Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10041248
Kasim Mohammad, Sarhan M. Musa
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.
{"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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008301
Varun Mishra, Monika Tripathi
In the most significant issue now plaguing social networking platforms and online communities is toxicity identification. Therefore, it is necessary to create an automatic hazardous identification system to block and restrict individual from certain online environments. We introduce multichannel Convolutional Neural Network (CNN) approach in this paper for the detection of toxic comments in a multi-label context. With the help of pre-trained word embeddings, the suggested model produces word vectors. Also, to model input words with long-term dependency, this hybrid model extracts local characteristics using a variety of filters and kernel sizes. Then, to forecast multi-label categories, we integrate numerous channels with three layers as fully linked, normalization, and an output layer. The results of the experiments show that the suggested model performs where we are presenting the fresh modeling CNN approach to detect the toxicity of textual content present on the social media platforms and we categorized the toxicity into positive and negative impact on our society.
{"title":"Detecting Toxic Comments Using Convolutional Neural Network Approach","authors":"Varun Mishra, Monika Tripathi","doi":"10.1109/CICN56167.2022.10008301","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008301","url":null,"abstract":"In the most significant issue now plaguing social networking platforms and online communities is toxicity identification. Therefore, it is necessary to create an automatic hazardous identification system to block and restrict individual from certain online environments. We introduce multichannel Convolutional Neural Network (CNN) approach in this paper for the detection of toxic comments in a multi-label context. With the help of pre-trained word embeddings, the suggested model produces word vectors. Also, to model input words with long-term dependency, this hybrid model extracts local characteristics using a variety of filters and kernel sizes. Then, to forecast multi-label categories, we integrate numerous channels with three layers as fully linked, normalization, and an output layer. The results of the experiments show that the suggested model performs where we are presenting the fresh modeling CNN approach to detect the toxicity of textual content present on the social media platforms and we categorized the toxicity into positive and negative impact on our society.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 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":"130721233","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008366
Ertie Abana, Marion James Ladia, Christine Fernando, Nicole Emmanuelle Pagalilauan, Jay Vee Miranda, Ailyn Samontina, Rouxanne Macoco
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.
本研究开发了一种微型水轮发电机水轮机，利用水流进入单一进水管，使水轮机在一定的水压下连续旋转。该装置旨在连接到家庭的主要供水管道，以产生能量并将其转化为电力，以便在紧急停电时运行小型设备。它包括涡轮机、发电机、降压电压、充电器模块、可充电电池和dc-dc升压模块。从所进行的测试中，该设备产生的平均电压，电流和功率为4.99 V, 0.48 A, 35 psi 2.40 W, 4.36 V, 0.35 A, 20 psi 1.54 W。该装置在35 psi和20 psi下的功率效率分别为23.97%和15.44%。在高压和低压下，平均9分钟和14.6分钟后，内置电池的电量增加1%。结果表明，该装置产生的能量足以为智能手机、充电宝、便携式灯和便携式风扇等额定电压为5伏的小型设备供电。
{"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}
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.
{"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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008315
Shatha Al Ghazali, Saad Harous, S. Turaev
The applications of artificial intelligence in education became a very attractive topic especially during the COVID-19 pandemic due to the high level of uncertainty surrounded the decision making process within the educational institutions. The objective of this study is to create a model that is able to predict the student's score in the SAT test based on the student's performance in the internal assessments of the school and other demographic attributes. The sample includes 37 students of both genders from a private school in the United Arab Emirates (UAE). The findings suggest that it is possible to implement artificial neural networks to estimate the student's performance in the SAT exam based on internal school data. The model accuracy is 87.4 % however, some attributes can be identified as noise data and can be further removed to increase the accuracy. Scholastic Assessment Test Artificial Neural Network Machine learning Students performance.
{"title":"Application of Artificial Neural Network to Estimate Students Performance in Scholastic Assessment Test","authors":"Shatha Al Ghazali, Saad Harous, S. Turaev","doi":"10.1109/CICN56167.2022.10008315","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008315","url":null,"abstract":"The applications of artificial intelligence in education became a very attractive topic especially during the COVID-19 pandemic due to the high level of uncertainty surrounded the decision making process within the educational institutions. The objective of this study is to create a model that is able to predict the student's score in the SAT test based on the student's performance in the internal assessments of the school and other demographic attributes. The sample includes 37 students of both genders from a private school in the United Arab Emirates (UAE). The findings suggest that it is possible to implement artificial neural networks to estimate the student's performance in the SAT exam based on internal school data. The model accuracy is 87.4 % however, some attributes can be identified as noise data and can be further removed to increase the accuracy. Scholastic Assessment Test Artificial Neural Network Machine learning Students performance.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"33 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114086724","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008322
Hadeer A. Hassan, Mohab Mohammed Eid, M. M. Elmesalawy, Ahmed M. Abd El-Haleem
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.
{"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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008247
Truc Duong, Anna Wisniewska, Nirnimesh Ghose
The rapid growth of wireless devices as societies adapt to the Internet of Everything (IoE) has led to saturation of spectrum resources. Dynamic spectrum access has been considered a promising solution to alleviate congested channels by allowing unlicensed users to access licensed channels when the licensed users are idle. Various coexistence challenges arise as unlicensed users compete over a limited amount of channel resources. In this article, we build on a previously defined bio-social inspired dynamic spectrum access coexistence scheme where unlicensed users achieve fair sharing of resources by choosing to defer to nodes with more urgent transmission needs. To prevent selfish nodes from taking advantage of the deference mechanism, we propose a decentralized rogue node detection behavioral model. While foraging for resources, each node performs rogue node detection using hardware fingerprinting. We show that we can achieve 99% rogue node detection accuracy with fast detection convergence time and low communication/coordination overhead.
{"title":"Bio-inspired Decentralized Rogue Node Detection in Fair Dynamic Spectrum Access Networks","authors":"Truc Duong, Anna Wisniewska, Nirnimesh Ghose","doi":"10.1109/CICN56167.2022.10008247","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008247","url":null,"abstract":"The rapid growth of wireless devices as societies adapt to the Internet of Everything (IoE) has led to saturation of spectrum resources. Dynamic spectrum access has been considered a promising solution to alleviate congested channels by allowing unlicensed users to access licensed channels when the licensed users are idle. Various coexistence challenges arise as unlicensed users compete over a limited amount of channel resources. In this article, we build on a previously defined bio-social inspired dynamic spectrum access coexistence scheme where unlicensed users achieve fair sharing of resources by choosing to defer to nodes with more urgent transmission needs. To prevent selfish nodes from taking advantage of the deference mechanism, we propose a decentralized rogue node detection behavioral model. While foraging for resources, each node performs rogue node detection using hardware fingerprinting. We show that we can achieve 99% rogue node detection accuracy with fast detection convergence time and low communication/coordination overhead.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"34 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":"134461825","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008270
Khalid Albulayhi, Q. A. Al-Haija
In this study, we propose a predictive model for forecasting future ransomware and malware attacks based on the previous time series data from 2005–2021. We use a time-series regression technique that relies on the neural network algorithm to estimate the forecasting of ransomware and malware attacks in future years over time. Our experiment has applied two hidden layers with the optimal parameter (weight and biases). We modify our model in terms of building time series to predict short-term future values up to 2026. To reach the minimum potential training error, we train our model on 60 epochs to achieve Mean Square Error (MSE) at minimum values. We have achieved the highest accuracy of 99% for forecasting malicious activities (ransomware and malware). The predictive model shows a massive increase in ransomware risk. The current lines of defense cannot keep up with the evolution of ransomware to prevent them.
{"title":"Early-stage Malware and Ransomware Forecasting in the Short-Term Future Using Regression-based Neural Network Technique","authors":"Khalid Albulayhi, Q. A. Al-Haija","doi":"10.1109/CICN56167.2022.10008270","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008270","url":null,"abstract":"In this study, we propose a predictive model for forecasting future ransomware and malware attacks based on the previous time series data from 2005–2021. We use a time-series regression technique that relies on the neural network algorithm to estimate the forecasting of ransomware and malware attacks in future years over time. Our experiment has applied two hidden layers with the optimal parameter (weight and biases). We modify our model in terms of building time series to predict short-term future values up to 2026. To reach the minimum potential training error, we train our model on 60 epochs to achieve Mean Square Error (MSE) at minimum values. We have achieved the highest accuracy of 99% for forecasting malicious activities (ransomware and malware). The predictive model shows a massive increase in ransomware risk. The current lines of defense cannot keep up with the evolution of ransomware to prevent them.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"2018 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":"133028552","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008237
Reem Alzaher, Wafa Hantom, Alanoud Aldweesh, Nasro Min Allah
The RSA algorithm is an asymmetric encryption algorithm used to ensure the confidentiality and integrity of data as it travels across networks. Security has grown in importance over time, resulting into more data requiring encryption. Parallelization represents an ideal solution to speed up the encryption and decryption processes. An advance implementation of RSA using parallelization concept leads to improve security and performance. In this paper, we represent a parallelized version of Multi-Keys RSA algorithm implemented using OpenMP library. Furthermore, we provide parallel implementation of Multi-Keys RSA under both static and dynamic scheduling with different chunk sizes, and our experimental results show that static scheduling is more optimum for RSA cryptography as compared to dynamic. As a final result, we have achieved an average speed up of 4.4 and efficiency of 0.7.
{"title":"Parallelizing Multi-Keys RSA Encryption Algorithm Using OpenMP","authors":"Reem Alzaher, Wafa Hantom, Alanoud Aldweesh, Nasro Min Allah","doi":"10.1109/CICN56167.2022.10008237","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008237","url":null,"abstract":"The RSA algorithm is an asymmetric encryption algorithm used to ensure the confidentiality and integrity of data as it travels across networks. Security has grown in importance over time, resulting into more data requiring encryption. Parallelization represents an ideal solution to speed up the encryption and decryption processes. An advance implementation of RSA using parallelization concept leads to improve security and performance. In this paper, we represent a parallelized version of Multi-Keys RSA algorithm implemented using OpenMP library. Furthermore, we provide parallel implementation of Multi-Keys RSA under both static and dynamic scheduling with different chunk sizes, and our experimental results show that static scheduling is more optimum for RSA cryptography as compared to dynamic. As a final result, we have achieved an average speed up of 4.4 and efficiency of 0.7.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"61 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":"123828602","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008300
Abhishek Bajpai, A. Singh, Abhineet Verma
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 %
{"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}
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