Pub Date : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987786
T. V. Kumar, F. V. A. Raj, B. Gopinath, B. Suresh, S. Tamizharasi
Following moving articles alongside their development through video groupings are perhaps of the most essential and most vital undertaking in PC vision. This fills in as the establishment for various more significant level mechanized applications in various spaces, including observation, expanded reality and movement catch in moving item discovery. Object following is key component of an IVS framework which can additionally be demonstrated for some dubious movement identification frameworks. There are numerous approaches and proposed algorithms for object tracking, but the article proposed Scale Adaptive Kernel Support Correlation Filter Algorithm (SKSCF), which is the basis for the implementation of IVS in this paper. It also derives an equivalent formulation of an SVM model with the circulant matrix expression and presents an effective alternating optimization method for visual tracking. The proposed work characterized to meet following goals: to make a video grouping for moving item following; to plan an exploratory set ready for moving item discovery; and, to plan and carry out moving item following calculation, the proposed calculation was carried out on a caught video succession. Object was identified first as per the picture info, and afterward followed in ensuing casings. The exploratory execution could play out the article following without missing any edge and could effectively overlay bouncing box. It could effectively create a picture grouping after the total execution of Mean Shift Flowchart. The presentation of calculation was checked by effectively following the client characterized object at any climate and playing out the overlay capability in the recognized article.
通过视频分组跟踪移动文章的发展可能是PC视觉中最重要和最重要的工作。这填补了各种更重要的层次机械化应用在各个空间的建立，包括观察，扩展现实和移动物品发现中的运动捕捉。对象跟踪是IVS框架的关键组成部分，它还可以为一些可疑的运动识别框架进行演示。目标跟踪的方法和算法有很多，但本文提出了Scale Adaptive Kernel Support Correlation Filter Algorithm (SKSCF)，这是本文实现IVS的基础。推导了循环矩阵表示的支持向量机模型的等价表达式，提出了一种有效的视觉跟踪交替优化方法。提出的工作主要实现以下目标:制作一个移动项目跟随的视频分组;为移动项目发现计划一个探索集;为了规划和执行移动项跟随计算，对捕获的视频序列进行了所提出的计算。首先根据图片信息确定物体，然后在随后的弹壳中确定。探索性执行可以在不丢失任何边缘的情况下播放文章，并且可以有效地覆盖弹跳框。它可以在平均移位流程图的总执行后有效地创建一个图片分组。通过在任何气候下有效地跟踪客户特征对象并在识别的文章中发挥覆盖能力来检查计算的呈现。
{"title":"Real-time Visual Detection and Tracking is Implemented in a Clustered Environment using an Adaptive Kernel-Supported Correlation Filter Algorithm","authors":"T. V. Kumar, F. V. A. Raj, B. Gopinath, B. Suresh, S. Tamizharasi","doi":"10.1109/ICTACS56270.2022.9987786","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987786","url":null,"abstract":"Following moving articles alongside their development through video groupings are perhaps of the most essential and most vital undertaking in PC vision. This fills in as the establishment for various more significant level mechanized applications in various spaces, including observation, expanded reality and movement catch in moving item discovery. Object following is key component of an IVS framework which can additionally be demonstrated for some dubious movement identification frameworks. There are numerous approaches and proposed algorithms for object tracking, but the article proposed Scale Adaptive Kernel Support Correlation Filter Algorithm (SKSCF), which is the basis for the implementation of IVS in this paper. It also derives an equivalent formulation of an SVM model with the circulant matrix expression and presents an effective alternating optimization method for visual tracking. The proposed work characterized to meet following goals: to make a video grouping for moving item following; to plan an exploratory set ready for moving item discovery; and, to plan and carry out moving item following calculation, the proposed calculation was carried out on a caught video succession. Object was identified first as per the picture info, and afterward followed in ensuing casings. The exploratory execution could play out the article following without missing any edge and could effectively overlay bouncing box. It could effectively create a picture grouping after the total execution of Mean Shift Flowchart. The presentation of calculation was checked by effectively following the client characterized object at any climate and playing out the overlay capability in the recognized article.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"43 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":"132094434","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/ICTACS56270.2022.9988385
Neha Sharma, Deeksha Kumari
Coronavirus Disease 2019 is occurred as a challenging disease among the scientist worldwide. The disease is developed at an extensive level. Thus, the disease must be detected, reported, isolated, diagnosed and cured at initial phase for mitigating its growth rate. This research paper is conducted on the basisof predicting covid-19 ML algorithms. The methods of predicting this disease consist of diverse stages inwhich data is added as input, pre-processed, attributes are extracted and data is classified. This research work focuses on gathering the authentic dataset which get pre-processed for the classification. In the phase of feature extraction,PCA and k-mean algorithms are applied. The votingclassification method is applied in this work in which GNB, BNB, RF and Support Vector Machine algorithms are integrated. Python is executed to implement the introduced method. Diverse metrics are considered to analyze the outcomes. Using supervised machine learning, we create this model. The branch of ML focuses on implementing intelligent models so that various complicated issues can be tackled. The introduced method offers higher accuracy, precisionand recall in comparison with other classifiers.
{"title":"Voting Classification Approach for Covid-19 Prediction with K-mean and PCA","authors":"Neha Sharma, Deeksha Kumari","doi":"10.1109/ICTACS56270.2022.9988385","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988385","url":null,"abstract":"Coronavirus Disease 2019 is occurred as a challenging disease among the scientist worldwide. The disease is developed at an extensive level. Thus, the disease must be detected, reported, isolated, diagnosed and cured at initial phase for mitigating its growth rate. This research paper is conducted on the basisof predicting covid-19 ML algorithms. The methods of predicting this disease consist of diverse stages inwhich data is added as input, pre-processed, attributes are extracted and data is classified. This research work focuses on gathering the authentic dataset which get pre-processed for the classification. In the phase of feature extraction,PCA and k-mean algorithms are applied. The votingclassification method is applied in this work in which GNB, BNB, RF and Support Vector Machine algorithms are integrated. Python is executed to implement the introduced method. Diverse metrics are considered to analyze the outcomes. Using supervised machine learning, we create this model. The branch of ML focuses on implementing intelligent models so that various complicated issues can be tackled. The introduced method offers higher accuracy, precisionand recall in comparison with other classifiers.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"53 3 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":"124300512","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}
This paper presents a triple band antenna integration with a band-pass filter by optimizing the impedance at the interface between the two. A miniaturized antenna is designed at three different frequencies and integrated with a filter and at the output a single frequency of 2.4 GHz is achieved. The filtenna is made-up on FR-4 epoxy substrate and 4.4 is the dielectric constant, and having 1.6mm thickness. The proposed structure is having low cost, miniaturized in size and gives good filtering performance.
{"title":"Antenna-Filter Integration for Wireless Applications","authors":"Shilpam Saxena, Apurva Shrivastava, Sudhanshu Tripathi","doi":"10.1109/ICTACS56270.2022.9988430","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988430","url":null,"abstract":"This paper presents a triple band antenna integration with a band-pass filter by optimizing the impedance at the interface between the two. A miniaturized antenna is designed at three different frequencies and integrated with a filter and at the output a single frequency of 2.4 GHz is achieved. The filtenna is made-up on FR-4 epoxy substrate and 4.4 is the dielectric constant, and having 1.6mm thickness. The proposed structure is having low cost, miniaturized in size and gives good filtering performance.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"34 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":"124314970","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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