Recent Trends in Intensive Computing最新文献_第7页

A Comprehensive Assessment on IOT Devices with Data Mining Techniques 基于数据挖掘技术的物联网设备综合评估

Recent Trends in Intensive Computing Pub Date : 2021-12-01 DOI: 10.3233/apc210211

G. Vijay Kumar, M. Sreedevi, Arvind Yadav, B. Aruna

引用次数: 0

Live Human Detection Robot in Earthquake Conditions 地震条件下的活体人体探测机器人

Recent Trends in Intensive Computing Pub Date : 2021-12-01 DOI: 10.3233/apc210286

R. Kabilan, K. Lakshmi Narayanan, M. Venkatesh, V. Vikram Bhaskaran, G. Viswanathan, S.G. Yogesh Rajan

引用次数: 1

Quantum Information Transmission Using CNOT Gate 基于CNOT门的量子信息传输

Recent Trends in Intensive Computing Pub Date : 2021-12-01 DOI: 10.3233/apc210219

Ankit Sharma, M. Nene

引用次数: 0

Regenerative and LoRa Based Trooper Monitoring System for Armed Forces 武装部队基于可再生和LoRa的士兵监控系统

Recent Trends in Intensive Computing Pub Date : 2021-12-01 DOI: 10.3233/apc210265

Hema R, Dathathreya P, Athitya V, Anumitha B

引用次数: 1

IoT Based Solar Panel Tracking System with Weather Monitoring System 基于物联网的太阳能电池板跟踪系统与天气监测系统

Recent Trends in Intensive Computing Pub Date : 2021-12-01 DOI: 10.3233/apc210282

K. Dinesh, Lakshmi Priya. A, Preethi. T, Sandhya. M, Sangeetha. P

引用次数: 1

Machine Learning Using Big Data Link Stability Based Node Observation for IoT Security 基于节点观察的大数据链路稳定性机器学习用于物联网安全

Recent Trends in Intensive Computing Pub Date : 2021-12-01 DOI: 10.3233/apc210299

R. Ganesh Babu, S. Yuvaraj, A. Vedanthsrivatson, T. Ramachandran, G. Vikram, N. Niffarudeen

{"title":"Machine Learning Using Big Data Link Stability Based Node Observation for IoT Security","authors":"R. Ganesh Babu, S. Yuvaraj, A. Vedanthsrivatson, T. Ramachandran, G. Vikram, N. Niffarudeen","doi":"10.3233/apc210299","DOIUrl":"https://doi.org/10.3233/apc210299","url":null,"abstract":"IoT systems create a multi-hop organizational structure among mobile devices in required to send on data groups. The remarkable properties of gadgets frameworks cause communications to interconnect among competing handheld devices. Most physiological directing displays don’t believe secure associations all through bundle communication to organize high communicate ability and genetic blocks that also prompts increased delay as well as bundle decreasing in mastermind. Only with continued growth and transformation of IoT networks, attacks on such IoT systems are increasing at an alarming rate. Our purpose will provide researchers with a research resource on latest research patterns in IoT security. As the primary driver of with us research problem concerning IoT security as well as machine learning. This analysis of the literature among the most research literature in IoT security recognized some very key current research which will generate organizational investigations. Only with fast emergence of different IoT threats, it is essential to develop frameworks that could integrate cutting-edge big data analytics and machine learning advanced technologies. Effectiveness are critical quality variables in shaping the best methods and algorithms for detecting IoT threats in real-time or close to real time.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116882335","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

Performance Analysis of ML Algorithms to Detect Gender Based on Voice 基于语音的ML性别检测算法性能分析

Recent Trends in Intensive Computing Pub Date : 2021-12-01 DOI: 10.3233/apc210192

Raz Mohammad Sahar, Dr. T. Srivinasa Rao, Dr. S. Anuradha, Dr. B. Srinivasa, Rao

{"title":"Performance Analysis of ML Algorithms to Detect Gender Based on Voice","authors":"Raz Mohammad Sahar, Dr. T. Srivinasa Rao, Dr. S. Anuradha, Dr. B. Srinivasa, Rao","doi":"10.3233/apc210192","DOIUrl":"https://doi.org/10.3233/apc210192","url":null,"abstract":"Gender classification is amongst the significant problems in the area of signal processing; previously, the problem was handled using different image classification methods, which mainly involve data extraction from a collection of images. Nevertheless, researchers over the globe have recently shown interest in gender classification using voiced features. The classification of gender goes beyond just the frequency and pitch of a human voice, according to a critical study of some of the human vocal attributes. Feature selection, which is from a technical point of view termed dimensionality reduction, is amongst the difficult problems encountered in machine learning. A similar obstacle is encountered when choosing gender particular features—which presents an analytical purpose in analyzing a human’s gender. This work will examine the effectiveness and importance of classification algorithms to the classification of gender via voice problems. Audial data, for example, pitch, frequency, etc., help in determining gender. Machine learning offers encouraging outcomes for classification problems in all domains. An area’s algorithms can be evaluated using performance metrics. This paper evaluates five different classification Algorithms of machine learning based on the classification of gender from audial data. The plan is to recognize gender using five different algorithms: Gradient Boosting, Decision Trees, Random Forest, Neural network, and Support Vector Machine. The major parameter in assessing any algorithm must be performance. Misclassifying rate ratio should not be more in classifying problems. In business markets, the location and gender of people are essentially related to AdSense. This research aims at comparing various machine learning algorithms in order to find the most suitable fitting for gender identification in audial data.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123721142","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 and Analysis of 64 GHz Millimetre Wave Microstrip Patch Antenna 64 GHz毫米波微带贴片天线的设计与分析

Recent Trends in Intensive Computing Pub Date : 2021-12-01 DOI: 10.3233/apc210262

Swagata B. Sarkar, Siva Nagappan, Shafin Kadhir Badhusha

引用次数: 0

DSAE – Deep Stack Auto Encoder and RCBO – Rider Chaotic Biogeography Optimization Algorithm for Big Data Classification DSAE -深度堆栈自动编码器和RCBO - Rider大数据分类混沌生物地理优化算法

Recent Trends in Intensive Computing Pub Date : 2021-12-01 DOI: 10.3233/apc210198

A. Brahmane, D. B. C. Krishna

{"title":"DSAE – Deep Stack Auto Encoder and RCBO – Rider Chaotic Biogeography Optimization Algorithm for Big Data Classification","authors":"A. Brahmane, D. B. C. Krishna","doi":"10.3233/apc210198","DOIUrl":"https://doi.org/10.3233/apc210198","url":null,"abstract":"In today’s era Big data classification is a very crucial and equally widely arise issue is many applications. Not only engineering applications but also in social, agricultural, banking, educational and many more applications are there in science and engineering where accurate big data classification is required. We proposed a very novel and efficient methodology for big data classification using Deep stack encoder and Rider chaotic biogeography algorithms. Our proposed algorithms are the combinations of two algorithms. First one is Rider Optimization algorithm and second one is chaotic biogeography-based optimization algorithm. So, we named it as RCBO which is integration is ROA and CBBO. Our proposed system also uses the Deep stack auto encoder for the purpose of training the system which actually produced the accurate classification. The Apache spark platform is used initial distribution of the data from master node to slave nodes. Our proposed system is tested and executed on the UCI Machine learning data set which gives the excellent results while comparing with other algorithms such as KNN classification, Extreme Learning Machine Random Forest algorithms.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125589505","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

Parallel Deep Learning Framework for Video Surveillance System 视频监控系统并行深度学习框架

Recent Trends in Intensive Computing Pub Date : 2021-12-01 DOI: 10.3233/apc210191

Mayuri Karvande, Apoorv Katkar, Nikhil Koli, Amit D. Joshi, S. Sawant

{"title":"Parallel Deep Learning Framework for Video Surveillance System","authors":"Mayuri Karvande, Apoorv Katkar, Nikhil Koli, Amit D. Joshi, S. Sawant","doi":"10.3233/apc210191","DOIUrl":"https://doi.org/10.3233/apc210191","url":null,"abstract":"In today’s world, the security of every individual has become an important aspect. There is a need for constant monitoring in public places. A Manual operating camera system is an unreliable and very basic and poor method for this purpose. Intelligent Video Surveillance is an approach where multiple CCTVs constantly record the scenes and proper algorithms are deployed in order to detect and monitor activities. Deep Learning frameworks and algorithms like Kera’s, YOLO, Convolutional Neural Networks or backbones for image detection like VGG16, Mobile net, Resnet101 have been used for human and weapon detection. The paper focuses on deep learning techniques and threading to collectively develop a Parallel Deep Learning Framework for Video Surveillance that aims at striking the right balance between accuracy and system performance or stability. Threading is used in terms of implementation of a uniquely proposed Dynamic Selection Algorithm that uses two backbones for object detection and switches between them based on the queue status for achieving system stability. A uniquely designed logistic regression filter is also implemented that boosts the system performance.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126992449","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