Pub Date : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987754
K. Patidar, D. Tiwari
Although numerous efforts have been made to develop feature selection framework which is efficient in Big Data technology, complexity of processing big data remains a significant barrier. As a result, the computational complexity and intricacy of big data may block the data mining process. The feature selection method means, a required pre-processing approach to minimize dataset dimensionality for great advanced features and classifier performance optimization. In order to increase performance, feature selection are regarded to constitute the core of big data technologies. In recent years, many academics have moved their focus to data science and analytics for application scenarios leveraging integrating tools of big data. People take quite some time to engage, when it comes to big data. As a consequence, in a decentralized system with a high workload, it is crucial in making feature selection dynamic and adaptable. Multi objective optimal strategies for feature selection are provided in this work. This research adds to the creation of a strategy for enhancing feature selection efficiency in large, complex data sets. In this paper, a multi-objective clustering-based gray-wolf optimization algorithm (MOCGWO) is proposed for classification problems. Five datasets were used to show the robustness of proposed algorithm. The result analysis was compared with other optimization methodology such as GWO and PSO. This shows efficacy of MOCGWO algorithm.
{"title":"Feature Selection using Multi-Objective Clustering based Gray Wolf Optimization for Big Data Analytics","authors":"K. Patidar, D. Tiwari","doi":"10.1109/ICTACS56270.2022.9987754","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987754","url":null,"abstract":"Although numerous efforts have been made to develop feature selection framework which is efficient in Big Data technology, complexity of processing big data remains a significant barrier. As a result, the computational complexity and intricacy of big data may block the data mining process. The feature selection method means, a required pre-processing approach to minimize dataset dimensionality for great advanced features and classifier performance optimization. In order to increase performance, feature selection are regarded to constitute the core of big data technologies. In recent years, many academics have moved their focus to data science and analytics for application scenarios leveraging integrating tools of big data. People take quite some time to engage, when it comes to big data. As a consequence, in a decentralized system with a high workload, it is crucial in making feature selection dynamic and adaptable. Multi objective optimal strategies for feature selection are provided in this work. This research adds to the creation of a strategy for enhancing feature selection efficiency in large, complex data sets. In this paper, a multi-objective clustering-based gray-wolf optimization algorithm (MOCGWO) is proposed for classification problems. Five datasets were used to show the robustness of proposed algorithm. The result analysis was compared with other optimization methodology such as GWO and PSO. This shows efficacy of MOCGWO algorithm.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123466892","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988551
V. Meena, Kanika Sharma, Amod Kumar
Wireless sensor networks (WSNs) are used to gather data and detect events in a real-time setting. WSN deployment in remote, inaccessible, and hostile regions has sparked tremendous attention; nonetheless, such deployment poses numerous problems. WSNs have several advantages, but one major disadvantage is that the sensor node's lifetime is determined by the battery life. The regularity of sensed data and the temperature are two important factors that influence battery life. To prolong the longevity of sensor nodes, some energy-efficient routing methods have been devised and deployed. These protocols are designed to optimize network paths. In this paper, a brief review of various existing meta-heuristic and non-metaheuristic routing methods, aiming to enhance network longevity, is presented. Particularly, the cluster-based optimized routing methods are being focused on as they are found to be more energy-efficient than the other genres. The selection of Cluster-Head (CH) for enhancing network efficiency has been a matter of research for a long. This review paper analyzes the existing methods pertaining to the design of an enhanced version of the optimized routing method.
{"title":"Study of State-of-the-Art Optimized Routing Methods in WSNs for Various Applications","authors":"V. Meena, Kanika Sharma, Amod Kumar","doi":"10.1109/ICTACS56270.2022.9988551","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988551","url":null,"abstract":"Wireless sensor networks (WSNs) are used to gather data and detect events in a real-time setting. WSN deployment in remote, inaccessible, and hostile regions has sparked tremendous attention; nonetheless, such deployment poses numerous problems. WSNs have several advantages, but one major disadvantage is that the sensor node's lifetime is determined by the battery life. The regularity of sensed data and the temperature are two important factors that influence battery life. To prolong the longevity of sensor nodes, some energy-efficient routing methods have been devised and deployed. These protocols are designed to optimize network paths. In this paper, a brief review of various existing meta-heuristic and non-metaheuristic routing methods, aiming to enhance network longevity, is presented. Particularly, the cluster-based optimized routing methods are being focused on as they are found to be more energy-efficient than the other genres. The selection of Cluster-Head (CH) for enhancing network efficiency has been a matter of research for a long. This review paper analyzes the existing methods pertaining to the design of an enhanced version of the optimized routing method.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125884691","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988247
C. K, K. S. Kumar
Student performance during their entire carrier and also a previous academic performance impact the chance of getting a job offer at the end of graduation. Many factors like student technical, analytical, and communication skills are essential to procuring a job. However, our effort is to find how academic skills and scores affect their chances. Machine learning algorithms play a significant role in analyzing and predicting the chance of students in placements based on their previous academic outcomes. In this paper, we collected student data from a reputed technical institute. The data set comprises different factors that influence the student chances; these influencing factors are studied and represented using visualizations. On this data set, we tried to analyze the data and draw visualizations and insights before performing or applying machine algorithms to the data. In this paper, our main motto is to analyze and understand the data and perform preprocessing of the data.
{"title":"Data Preprocessing and Visualizations Using Machine Learning for Student Placement Prediction","authors":"C. K, K. S. Kumar","doi":"10.1109/ICTACS56270.2022.9988247","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988247","url":null,"abstract":"Student performance during their entire carrier and also a previous academic performance impact the chance of getting a job offer at the end of graduation. Many factors like student technical, analytical, and communication skills are essential to procuring a job. However, our effort is to find how academic skills and scores affect their chances. Machine learning algorithms play a significant role in analyzing and predicting the chance of students in placements based on their previous academic outcomes. In this paper, we collected student data from a reputed technical institute. The data set comprises different factors that influence the student chances; these influencing factors are studied and represented using visualizations. On this data set, we tried to analyze the data and draw visualizations and insights before performing or applying machine algorithms to the data. In this paper, our main motto is to analyze and understand the data and perform preprocessing of the data.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"68 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129902608","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988569
Kakularam Vikas Reddy, L. Parvathy
The primary goal of this study is to propose and compare an automatic melanoma cancer detection system based on mobilenet architecture algorithm and convolutional neural network algorithm (CNN) to detect melanoma cancer. With a sample size of 10, Group 1 was the MobileNet architecture, and Group 2 was the Convolutional Neural Network algorithm. They were iterated 20 times to predict the accuracy percentage of melanoma cancer detection. The accuracy of the Mobilenet architecture algorithm (75%) is significantly higher than that of the Convolutional Neural Network (60%). The mobilenet architecture algorithm has a high statistical significance (p0.05 Independent Sample T-test). Within the scope of this study, the Mobilenet architecture algorithm outperforms Convolutional Neural networks in melanoma skin cancer detection.
{"title":"An Innovative Analysis of predicting Melanoma Skin Cancer using MobileNet and Convolutional Neural Network Algorithm","authors":"Kakularam Vikas Reddy, L. Parvathy","doi":"10.1109/ICTACS56270.2022.9988569","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988569","url":null,"abstract":"The primary goal of this study is to propose and compare an automatic melanoma cancer detection system based on mobilenet architecture algorithm and convolutional neural network algorithm (CNN) to detect melanoma cancer. With a sample size of 10, Group 1 was the MobileNet architecture, and Group 2 was the Convolutional Neural Network algorithm. They were iterated 20 times to predict the accuracy percentage of melanoma cancer detection. The accuracy of the Mobilenet architecture algorithm (75%) is significantly higher than that of the Convolutional Neural Network (60%). The mobilenet architecture algorithm has a high statistical significance (p0.05 Independent Sample T-test). Within the scope of this study, the Mobilenet architecture algorithm outperforms Convolutional Neural networks in melanoma skin cancer detection.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128640663","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987834
C. Ananth, B. Revathi, I. Poonguzhali, A. Anitha, T. Ananth Kumar
Smart wearables are redefining the way people move and behave in real-time. Workers will be alerted to the presence of toxic gases as well as be tracked in the event of an accident if this system is implemented. Additionally, the instrument has sensors for methane and carbon monoxide gases included in its design. The prototype can detect gas in the air, the rate of the miner's breathing, the change in temperature and humidity, and the miner's location at all times. Wi-Fi will be used to transmit all of these parameters to a dynamic internet protocol. Every one of them will be able to make it through the shield. This way, all mineworkers can be monitored, and if something goes wrong, the miner can be rescued as quickly as possible. Using a pulse sensor on the miner's body, the base camp can track the miner's GPS location. It may be necessary to dig a coal mine as soon as possible to save the most people in a disaster. With the help of IoT, we can build a database and, if necessary, communicate with a nearby hospital. Our final consideration will look at market trends and challenges for WHDs to keep in mind.
{"title":"Wearable Smart Jacket for Coal Miners Using IoT","authors":"C. Ananth, B. Revathi, I. Poonguzhali, A. Anitha, T. Ananth Kumar","doi":"10.1109/ICTACS56270.2022.9987834","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987834","url":null,"abstract":"Smart wearables are redefining the way people move and behave in real-time. Workers will be alerted to the presence of toxic gases as well as be tracked in the event of an accident if this system is implemented. Additionally, the instrument has sensors for methane and carbon monoxide gases included in its design. The prototype can detect gas in the air, the rate of the miner's breathing, the change in temperature and humidity, and the miner's location at all times. Wi-Fi will be used to transmit all of these parameters to a dynamic internet protocol. Every one of them will be able to make it through the shield. This way, all mineworkers can be monitored, and if something goes wrong, the miner can be rescued as quickly as possible. Using a pulse sensor on the miner's body, the base camp can track the miner's GPS location. It may be necessary to dig a coal mine as soon as possible to save the most people in a disaster. With the help of IoT, we can build a database and, if necessary, communicate with a nearby hospital. Our final consideration will look at market trends and challenges for WHDs to keep in mind.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132427807","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988613
H. S, P. Ramkumar, R. Balakrishna, Sunitha Rani. N, P. S.
Nowadays the entire world has been suffered by a virus called corona which creates panic to the entire world. Even though the world has reached out its advanced level in medical and all other techniques this unseen virus has created an impact to the entire world. This virus has been explored in Wuhan at china, then it spread the entire world and the effect is being very dangerous. In this regard although there is been many researchers have given different solution to predict the root causes of this disease still it is a challenging task. So, this article addressed about the possibility of prediction rate using KNN algorithm. This proposed method would produce 85% of prediction accuracy and 1.4% to 3.4% accuracy improvement when compared with other algorithm. When compared with all other algorithm K- Nearest neighbour algorithm has given better classification than other machine learning algorithm for predicting the COVID 19 possibilities also it diminishes the error rate of prediction accuracy.
{"title":"Possibilities of Prediction of COVID 19 using K-Nearest Neighbour Algorithm","authors":"H. S, P. Ramkumar, R. Balakrishna, Sunitha Rani. N, P. S.","doi":"10.1109/ICTACS56270.2022.9988613","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988613","url":null,"abstract":"Nowadays the entire world has been suffered by a virus called corona which creates panic to the entire world. Even though the world has reached out its advanced level in medical and all other techniques this unseen virus has created an impact to the entire world. This virus has been explored in Wuhan at china, then it spread the entire world and the effect is being very dangerous. In this regard although there is been many researchers have given different solution to predict the root causes of this disease still it is a challenging task. So, this article addressed about the possibility of prediction rate using KNN algorithm. This proposed method would produce 85% of prediction accuracy and 1.4% to 3.4% accuracy improvement when compared with other algorithm. When compared with all other algorithm K- Nearest neighbour algorithm has given better classification than other machine learning algorithm for predicting the COVID 19 possibilities also it diminishes the error rate of prediction accuracy.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126637331","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}
The IEEE 802.16 specification defines WiMAX, a wireless broadband data transmission technology. It enables high-speed data transmission over a wide range and remains inexpensive. This is a point-to-multipoint wireless network technology that can also be used in other network applications such as wireless sensor networks. In this article, we will use MATLAB Simulink to analyze the MAC tier model on WiMAX. This MAC tier model can be used to evaluate WiMAX performance in multiple scenarios such as high data rates, modulation schemes, and channel conditions. The proposed simulation model has reduced simulation time and performance. In this analysis, various modulation techniques such as QPSK and QAM were used on the AWGN channel and the simulation results were compared by SNR and BER.
{"title":"An Empirical Study and Simulation Analysis of the MAC Layer Model Using the AWGN Channel on WiMAX Technology","authors":"Mukesh Patidar, Garima Bhardwaj, Ankit Jain, Bhasker Pant, Deepak Kumar Ray, Sandeep Sharma","doi":"10.1109/ICTACS56270.2022.9988033","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988033","url":null,"abstract":"The IEEE 802.16 specification defines WiMAX, a wireless broadband data transmission technology. It enables high-speed data transmission over a wide range and remains inexpensive. This is a point-to-multipoint wireless network technology that can also be used in other network applications such as wireless sensor networks. In this article, we will use MATLAB Simulink to analyze the MAC tier model on WiMAX. This MAC tier model can be used to evaluate WiMAX performance in multiple scenarios such as high data rates, modulation schemes, and channel conditions. The proposed simulation model has reduced simulation time and performance. In this analysis, various modulation techniques such as QPSK and QAM were used on the AWGN channel and the simulation results were compared by SNR and BER.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126240517","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987856
V. J, L. Hema
The hybrid Renewable Power Generation System (RPG) has been developed in recent years because of its unique properties during the power extraction process, such as cleanness, noiselessness, and environmentally friendly nature. The micro grid idea presents the decrease of various transformations inside an individual A.C. or D.C. network. It encourages the association of sustainable variable A.C. and D.C. sources and loads in power frameworks. Here P.V. systems, wind turbine generators, and batteries are used for power management strategies are employed in this system. For the energy stabilization of the RPGS, the DC-DC converters are commonly used in a wide range of applications, and they offer significant benefits when used appropriately. The proposed design technique describes the circuit analysis of a Dual Active Bridge (DAC) converter that generates common waveforms based on the circuit behavior. The evaluation of a dual active bridge converter interacting with a Renewable Energy System (RES) tracked by a maximum power point tracking technique is then used to develop a RES system for the stable power generation of the proposed DAC. The proposed Intrinsic Power Transformation Algorithm (IPTA) method stabilizes on three-time scales. The first two sub-gate levels are executed. Finally, the system level is the executed power variation. Through these two facilitated control techniques, vacillations in power utilization are steady for the most part. On the third time scale, the steady-state error of the varying load system is analyzed and optimized using the IPTA. The proposed IPTA control scheme is verified through steady-state error (%), Total Harmonics Distortions (THD) %, and efficiency (%) of the system.
{"title":"A Novel Intrinsic Power Transformation Algorithm Employed in Dual Active Bridge Converter for the Performance Improvement in Hybrid Microgrid","authors":"V. J, L. Hema","doi":"10.1109/ICTACS56270.2022.9987856","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987856","url":null,"abstract":"The hybrid Renewable Power Generation System (RPG) has been developed in recent years because of its unique properties during the power extraction process, such as cleanness, noiselessness, and environmentally friendly nature. The micro grid idea presents the decrease of various transformations inside an individual A.C. or D.C. network. It encourages the association of sustainable variable A.C. and D.C. sources and loads in power frameworks. Here P.V. systems, wind turbine generators, and batteries are used for power management strategies are employed in this system. For the energy stabilization of the RPGS, the DC-DC converters are commonly used in a wide range of applications, and they offer significant benefits when used appropriately. The proposed design technique describes the circuit analysis of a Dual Active Bridge (DAC) converter that generates common waveforms based on the circuit behavior. The evaluation of a dual active bridge converter interacting with a Renewable Energy System (RES) tracked by a maximum power point tracking technique is then used to develop a RES system for the stable power generation of the proposed DAC. The proposed Intrinsic Power Transformation Algorithm (IPTA) method stabilizes on three-time scales. The first two sub-gate levels are executed. Finally, the system level is the executed power variation. Through these two facilitated control techniques, vacillations in power utilization are steady for the most part. On the third time scale, the steady-state error of the varying load system is analyzed and optimized using the IPTA. The proposed IPTA control scheme is verified through steady-state error (%), Total Harmonics Distortions (THD) %, and efficiency (%) of the system.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126095330","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988055
K. Vandhana, P. Sriramya
The objective of this research is to enhance security and storage for data stored in the public cloud using novel cube based obfuscation and steganography compared with side channel attack with minimal cost. The two groups that are considered are Secure Channel Attack and Novel Cube Based Obfuscation methods. This proposed approach makes the data in a non-understandable format so no attacker can identify the image and cannot extract steganographic images and also occupies less storage space. The sample size considered for implementing this work was N=20 for each of the groups considered. The sample size calculation was done using clinical. The pretest analysis was kept 80%. Based on the statistical analysis the significance value for calculating image size was found to be 0.95. In this research it is observed that cube based obfuscation seems to be secure and occupies less storage space than secure channel attack with 2861 bytes image size. Based on the experimental and statistical results achieved it is concluded that Novel Cube based obfuscation (CBO) seems to be secure and takes less storage space than secure channel attack (SCA).
{"title":"Security Enhancement and Efficient Storage Enhancement in Public Cloud using Novel Cube Based Obfuscation and Steganography Comparing with SCA with Minimal Cost","authors":"K. Vandhana, P. Sriramya","doi":"10.1109/ICTACS56270.2022.9988055","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988055","url":null,"abstract":"The objective of this research is to enhance security and storage for data stored in the public cloud using novel cube based obfuscation and steganography compared with side channel attack with minimal cost. The two groups that are considered are Secure Channel Attack and Novel Cube Based Obfuscation methods. This proposed approach makes the data in a non-understandable format so no attacker can identify the image and cannot extract steganographic images and also occupies less storage space. The sample size considered for implementing this work was N=20 for each of the groups considered. The sample size calculation was done using clinical. The pretest analysis was kept 80%. Based on the statistical analysis the significance value for calculating image size was found to be 0.95. In this research it is observed that cube based obfuscation seems to be secure and occupies less storage space than secure channel attack with 2861 bytes image size. Based on the experimental and statistical results achieved it is concluded that Novel Cube based obfuscation (CBO) seems to be secure and takes less storage space than secure channel attack (SCA).","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131479692","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}
Covid is a respiratory disease that ultimately results in death. It is of the utmost importance to determine whether or not a person has covid. Since it first appeared in December 2019, the COVID-19 pandemic has been a problem all across the world. For individuals who may have COVID-19, getting a timely and accurate diagnosis is absolutely necessary in order to receive medical treatment. In order to put a stop to the COVID-19 epidemic, chest X-rays will need to be capable of making an independent diagnosis of the virus using Machine Learning. This study provides evidence that the use of ensemble deep transfer learning for the early diagnosis of COVID-19 patients is both effective and efficient. If you follow these instructions, you will be able to report suspected COVID-19 activity and receive responses as they become available. With the help of medical sensors and the deep ensemble model of a cloud server, chest X-ray modalities can identify the presence of an infection. The authors of this study educated a Convolutional Neural Network system to reliably predict Covid-19 by using chest X-ray images as their training data. The researchers were the ones who developed the CNN algorithm. During the model's creation and training, they encountered difficulties, which they addressed and developed solutions for.
{"title":"Covid-19 Disease Detection using Chest X-Ray Images by Means of CNN","authors":"Ajay Reddy Yeruva, Pragati Choudhari, Anurag Shrivastava, Devvret Verma, Sanchita Shaw, A. Rana","doi":"10.1109/ICTACS56270.2022.9988148","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988148","url":null,"abstract":"Covid is a respiratory disease that ultimately results in death. It is of the utmost importance to determine whether or not a person has covid. Since it first appeared in December 2019, the COVID-19 pandemic has been a problem all across the world. For individuals who may have COVID-19, getting a timely and accurate diagnosis is absolutely necessary in order to receive medical treatment. In order to put a stop to the COVID-19 epidemic, chest X-rays will need to be capable of making an independent diagnosis of the virus using Machine Learning. This study provides evidence that the use of ensemble deep transfer learning for the early diagnosis of COVID-19 patients is both effective and efficient. If you follow these instructions, you will be able to report suspected COVID-19 activity and receive responses as they become available. With the help of medical sensors and the deep ensemble model of a cloud server, chest X-ray modalities can identify the presence of an infection. The authors of this study educated a Convolutional Neural Network system to reliably predict Covid-19 by using chest X-ray images as their training data. The researchers were the ones who developed the CNN algorithm. During the model's creation and training, they encountered difficulties, which they addressed and developed solutions for.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127729392","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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