2021.9708201

J. H. Kishore, B. Yamuna, Karthi Balasubramanian

引用次数: 0