Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10022407
Sumaira Sarwar, Sidra Tahir, M. Humayun, M. Almufareh, Noor Zaman Jhanjhi, Bushra Hamid
Consumers now have more options because to the growth of e-commerce, but there is also an abundance of information. Users are looking for technologies that will allow websites to automatically present goods that they may be interested in so they may swiftly locate preferred products from enormous resources. In order to automate the suggestion process, recommender systems are developed. The accuracy criterion of the suggestion in the area of collaborative filtering. One algorithm's implementation is never simple or straightforward due to an algorithm. We suggest a slope one technique that may be used in various recommender systems to address these issues. It is based on the combination of reliable data and user similarity. Three methods make up this algorithm. We should choose reliable facts first. The similarity between users should be calculated second. Third, we must the final suggestion is obtained by weighting this similarity with the modified slope one method equation. Using the [1] Kaggle dataset, we conducted a number of trials, and the findings show that our recommender superior to the conventional slope one method in terms of performance.
{"title":"Recommendation of Smart Devices Using Collaborative Filter Approach","authors":"Sumaira Sarwar, Sidra Tahir, M. Humayun, M. Almufareh, Noor Zaman Jhanjhi, Bushra Hamid","doi":"10.1109/MACS56771.2022.10022407","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022407","url":null,"abstract":"Consumers now have more options because to the growth of e-commerce, but there is also an abundance of information. Users are looking for technologies that will allow websites to automatically present goods that they may be interested in so they may swiftly locate preferred products from enormous resources. In order to automate the suggestion process, recommender systems are developed. The accuracy criterion of the suggestion in the area of collaborative filtering. One algorithm's implementation is never simple or straightforward due to an algorithm. We suggest a slope one technique that may be used in various recommender systems to address these issues. It is based on the combination of reliable data and user similarity. Three methods make up this algorithm. We should choose reliable facts first. The similarity between users should be calculated second. Third, we must the final suggestion is obtained by weighting this similarity with the modified slope one method equation. Using the [1] Kaggle dataset, we conducted a number of trials, and the findings show that our recommender superior to the conventional slope one method in terms of performance.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"108 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126074292","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 : 2022-11-12DOI: 10.1109/MACS56771.2022.10022602
K. Reddy, M. Ayyadurai
By combining a distributed energy optimization protocol and the Crow Search Algorithm, An underwater acoustic sensor network's sensor nodes can be made to use less energy. $(DEO_CSA)mathbf{in}$ contrast to the DBR protocol for depth-based routing. The Underwater Acoustic Sensor Network (UWASN) uses a 3D geographic zone for cooperative sampling to gather data and uses the crow search algorithm to distribute the data among the nodes.20 samples from each group were collected with a pre-test power of 80%, an error of 0.05, a confidence level of 95%, and 0.05 was chosen as the cutoff point for training the data sets. By changing the node distance, the proposed algorithm routing metrics are examined in a virtual underwater environment using the Aquasim patch and NS2 simulator. When compared to DBR's energy (1mJ) with delay, the proposed DEOCSA performs best for dynamically changing environmental and geographical topological conditions (850ms) The statistical research demonstrates that the least significant value (P0.05) for energy optimization is energy $(mathbf{P}=0.05)$. The simulation results show that by using the recommended Crow Search algorithm rather than Depth Based Routing Algorithm, the sensor network's energy efficiency is increased by shortening the time spent choosing the best nodes.
{"title":"Distributed Energy Optimization Protocol using Crow Search Algorithm in Underwater Acoustic Sensor Network for Energy Enhancement Comparing with Depth Based Routing Algorithm","authors":"K. Reddy, M. Ayyadurai","doi":"10.1109/MACS56771.2022.10022602","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022602","url":null,"abstract":"By combining a distributed energy optimization protocol and the Crow Search Algorithm, An underwater acoustic sensor network's sensor nodes can be made to use less energy. $(DEO_CSA)mathbf{in}$ contrast to the DBR protocol for depth-based routing. The Underwater Acoustic Sensor Network (UWASN) uses a 3D geographic zone for cooperative sampling to gather data and uses the crow search algorithm to distribute the data among the nodes.20 samples from each group were collected with a pre-test power of 80%, an error of 0.05, a confidence level of 95%, and 0.05 was chosen as the cutoff point for training the data sets. By changing the node distance, the proposed algorithm routing metrics are examined in a virtual underwater environment using the Aquasim patch and NS2 simulator. When compared to DBR's energy (1mJ) with delay, the proposed DEOCSA performs best for dynamically changing environmental and geographical topological conditions (850ms) The statistical research demonstrates that the least significant value (P0.05) for energy optimization is energy $(mathbf{P}=0.05)$. The simulation results show that by using the recommended Crow Search algorithm rather than Depth Based Routing Algorithm, the sensor network's energy efficiency is increased by shortening the time spent choosing the best nodes.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126969722","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 : 2022-11-12DOI: 10.1109/MACS56771.2022.10022738
V. Iswarya, T. Yuvaraj, R. Navaneethan
This paper presents the comparative study of Reverse Osmosis and Advanced Oxidation process for reducing the Total Suspended Solids to reduce the organic content in the wastewater. Reverse Osmosis and Advanced Oxidation methods are simulated by varying the membrane parameters or the oxidant dosage, to reduce the Total SuspendedSolids. In the first set of Reverse Osmosis, the dosage applied is 10.61 (m2) and the TSS in the effluent is 0.0 (kg/d). In the first set of Advanced Oxidation process, the oxidant dosage is 8.765 (mg/l) and TSS in the effluent is 7.229 (kg/d). From the obtained results, Reverse Osmosis removes Total Suspended Solids with better efficiency than Advanced Oxidation process.
{"title":"Reduction of Organic Content in Industrial and Residential Wastewater Using Reverse Osmosis and Advanced Oxidation Method by limiting the Total Suspended Solids","authors":"V. Iswarya, T. Yuvaraj, R. Navaneethan","doi":"10.1109/MACS56771.2022.10022738","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022738","url":null,"abstract":"This paper presents the comparative study of Reverse Osmosis and Advanced Oxidation process for reducing the Total Suspended Solids to reduce the organic content in the wastewater. Reverse Osmosis and Advanced Oxidation methods are simulated by varying the membrane parameters or the oxidant dosage, to reduce the Total SuspendedSolids. In the first set of Reverse Osmosis, the dosage applied is 10.61 (m2) and the TSS in the effluent is 0.0 (kg/d). In the first set of Advanced Oxidation process, the oxidant dosage is 8.765 (mg/l) and TSS in the effluent is 7.229 (kg/d). From the obtained results, Reverse Osmosis removes Total Suspended Solids with better efficiency than Advanced Oxidation process.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116102968","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 : 2022-11-12DOI: 10.1109/MACS56771.2022.10022662
Taher M. Ghazal, M Zahid Hasan, Haitham M. Alzoubi, Nidal A. Al-Dmour, Alyazia Alsuwaidi, Yousuf Agha
The HCI in the industry, and IT has increased the feasibility of data exchange. Multiple transactions are carried out online, which has led to overwhelming increasing shopping behavior. As a result of which the personal information of people like account details, and others are made available to the digital medium. However, that exposure of information is triggering data privacy issues, which is a major problem associated with HCI in the IT and industries. The proposed solution to this problem is to spread awareness among people about managing data online and make them conscious about setting strong passwords for making the accounts secure.
{"title":"The Application of HCI in Industry and IT","authors":"Taher M. Ghazal, M Zahid Hasan, Haitham M. Alzoubi, Nidal A. Al-Dmour, Alyazia Alsuwaidi, Yousuf Agha","doi":"10.1109/MACS56771.2022.10022662","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022662","url":null,"abstract":"The HCI in the industry, and IT has increased the feasibility of data exchange. Multiple transactions are carried out online, which has led to overwhelming increasing shopping behavior. As a result of which the personal information of people like account details, and others are made available to the digital medium. However, that exposure of information is triggering data privacy issues, which is a major problem associated with HCI in the IT and industries. The proposed solution to this problem is to spread awareness among people about managing data online and make them conscious about setting strong passwords for making the accounts secure.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123037001","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 : 2022-11-12DOI: 10.1109/MACS56771.2022.10022680
G.Sai Ruchitha, V. Karthick, I. Nasim
The aim of this research is to identify the credit card fraud detection model using an improved K-nearest neighbor classifier method to find the credit card fraudulent transactions with accuracy percentage compared with Logistic regression algorithm. Materials and Methods: Logistic regression algorithm with sample size is 26 and K-nearest neighbor classifier method with sample size is 26 was iterated for predicting accuracy percentage of credit card fraud detection. The study contains two groups where the K-nearest neighbor classifier method algorithm is analysed in the first group, the logistic regression algorithm is analysed in the second group. The G power test value is 0.8 and the accuracies of each algorithm are compared. Results: Logistic regression algorithm is significantly better accuracy 92% compared to K-nearest neighbor 90% with the power value of 80% The two algorithms KNN and logistic regression are statistically not satisfied with the independent sample T-Test ${(alpha=.881)}$ value ${(mathrm{p}=0.05)}$ with a confidence level of 95%. Conclusion: The outcome of the proposed algorithm was compared with the K-nearest neighbor classifier method algorithm and the proposed algorithm has significantly better accuracy of 92% compared with Logistic regression algorithm.
本研究的目的是利用改进的k近邻分类器方法识别信用卡欺诈检测模型，与Logistic回归算法相比，发现具有准确率百分比的信用卡欺诈交易。材料与方法:对样本量为26的Logistic回归算法和样本量为26的k近邻分类器方法进行迭代，预测信用卡欺诈检测的准确率。本研究分为两组，第一组分析k近邻分类器方法算法，第二组分析逻辑回归算法。G功率测试值为0.8，比较了各算法的精度。结果:Logistic回归算法的准确率为92%，而k近邻算法的准确率为90%，幂值为80%，两种算法的KNN和Logistic回归在统计上不满足独立样本t检验${(alpha=.881)}$ value ${( maththrm {p}=0.05)}$，置信水平为95%。结论:与k近邻分类器方法算法的结果进行了比较，与Logistic回归算法相比，该算法的准确率为92%，显著提高。
{"title":"A Novel Approach to Find Accuracy in Credit Card Fraud Detection Using Improved K-Nearest Neighbor Classifier Method Comparing with Logistic Regression Algorithm","authors":"G.Sai Ruchitha, V. Karthick, I. Nasim","doi":"10.1109/MACS56771.2022.10022680","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022680","url":null,"abstract":"The aim of this research is to identify the credit card fraud detection model using an improved K-nearest neighbor classifier method to find the credit card fraudulent transactions with accuracy percentage compared with Logistic regression algorithm. Materials and Methods: Logistic regression algorithm with sample size is 26 and K-nearest neighbor classifier method with sample size is 26 was iterated for predicting accuracy percentage of credit card fraud detection. The study contains two groups where the K-nearest neighbor classifier method algorithm is analysed in the first group, the logistic regression algorithm is analysed in the second group. The G power test value is 0.8 and the accuracies of each algorithm are compared. Results: Logistic regression algorithm is significantly better accuracy 92% compared to K-nearest neighbor 90% with the power value of 80% The two algorithms KNN and logistic regression are statistically not satisfied with the independent sample T-Test ${(alpha=.881)}$ value ${(mathrm{p}=0.05)}$ with a confidence level of 95%. Conclusion: The outcome of the proposed algorithm was compared with the K-nearest neighbor classifier method algorithm and the proposed algorithm has significantly better accuracy of 92% compared with Logistic regression algorithm.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129873126","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 : 2022-11-12DOI: 10.1109/MACS56771.2022.10022836
Pattem Chetana, M. Gunasekaran, Saroj Kumar Tiwari
The object of the study is to provide an effective feedback system for robotic restaurants which uses customer's facial expressions to acquire ratings about customers experience. Materials and methods: In this study we used the FER2013 dataset inorder to implement the system. The proposed system is built based on an Improved support vector machine algorithm with a novel kernel trick method. This system uses the customer's facial expression in order to obtain the rating. Two sample groups have been considered for study in this article such as Improved support vector machine and Flattened convolutional neural networks with sample size 51 for each. Results: The proposed system performed better by obtaining higher accuracy i.e. 82.14% than the Flattened convolutional neural networks where the accuracy was 61.16% The statistical significance of two algorithms observed is $mathbf{p} < 0.001 (2- text{tailed})$ from the independent sample $mathbf{T}$ test. Conclusion: From the results it can be clearly seen that Improved support vector machine show higher accuracy than Flattened convolution neural networks.
{"title":"An Efficient System for Classifying Customer Facial Expressions to Obtain Feedback with Accuracy at Unmanned Restaurants using Improved Support Vector Machine and Flattened Convolutional Neural Networks","authors":"Pattem Chetana, M. Gunasekaran, Saroj Kumar Tiwari","doi":"10.1109/MACS56771.2022.10022836","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022836","url":null,"abstract":"The object of the study is to provide an effective feedback system for robotic restaurants which uses customer's facial expressions to acquire ratings about customers experience. Materials and methods: In this study we used the FER2013 dataset inorder to implement the system. The proposed system is built based on an Improved support vector machine algorithm with a novel kernel trick method. This system uses the customer's facial expression in order to obtain the rating. Two sample groups have been considered for study in this article such as Improved support vector machine and Flattened convolutional neural networks with sample size 51 for each. Results: The proposed system performed better by obtaining higher accuracy i.e. 82.14% than the Flattened convolutional neural networks where the accuracy was 61.16% The statistical significance of two algorithms observed is $mathbf{p} < 0.001 (2- text{tailed})$ from the independent sample $mathbf{T}$ test. Conclusion: From the results it can be clearly seen that Improved support vector machine show higher accuracy than Flattened convolution neural networks.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129021877","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 : 2022-11-12DOI: 10.1109/MACS56771.2022.10022708
Tayyaba Ajaz, Humaira Ashraf, Ghadah Alwakid
this paper proposes a color image encryption algorithm based on DNA encoding in conjunction with a 1D logistic map and a high-dimensional chaotic map. The XOR function is used to combine low and high-dimensional chaotic maps, resulting in a cipher image. For image encryption, the paper also proposes a system of confusion and diffusion. Confusion and Diffusion apply to high-dimensional chaotic maps. In a low-dimensional logistic map, the keyspace is smaller, and chaotic sequences are used directly from the chaotic map, which has an impact on the encryption process. Recent image encryption focuses primarily on a gray image scale. We present color image encryption that uses non-RGB color spaces like YCbCr, YIQ, HSV, and L*a*b*. The high connection between RGB color spaces red, green, and blue is commonly used in data storage and handling. RGB color spaces merge Luminance and Chroma channels so color analysis becomes more complex so we use non -RGB spaces. Using the logistic sine map or tent map association, different chaotic systems can be found by random number attacks and dictionary attacks. Gives the security analysis of encryption using DNA encoding and chaotic map and explains that it's vulnerable to plaintext-choice attacks we propose a new and more efficient approach and reduce time complexity then- existing approach and become encryption more secure.
{"title":"Hybrid security scheme for a colored image using DNA and chaotic map","authors":"Tayyaba Ajaz, Humaira Ashraf, Ghadah Alwakid","doi":"10.1109/MACS56771.2022.10022708","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022708","url":null,"abstract":"this paper proposes a color image encryption algorithm based on DNA encoding in conjunction with a 1D logistic map and a high-dimensional chaotic map. The XOR function is used to combine low and high-dimensional chaotic maps, resulting in a cipher image. For image encryption, the paper also proposes a system of confusion and diffusion. Confusion and Diffusion apply to high-dimensional chaotic maps. In a low-dimensional logistic map, the keyspace is smaller, and chaotic sequences are used directly from the chaotic map, which has an impact on the encryption process. Recent image encryption focuses primarily on a gray image scale. We present color image encryption that uses non-RGB color spaces like YCbCr, YIQ, HSV, and L*a*b*. The high connection between RGB color spaces red, green, and blue is commonly used in data storage and handling. RGB color spaces merge Luminance and Chroma channels so color analysis becomes more complex so we use non -RGB spaces. Using the logistic sine map or tent map association, different chaotic systems can be found by random number attacks and dictionary attacks. Gives the security analysis of encryption using DNA encoding and chaotic map and explains that it's vulnerable to plaintext-choice attacks we propose a new and more efficient approach and reduce time complexity then- existing approach and become encryption more secure.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125087009","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 : 2022-11-12DOI: 10.1109/MACS56771.2022.10022971
Mohit Lalit, Sunil K. Chawla, A. Rana, K. Nisar, Tariq Rahim Soomro, Muhammad Asghar Khan
Enabling objects to work intelligently through various communication technologies is a common phenomenon. Objects become intelligent and are allowed to work autonomously through the internet, and the concept is popularly known as the Internet of Things (IoT). Due to the diversity in application areas of IoT, it has various aspects to be concerned about too. The part researchers are more worried about is transferring data among objects securely. This paper aims to discuss numerous security concerns of application layer protocols and to look into various types of attacks that the application layer might suffer.
{"title":"IoT Networks: Security Vulnerabilities of Application Layer Protocols","authors":"Mohit Lalit, Sunil K. Chawla, A. Rana, K. Nisar, Tariq Rahim Soomro, Muhammad Asghar Khan","doi":"10.1109/MACS56771.2022.10022971","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022971","url":null,"abstract":"Enabling objects to work intelligently through various communication technologies is a common phenomenon. Objects become intelligent and are allowed to work autonomously through the internet, and the concept is popularly known as the Internet of Things (IoT). Due to the diversity in application areas of IoT, it has various aspects to be concerned about too. The part researchers are more worried about is transferring data among objects securely. This paper aims to discuss numerous security concerns of application layer protocols and to look into various types of attacks that the application layer might suffer.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123518475","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 : 2022-11-12DOI: 10.1109/MACS56771.2022.10022408
P. R. Reddy, P. Kalyanasundaram, V. Suresh
The main objective of this research is to detect the forest fire by sensing temperature and atmospheric carbon dioxide (CO2) levels to prevent the forest fire and to provide exact information using IOT at faster speed. The efficiency of detection using Node Microcontroller Unit (NodeMCU) is compared with Arduino microcontroller. A total of 40 samples are taken from the Serial monitor of the Arduino IDE. Group 1 has temperature values (n = 10) and atmospheric carbon dioxide (CO2) levels (n = 10) with the Node Microcontroller Unit (Node MCU). Group 2 has temperature values (n = 10) and atmospheric carbon dioxide (CO2) levels (n = 10) using Arduino Microcontroller. In this novel forest fire detection, the G-power analysis was done to the samples and the minimum power is acquired to be 0.8 for the system with an error correction of 0.5. The significance values for the temperature sensor are 0.129 and 0.132 for NodeMCU and Arduino Microcontroller respectively. The significance values for atmospheric carbon dioxide (CO2) levels are 0.212 and 0.224 for NodeMCU and Arduino Microcontroller respectively. Results: Through the implementation of this novel forest fire detection, it is observed that the efficiency of NodeMCU is 92.9 % and efficiency of Arduino microcontroller is 89.95 %. This innovative approach with NodeMCU appears to be more efficient (92.9 %) in detecting the occurrence of forest fire using Arduino Microcontroller with the significance value of temperature and atmospheric carbon dioxide level of 0.129 and 0.212 respectively.
{"title":"IOT based Novel speedy Detection of Forest fire using Sensors with improved accuracy by sensing Temperature and Atmospheric Carbon Dioxide Level using Node Microcontroller Unit in comparison with Arduino Microcontroller","authors":"P. R. Reddy, P. Kalyanasundaram, V. Suresh","doi":"10.1109/MACS56771.2022.10022408","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022408","url":null,"abstract":"The main objective of this research is to detect the forest fire by sensing temperature and atmospheric carbon dioxide (CO2) levels to prevent the forest fire and to provide exact information using IOT at faster speed. The efficiency of detection using Node Microcontroller Unit (NodeMCU) is compared with Arduino microcontroller. A total of 40 samples are taken from the Serial monitor of the Arduino IDE. Group 1 has temperature values (n = 10) and atmospheric carbon dioxide (CO2) levels (n = 10) with the Node Microcontroller Unit (Node MCU). Group 2 has temperature values (n = 10) and atmospheric carbon dioxide (CO2) levels (n = 10) using Arduino Microcontroller. In this novel forest fire detection, the G-power analysis was done to the samples and the minimum power is acquired to be 0.8 for the system with an error correction of 0.5. The significance values for the temperature sensor are 0.129 and 0.132 for NodeMCU and Arduino Microcontroller respectively. The significance values for atmospheric carbon dioxide (CO2) levels are 0.212 and 0.224 for NodeMCU and Arduino Microcontroller respectively. Results: Through the implementation of this novel forest fire detection, it is observed that the efficiency of NodeMCU is 92.9 % and efficiency of Arduino microcontroller is 89.95 %. This innovative approach with NodeMCU appears to be more efficient (92.9 %) in detecting the occurrence of forest fire using Arduino Microcontroller with the significance value of temperature and atmospheric carbon dioxide level of 0.129 and 0.212 respectively.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116217602","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 : 2022-11-12DOI: 10.1109/MACS56771.2022.10023180
B. Sai kumar, A. Akilandeswari, Emg Subramanian
The aim of this research is to develop a 4:1 mul-tiplexer using the advanced GDI logic. As compared to CMOS transistor logic, GDI technique reduces the power consumption and propagation delay. Materials and Methods: There are two groups namely CMOS logic and GDI. A total of 20 samples are used to compare the propagation delay and power in which CMOS of group size 10 and GDI of group size 10. The samples are calculated with a pretest power of 80%, a confidence interval of 95% and alpha value of 0.05. Result: The propagation delay of the GDI approach is 0.4550 ns, power dissipation of 1.5070 μW, while CMOS transistor logic has more propagation delay and power consumption. It has a propagation delay of 0.8350 ns and a power dissipation of 3.5000 μW with significance of 0.02. Conclusion: The new GDI approach delivers reduced propagation delay and power consumption than CMOS transistor logic.
{"title":"Design of 4:1 Multiplexer using Gate Diffusion Input Technique and Comparison of Delay and Power Performance with CMOS Logic","authors":"B. Sai kumar, A. Akilandeswari, Emg Subramanian","doi":"10.1109/MACS56771.2022.10023180","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10023180","url":null,"abstract":"The aim of this research is to develop a 4:1 mul-tiplexer using the advanced GDI logic. As compared to CMOS transistor logic, GDI technique reduces the power consumption and propagation delay. Materials and Methods: There are two groups namely CMOS logic and GDI. A total of 20 samples are used to compare the propagation delay and power in which CMOS of group size 10 and GDI of group size 10. The samples are calculated with a pretest power of 80%, a confidence interval of 95% and alpha value of 0.05. Result: The propagation delay of the GDI approach is 0.4550 ns, power dissipation of 1.5070 μW, while CMOS transistor logic has more propagation delay and power consumption. It has a propagation delay of 0.8350 ns and a power dissipation of 3.5000 μW with significance of 0.02. Conclusion: The new GDI approach delivers reduced propagation delay and power consumption than CMOS transistor logic.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"23 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132531857","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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