2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)最新文献_第7页

Capacitive Model of CSDG MOSFET at Pinch-off for Switching Characteristics CSDG MOSFET在关断开关特性的电容性模型

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944904

Abha Dargar, V. Srivastava

引用次数: 3

Secure Ambient Assisted Living System using Elliptic Curve Cryptography based CPABE 基于椭圆曲线密码的安全环境辅助生活系统

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944521

K. Sowjanya, Mou Dasgupta

引用次数: 2

Position Control of BLDC Motor Using S-Curve for Trajectory Planning and Feedforward Control Design 基于s曲线轨迹规划和前馈控制设计的无刷直流电机位置控制

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944914

Aishwarya Gavai, A. Kasbekar, A. Mulay

引用次数: 0

MMAS on GPU for Large TSP Instances 大型TSP实例GPU上的MMAS

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944770

Pramod Yelmewad, Aniket Kumar, B. Talawar

引用次数: 2

Art is Key to the Success of a Modern Game 美术是现代游戏成功的关键

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944678

Chuyang Wang, Samuel Mondier, P. Duraisamy

引用次数: 2

Enhanced Prediction of plant virus-encoded RNA silencing suppressors by incorporating Reduced Set of Sequence Features using SMOTE followed by Fuzzy-Rough Feature Selection Technique 利用SMOTE和模糊-粗糙特征选择技术结合减少序列特征集增强对植物病毒编码RNA沉默抑制子的预测

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944442

Pankhuri Jain, A. Tiwari, T. Som

{"title":"Enhanced Prediction of plant virus-encoded RNA silencing suppressors by incorporating Reduced Set of Sequence Features using SMOTE followed by Fuzzy-Rough Feature Selection Technique","authors":"Pankhuri Jain, A. Tiwari, T. Som","doi":"10.1109/icccnt45670.2019.8944442","DOIUrl":"https://doi.org/10.1109/icccnt45670.2019.8944442","url":null,"abstract":"Plant viruses are observed to be the natural focuses of RNA silencing. Highly diverse silencing suppressor proteins have been developed by them due to their counter defensive strategy. Silencing suppressor proteins groups share very low sequence as well as structural similarities among them. Therefore, these proteins obstruct their annotation using sequence similarity-based search techniques. Machine learning techniques can offer an alternative and effective way to solve this problem. However, the optimal performance through machine learning based techniques is being predominantly affected by diverse factors, such as availability of irrelevant and/or redundant features, class imbalance, and selection of suitable learning algorithm. In the current study, we present a novel approach to improve the prediction performance for the RNA silencing suppressors by using fuzzy rough feature selection technique with rank as well as evolutionary search on optimally balanced dataset. From the experimental results, it is obvious that fuzzy rough feature selection technique with evolutionary search on optimally balanced dataset by Synthetic Minority Over-sampling Technique (SMOTE) produces the best results with the sensitivity of 98.90%, specificity of 95.30%, overall accuracy of 96.60%, AUC of 0.993, and MCC of 0.934 using boosted random forest algorithm. On the basis of conducted experimental results, it can be observed that the proposed technique is producing the best results till date. These results can be achieved by suitably modifying the class distribution by using SMOTE followed by choosing the relevant and non-redundant features from training sets using fuzzy rough feature subset selection with evolutionary search.","PeriodicalId":438405,"journal":{"name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127278957","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: 2

A Non-Database Operations Aware Priority Ceiling Protocol for Hard Real-Time Database Systems 面向硬实时数据库系统的非数据库操作感知优先级上限协议

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944482

Ritesh K. Singh, Sarvesh Pandey, Udai Shanker

引用次数: 9

Influence of IoT &AI in place making and creating Smart Cities 物联网和人工智能对智慧城市的影响

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944477

A. Chaudhry, Banafsha Rajput, Rajneesh Mishra

{"title":"Influence of IoT &AI in place making and creating Smart Cities","authors":"A. Chaudhry, Banafsha Rajput, Rajneesh Mishra","doi":"10.1109/icccnt45670.2019.8944477","DOIUrl":"https://doi.org/10.1109/icccnt45670.2019.8944477","url":null,"abstract":"It has been many years now the concept of smart city evolved, with the beginning of a new era in urban innovation called Smart cities. Since that time there has been many studies made to discuss and understand this new concept with its application and acceptance worldwide. It is very obvious that smart cities will make things much complex but also ease our life style with high-tech solutions at the urban context. The study discusses various smart initiatives of top three ranked smart cities (London, Singapore & Seoul) by the smart government and the user experience. However research still fall short to understand the success of participatory design in use of smart concepts and the smart knowledge in day-to-day life. Policy makers and practitioners both need to deal with these progressive developments and digital connections. One example of this is the popular anonymous England based street artist who is known for his satirical street art regarding political activism, social causes and ironic realities of civic life. This work can truly be experienced in person yet does not require any digital media to enhance the experience. In order to explore the recent scenario of smart cities a survey is conducted to analyze the role of Internet of Things (IoT) and Artificial Intelligence (AI) in place making of Dubai. Though smart cities bring many complexities with them but same time they also ease and improve lifestyles. IoT and AI are very essential key elements during the entire process of planning for smart cities.","PeriodicalId":438405,"journal":{"name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128916618","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: 13

Convolutional Neural Network for Semiconductor Wafer Defect Detection 基于卷积神经网络的半导体晶圆缺陷检测

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944584

B. Devika, Neetha George

{"title":"Convolutional Neural Network for Semiconductor Wafer Defect Detection","authors":"B. Devika, Neetha George","doi":"10.1109/icccnt45670.2019.8944584","DOIUrl":"https://doi.org/10.1109/icccnt45670.2019.8944584","url":null,"abstract":"Semiconductor wafer defects are major problem which causes huge loss in the manufacturing industry. The Wafer Bin Maps (WBMs) gives pictorial representation of location of defective integrated circuits (ICs) on the wafer. In WBMs, the defective ICs forms specific patterns. Determining these specific patterns is important, because each defect pattern is related to different fabrication errors. If these patterns were to be identified correctly, then the root fabrication problem can be recognized and can fix it to avoid further loss. An efficient defect detection method can reduce wafer test time and improve yield. In this paper, we propose a deep learning(DL) based model using convolutional neural networks (CNNs) to identify wafer defect patterns. In CNNs, convolution and pooling layer does feature extraction and fully connected layer does classification. In our model, there are eight convolution layer and three fully connected layers. CNN is robust to random noise and performs effectively not only for single defect detection but also for multiple defect detection. Thus can classify diverse defect patterns and achieve a better overall performance.","PeriodicalId":438405,"journal":{"name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131049227","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: 13

Detection of Hepatitis Viruses Based on J48, KStar and Naïve Bayes Classifier 基于J48、KStar和Naïve贝叶斯分类器的肝炎病毒检测

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944619

Shimoul Uddin Emon, Tahira Islam Trishna, Romana Rahman Ema, Gazi Imran Hossen Sajal, Shuvodip Kundu, Tajul Islam

{"title":"Detection of Hepatitis Viruses Based on J48, KStar and Naïve Bayes Classifier","authors":"Shimoul Uddin Emon, Tahira Islam Trishna, Romana Rahman Ema, Gazi Imran Hossen Sajal, Shuvodip Kundu, Tajul Islam","doi":"10.1109/icccnt45670.2019.8944619","DOIUrl":"https://doi.org/10.1109/icccnt45670.2019.8944619","url":null,"abstract":"In this paper, the target is to detect hepatitis according to symptoms, mode of transmission and relevant tests. Hepatitis is not only the diseases of liver but also infect other sites of the body. It can attack people of any age. Many viruses cause hepatitis. From there mainly five viruses are mentioned as hepatitis viruses because they are mainly tainted in the liver. They are HAV, HBV, HCV, HDV, and HEV. The Naive Bayes, KStar and J48 classifiers are used in WEKA software to calculate the result. Naïve Bayes is the algorithm used for solving text classification problems. Basically, it is used for text classification that includes high dimensional training data sets. KStar is an instance-based learner. It conducts an entropy-based distance function. J48 classifier in weka is actually an algorithm generally known as C4.5 algorithm. This algorithm is often called a statistical classifier. In Naive Bayes the accuracy of result 93.8% stands for 10 fold cross-validation. In J48 classifier the accuracy is 98.6% by using 10 fold cross-validation and in KStar it is 97.2% by using 10 fold cross-validation.","PeriodicalId":438405,"journal":{"name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128879619","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: 3