Pub Date : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988553
T. Aruna, P. Naresh, A. Rajeshwari, M. Hussan, K. G. Guptha
The sheer quantity of big information has created tremendous prospects for forecasts and study. Infographic is a normal sight in everyday life. Several trend lines explain the pragmatic approach to weather assessment using interactive media. Because it was Previously unable to evaluate huge data, visual analytic tools have made plotting the system quality. For a good knowledge of the conditions, maps are used. Graphing trends like The precipitation of India have been presented with the maximum, minimum, and medium precipitation in the U.s Districts. The precipitation trend in India's provinces and territories was correctly illustrated in this article. The recurring sequence highlights extremely dry areas.
{"title":"Visualization and Prediction of Rainfall Using Deep Learning and Machine Learning Techniques","authors":"T. Aruna, P. Naresh, A. Rajeshwari, M. Hussan, K. G. Guptha","doi":"10.1109/ICTACS56270.2022.9988553","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988553","url":null,"abstract":"The sheer quantity of big information has created tremendous prospects for forecasts and study. Infographic is a normal sight in everyday life. Several trend lines explain the pragmatic approach to weather assessment using interactive media. Because it was Previously unable to evaluate huge data, visual analytic tools have made plotting the system quality. For a good knowledge of the conditions, maps are used. Graphing trends like The precipitation of India have been presented with the maximum, minimum, and medium precipitation in the U.s Districts. The precipitation trend in India's provinces and territories was correctly illustrated in this article. The recurring sequence highlights extremely dry areas.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"10 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":"126426533","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/ICTACS56270.2022.9988462
Apeksha Koul, Yogesh Kumar, Anish Gupta
Bladder cancer is currently the most frequent and worst cancer in the United States. Over the last several decades, bladder cancer detection and therapy breakthroughs have significantly reduced its mortality. Cystoscopy treatment has been considered useful for detecting and treating bladder cancer (BCa), but it is also prone to certain complications. Hence, this study has explored numerous research methodologies for identifying and diagnosing bladder cancer using AI techniques such as machine learning and deep learning models. The paper also emphasizes the accomplishments and challenges of researchers in this field. The assessment of the various techniques has also been compared to draw some conclusions.
{"title":"A Study on Bladder Cancer Detection using AI-based Learning Techniques","authors":"Apeksha Koul, Yogesh Kumar, Anish Gupta","doi":"10.1109/ICTACS56270.2022.9988462","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988462","url":null,"abstract":"Bladder cancer is currently the most frequent and worst cancer in the United States. Over the last several decades, bladder cancer detection and therapy breakthroughs have significantly reduced its mortality. Cystoscopy treatment has been considered useful for detecting and treating bladder cancer (BCa), but it is also prone to certain complications. Hence, this study has explored numerous research methodologies for identifying and diagnosing bladder cancer using AI techniques such as machine learning and deep learning models. The paper also emphasizes the accomplishments and challenges of researchers in this field. The assessment of the various techniques has also been compared to draw some conclusions.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"21 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":"127223099","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/ICTACS56270.2022.9987971
H. S, S. Raman, Pitty Sanjay, S. Latha, P. Muthu, S. Dhanalakshmi
On comparing diseases that cause major mortality, skin lesions are frequently considered of as minor players in the worldwide league of illness. Melanoma and Melanocytic nevus are skin cancers that have a high fatality rate. In the early stages of skin lesions, accurate classification can help doctors save a patient's life. Even when dermatologists utilize photos to diagnose, specialists' correct diagnosis rates are believed to be 75–84 percent. The purpose of this study is to use machine learning to pre-classify skin lesions as Melanoma or Melanocytic nevus, and to build a decision support system to assist doctors and differential diagnosticians in making better decisions.
{"title":"Skin Lesion Classification using Machine Learning Algorithm for Differential Diagnosis","authors":"H. S, S. Raman, Pitty Sanjay, S. Latha, P. Muthu, S. Dhanalakshmi","doi":"10.1109/ICTACS56270.2022.9987971","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987971","url":null,"abstract":"On comparing diseases that cause major mortality, skin lesions are frequently considered of as minor players in the worldwide league of illness. Melanoma and Melanocytic nevus are skin cancers that have a high fatality rate. In the early stages of skin lesions, accurate classification can help doctors save a patient's life. Even when dermatologists utilize photos to diagnose, specialists' correct diagnosis rates are believed to be 75–84 percent. The purpose of this study is to use machine learning to pre-classify skin lesions as Melanoma or Melanocytic nevus, and to build a decision support system to assist doctors and differential diagnosticians in making better decisions.","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":"125952776","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/ICTACS56270.2022.9988059
Preetkamal Singh, J. Kaur
Since the development of sensing technology, Wireless Sensor Networks (WSNs) have been playing an amazing role in monitoring and early detection of the target applications be it forest fire detection, agricultural, industrial, flood detection, etc. The limited battery of sensor nodes restricts the potential of WSNs for covering various applications. The routing methods can save the energy of sensor nodes meant for various applications. In this paper, we present a review of various routing techniques that are meant for various applications namely, detection of forest fire, landslide, flood, continuous monitoring of the environment, agricultural, underwater, intelligent transportation, etc. To our best knowledge, this is the first-ever review that considers the routing method pertaining to the diversified application of WSN. We focus on the cluster-based routing methods due to their various advantages namely, scalability, energy-preservation, load balancing, etc.
{"title":"A Review of Routing Techniques for Different Applications in Wireless Sensor Network","authors":"Preetkamal Singh, J. Kaur","doi":"10.1109/ICTACS56270.2022.9988059","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988059","url":null,"abstract":"Since the development of sensing technology, Wireless Sensor Networks (WSNs) have been playing an amazing role in monitoring and early detection of the target applications be it forest fire detection, agricultural, industrial, flood detection, etc. The limited battery of sensor nodes restricts the potential of WSNs for covering various applications. The routing methods can save the energy of sensor nodes meant for various applications. In this paper, we present a review of various routing techniques that are meant for various applications namely, detection of forest fire, landslide, flood, continuous monitoring of the environment, agricultural, underwater, intelligent transportation, etc. To our best knowledge, this is the first-ever review that considers the routing method pertaining to the diversified application of WSN. We focus on the cluster-based routing methods due to their various advantages namely, scalability, energy-preservation, load balancing, etc.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"15 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":"124067887","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/ICTACS56270.2022.9988238
Ira Gaba, B. Ramamurthy
This Identification and prediction of the tea quality is the essential research focus nowadays in the field of agriculture. Nowadays the Artificial Intelligence has become the latest topic in the region of pattern recognition. The various combination and permutation of the different techniques has resulted in proper solving the problem as well as have better accuracy in recognition. Therefore, there is urge need of a detailed survey AI techniques used for the identification of the tea leaf quality for the different grades of tea plants. In this paper, we aim on the various methods used for the pre- processing of the input image to extract the processed image which will further be useful for the feature extraction and the classification of the proposed image. It is very important to get the effective and accurate processed data which will further act as an input for the next level modules. This paper shows various methods of edge detection are applied on the image like Canny, Sobel and Laplacian are used. The further results are compared for quality metrics parameters such as the Mean Square Error (MSE) & Structural Similarity Index Metric (SSIM). The main agenda of this paper is to perform the edge detection and to check the quality measure of the processed image. The software used here is python.
{"title":"Image Pre-Processing Algorithms for the Quality Detection of Tea Leaves","authors":"Ira Gaba, B. Ramamurthy","doi":"10.1109/ICTACS56270.2022.9988238","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988238","url":null,"abstract":"This Identification and prediction of the tea quality is the essential research focus nowadays in the field of agriculture. Nowadays the Artificial Intelligence has become the latest topic in the region of pattern recognition. The various combination and permutation of the different techniques has resulted in proper solving the problem as well as have better accuracy in recognition. Therefore, there is urge need of a detailed survey AI techniques used for the identification of the tea leaf quality for the different grades of tea plants. In this paper, we aim on the various methods used for the pre- processing of the input image to extract the processed image which will further be useful for the feature extraction and the classification of the proposed image. It is very important to get the effective and accurate processed data which will further act as an input for the next level modules. This paper shows various methods of edge detection are applied on the image like Canny, Sobel and Laplacian are used. The further results are compared for quality metrics parameters such as the Mean Square Error (MSE) & Structural Similarity Index Metric (SSIM). The main agenda of this paper is to perform the edge detection and to check the quality measure of the processed image. The software used here is python.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"66 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":"124535649","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/ICTACS56270.2022.9988330
Shubhajit Panda, Mahesh Jangid, Ashish Jain
With the advent of AI and Machine learning based learning, the overall process of cancer diagnosis became much smoother and faster through automated techniques. Because of the presence of artefacts that cause color changes in H&E stained histopathology images, color normalization is an important pre-processing step for cancer identification. However, the existing color normalization methods suffers from two major issues: Loss of information that leads to poor background luminance and huge computational complexity. To address this issue, we developed a modified Reinhard approach for color normalizing on the CRC dataset in order to improve the background luminance of H&E stained colorectal cancer histopathology photographs. Our proposed algorithm not only mitigate the limitations of the previous reinhard method but statistically satisfy all four hypothesis of the color normalization by incorporating a global feature along with local one. Our algorithm's performance was also compared to that of other current color normalization algorithms, and it was shown to be superior in both quantitative and qualitative terms.
{"title":"Enhancing Background Luminance for Colorectal Cancer H and E Stained Images using Modified Reinhard Technique","authors":"Shubhajit Panda, Mahesh Jangid, Ashish Jain","doi":"10.1109/ICTACS56270.2022.9988330","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988330","url":null,"abstract":"With the advent of AI and Machine learning based learning, the overall process of cancer diagnosis became much smoother and faster through automated techniques. Because of the presence of artefacts that cause color changes in H&E stained histopathology images, color normalization is an important pre-processing step for cancer identification. However, the existing color normalization methods suffers from two major issues: Loss of information that leads to poor background luminance and huge computational complexity. To address this issue, we developed a modified Reinhard approach for color normalizing on the CRC dataset in order to improve the background luminance of H&E stained colorectal cancer histopathology photographs. Our proposed algorithm not only mitigate the limitations of the previous reinhard method but statistically satisfy all four hypothesis of the color normalization by incorporating a global feature along with local one. Our algorithm's performance was also compared to that of other current color normalization algorithms, and it was shown to be superior in both quantitative and qualitative terms.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"17 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":"127863655","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/ICTACS56270.2022.9988652
Sandeep Kumar, Keerthi Gudiseva, Aalla Iswarya, S. Rani, K. Prasad, Yogesh Kumar Sharma
Musicians or artists build on what has been generated utilizing the system and bring their original work. Music composition is an exciting topic that helps us to realize the composer's creativity. With the rapid improvement of the era, the form of music has ended up extra various and unfolds faster. The cost of making music, on the other hand, remains very high. Deep learning should really be capable of producing music that sounds like it was made by a person if it has sufficient data and the right algorithm. The purpose of this research is to set up a track-based and machine-learning-based device that can automatically put together songs. The device is composed of a set of piano MIDI records from the MAESTRO dataset that are used to build song segments. Fully connected and convolutional layers take advantage of the rich features in the frequency area to improve the quality of the music that is made.
{"title":"Automatic Music Generation System based on RNN Architecture","authors":"Sandeep Kumar, Keerthi Gudiseva, Aalla Iswarya, S. Rani, K. Prasad, Yogesh Kumar Sharma","doi":"10.1109/ICTACS56270.2022.9988652","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988652","url":null,"abstract":"Musicians or artists build on what has been generated utilizing the system and bring their original work. Music composition is an exciting topic that helps us to realize the composer's creativity. With the rapid improvement of the era, the form of music has ended up extra various and unfolds faster. The cost of making music, on the other hand, remains very high. Deep learning should really be capable of producing music that sounds like it was made by a person if it has sufficient data and the right algorithm. The purpose of this research is to set up a track-based and machine-learning-based device that can automatically put together songs. The device is composed of a set of piano MIDI records from the MAESTRO dataset that are used to build song segments. Fully connected and convolutional layers take advantage of the rich features in the frequency area to improve the quality of the music that is made.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"1 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":"128764469","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}
Numerous statistical methods, including “machine learning”, “predictive modeling” and “data mining” are included in predictive analysis.. One of the most intriguing and fascinating recent developments in artificial intelligence is machine learning. With the rise in technology the numbers of algorithms are also increasing for training models and based on the dataset the algorithms are being selected for training a good model with higher accuracy. In this paper I have used a stars dataset imported from Kaggle for predicting the spectral classes of the stars M and O based on the temperature, and have used regression algorithms for predicting it, since it contains continuous real values and regression algorithms work best for this type of cases for predictions and outputs with higher accuracy. By implementing the algorithms, I found that Random Forest Regressor works best with a higher R2_score.
{"title":"Predicting preeminent Machine Learning Approach on Stars","authors":"Soumobrata Manna, Vikas Jalodia, K. Kumar, Vikas Tripathi, Smita Sharma, Deepika Arora","doi":"10.1109/ICTACS56270.2022.9988044","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988044","url":null,"abstract":"Numerous statistical methods, including “machine learning”, “predictive modeling” and “data mining” are included in predictive analysis.. One of the most intriguing and fascinating recent developments in artificial intelligence is machine learning. With the rise in technology the numbers of algorithms are also increasing for training models and based on the dataset the algorithms are being selected for training a good model with higher accuracy. In this paper I have used a stars dataset imported from Kaggle for predicting the spectral classes of the stars M and O based on the temperature, and have used regression algorithms for predicting it, since it contains continuous real values and regression algorithms work best for this type of cases for predictions and outputs with higher accuracy. By implementing the algorithms, I found that Random Forest Regressor works best with a higher R2_score.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"7 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":"115223498","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/ICTACS56270.2022.9987881
Ajay Reddy Yeruva, C. S. L Vijaya Durga, Gokulavasan B, Kumud Pant, Prateek Chaturvedi, A. Srivastava
Access to adequate medical care can be difficult in many countries, particularly in economically developing countries. There aren't enough medical professionals, such as doctors or nurses, and the nearest hospitals are too far away. Because there is such a severe shortage of resources, it is extremely challenging to provide preventative therapy to persons who are ill. As a direct consequence of this, even persons in good health are falling more behind in their surveillance of their fitness. It is crucial to have a plan in place to address this problem in order to guarantee that persons will not experience a disruption in their capacity to get necessary medical care in the event that this problem arises. Applications of the Internet of Things include ensuring public safety and improving operational efficiencies in transportation, municipal management, manufacturing, and physical activity (IoT). This study investigates its application in medical equipment and suggests an innovative approach to combining the ideas of fog computing and the Internet of Things. A poor health care system that focuses on clinics can be converted into a high-quality system that puts patients at the center with the help of the framework that has been proposed.
{"title":"A Smart Healthcare Monitoring System Based on Fog Computing Architecture","authors":"Ajay Reddy Yeruva, C. S. L Vijaya Durga, Gokulavasan B, Kumud Pant, Prateek Chaturvedi, A. Srivastava","doi":"10.1109/ICTACS56270.2022.9987881","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987881","url":null,"abstract":"Access to adequate medical care can be difficult in many countries, particularly in economically developing countries. There aren't enough medical professionals, such as doctors or nurses, and the nearest hospitals are too far away. Because there is such a severe shortage of resources, it is extremely challenging to provide preventative therapy to persons who are ill. As a direct consequence of this, even persons in good health are falling more behind in their surveillance of their fitness. It is crucial to have a plan in place to address this problem in order to guarantee that persons will not experience a disruption in their capacity to get necessary medical care in the event that this problem arises. Applications of the Internet of Things include ensuring public safety and improving operational efficiencies in transportation, municipal management, manufacturing, and physical activity (IoT). This study investigates its application in medical equipment and suggests an innovative approach to combining the ideas of fog computing and the Internet of Things. A poor health care system that focuses on clinics can be converted into a high-quality system that puts patients at the center with the help of the framework that has been proposed.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"39 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":"123358837","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/ICTACS56270.2022.9988299
Chitranjan Prasad Sah
By the means of asymptotic security of cryptographic security mechanism we can get knowledge about efficiency and tolerable features against various type of attacks compromised on it. Analytical study about how zero-knowledge proofs can be used with Diffie Hellman problem (DHP) are presented in this research. One of the better algorithms of discrete logarithm problem which is suggested by Henry for zero knowledge proofs is suitable for DHP problem for the robustness analysis of it. The efficiency of discrete logarithm algorithm for DHP problem and integer factorization problem are analyzed and made comparison between them and covariance and correlation between their asymptotic functions is obtained as final result which clearly give us idea about strong relationship between each other and correlation factor between them is high, so they are similar in nature.
{"title":"Robustness Analysis of Zero Knowledge Proofs using Diffie Hellman Problem","authors":"Chitranjan Prasad Sah","doi":"10.1109/ICTACS56270.2022.9988299","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988299","url":null,"abstract":"By the means of asymptotic security of cryptographic security mechanism we can get knowledge about efficiency and tolerable features against various type of attacks compromised on it. Analytical study about how zero-knowledge proofs can be used with Diffie Hellman problem (DHP) are presented in this research. One of the better algorithms of discrete logarithm problem which is suggested by Henry for zero knowledge proofs is suitable for DHP problem for the robustness analysis of it. The efficiency of discrete logarithm algorithm for DHP problem and integer factorization problem are analyzed and made comparison between them and covariance and correlation between their asymptotic functions is obtained as final result which clearly give us idea about strong relationship between each other and correlation factor between them is high, so they are similar in nature.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"21 7 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":"125778464","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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