Pub Date : 2021-11-26DOI: 10.1109/iccica52458.2021.9697292
Sonali R. Chavan, S. Sherekar, V. Thakre
Face recognition is one of the most successful application & has recently gain popularity with significant attention. Extensive research has been done in recognising the identity of the user from their facial image. Security issue on face recognition systems persists as a primary concern. although there are so many detection methods have been proposed but still it has some drawbacks in terms of parameters performance, size of datasets and generalisation ability to detect unseen face attacks So it is a challenging task to the researchers to proposed a robust face detection technique. This paper adopted comprehensive presentation of proposed Anti-spoofing techniques followed by features, datasets, parameters. Paper also provides experimental view on extensive comparative analysis of parameters, classifiers and databases which will be use to protect from various types of Face Spoofing attacks and depicted the purely CNN based existing methodology with general Face Spoofing detection module.
{"title":"Factors Related To The Improvement of Face Anti-Spoofing Detection Techniques With CNN Classifier","authors":"Sonali R. Chavan, S. Sherekar, V. Thakre","doi":"10.1109/iccica52458.2021.9697292","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697292","url":null,"abstract":"Face recognition is one of the most successful application & has recently gain popularity with significant attention. Extensive research has been done in recognising the identity of the user from their facial image. Security issue on face recognition systems persists as a primary concern. although there are so many detection methods have been proposed but still it has some drawbacks in terms of parameters performance, size of datasets and generalisation ability to detect unseen face attacks So it is a challenging task to the researchers to proposed a robust face detection technique. This paper adopted comprehensive presentation of proposed Anti-spoofing techniques followed by features, datasets, parameters. Paper also provides experimental view on extensive comparative analysis of parameters, classifiers and databases which will be use to protect from various types of Face Spoofing attacks and depicted the purely CNN based existing methodology with general Face Spoofing detection module.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116895797","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697143
Gawande Apeksha R., Sherekar Swati S.
The intention of this entire survey is to evaluate the importance and impact of the articles which have been posted with the identify device gaining knowledge of-primarily based totally early detection of crop disorder or prediction of fungal illnesses on vegetation with the help of device gaining knowledge of and facts mining techniques at some stage in the duration 2016-2020. It likewise uncovers that the territory of plant disorder has gotten elevated and hobby with the aid of using researchers, studies investment institutions, and experts. The electronically available peer-review journal papers from Google Scholar, Web of Sciences, and papers available at Mendeley desktop application databases were reviewed. The following parameters were considered while reviewing the papers. 1. Which machine learning or data mining algorithmic approach was used? 2. Which performance metrics were used? 3. Which plant diseases data set was used? 4. How was the performance analysis carried out? 5. Whether the results were compared with some other techniques? The computer algorithms-based articles deal with the early detection of plant disease and were published between 2016 and 2020 were reviewed. From the top-refered to explore distributions relating to AI based expectation of plant infection, it is seen that mixture models were broadly used over a singular order model. A broadly utilized relapse model with SVM, variations of choice trees, and Naive Bayes models are having the best exhibition for early expectation of yield infections.
整个调查的目的是评估在2016-2020年期间的某个阶段，通过识别设备获取知识(主要基于作物病害的完全早期检测或植被真菌疾病的预测)发布的文章的重要性和影响，该设备获取知识和事实挖掘技术。它还揭示了在研究人员、研究投资机构和专家的帮助下，植物紊乱的领域得到了提升和重视。从Google Scholar, Web of Sciences和Mendeley桌面应用程序数据库中获得的电子同行评议期刊论文进行了审查。在审查论文时考虑了以下参数。1. 使用了哪种机器学习或数据挖掘算法?2. 使用了哪些性能指标?3.使用了哪个植物病害数据集?4. 性能分析是如何进行的?5. 结果是否与其他技术进行了比较?回顾了2016年至2020年间发表的基于计算机算法的植物病害早期检测文章。从顶部参考的探索与基于人工智能的植物感染预期相关的分布，可以看出混合模型被广泛使用于单阶模型。基于支持向量机的复发模型、选择树的变化和朴素贝叶斯模型在产量感染的早期预期中有最好的表现。
{"title":"A brief study on the prediction of crop disease using machine learning approaches","authors":"Gawande Apeksha R., Sherekar Swati S.","doi":"10.1109/iccica52458.2021.9697143","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697143","url":null,"abstract":"The intention of this entire survey is to evaluate the importance and impact of the articles which have been posted with the identify device gaining knowledge of-primarily based totally early detection of crop disorder or prediction of fungal illnesses on vegetation with the help of device gaining knowledge of and facts mining techniques at some stage in the duration 2016-2020. It likewise uncovers that the territory of plant disorder has gotten elevated and hobby with the aid of using researchers, studies investment institutions, and experts. The electronically available peer-review journal papers from Google Scholar, Web of Sciences, and papers available at Mendeley desktop application databases were reviewed. The following parameters were considered while reviewing the papers. 1. Which machine learning or data mining algorithmic approach was used? 2. Which performance metrics were used? 3. Which plant diseases data set was used? 4. How was the performance analysis carried out? 5. Whether the results were compared with some other techniques? The computer algorithms-based articles deal with the early detection of plant disease and were published between 2016 and 2020 were reviewed. From the top-refered to explore distributions relating to AI based expectation of plant infection, it is seen that mixture models were broadly used over a singular order model. A broadly utilized relapse model with SVM, variations of choice trees, and Naive Bayes models are having the best exhibition for early expectation of yield infections.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"799 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117042825","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697319
A. Raut, S. Khandait
Wireless sensor network (WSN) the unique and utmost encouraging tools for monitoring the real-time applications. It has been utilized in various areas particularly for offering real-time monitoring and control applications which attempts to monitor and record the environmental parameters and takes the appropriate decisions on time in a difficult situation. In recent enlargements Machine Learning (ML) techniques has been used to solve different problems in WSNs to ensure that good decisions can be made in the complex situations in time. Applying ML will help in boosting the efficiency of WSNs, as well as limiting humanoid intervention or re-programming. We have studied previous work for addressing the issues in Quality of Service (QoS) provisioning in WSNs. In addition we done the survey of ML based techniques used to address the issues in WSNs in the recent era.
{"title":"Machine Learning Algorithms in WSNs and its Applications","authors":"A. Raut, S. Khandait","doi":"10.1109/iccica52458.2021.9697319","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697319","url":null,"abstract":"Wireless sensor network (WSN) the unique and utmost encouraging tools for monitoring the real-time applications. It has been utilized in various areas particularly for offering real-time monitoring and control applications which attempts to monitor and record the environmental parameters and takes the appropriate decisions on time in a difficult situation. In recent enlargements Machine Learning (ML) techniques has been used to solve different problems in WSNs to ensure that good decisions can be made in the complex situations in time. Applying ML will help in boosting the efficiency of WSNs, as well as limiting humanoid intervention or re-programming. We have studied previous work for addressing the issues in Quality of Service (QoS) provisioning in WSNs. In addition we done the survey of ML based techniques used to address the issues in WSNs in the recent era.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123923901","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697266
Shashwat Pandey, Aditya Rohatgi
The process of transforming a physical document to a digital version leaves loose ends in several portions. There is a lack of solutions that offer end-to-end conversion of hard copies entailing images, graphs, tables, and other details into soft copies. To this end, we attempt to develop a computationally efficient algorithm to convert a document into its digital version through LATEX representations of the hard copy. Our research efforts take the problem of using OCR techniques into account for converting an image of a typesetted document into LATEX. This work serves as a proof of concept that equation layouts can be learned and individual character recognition is possible with not so sophisticated OCR techniques. The method we created to break the problem down step by step helped modularize and compartmentalize the tasks so that each can focus on the different types of issues that can occur at different levels of granularity.
{"title":"Using OCR to automate document conversion to LATEX","authors":"Shashwat Pandey, Aditya Rohatgi","doi":"10.1109/iccica52458.2021.9697266","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697266","url":null,"abstract":"The process of transforming a physical document to a digital version leaves loose ends in several portions. There is a lack of solutions that offer end-to-end conversion of hard copies entailing images, graphs, tables, and other details into soft copies. To this end, we attempt to develop a computationally efficient algorithm to convert a document into its digital version through LATEX representations of the hard copy. Our research efforts take the problem of using OCR techniques into account for converting an image of a typesetted document into LATEX. This work serves as a proof of concept that equation layouts can be learned and individual character recognition is possible with not so sophisticated OCR techniques. The method we created to break the problem down step by step helped modularize and compartmentalize the tasks so that each can focus on the different types of issues that can occur at different levels of granularity.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129066843","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697136
Suhashini Chaurasia, S. Sherekar, Vilas Thakare
Social media is the richest source of text generated by the user. So there is a necessity to automate the system to help organizing and classifying the opinions posted on social media sites. Proposed methodology framework using Artificial Recurrent Neural Network (ARNN) with bi-directional long short term memory (LSTM) has been used for the classification of sentiments. Structure for RNN with bidirectional LSTM is depicted. US airline Twitter sentiment dataset has been analysed using bidirectional LSTM model. Text with varying length is taken for the experiment. Graphical representation of the analysis has been depicted in this paper. Confusion matrix shows the result. At the end it is concluded that the sentiments are analysed and classified as positive, negative or neutral.
{"title":"Twitter Sentiment Analysis using Natural Language Processing","authors":"Suhashini Chaurasia, S. Sherekar, Vilas Thakare","doi":"10.1109/iccica52458.2021.9697136","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697136","url":null,"abstract":"Social media is the richest source of text generated by the user. So there is a necessity to automate the system to help organizing and classifying the opinions posted on social media sites. Proposed methodology framework using Artificial Recurrent Neural Network (ARNN) with bi-directional long short term memory (LSTM) has been used for the classification of sentiments. Structure for RNN with bidirectional LSTM is depicted. US airline Twitter sentiment dataset has been analysed using bidirectional LSTM model. Text with varying length is taken for the experiment. Graphical representation of the analysis has been depicted in this paper. Confusion matrix shows the result. At the end it is concluded that the sentiments are analysed and classified as positive, negative or neutral.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114281524","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697173
D. Singh, S. Kediya, R. Mahajan, P. Asthana
The paper has attempted to understand and unveil the non-technical causes of power electric losses. Many studies have covered the reasons for technical losses but here the author has covered the power losses due to manpower employed in MSEB (Maharashtra state electricity Board). The major findings of the study were that employees needs to be made aware about power losses. Employees are uninterested in continuing their inquisitiveness. Furthermore, they are unconcerned in learning new skills. Hence these factors led to negative outcome regarding power loss. They need to be given more training so that they can take effective measures to check the issue.
{"title":"Study of non technical factors responsible for power losses at MSEB","authors":"D. Singh, S. Kediya, R. Mahajan, P. Asthana","doi":"10.1109/iccica52458.2021.9697173","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697173","url":null,"abstract":"The paper has attempted to understand and unveil the non-technical causes of power electric losses. Many studies have covered the reasons for technical losses but here the author has covered the power losses due to manpower employed in MSEB (Maharashtra state electricity Board). The major findings of the study were that employees needs to be made aware about power losses. Employees are uninterested in continuing their inquisitiveness. Furthermore, they are unconcerned in learning new skills. Hence these factors led to negative outcome regarding power loss. They need to be given more training so that they can take effective measures to check the issue.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125328154","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697270
Swapna Choudhary, S. Dorle
Vehicular ad hoc networks (VANETs) are used in intelligent transportation systems to provide safety and security with a reduction in traffic jams using vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) unit communications. During the communication, nodes are always under various security threads. In order to reduce the possibility of these attacks and to normalize traffic flow in the network, a software-defined network (SDN) is used. SDN will improve centralized visibility as all the underlying open flow switches are connected to the controller, which will reduce the routing load in the network. SDN doesn’t provide a high level of security to the network, hence protocols like encryption, hashing, etc. are applied to the VANET. In the paper, SDN based blockchain algorithm is applied, which coordinates network traffic and improves the overall security of the network. Security analysis of the proposed algorithm demonstrates that blockchain with encrypted SDN removes more than 90% of the network attacks as compared to its non- blockchain SDN.
{"title":"SDN based Blockchain Architecture for Security Performance of VANETs","authors":"Swapna Choudhary, S. Dorle","doi":"10.1109/iccica52458.2021.9697270","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697270","url":null,"abstract":"Vehicular ad hoc networks (VANETs) are used in intelligent transportation systems to provide safety and security with a reduction in traffic jams using vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) unit communications. During the communication, nodes are always under various security threads. In order to reduce the possibility of these attacks and to normalize traffic flow in the network, a software-defined network (SDN) is used. SDN will improve centralized visibility as all the underlying open flow switches are connected to the controller, which will reduce the routing load in the network. SDN doesn’t provide a high level of security to the network, hence protocols like encryption, hashing, etc. are applied to the VANET. In the paper, SDN based blockchain algorithm is applied, which coordinates network traffic and improves the overall security of the network. Security analysis of the proposed algorithm demonstrates that blockchain with encrypted SDN removes more than 90% of the network attacks as compared to its non- blockchain SDN.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131593874","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697282
Pramod Jadhav, A. Shelke, C. Sonar
The cognitive intelligence is vital for human adaptation and subsistence. It encompasses wisdom and mental ability in regards to learning, evaluation and solving the problems. In almost all sectors, companies are facing acute competition. Employing cognitive intelligence, industries are adopting enormous operational excellence measures to thrive their success. Hence cognitive leadership is important driving force that influences the organizational success through the human capital. This research endeavors to study such cognitive leadership attempts in anticipating the vulnerability, defining and applying various strategies in creation of innovation nurturing environment. An influence of cognitive leadership in influencing the risk mitigation and non-technical innovation strategies is analyzed while examining their impact on the business success within a theoretical lens of socio-cognitive space and capabilities-based view (CBV) of strategic management frameworks using partial least squares (PLS) method of structural equations modelling (SEM).
{"title":"Effect of Leadership and Innovations on Business Performance: A Structural Equation Modelling Analysis","authors":"Pramod Jadhav, A. Shelke, C. Sonar","doi":"10.1109/iccica52458.2021.9697282","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697282","url":null,"abstract":"The cognitive intelligence is vital for human adaptation and subsistence. It encompasses wisdom and mental ability in regards to learning, evaluation and solving the problems. In almost all sectors, companies are facing acute competition. Employing cognitive intelligence, industries are adopting enormous operational excellence measures to thrive their success. Hence cognitive leadership is important driving force that influences the organizational success through the human capital. This research endeavors to study such cognitive leadership attempts in anticipating the vulnerability, defining and applying various strategies in creation of innovation nurturing environment. An influence of cognitive leadership in influencing the risk mitigation and non-technical innovation strategies is analyzed while examining their impact on the business success within a theoretical lens of socio-cognitive space and capabilities-based view (CBV) of strategic management frameworks using partial least squares (PLS) method of structural equations modelling (SEM).","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"340 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131836123","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697291
C. Sekhar, K. Pavani, M. Rao
Daily, large amounts of data are generated. Unauthorized users should be kept away from the data. Issues and problems arose one after the other as a result of the continuous development of network security. To avoid these malicious attacks, deep learning and machine learning methodologies are frequently used. Machine learning is a branch of the computer field that studies computational algorithms to convert empirical data into usable models. This field originated from the communities of traditional statics and intelligent retrieval. Machine learning includes deep learning as a subset. A system that can be trained to recognise objects using raw input has referred to as a deep learning system. In this study, we are applying DL techniques such as CNN, DNN, LSTM and RNN on NSL-KDD dataset. In this paper, we conduct a comparative analysis of multiple algorithms to determine which model is best for network security based on the network conditions and environment.
{"title":"Comparative analysis on Intrusion Detection system through ML and DL Techniques: Survey","authors":"C. Sekhar, K. Pavani, M. Rao","doi":"10.1109/iccica52458.2021.9697291","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697291","url":null,"abstract":"Daily, large amounts of data are generated. Unauthorized users should be kept away from the data. Issues and problems arose one after the other as a result of the continuous development of network security. To avoid these malicious attacks, deep learning and machine learning methodologies are frequently used. Machine learning is a branch of the computer field that studies computational algorithms to convert empirical data into usable models. This field originated from the communities of traditional statics and intelligent retrieval. Machine learning includes deep learning as a subset. A system that can be trained to recognise objects using raw input has referred to as a deep learning system. In this study, we are applying DL techniques such as CNN, DNN, LSTM and RNN on NSL-KDD dataset. In this paper, we conduct a comparative analysis of multiple algorithms to determine which model is best for network security based on the network conditions and environment.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116609091","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697122
Shatrughan Dubey, Yogadhar Pandey
This paper proposed a new model which isi based oni the classification methods such asi support vector machine neurali network andi optimization methods which isi bi-logically inspired method for the improving the classifier results in the terms ofisome performance parameters such as accuracy, precision, recall etc., here we measure the all performance parameters for the various dataset such as heart patients, liver patients andi cancer patients and improve the rate of classification or resultsi with compare than other existing techniques. The alli patient’s dataset whichi is taken fromitheiuci machine learning repository whichi providei the authentic dataset for the research work and thei simulation software isimatlab. Ini thisi paper our experimental results shows thati theibetter detectioniratei of classification for performance parameters thani other existingi techniques.
{"title":"Machine Learning Based Automated Approach To Detect Brain Disease Anomalies","authors":"Shatrughan Dubey, Yogadhar Pandey","doi":"10.1109/iccica52458.2021.9697122","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697122","url":null,"abstract":"This paper proposed a new model which isi based oni the classification methods such asi support vector machine neurali network andi optimization methods which isi bi-logically inspired method for the improving the classifier results in the terms ofisome performance parameters such as accuracy, precision, recall etc., here we measure the all performance parameters for the various dataset such as heart patients, liver patients andi cancer patients and improve the rate of classification or resultsi with compare than other existing techniques. The alli patient’s dataset whichi is taken fromitheiuci machine learning repository whichi providei the authentic dataset for the research work and thei simulation software isimatlab. Ini thisi paper our experimental results shows thati theibetter detectioniratei of classification for performance parameters thani other existingi techniques.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131663763","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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