2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)最新文献_第7页

Optimization of Solar Energy Using Recurrent Neural Network Controller 基于递归神经网络控制器的太阳能优化

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10041248

Kasim Mohammad, Sarhan M. Musa

{"title":"Optimization of Solar Energy Using Recurrent Neural Network Controller","authors":"Kasim Mohammad, Sarhan M. Musa","doi":"10.1109/CICN56167.2022.10041248","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10041248","url":null,"abstract":"The use of solar panels has some advantages over other conventional electrical generating methods, as there is no sound pollution in collecting solar energy using solar panels, and also it has a minimum need for maintenance. In addition, it helps in the greenhouse effect which does not contribute to any CO2 pollution, as the conversion of light to electricity does not contain any chemical reactions. Using photovoltaic (PV) systems that are connected to a load will require a Maximum Power Point Tracker (MPPT) to maintain the highest possible efficiency of power generated. The resistance of the PV panels is different from the load resistance, the MPPT will control the duty cycle of the Insulated Gate Bipolar Transistor (IGBT) in the DC-DC converter to match the PV and load resistance for best efficacy. However, the use of MPPT with the connection to a controller collecting the maximum power generated from the PV system. In this paper, we design and implement a Recurrent Neural Network (RNN) based MPPT method to improve the efficiency of the power observation for the PV system for any value of irradiation (G) and temperature (T). Mainly, we compare two controller methods, using 104 sets of data for an ANN controller that was designed and tested in the past, with the same 104 sets of data to train the proposed RNN controller, as ANN used prediction in its calculations to find the best output efficiency, RNN will use a recurrent connection in the hidden layers that allow information to flow from one input to another.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115012714","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

Detecting Toxic Comments Using Convolutional Neural Network Approach 使用卷积神经网络方法检测有毒评论

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008301

Varun Mishra, Monika Tripathi

引用次数: 0

Micro Hydro Generator Turbine 微型水轮发电机

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008366

Ertie Abana, Marion James Ladia, Christine Fernando, Nicole Emmanuelle Pagalilauan, Jay Vee Miranda, Ailyn Samontina, Rouxanne Macoco

{"title":"Micro Hydro Generator Turbine","authors":"Ertie Abana, Marion James Ladia, Christine Fernando, Nicole Emmanuelle Pagalilauan, Jay Vee Miranda, Ailyn Samontina, Rouxanne Macoco","doi":"10.1109/CICN56167.2022.10008366","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008366","url":null,"abstract":"This study developed a micro-hydro generator turbine utilizing water flowing into a single inflow pipe which makes the turbine rotate continuously within a specific water pressure. The device is intended to be connected to the household's main water pipeline to generate energy and convert it into electricity that can operate small devices during emergency power outages. It comprises a turbine, generator, step-down voltage, charger module, rechargeable battery, and dc-dc boost module. From the tests conducted, the device generated an average voltage, current, and power of 4.99 V, 0.48 A, 2.40 W at 35 psi, and 4.36 V, 0.35 A, 1.54 W at 20 psi. The power efficiencies of the device at 35 psi and 20 psi were 23.97% and 15.44%, respectively. The percent charge of the built-in battery increases by 1% after an average of 9 minutes and 14.6 minutes for high and low pressure, respectively. The results showed that the device generated enough energy to supply small devices rated 5 volts like smartphones, power banks, portable lamps, and portable fans.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"39 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130946367","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

An Efficient Algorithm for Plant Disease Detection Using Deep Convolutional Networks 一种基于深度卷积网络的植物病害检测算法

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008235

Pratibha Nayar, Shivank Chhibber, Ashwani Kumar Dubey

{"title":"An Efficient Algorithm for Plant Disease Detection Using Deep Convolutional Networks","authors":"Pratibha Nayar, Shivank Chhibber, Ashwani Kumar Dubey","doi":"10.1109/CICN56167.2022.10008235","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008235","url":null,"abstract":"Plant diseases and pests are important factors in determining crop yield and quality. Plant diseases are not only a threat to food security on a global scale but can also have devastating consequences for farmers whose livelihood depend on healthy crops. The detection of plant diseases is of fundamental importance in practical agricultural production. It controls the growth and health of the plant and ensures the regular operation and successful harvest of agricultural plantations. The disease affecting the plants is determined by factors such as the climate. This paper examines an alternative approach to developing a disease detection model supported by leaf classification using deep convolutional networks. Growth in computer vision present a scope to broaden and boost the practice of precision crop protection and expand the market for computer vision applications in precision agriculture, a completely unique form of training and therefore the technique used allows for quick and direct implementation of the system in practice. The database used in this paper consists of 77,000 images of healthy and infected plant leaves. We were able to train a CNN model for classifying plant diseases that is, they are present or not, and then another model was trained with YOLOv7 to detect the disease. The trained classification model achieved an accuracy of 99.5% and the detection model was able to achieve mA$P$, precision, recall of 0.65, 0.59and 0.65 respectively.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132377059","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: 5

Application of Artificial Neural Network to Estimate Students Performance in Scholastic Assessment Test 人工神经网络在学生学业评估中的应用

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008315

Shatha Al Ghazali, Saad Harous, S. Turaev

引用次数: 0

A New Intelligent System for Evaluating and Assisting Students in Laboratory Learning Management System 一种新型的智能学生评价与辅助实验室学习管理系统

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008322

Hadeer A. Hassan, Mohab Mohammed Eid, M. M. Elmesalawy, Ahmed M. Abd El-Haleem

{"title":"A New Intelligent System for Evaluating and Assisting Students in Laboratory Learning Management System","authors":"Hadeer A. Hassan, Mohab Mohammed Eid, M. M. Elmesalawy, Ahmed M. Abd El-Haleem","doi":"10.1109/CICN56167.2022.10008322","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008322","url":null,"abstract":"Due to the Covid-19 epidemic the need for digital E-learning systems become mandatory. Also, most sectors that faced a shortage in E-learning systems are performing laboratory experiments remotely. For this reason, this research paper focuses on providing a complete Laboratory Learning Management System (LLMS) with generic and intelligent performance evaluation for experiments. The new LLMS offers many services from intelligently and automatically doing performance assessments and assistance for the students while performing the experiments online. The new performance assessment module provides regular assessment for experimental steps added to it the intelligent automatic assessment that detects if the students performed the experiments correctly from their mouse dynamics using an AI algorithm. Moreover, the new LLMS uses an analytic module to provide the teachers with analyzed results and charts to describe the behavior of students in various performed experiments. Regarding, the new performance assistant module provides students with complete assistance by pressing the help button to trigger the virtual tutor to explain any experimental steps. Furthermore, it intelligently to collects the mouse dynamics of the student performing the experiments and uses AI algorithms to detect if students face difficulties and provide them with suitable help automatically. Moreover, it can open a chat session with a real teaching assistant or a classmate to help the students. Furthermore, the new performance assessment and assistant services are considered generic because they used the mouse dynamic behavior of students which is suitable for any type of software used in the laboratory, without the need for a special device or extra cost.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114560687","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

Bio-inspired Decentralized Rogue Node Detection in Fair Dynamic Spectrum Access Networks 公平动态频谱接入网络中受生物启发的分散流氓节点检测

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008247

Truc Duong, Anna Wisniewska, Nirnimesh Ghose

引用次数: 0

Early-stage Malware and Ransomware Forecasting in the Short-Term Future Using Regression-based Neural Network Technique 基于回归神经网络技术的早期恶意软件和勒索软件短期预测

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008270

Khalid Albulayhi, Q. A. Al-Haija

引用次数: 0

Parallelizing Multi-Keys RSA Encryption Algorithm Using OpenMP 基于OpenMP的并行多密钥RSA加密算法

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008237

Reem Alzaher, Wafa Hantom, Alanoud Aldweesh, Nasro Min Allah

引用次数: 0

An Efficient Method to Predict the Tata- Motors Stock Price using Hybrid Machine Learning Methods 一种利用混合机器学习方法预测塔塔汽车股价的有效方法

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008300

Abhishek Bajpai, A. Singh, Abhineet Verma

{"title":"An Efficient Method to Predict the Tata- Motors Stock Price using Hybrid Machine Learning Methods","authors":"Abhishek Bajpai, A. Singh, Abhineet Verma","doi":"10.1109/CICN56167.2022.10008300","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008300","url":null,"abstract":"Stock market analysis has always been an important aspect of every country's financial sector. As of now, various research has been done to predict the stock market prices but only considering the technical stock data. However, the problem lies in combining the technical data of stock prices and news sentiments from financial news data so that prediction can be done with much greater accuracy. In our paper, we have designed a stock price prediction system and proposed an approach in which technical stock Data is evaluated by technical means and news sentiment data is represented in the form of sentiment vectors using sentiment analysis. We have deployed Particle Swarm Optimization (PSO) to tune the hyper- parameters of the Support Vector Machine for regression (SVR), thus providing better results. We have done experiments on the Tata Motors stock price data and compared our approach with [1] who have deployed the SVM-PSO model with basic technical features taken into consideration. Our model SVR-PSO with financial news data gives a Mean Absolute Percentage Error of 0.29% as compared to the standard SVM- PSO which gives a Mean Absolute Percentage Error of 0.71 %","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125591774","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