Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315778
Rakesh Ahuja, Manish Sharma, Mohd. Junedul Haque
The paper described a new MPEG-2 compressed domain based imperceptible, blind, robust, and secure digital video watermarking method. The scheme exploited the intra coding part of MPEG compression algorithm for inserting the watermark image as copyright information. Three cryptographic keys are used to extract the watermark. This process further enhances the security of overall watermarking algorithm. Therefore, proper extraction would never possible without negotiating the unique keys. The strength of the proposed algorithm evaluated the robustness by applying wide variety of frame-based attacks to measure grading of resemblance and divergence between the extracted and original watermark respectively. The dominance of the projected technique is to obtain the admirable result for robustness and perceptibility both. The perceptibility is maintained as the scheme doesn't require to change the motion vectors resultant from DPCM process while encoding the video through MPEG-2 compression standard.
{"title":"A Compressed domain Based Robust and Imperceptible Digital Video Watermarking Scheme","authors":"Rakesh Ahuja, Manish Sharma, Mohd. Junedul Haque","doi":"10.1109/PDGC50313.2020.9315778","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315778","url":null,"abstract":"The paper described a new MPEG-2 compressed domain based imperceptible, blind, robust, and secure digital video watermarking method. The scheme exploited the intra coding part of MPEG compression algorithm for inserting the watermark image as copyright information. Three cryptographic keys are used to extract the watermark. This process further enhances the security of overall watermarking algorithm. Therefore, proper extraction would never possible without negotiating the unique keys. The strength of the proposed algorithm evaluated the robustness by applying wide variety of frame-based attacks to measure grading of resemblance and divergence between the extracted and original watermark respectively. The dominance of the projected technique is to obtain the admirable result for robustness and perceptibility both. The perceptibility is maintained as the scheme doesn't require to change the motion vectors resultant from DPCM process while encoding the video through MPEG-2 compression standard.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128932660","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 : 2020-11-06DOI: 10.1109/pdgc50313.2020.9315790
H. Saini, Chief Guest
It is our proud privilege to extend a sincere welcome to our today's Honorable Chief Guest, Professor Shayam sundar Pattnaik, Director, National Institute of Technical Teachers and Research, Chandigarh along with our Honorable Vice Chancellor Professor Vinod Kumar Vice Chancellor, JUIT, waknaghat, Professor Samir Dev Gupta, Dean (Academic) & HoD-CSE & IT, JUIT, waknaghat and Maj Gen Rakesh Bassi (Retd.), Registrar and Dean of Students, JUIT, waknaghat on the behalf of the Organizing Committee of PDGC-2020.
我们非常荣幸地向我们今天的首席嘉宾，昌迪加尔国家技术教师研究所所长Shayam sundar Pattnaik教授，以及我们尊敬的副校长Vinod Kumar教授，印度理工大学瓦格纳哈特分校副校长，印度理工大学瓦格纳哈特分校教务长兼学生主任Samir Dev Gupta教授，印度理工大学瓦格纳哈特分校教务长兼学生主任Rakesh Bassi少将(已退休)表示诚挚的欢迎。谨代表PDGC-2020组委会
{"title":"Welcome Message","authors":"H. Saini, Chief Guest","doi":"10.1109/pdgc50313.2020.9315790","DOIUrl":"https://doi.org/10.1109/pdgc50313.2020.9315790","url":null,"abstract":"It is our proud privilege to extend a sincere welcome to our today's Honorable Chief Guest, Professor Shayam sundar Pattnaik, Director, National Institute of Technical Teachers and Research, Chandigarh along with our Honorable Vice Chancellor Professor Vinod Kumar Vice Chancellor, JUIT, waknaghat, Professor Samir Dev Gupta, Dean (Academic) & HoD-CSE & IT, JUIT, waknaghat and Maj Gen Rakesh Bassi (Retd.), Registrar and Dean of Students, JUIT, waknaghat on the behalf of the Organizing Committee of PDGC-2020.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133157615","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315800
G. Bathla
Stock price movement is non-linear and complex. Several research works have been carried out to predict stock prices. Traditional approaches such as Linear Regression and Support Vector Regression were used but accuracy was not adequate. Researchers have tried to improve stock price prediction using ARIMA. Due to very high variations in stock prices, deep learning techniques are applied due to its proven accuracy in various analytics fields. Artificial Neural Network was deployed to predict stock prices but as stock prices are time-series based, recurrent neural network was applied to further improve prediction accuracy. In RNN, there is limitation of not able to store high dependencies and also vanishing gradient descent issue exists. Therefore, data scientists and analysts applied LSTM to predict stock price movement. In this paper, LSTM is compared with SVR using various stock index data such as S& P 500, NYSE, NSE, BSE, NASDAQ and Dow Jones industrial Average for experiment analysis. Experiment analysis proves that LSTM provides better accuracy as compared to SVR.
{"title":"Stock Price prediction using LSTM and SVR","authors":"G. Bathla","doi":"10.1109/PDGC50313.2020.9315800","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315800","url":null,"abstract":"Stock price movement is non-linear and complex. Several research works have been carried out to predict stock prices. Traditional approaches such as Linear Regression and Support Vector Regression were used but accuracy was not adequate. Researchers have tried to improve stock price prediction using ARIMA. Due to very high variations in stock prices, deep learning techniques are applied due to its proven accuracy in various analytics fields. Artificial Neural Network was deployed to predict stock prices but as stock prices are time-series based, recurrent neural network was applied to further improve prediction accuracy. In RNN, there is limitation of not able to store high dependencies and also vanishing gradient descent issue exists. Therefore, data scientists and analysts applied LSTM to predict stock price movement. In this paper, LSTM is compared with SVR using various stock index data such as S& P 500, NYSE, NSE, BSE, NASDAQ and Dow Jones industrial Average for experiment analysis. Experiment analysis proves that LSTM provides better accuracy as compared to SVR.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127818677","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315741
G. C. Sampada, T. I. Sake, Megha Chhabra
The requirement engineering stage is a significant stage during the development of the software. All the eventual stages in the development of the software are resolved by this stage. If this phase is dominated, then the software may not be developed as per the expectation of the client. The automation in requirement engineering provides a peril for the developers to amend the activities during the process. This paper reviews different approaches staged by the researchers to automate the requirement elicitation process of the software development cycle.
{"title":"A Review on Advanced Techniques of Requirement Elicitation and Specification in Software Development Stages","authors":"G. C. Sampada, T. I. Sake, Megha Chhabra","doi":"10.1109/PDGC50313.2020.9315741","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315741","url":null,"abstract":"The requirement engineering stage is a significant stage during the development of the software. All the eventual stages in the development of the software are resolved by this stage. If this phase is dominated, then the software may not be developed as per the expectation of the client. The automation in requirement engineering provides a peril for the developers to amend the activities during the process. This paper reviews different approaches staged by the researchers to automate the requirement elicitation process of the software development cycle.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133803274","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315750
Deepak Mehta, S. Saxena
The applications of Wireless Sensor Networks have been envisioned in numerous spheres of life in modern time. Wireless sensors enabled IoT based applications are changing the way modern life is being lived with applications into battle field surveillance, habitat monitoring, structural health, monitoring of vital parameters of human body and many more. Major constraint with these resource constrained networks is energy depletion during transmission of information with energy depletion increasing as the distance to which data is communicated increases. Energy efficient routing protocols have proved to be a formidable mechanism to save energy in WSNs. Moreover, hierarchical routing protocols are considered to be providing highest energy efficiency among all types of routing protocols. In recent times extensive research on nature inspired, swarm intelligence-based routing protocols has been observed. These optimization-based protocols not only have been established to be more energy efficient but have also performed better on several performance parameters including throughput, packet delivery ratio, delay and other QoS parameters, thus optimizing energy and other QoS based factors in a Wireless Sensor Network. This paper presents a taxonomy of approaches based on swarm intelligence and a analyzes the state-of-the-art swarm intelligence-based hierarchical routing protocols.
{"title":"Swarm Intelligence based Hierarchical Routing Protocols Study in WSNs","authors":"Deepak Mehta, S. Saxena","doi":"10.1109/PDGC50313.2020.9315750","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315750","url":null,"abstract":"The applications of Wireless Sensor Networks have been envisioned in numerous spheres of life in modern time. Wireless sensors enabled IoT based applications are changing the way modern life is being lived with applications into battle field surveillance, habitat monitoring, structural health, monitoring of vital parameters of human body and many more. Major constraint with these resource constrained networks is energy depletion during transmission of information with energy depletion increasing as the distance to which data is communicated increases. Energy efficient routing protocols have proved to be a formidable mechanism to save energy in WSNs. Moreover, hierarchical routing protocols are considered to be providing highest energy efficiency among all types of routing protocols. In recent times extensive research on nature inspired, swarm intelligence-based routing protocols has been observed. These optimization-based protocols not only have been established to be more energy efficient but have also performed better on several performance parameters including throughput, packet delivery ratio, delay and other QoS parameters, thus optimizing energy and other QoS based factors in a Wireless Sensor Network. This paper presents a taxonomy of approaches based on swarm intelligence and a analyzes the state-of-the-art swarm intelligence-based hierarchical routing protocols.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115627557","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315824
Punit Gupta, Navaditya Gaur, R. Tripathi, M. Goyal, Ankit Mundra
Iot plays a vital role in solving real time problems in the field of Healthcare. Abundant problems can be rectified with optimal use of IOT Healthcare. It can be applied to detect Diabetes at early stages, detection of Foot ulcers, anomaly in heart rate and similar scenario. The paper proposed the plan and its working in Healthcare using IOT to detect ulcer in the foot of diabetic patients. The given model examines the medical condition of ulcer cause by diabetic and notify in case of aberration. Node MCU development board plays a vital role in its model development and stores and tracks the medical report of the Patient. It also helps in real time sharing of large chunks of data with great efficiency. Indeed this model slack off maj or time consuming efforts like regular visits to doctor and provide real time update with regards to patient.
{"title":"IoT and Cloud Based Healthcare Solution for Diabetic Foot Ulcer","authors":"Punit Gupta, Navaditya Gaur, R. Tripathi, M. Goyal, Ankit Mundra","doi":"10.1109/PDGC50313.2020.9315824","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315824","url":null,"abstract":"Iot plays a vital role in solving real time problems in the field of Healthcare. Abundant problems can be rectified with optimal use of IOT Healthcare. It can be applied to detect Diabetes at early stages, detection of Foot ulcers, anomaly in heart rate and similar scenario. The paper proposed the plan and its working in Healthcare using IOT to detect ulcer in the foot of diabetic patients. The given model examines the medical condition of ulcer cause by diabetic and notify in case of aberration. Node MCU development board plays a vital role in its model development and stores and tracks the medical report of the Patient. It also helps in real time sharing of large chunks of data with great efficiency. Indeed this model slack off maj or time consuming efforts like regular visits to doctor and provide real time update with regards to patient.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117215061","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315774
Y. Pant, Ravindra Sharma
This work emphasizes to increase the network lifetime by using an appropriate data collection scheme and machine learning technique. The routing mechanism is one of the best approaches to decrease energy consumption and increase the lifetime of the network as well. We have used PSO with an updated scheme where we are selecting the random values to find best fitness value then the final route will be calculated. Genetic methods like mutation and crossover are implemented over the final routes to get alternate routes and then performance will be calculated. We have compared the lifetime and stability of network with existing protocols like Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information Systems (PEGASIS), and Ant Colony Routing (ACR). In this work, we have added active-sleep feature with our network to enhance the network lifetime and the machine learning technique is used to predict the data of the network in sleep state. MATLAB is used to validate our mathematical framework; we have performed analytical simulations by choosing the network area, the number of nodes in each cluster. The lifetime and stability period is analyzed and compared with other optimization methods.
{"title":"RPSO Optimization with machine learning in WSN","authors":"Y. Pant, Ravindra Sharma","doi":"10.1109/PDGC50313.2020.9315774","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315774","url":null,"abstract":"This work emphasizes to increase the network lifetime by using an appropriate data collection scheme and machine learning technique. The routing mechanism is one of the best approaches to decrease energy consumption and increase the lifetime of the network as well. We have used PSO with an updated scheme where we are selecting the random values to find best fitness value then the final route will be calculated. Genetic methods like mutation and crossover are implemented over the final routes to get alternate routes and then performance will be calculated. We have compared the lifetime and stability of network with existing protocols like Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information Systems (PEGASIS), and Ant Colony Routing (ACR). In this work, we have added active-sleep feature with our network to enhance the network lifetime and the machine learning technique is used to predict the data of the network in sleep state. MATLAB is used to validate our mathematical framework; we have performed analytical simulations by choosing the network area, the number of nodes in each cluster. The lifetime and stability period is analyzed and compared with other optimization methods.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125758252","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315767
Aina Mehta, Jasminder Kaur Sandhu, Luxmi Sapra
Wireless Sensor Networks consist of spatially dispersed autonomous sensor nodes which collect data from the environment and forward to the other gateway for processing. These network controls the dynamic environment that changes frequently with time. This effectual behavior is created or initialized by outward parameters such as temperature, sound, light, events. To adjust with such situations these networks follow Machine Learning techniques. In this paper, a review on the Machine Learning techniques that can be applied on these networks is presented. These networks are the most trending technologies for some real applications because of its features such as low-cost, tiny and mobility. Further, a relative guide to the network designers is suggested for developing appropriate Machine Learning solutions for requisite application.
{"title":"Machine Learning in Wireless Sensor Networks: A Retrospective","authors":"Aina Mehta, Jasminder Kaur Sandhu, Luxmi Sapra","doi":"10.1109/PDGC50313.2020.9315767","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315767","url":null,"abstract":"Wireless Sensor Networks consist of spatially dispersed autonomous sensor nodes which collect data from the environment and forward to the other gateway for processing. These network controls the dynamic environment that changes frequently with time. This effectual behavior is created or initialized by outward parameters such as temperature, sound, light, events. To adjust with such situations these networks follow Machine Learning techniques. In this paper, a review on the Machine Learning techniques that can be applied on these networks is presented. These networks are the most trending technologies for some real applications because of its features such as low-cost, tiny and mobility. Further, a relative guide to the network designers is suggested for developing appropriate Machine Learning solutions for requisite application.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"6 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132371039","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}
Multi-class Classification is often used for classification and categorization purposes under Machine Learning wherein vast datasets can be classified into multiple labels/classes. It is often perceived as more complex than binary classification and is still being explored and studied. The main objective of this paper is to perform a comparative study of Genetic Algorithm and Artificial Neural Network to identify the algorithm that enhances the accuracy of multi-class classification. The experimental results obtained in the comparative study are evaluated using our model developed for Type-2 Diabetes Individualistic Treatment Recommendation, which successfully implements multiclass classification of patients into 7 classes(Treatment Line). Presently, doctors prescribe drugs by using their knowledge and experience, but they require a faster and more efficient system to assist them in taking the final decision by providing a suitable suggestion about the treatment line. The dataset used by our model consists of 24 input attributes and 7 output class of 2430 individuals having different characteristics like hypertension etc to make it as diverse as possible. While comparing the benefits and drawbacks of these two algorithms on our model, we have considered factors such as accuracy, training, testing and complexity. Among the two types of classifier the ANN classifier leverages the performance of the system by giving the most accurate result and generating the prediction accuracy of 92%. Thus, based on the comparative study ANN classifier demonstrates better prediction results than evolutionary Genetic Algorithm.
{"title":"Comparative Study of Genetic Algorithm and Artificial Neural Network for Multi-class Classification based on Type-2 Diabetes Treatment Recommendation model","authors":"Siddhi Khanse, Payal Bhandari, Rumjhum Singru, Neha Runwal, Atharva Dharane","doi":"10.1109/PDGC50313.2020.9315837","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315837","url":null,"abstract":"Multi-class Classification is often used for classification and categorization purposes under Machine Learning wherein vast datasets can be classified into multiple labels/classes. It is often perceived as more complex than binary classification and is still being explored and studied. The main objective of this paper is to perform a comparative study of Genetic Algorithm and Artificial Neural Network to identify the algorithm that enhances the accuracy of multi-class classification. The experimental results obtained in the comparative study are evaluated using our model developed for Type-2 Diabetes Individualistic Treatment Recommendation, which successfully implements multiclass classification of patients into 7 classes(Treatment Line). Presently, doctors prescribe drugs by using their knowledge and experience, but they require a faster and more efficient system to assist them in taking the final decision by providing a suitable suggestion about the treatment line. The dataset used by our model consists of 24 input attributes and 7 output class of 2430 individuals having different characteristics like hypertension etc to make it as diverse as possible. While comparing the benefits and drawbacks of these two algorithms on our model, we have considered factors such as accuracy, training, testing and complexity. Among the two types of classifier the ANN classifier leverages the performance of the system by giving the most accurate result and generating the prediction accuracy of 92%. Thus, based on the comparative study ANN classifier demonstrates better prediction results than evolutionary Genetic Algorithm.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"19 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132496990","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315747
Savita, Ganga Sharma, Geeta Rani, Vijaypal Singh Dhaka
Cardiovascular disease is a major cause of death worldwide. The detection of these diseases at a premature phase is imperative to rescue the lives of people. Implying machine learning classification techniques into health care organization gives extraordinary results which assist health care professionals for immediate and accurate diagnosis of these diseases. Healthcare organizations generate a huge amount of data which is still not perfectly utilized by researchers. Machine learning techniques and tools help in extracting effective knowledge from datasets for more precise results. Exploring numerous combinations of algorithms and finding out efficient techniques from the recent research papers is the objective of this research. The novelty of our work is associated with uses of optimization algorithms over classification algorithms such as Genetic algorithm (GA), Naïve Bayes (NB), Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machine SVM), etc. used so far. Feature optimization techniques (Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) with machine learning techniques (K-Nearest Neighbor (KNN) and Random Forest (RF)) give maximum accuracy of 99.65% which is examined from the survey work. The future works can emphasize on developing an advanced model by integrating different optimization techniques using machine learning which could help the health care professionals in making felicitous decisions.
{"title":"A Review on Machine Learning Techniques for Prediction of Cardiovascular Diseases","authors":"Savita, Ganga Sharma, Geeta Rani, Vijaypal Singh Dhaka","doi":"10.1109/PDGC50313.2020.9315747","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315747","url":null,"abstract":"Cardiovascular disease is a major cause of death worldwide. The detection of these diseases at a premature phase is imperative to rescue the lives of people. Implying machine learning classification techniques into health care organization gives extraordinary results which assist health care professionals for immediate and accurate diagnosis of these diseases. Healthcare organizations generate a huge amount of data which is still not perfectly utilized by researchers. Machine learning techniques and tools help in extracting effective knowledge from datasets for more precise results. Exploring numerous combinations of algorithms and finding out efficient techniques from the recent research papers is the objective of this research. The novelty of our work is associated with uses of optimization algorithms over classification algorithms such as Genetic algorithm (GA), Naïve Bayes (NB), Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machine SVM), etc. used so far. Feature optimization techniques (Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) with machine learning techniques (K-Nearest Neighbor (KNN) and Random Forest (RF)) give maximum accuracy of 99.65% which is examined from the survey work. The future works can emphasize on developing an advanced model by integrating different optimization techniques using machine learning which could help the health care professionals in making felicitous decisions.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130230409","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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