Pub Date : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-000189
K. Santhosh, S. Ajitha
Analytics of gene expression data is the prolonged research area of present situation. Analysis of gene expression data requires enormous amount of work and huge set of algorithms. Using agent computing we deal with complex systems which are discovered many opportunities for developing data mining systems in a different ways. Hence to create predictive models, there is a huge need for intelligent and autonomous software agents which can procure useful information from the large datasets of raw information. Predictive analytics models can be created from these datasets which can be further used for various applications in security, future prediction etc. This research paper gives an overall function of multi agent systems in analytics of gene expression data, in terms of characteristics, adaptability, reliability and robotics of agents. Analytics on gene expression data is one of the emerging research fields. A large set of methodology and algorithms are existing in the field but in the application of agent technology in the field of gene expression data is at the infant stage. So the aim of this review paper is to integrate agent technology in the gene expression data analytics.
{"title":"Agent Technology for Data Analytics of Gene Expression Data: A Literature Review","authors":"K. Santhosh, S. Ajitha","doi":"10.1109/ICCMC48092.2020.ICCMC-000189","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000189","url":null,"abstract":"Analytics of gene expression data is the prolonged research area of present situation. Analysis of gene expression data requires enormous amount of work and huge set of algorithms. Using agent computing we deal with complex systems which are discovered many opportunities for developing data mining systems in a different ways. Hence to create predictive models, there is a huge need for intelligent and autonomous software agents which can procure useful information from the large datasets of raw information. Predictive analytics models can be created from these datasets which can be further used for various applications in security, future prediction etc. This research paper gives an overall function of multi agent systems in analytics of gene expression data, in terms of characteristics, adaptability, reliability and robotics of agents. Analytics on gene expression data is one of the emerging research fields. A large set of methodology and algorithms are existing in the field but in the application of agent technology in the field of gene expression data is at the infant stage. So the aim of this review paper is to integrate agent technology in the gene expression data analytics.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121102353","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 : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-000124
P. Bute, S. K. Mittal
In this Paper, fuzzy logic approach is implemented as a switching technique. The traditional logic gate design is eliminated by using the proposed technique. There is a pulse generator which is designed by fuzzy logic acts as a pulse generator and a look-up table also. An input is based on a modulation index, well controlled membership functions used and the related fuzzy logic controller’s rules can be used to produce pulses directly without using any extra block. The mentioned technique is being implemented by using the cascaded multilevel H-Bridge inverter with symmetric operation using pulse width modulation with selective harmonic elimination technique (SHE-PWM). MFs are designed as per the calculated firing angles for various modulation index.
{"title":"Simulation Study of Cascade H-bridge Multilevel Inverter 7-Level Inverter by SHE Technique","authors":"P. Bute, S. K. Mittal","doi":"10.1109/ICCMC48092.2020.ICCMC-000124","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000124","url":null,"abstract":"In this Paper, fuzzy logic approach is implemented as a switching technique. The traditional logic gate design is eliminated by using the proposed technique. There is a pulse generator which is designed by fuzzy logic acts as a pulse generator and a look-up table also. An input is based on a modulation index, well controlled membership functions used and the related fuzzy logic controller’s rules can be used to produce pulses directly without using any extra block. The mentioned technique is being implemented by using the cascaded multilevel H-Bridge inverter with symmetric operation using pulse width modulation with selective harmonic elimination technique (SHE-PWM). MFs are designed as per the calculated firing angles for various modulation index.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126630454","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 : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-00012
Prajakta Karemore, P. Jagtap
In recent years, Industrial Control [IC] is emerging as a fundamental aspect to gather all the pertinent data, insights and information identified with different mechanical procedures, engines, machines, and gadgets utilized in industrial premises. This focuses on delivering controlled access, better efficiency and great aftereffects of the modern items being manufactured. In this new period of innovative research improvements in remote control and monitoring by means of correspondence methods, for example, ZigBee, RF, Infrared, systems have been generally utilized in industries. In any case, these remote correspondence strategies are commonly confined to basic applications on account of their moderate correspondence paces, separations, and information security. The recently proposed framework is evolving as the fundamental need of the industry for monitoring, control, security and wellbeing of various exercises. The monitoring framework incorporates sensors like fire sensor, smoke sensor, ultrasonic sensor, moisture and temperature sensor, current and voltage with Wi-Fi module for control operations. With the benefits of unusual exercises, reasonable activities will be activated. This framework can likewise be controlled by using remote server with application in PC/Laptop. This undertaking additionally incorporates facial recognition by utilizing an open CV to perceive the essence of the approved individual of the industry to sign in / log out and the subtleties will be put away in the database sheet or refreshed to cloud. On the off chance that, any invalid section or attempt to the break-in, immediately an alarm email will be sent to the particular approved authority/individual/group.
{"title":"A Review of IoT Based Smart Industrial System for Controlling and Monitoring","authors":"Prajakta Karemore, P. Jagtap","doi":"10.1109/ICCMC48092.2020.ICCMC-00012","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00012","url":null,"abstract":"In recent years, Industrial Control [IC] is emerging as a fundamental aspect to gather all the pertinent data, insights and information identified with different mechanical procedures, engines, machines, and gadgets utilized in industrial premises. This focuses on delivering controlled access, better efficiency and great aftereffects of the modern items being manufactured. In this new period of innovative research improvements in remote control and monitoring by means of correspondence methods, for example, ZigBee, RF, Infrared, systems have been generally utilized in industries. In any case, these remote correspondence strategies are commonly confined to basic applications on account of their moderate correspondence paces, separations, and information security. The recently proposed framework is evolving as the fundamental need of the industry for monitoring, control, security and wellbeing of various exercises. The monitoring framework incorporates sensors like fire sensor, smoke sensor, ultrasonic sensor, moisture and temperature sensor, current and voltage with Wi-Fi module for control operations. With the benefits of unusual exercises, reasonable activities will be activated. This framework can likewise be controlled by using remote server with application in PC/Laptop. This undertaking additionally incorporates facial recognition by utilizing an open CV to perceive the essence of the approved individual of the industry to sign in / log out and the subtleties will be put away in the database sheet or refreshed to cloud. On the off chance that, any invalid section or attempt to the break-in, immediately an alarm email will be sent to the particular approved authority/individual/group.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127261398","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 : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-00080
Minu Eliz Pothen, Dr. Maya L Pai
Diseases infected on plant leaves particularly in rice leaves are one of the significant issues faced by the farmers. As a result, it is extremely hard to deliver the quantity of food needed for the growing human population. Rice diseases have caused production and economic losses in the agricultural sector. It will like-wise influence the earnings of farmers who rely upon agriculture and nowadays farmers commit suicide because of misfortune experienced in agriculture. Detection of definite disease infected on plants will assist to plan various disease control procedures. Proposed method describes different strategies utilized for rice leaf disease classification purpose. Bacterial leaf blight, Leaf smut and Brown spot diseased images are segmented using Otsu’s method. From the segmented area. various features are separated utilizing “Local Binary Patterns (LBP)” and “Histogram of Oriented Gradients (HOG)”. Then the features are classified with the assistance of Support Vector Machine (SVM) and accomplished 94.6% with polynomial Kernel SVM and HOG.
{"title":"Detection of Rice Leaf Diseases Using Image Processing","authors":"Minu Eliz Pothen, Dr. Maya L Pai","doi":"10.1109/ICCMC48092.2020.ICCMC-00080","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00080","url":null,"abstract":"Diseases infected on plant leaves particularly in rice leaves are one of the significant issues faced by the farmers. As a result, it is extremely hard to deliver the quantity of food needed for the growing human population. Rice diseases have caused production and economic losses in the agricultural sector. It will like-wise influence the earnings of farmers who rely upon agriculture and nowadays farmers commit suicide because of misfortune experienced in agriculture. Detection of definite disease infected on plants will assist to plan various disease control procedures. Proposed method describes different strategies utilized for rice leaf disease classification purpose. Bacterial leaf blight, Leaf smut and Brown spot diseased images are segmented using Otsu’s method. From the segmented area. various features are separated utilizing “Local Binary Patterns (LBP)” and “Histogram of Oriented Gradients (HOG)”. Then the features are classified with the assistance of Support Vector Machine (SVM) and accomplished 94.6% with polynomial Kernel SVM and HOG.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124042392","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 : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-00056
M. Vasuki, S. Revathy
Cluster ensemble approaches are combining different clustering results into single partitions. To enhance the quality of single partitions, this paper examines a comparative study of different methods with advantage and drawbacks. Performing spectral ensemble cluster (SEC) via weighted k-means are not efficient to handle incomplete basic partitions and big data problems. To overcome the problems in SEC, Greedy k-means consensus clustering is combined with SEC. By solving the above challenges, named spectral greedy k-means consensus clustering (SGKCC) is proposed. The proposed SGKCC efficient to handle incomplete basic partitions in big data which enhance the quality of single partition. Extensive evaluation NMI and RI used to calculate the performance efficiency compared with existing approach proving the result of proposed algorithm.
{"title":"Efficient Handling of Incomplete basic Partitions by Spectral Greedy K-Means Consensus Clustering","authors":"M. Vasuki, S. Revathy","doi":"10.1109/ICCMC48092.2020.ICCMC-00056","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00056","url":null,"abstract":"Cluster ensemble approaches are combining different clustering results into single partitions. To enhance the quality of single partitions, this paper examines a comparative study of different methods with advantage and drawbacks. Performing spectral ensemble cluster (SEC) via weighted k-means are not efficient to handle incomplete basic partitions and big data problems. To overcome the problems in SEC, Greedy k-means consensus clustering is combined with SEC. By solving the above challenges, named spectral greedy k-means consensus clustering (SGKCC) is proposed. The proposed SGKCC efficient to handle incomplete basic partitions in big data which enhance the quality of single partition. Extensive evaluation NMI and RI used to calculate the performance efficiency compared with existing approach proving the result of proposed algorithm.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121457584","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 : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-000100
S. S. Rao, R. Shreyas, G. Maske, A. Choudhury
Iris recognition is one of the best methods in the biometric identification field because the iris has features that are not unique but also stay throughout the person’s lifetime. Iris recognition has multiple phases namely Image Acquisition, Iris Segmentation, Iris Localization, Feature Extraction and Matching. Image Acquisition is simply the capturing of the iris image at an optimal distance. Iris Segmentation is the process of obtaining all the different segments of the eye. Iris Localization is the process of finding inner and outer boundaries of the iris differentiating it from the sclera and pupil and mainly focusing on the iris alone. Feature extraction is the process of extracting the biometric template from the Iris, giving the unique data required for the next step. Matching is the process of finding the best match in the database for the extracted biometric template. The future implementation of this paper focuses only on the processes of Image Acquisition, Iris Segmentation and Iris Localization. The paper aims to optimise these processes in terms of image capture distance, computation time and memory requirement, using the Dynamic Reconfigurable Processor (DRP) technology along with suitable algorithms for segmentation and localization processes as described in sections 2.2 and 2.3 respectively.
{"title":"Survey of Iris Image Segmentation and Localization","authors":"S. S. Rao, R. Shreyas, G. Maske, A. Choudhury","doi":"10.1109/ICCMC48092.2020.ICCMC-000100","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000100","url":null,"abstract":"Iris recognition is one of the best methods in the biometric identification field because the iris has features that are not unique but also stay throughout the person’s lifetime. Iris recognition has multiple phases namely Image Acquisition, Iris Segmentation, Iris Localization, Feature Extraction and Matching. Image Acquisition is simply the capturing of the iris image at an optimal distance. Iris Segmentation is the process of obtaining all the different segments of the eye. Iris Localization is the process of finding inner and outer boundaries of the iris differentiating it from the sclera and pupil and mainly focusing on the iris alone. Feature extraction is the process of extracting the biometric template from the Iris, giving the unique data required for the next step. Matching is the process of finding the best match in the database for the extracted biometric template. The future implementation of this paper focuses only on the processes of Image Acquisition, Iris Segmentation and Iris Localization. The paper aims to optimise these processes in terms of image capture distance, computation time and memory requirement, using the Dynamic Reconfigurable Processor (DRP) technology along with suitable algorithms for segmentation and localization processes as described in sections 2.2 and 2.3 respectively.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"9 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120806530","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 : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-00043
R. Ramachandran, Gopika Ravichandran, Aswathi Raveendran
In this digital era, big data has very high dimension and requires large amount of space for its data storage. Hence a lossless data interpretation will be difficult when big data contains large dimension. But, all these dimensions in big data may not be relevant or they may be interrelated and hence redundancy may exist in attribute set. Dimensionality reduction is a technique which focusses on downsizing the attributes and complication of a high dimensional data. In this paper, a detailed study of different dimensionality reduction techniques namely principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), singular value decomposition (SVD), independent component analysis (ICA) has been proposed. Furthermore, it also provides comparative analysis based on various parameters.
{"title":"Evaluation of Dimensionality Reduction Techniques for Big data","authors":"R. Ramachandran, Gopika Ravichandran, Aswathi Raveendran","doi":"10.1109/ICCMC48092.2020.ICCMC-00043","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00043","url":null,"abstract":"In this digital era, big data has very high dimension and requires large amount of space for its data storage. Hence a lossless data interpretation will be difficult when big data contains large dimension. But, all these dimensions in big data may not be relevant or they may be interrelated and hence redundancy may exist in attribute set. Dimensionality reduction is a technique which focusses on downsizing the attributes and complication of a high dimensional data. In this paper, a detailed study of different dimensionality reduction techniques namely principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), singular value decomposition (SVD), independent component analysis (ICA) has been proposed. Furthermore, it also provides comparative analysis based on various parameters.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124404576","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 : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-000184
C. Z. Basha, M. Reddy, K. Nikhil, P. M. Venkatesh, A. Asish
Rapidly creative innovations are emerging day by day in various fields, particularly in restorative condition. Bone fracture is one of the most common human problem and happens when the high pressure is applied to the bones, or simply because of accidents. High precision diagnosis of bone fracture is an important feature in the medical profession. Owing to fewer physicians, remotely based hospitals cannot have any of the equipment to diagnose fractures. X-ray scans are used to assess the fractures. These X-rays are one of the less expensive techniques for identification of fractures. Harris corner based detection algorithm is proposed to extract features from the image and the extracted features from this algorithm can identify edges, fractures and corners present in the image.300 different X-ray images are collected from Osmania hospital, Hyderabad. Proposed method gives an accuracy of 92% which is better in recognizing fracture compared to the existing methods.
{"title":"Enhanced Computer Aided Bone Fracture Detection Employing X-Ray Images by Harris Corner Technique","authors":"C. Z. Basha, M. Reddy, K. Nikhil, P. M. Venkatesh, A. Asish","doi":"10.1109/ICCMC48092.2020.ICCMC-000184","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000184","url":null,"abstract":"Rapidly creative innovations are emerging day by day in various fields, particularly in restorative condition. Bone fracture is one of the most common human problem and happens when the high pressure is applied to the bones, or simply because of accidents. High precision diagnosis of bone fracture is an important feature in the medical profession. Owing to fewer physicians, remotely based hospitals cannot have any of the equipment to diagnose fractures. X-ray scans are used to assess the fractures. These X-rays are one of the less expensive techniques for identification of fractures. Harris corner based detection algorithm is proposed to extract features from the image and the extracted features from this algorithm can identify edges, fractures and corners present in the image.300 different X-ray images are collected from Osmania hospital, Hyderabad. Proposed method gives an accuracy of 92% which is better in recognizing fracture compared to the existing methods.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127762224","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 : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-000146
R. Salagar, Pushpa B. Patil
The presence of the skew in a captured document image through a photographic camera, mobile camera or scanner is inevitable. In a document image detection and correction of skew are challenging phases before further processing like segmentation and analysis. In this paper, Run Length Smoothing Algorithm (RLSA) is proposed for the detection and correction of skew for handwritten Kannada document images. The proposed work has mainly two parts, the first part is preprocessing of a document using methods like thresholding, the maximum gradient for extraction of text and text line area with no loss of any data. The second part is skew detection and correction. The algorithm RLSA is used row and column-wise of a document image. The RLSA is applied for skew detection to determine skew (slant) angle further the document is turned in the anti-clockwise direction with the preferred angle, which will remove the skew of a document that has occurred while taking the photocopy of the document. The performance proposed method is evaluated for handwritten Kannada documents; the experiment outcomes are significantly better.
{"title":"Application of RLSA for Skew Detection and Correction in Kannada Text Images","authors":"R. Salagar, Pushpa B. Patil","doi":"10.1109/ICCMC48092.2020.ICCMC-000146","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000146","url":null,"abstract":"The presence of the skew in a captured document image through a photographic camera, mobile camera or scanner is inevitable. In a document image detection and correction of skew are challenging phases before further processing like segmentation and analysis. In this paper, Run Length Smoothing Algorithm (RLSA) is proposed for the detection and correction of skew for handwritten Kannada document images. The proposed work has mainly two parts, the first part is preprocessing of a document using methods like thresholding, the maximum gradient for extraction of text and text line area with no loss of any data. The second part is skew detection and correction. The algorithm RLSA is used row and column-wise of a document image. The RLSA is applied for skew detection to determine skew (slant) angle further the document is turned in the anti-clockwise direction with the preferred angle, which will remove the skew of a document that has occurred while taking the photocopy of the document. The performance proposed method is evaluated for handwritten Kannada documents; the experiment outcomes are significantly better.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116940410","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 : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-000193
P. Tamilarasi, R. Rani
Crime against women has become a very big problem of our nation. Many countries are trying to control this offence continuously and its prevention is an essential task. In recent years crimes are significantly increasing against women. Currently the Indian government show interest to address this problem and give more importance to develop our society. Every year a huge amount of data collection is generated on the basis of the crime reporting. This data can be very useful for assessing and predicting crime, and can help us to some degree stop the crime. Data analysis is a process of examining, cleansing, transformation and modelling data with the goal of establish useful information, reporting conclusion and sustaining decision-making. Feature Scaling is one of the most important techniques to standardize the independent features to place the data in a fixed range. It is performed at the time of data pre-processing. K-fold cross-validation is a re-sampling method used for calculating machine learning models on a small sample of data. It is a common strategy since it is easy to understand and usually results in a model deftness calculation that is less biased or less negative than other approaches, such as a simple train or test divide. Machine learning plays a large part in data processing. This paper introduces six different types of Machine learning algorithms such as KNN and decision trees, Naïve Bayes, Linear Regression CART (Classification and Regression Tree) and SVM using similar characteristics on crime data. Those algorithms are tested for accuracy. The main objective of this research is to evaluate the efficacy and application of the machine learning algorithms in data analytics.
{"title":"Diagnosis of Crime Rate against Women using k-fold Cross Validation through Machine Learning","authors":"P. Tamilarasi, R. Rani","doi":"10.1109/ICCMC48092.2020.ICCMC-000193","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000193","url":null,"abstract":"Crime against women has become a very big problem of our nation. Many countries are trying to control this offence continuously and its prevention is an essential task. In recent years crimes are significantly increasing against women. Currently the Indian government show interest to address this problem and give more importance to develop our society. Every year a huge amount of data collection is generated on the basis of the crime reporting. This data can be very useful for assessing and predicting crime, and can help us to some degree stop the crime. Data analysis is a process of examining, cleansing, transformation and modelling data with the goal of establish useful information, reporting conclusion and sustaining decision-making. Feature Scaling is one of the most important techniques to standardize the independent features to place the data in a fixed range. It is performed at the time of data pre-processing. K-fold cross-validation is a re-sampling method used for calculating machine learning models on a small sample of data. It is a common strategy since it is easy to understand and usually results in a model deftness calculation that is less biased or less negative than other approaches, such as a simple train or test divide. Machine learning plays a large part in data processing. This paper introduces six different types of Machine learning algorithms such as KNN and decision trees, Naïve Bayes, Linear Regression CART (Classification and Regression Tree) and SVM using similar characteristics on crime data. Those algorithms are tested for accuracy. The main objective of this research is to evaluate the efficacy and application of the machine learning algorithms in data analytics.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132785991","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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