2020 International Conference on Computational Intelligence (ICCI)最新文献_第3页

An Initiative To Prevent Japanese Encephalitis Using Genetic Algorithm And Artificial Neural Network 利用遗传算法和人工神经网络预防日本脑炎的研究

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247744

R. Mehra, K.S. Pachpor, K. Kottilingam, A. Saranya

{"title":"An Initiative To Prevent Japanese Encephalitis Using Genetic Algorithm And Artificial Neural Network","authors":"R. Mehra, K.S. Pachpor, K. Kottilingam, A. Saranya","doi":"10.1109/ICCI51257.2020.9247744","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247744","url":null,"abstract":"Japanese Encephalitis primarily affects children. Most adults in endemic countries have natural immunity after childhood infection, but individuals of any age may be affected. This work deals with the data of those who are affected. The primary step is studying the data obtained to Figure out the unique and similar symptoms which are present in Japanese Encephalitis in comparison with normal Viral Fever. Machine Learning algorithms are used to carry out this work. The Genetic Algorithm is used for optimization and generation of fittest string for the input data. To obtain precise results along with the justification, the Attribute Selection algorithm is also used. The main objective of the work is to create preventive awareness of the disease at the initial stage. Extract the essential features of biotest from the affected person, which is taken into consideration with the genetic algorithm and Attribute Selection algorithm. Genetic algorithms give higher quality for the optimized problem and produce an approximate result using the Attribute Selection algorithm with factor analysis. The percentage of improvement on using these algorithms is 96%. OpenCV color change detection and Artificial Neural Network (ANN) is used to detect the change in the color and infection information of the Brain cell. The results outperform with the existing methodologies to detect whether the cell is parasitized or uninfected. The percentage of omprovement on using this algorithm is 99%.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123466017","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: 6

Efficient Energy Usage Model for WSN-IoT Environments 无线网络-物联网环境下的高效能源使用模型

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247795

Gunasekar Thangarasu, P. Dominic, K. Subrmanian, S. Kayalvizhi, Sajitha Smiley Masillamony

引用次数: 1

Mobile Application to Predict Future Risk of Depression 预测未来抑郁风险的手机应用程序

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247686

Jahizah Mohammed Jabarullah, W. Ahmad

引用次数: 0

Detection of Error-Related Potentials during the Robot Navigation Task by Humans 人类对机器人导航任务中错误相关电位的检测

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247790

Kentaro Nakamura, K. Natsume

{"title":"Detection of Error-Related Potentials during the Robot Navigation Task by Humans","authors":"Kentaro Nakamura, K. Natsume","doi":"10.1109/ICCI51257.2020.9247790","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247790","url":null,"abstract":"We have developed a system in which humans and autonomous robots can collaborate with each other. In the system, robots often exhibit behaviors not intended by the humans. To avoid this situation, it is necessary to convey the humans’ will to the robots. To do this, we have focused on electroencephalogram (EEG) error-related Potential (ErrP), using which we can detect the ErrP when a person observes an error by a robot. In our previous study, we recorded the ErrPs from subjects in a maze task when a robot moved in directions that the subjects did not intend. However, the mean epoch number of the ErrP per subject was small. It is necessary to collect a large number of data using a deep neural network. Generally, medical data and physiological data recorded from people are small. Few Shot Learning is necessary for a small number of data. Thus, Siamese neural networks have been proposed. In this study, we combined the Siamese deep neural network with a support vector machine to discriminate between EEG data with an error (ErrP) and that without an error. Consequently, we could obtain >70% of the maximum classification accuracy among subjects and 0.60 ± 0.22 of the area under curve.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131309296","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

A Proposed Framework for Identifying the Role of Data Science in Handling Future Pandemics for Malaysian SMEs through Technology Acceptance Model 通过技术接受模型确定数据科学在处理马来西亚中小企业未来流行病中的作用的拟议框架

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247745

J. Wong, Kesava Rao Alla, P. Dominic

{"title":"A Proposed Framework for Identifying the Role of Data Science in Handling Future Pandemics for Malaysian SMEs through Technology Acceptance Model","authors":"J. Wong, Kesava Rao Alla, P. Dominic","doi":"10.1109/ICCI51257.2020.9247745","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247745","url":null,"abstract":"The year 2020 will be written in the history as the year that has caused catastrophic impact on health, human lives, and most importantly the economy that has been rumbled in some countries to the levels of World War I and II. This pandemic also exposed the loopholes in the systems for few ‘Developed Nations’, ‘Established Public Health Systems’, and ‘Billion Dollar Forex Reserves’ that most of the countries relied upon in general. All these were challenged to the core once the COVID-19 pandemic started growing exponentially from March 2020 forcing the countries to go under lockdown which has curved down their economic charts. Malaysia too has suffered with a months-long lockdown, growing unemployment and shrinking economy. The SMEs in Malaysia are among the worst affected. In May 2020, almost 50% of the SMEs reached a position where their very existence was at stake. A potential second or third wave of COVID-19 or some other pandemic in future is not any surprise for Malaysia. But, how far the country and its SMEs are prepared to face such situation again is the question. A quick and accurate data analytics on historical pandemics, hospital data, infection rates, tracking, testing and treatments offered may help in predicting the primary signs that can protect from disasters to a great extent. This study applies ‘technology acceptance model’ to Malaysian SMEs to explore the possibility of Data Science in launching accurate forecasts that could keep them in a better position rather than getting caught in surprise lockdowns. Since the acceleration in the spread of infectious diseases lately around the globe is due to the growth in the human population and globalisation, Data Analytics can be used to predict where the potential outbreaks may unfold next and thereby to flag the early alert.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121364677","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

SMTrust: Proposing Trust-Based Secure Routing Protocol for RPL Attacks for IoT Applications SMTrust:针对物联网应用的RPL攻击，提出基于信任的安全路由协议

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247818

S. M. Muzammal, R. Murugesan, N. Jhanjhi, L. T. Jung

{"title":"SMTrust: Proposing Trust-Based Secure Routing Protocol for RPL Attacks for IoT Applications","authors":"S. M. Muzammal, R. Murugesan, N. Jhanjhi, L. T. Jung","doi":"10.1109/ICCI51257.2020.9247818","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247818","url":null,"abstract":"With large scale generation and exchange of data between IoT devices and constrained IoT security to protect data communication, it becomes easy for attackers to compromise data routes. In IoT networks, IPv6 Routing Protocol is the de facto routing protocol for Low Power and Lossy Networks (RPL). RPL offers limited security against several RPL-specific and WSN-inherited attacks in IoT applications. Additionally, IoT devices are limited in memory, processing, and power to operate properly using the traditional Internet and routing security solutions. Several mitigation schemes for the security of IoT networks and routing, have been proposed including Machine Learning-based, IDS-based, and Trust-based approaches. In existing trust-based methods, mobility of nodes is not considered at all or its insufficient for mobile sink nodes, specifically for security against RPL attacks. This research work proposes a conceptual design, named SMTrust, for security of routing protocol in IoT, considering the mobility-based trust metrics. The proposed solution intends to provide defense against popular RPL attacks, for example, Blackhole, Greyhole, Rank, Version Number attacks, etc. We believe that SMTrust shall provide better network performance for attacks detection accuracy, mobility and scalability as compared to existing trust models, such as, DCTM-RPL and SecTrust-RPL. The novelty of our solution is that it considers the mobility metrics of the sensor nodes as well as the sink nodes, which has not been addressed by the existing models. This consideration makes it suitable for mobile IoT environment. The proposed design of SMTrust, as secure routing protocol, when embedded in RPL, shall ensure confidentiality, integrity, and availability among the sensor nodes during routing process in IoT communication and networks.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114902548","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: 18

ICCI 2020 Cover Page ICCI 2020封面

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/icci51257.2020.9247633

引用次数: 0

Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications 求解石油工程地下动力学的无监督深度学习算法

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247667

Abhishek Kumar, S. Ridha, Suhaib Umer Ilyas

{"title":"Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications","authors":"Abhishek Kumar, S. Ridha, Suhaib Umer Ilyas","doi":"10.1109/ICCI51257.2020.9247667","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247667","url":null,"abstract":"Ordinary and partial differential equations play a significant role across various energy domain as they aid in approximating solution for complex mathematical problems. Drilling optimization and reservoir simulation are some common application that takes the form of differential equations and are dominated by their respective governing equations. Approximating the solution of such mathematical problems requires a fast and reliable methodology. However, the computational complexity increases with the dimension for the classical numerical techniques and the quality of the result is dependent upon the discretization and sampling methods of the subspace. Recent advances in deep learning techniques, based on universal approximation theorem of neural network seems promising to tackle the high dimensional problem. The solution provided by deep learning for a differential equation is in a closed analytical form which is differentiable and could be used in any subsequent computation. In the present study, the solution for the initial condition and boundary value problems in ordinary and partial differential equation by deep learning method have been analyzed. The propsed algorithm could be valuable aid for analyzing the fluid flow and reservoir simulation in an effective manner.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121200885","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

Refined Urdu Lexicon Development K-Means Clustering Based Computational Model Using Colloquial Romanized Urdu Dataset 基于口语化乌尔都语数据集的K-Means聚类计算模型

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247814

Faisal Baseer, J. Jaafar, I. Aziz, Asad Habib

{"title":"Refined Urdu Lexicon Development K-Means Clustering Based Computational Model Using Colloquial Romanized Urdu Dataset","authors":"Faisal Baseer, J. Jaafar, I. Aziz, Asad Habib","doi":"10.1109/ICCI51257.2020.9247814","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247814","url":null,"abstract":"Urdu is among the most widely used languages in the world for verbal and written communication. Due to lack of optimized and user friendly native Urdu-script support on various platforms, it is mostly written in Romanized script in soft form. In our research, we have developed a refined Urdu lexicon using tokens with the highest frequency of occurrence in the data set. This data set is basically a raw corpus of colloquial Urdu written in Romanized script. The corpus was collected from volunteer participants who used this language as a mode of communication on the Internet and text massaging. The raw corpus is passed through a series of steps such as Prepossessing, Tokenization and Annotation before passing it to computationally extensive subsequent steps. Edit Distance and K-means Clustering techniques are used for identification of candidate tokens and their potential selection/ inclusion in the refined lexicon. We have also identified most commonly used tokens, candidate tokens and other lingual attributes from the data collected. Based on analysis, we have proposed a computational model for refined colloquial Romanized Urdu lexicon development.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121993506","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

A Hybrid Model of Holt-Wintor and Neural Network Methods for Automobile Sales Forecasting 霍尔特-温特混合模型与神经网络方法在汽车销售预测中的应用

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247838

K. Subrmanian, Mohmod Bin Othman, R. Sokkalingam, Gunasekar Thangarasu, Kayalvizhi Subramanian

引用次数: 0