Pub Date : 2020-03-01DOI: 10.1109/ICCMC48092.2020.ICCMC-0003
Aathira M, G. Jeyakumar
The Differential Evolution (DE) algorithm, under the family of Evolutionary Algorithms (EAs), is one of the powerful algorithms used for solving continuous parameter optimization challenges. The simplistic nature and robustness of the classical DE algorithm have drawn researchers’ attention towards its progressive enhancement. This work reports on an investigation of the behavioral changes of the classical DE algorithm, evoked when its mutation and crossover components are fine tuned for enhancement of DE’s performance. The scope of this study covers the implementation of a mutation level enhancement and a crossover level enhancement, followed by their integration. The mutation and the crossover components are augmented by incorporation of Centroid DE and Superior-Superior & Superior-Inferior DE logics, respectively. The algorithms appraised in this inquiry were classical DE, Centroid based DE(cDE), Superior-Superior based DE (ssDE), Superior-Inferior DE (siDE), Centroid Superior-Superior DE (cssDE) and Centroid Superior-Inferior DE (csiDE). These algorithms were evaluated by comparison of the values of their mean objective function (MOV), and their speed, at solving the global optimization problems in a simple benchmarking function suite with 4 functions of different categories. The study concludes that the DE algorithm shows enhancement performance with modified mutation and crossover components. However, with a trend for inconsistency for varying values of its control parameters and benchmarking problems.
差分进化算法(DE)是进化算法家族中的一种，是解决连续参数优化问题的强大算法之一。经典DE算法的简单性和鲁棒性引起了研究人员对其逐步增强的关注。这项工作报告了经典DE算法的行为变化的研究，当它的突变和交叉成分被微调以提高DE的性能时，会引起行为变化。本研究的范围涵盖了突变水平增强和交叉水平增强的实施，随后是它们的整合。通过引入质心DE和Superior-Superior & superior -劣DE逻辑分别增强了突变分量和交叉分量。本研究评价的算法有经典DE、基于质心的DE(cDE)、基于优-优的DE(ssDE)、优-劣DE(siDE)、质心优-劣DE(cssDE)和质心优-劣DE(csiDE)。通过比较这些算法的平均目标函数(MOV)值，以及它们在一个包含4个不同类别函数的简单基准函数套件中解决全局优化问题的速度，对这些算法进行了评估。研究表明，改进变异和交叉分量后，DE算法具有较好的增强性能。然而，随着其控制参数值的变化而出现不一致的趋势和基准测试问题。
{"title":"Performance Enhancement of Mutation and Crossover Components by using Differential Evolution Algorithm","authors":"Aathira M, G. Jeyakumar","doi":"10.1109/ICCMC48092.2020.ICCMC-0003","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-0003","url":null,"abstract":"The Differential Evolution (DE) algorithm, under the family of Evolutionary Algorithms (EAs), is one of the powerful algorithms used for solving continuous parameter optimization challenges. The simplistic nature and robustness of the classical DE algorithm have drawn researchers’ attention towards its progressive enhancement. This work reports on an investigation of the behavioral changes of the classical DE algorithm, evoked when its mutation and crossover components are fine tuned for enhancement of DE’s performance. The scope of this study covers the implementation of a mutation level enhancement and a crossover level enhancement, followed by their integration. The mutation and the crossover components are augmented by incorporation of Centroid DE and Superior-Superior & Superior-Inferior DE logics, respectively. The algorithms appraised in this inquiry were classical DE, Centroid based DE(cDE), Superior-Superior based DE (ssDE), Superior-Inferior DE (siDE), Centroid Superior-Superior DE (cssDE) and Centroid Superior-Inferior DE (csiDE). These algorithms were evaluated by comparison of the values of their mean objective function (MOV), and their speed, at solving the global optimization problems in a simple benchmarking function suite with 4 functions of different categories. The study concludes that the DE algorithm shows enhancement performance with modified mutation and crossover components. However, with a trend for inconsistency for varying values of its control parameters and benchmarking problems.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"45 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":"115808349","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-000185
Sathuluri Mallikharjuna Rao, T. Saikumar, J. Reddy, V.Ravi Chowdary, Ammam.Jaya Apurva Rani
Modern era require modern solutions and modern technologies. Thereby modernizing such in the domain of antennas, a new type of patch antenna intended for C-band applications is designed printing over a FR4_epoxy substrate. whose dimensions, is W$_{1} times L_{1} times h$ as 35mm $times30$ mm $times1.6$ mm the simulations results showed that the antenna works at a single resonant frequency 5.9Ghz, hence covering the applications like military, weather forecasting, defense tracking and air traffic control. The antenna feed with co-planar wave guide (CPW) is a simulation-based design and the parameters of antenna designed are optimized by making use of ANSYS HFSS software.
{"title":"A CPW Fed Patch Antenna Design for Weather Monitoring, Air Traffic Control and Defense Tracking Applications","authors":"Sathuluri Mallikharjuna Rao, T. Saikumar, J. Reddy, V.Ravi Chowdary, Ammam.Jaya Apurva Rani","doi":"10.1109/ICCMC48092.2020.ICCMC-000185","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000185","url":null,"abstract":"Modern era require modern solutions and modern technologies. Thereby modernizing such in the domain of antennas, a new type of patch antenna intended for C-band applications is designed printing over a FR4_epoxy substrate. whose dimensions, is W$_{1} times L_{1} times h$ as 35mm $times30$ mm $times1.6$ mm the simulations results showed that the antenna works at a single resonant frequency 5.9Ghz, hence covering the applications like military, weather forecasting, defense tracking and air traffic control. The antenna feed with co-planar wave guide (CPW) is a simulation-based design and the parameters of antenna designed are optimized by making use of ANSYS HFSS software.","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":"115878939","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-000109
P. Seethalakshmi, K. Venkatalakshmi
The smart grid is a combination of smart network devices and systems that support the efficient generation, distribution and transmission of energy from source to destination. Energy is becoming one of the most important resources of daily life. In general, technology advancements are rapidly increasing and energy demand is also increasing due to the discovery of new electrical/electronic devices. Most of the conditions, there is a mismatch between energy generation and energy consumption. The big challenge is to maintain a balance between generating energy and using it. The service providers need to forecast the energy demand well in advance with minimal error to maintain the equilibrium state, even a small error in the predictive mechanism leads to a loss for both service providers and consumers. To address these problems we proposed an energy prediction model based on Long Short Term Memory (LSTM). It has emerged as a promising Artificial Neural Network (ANN) technique for predicting time series issues due to the properties of selective retrieval patterns for a long time. Further, the LSTM model is optimized by using Optimizer Ensembles to improve the efficiency of the proposed model. The simulation results show that the proposed LSTM achieves better predictive results (less error, high efficiency) compared to existing methods such as Moving Average (MA), Linear Regression (LR) and k-Nearest Neighbors (k-NN) techniques.
{"title":"Prediction of Energy Demand in Smart Grid Using Deep Neural Networks with Optimizer Ensembles","authors":"P. Seethalakshmi, K. Venkatalakshmi","doi":"10.1109/ICCMC48092.2020.ICCMC-000109","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000109","url":null,"abstract":"The smart grid is a combination of smart network devices and systems that support the efficient generation, distribution and transmission of energy from source to destination. Energy is becoming one of the most important resources of daily life. In general, technology advancements are rapidly increasing and energy demand is also increasing due to the discovery of new electrical/electronic devices. Most of the conditions, there is a mismatch between energy generation and energy consumption. The big challenge is to maintain a balance between generating energy and using it. The service providers need to forecast the energy demand well in advance with minimal error to maintain the equilibrium state, even a small error in the predictive mechanism leads to a loss for both service providers and consumers. To address these problems we proposed an energy prediction model based on Long Short Term Memory (LSTM). It has emerged as a promising Artificial Neural Network (ANN) technique for predicting time series issues due to the properties of selective retrieval patterns for a long time. Further, the LSTM model is optimized by using Optimizer Ensembles to improve the efficiency of the proposed model. The simulation results show that the proposed LSTM achieves better predictive results (less error, high efficiency) compared to existing methods such as Moving Average (MA), Linear Regression (LR) and k-Nearest Neighbors (k-NN) techniques.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"8 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":"123226121","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-000150
Shilpa Sridhar, Latha, Arpita Thakre
Dual mode Orthogonal Frequency Division Multiplexing with Index Modulation (DM-OFDM-IM) is a modified version of the orthogonal frequency division multiplexing with index modulation (OFDM-IM). In DMOFDM-IM, sub-carriers in each sub-block are divided into two groups. These two groups are modulated with two different signal constellations, chosen from available standard constellations, for example PSK or QAM. All the subcarriers are used to transmit data, while index bit is used to select the constellation. Therefore, in this system there is an improvement in the spectral efficiency which is always greater than OFDM-IM. In this paper we suggest a new signal constellation design to be used for DM-OFDM-IM that gives reduced bit error rate (BER) compared to conventional DMOFDM-IM system. We also discuss about the rationale between our design here.
{"title":"Constellation Design for Dual-Mode OFDM-IM","authors":"Shilpa Sridhar, Latha, Arpita Thakre","doi":"10.1109/ICCMC48092.2020.ICCMC-000150","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000150","url":null,"abstract":"Dual mode Orthogonal Frequency Division Multiplexing with Index Modulation (DM-OFDM-IM) is a modified version of the orthogonal frequency division multiplexing with index modulation (OFDM-IM). In DMOFDM-IM, sub-carriers in each sub-block are divided into two groups. These two groups are modulated with two different signal constellations, chosen from available standard constellations, for example PSK or QAM. All the subcarriers are used to transmit data, while index bit is used to select the constellation. Therefore, in this system there is an improvement in the spectral efficiency which is always greater than OFDM-IM. In this paper we suggest a new signal constellation design to be used for DM-OFDM-IM that gives reduced bit error rate (BER) compared to conventional DMOFDM-IM system. We also discuss about the rationale between our design here.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"16 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":"124927070","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-000169
S. Krishnendu, P. Lakshmi, L. Nitha
In India, the crime rate is increasing each day. In the current situation, recent technological influence, effects of social media and modern approaches help the offenders to achieve their crimes. Both analysis and prediction of crime is a systematized method that classifies and examines the crime patterns. There exist various clustering algorithms for crime analysis and pattern prediction but they do not reveal all the requirements. Among these, K means algorithm provides a better way for predicting the results. The proposed research work mainly focused on predicting the region with higher crime rates and age groups with more or less criminal tendencies. We propose an optimized K means algorithm to lower the time complexity and improve efficiency in the result.
{"title":"Crime Analysis and Prediction using Optimized K-Means Algorithm","authors":"S. Krishnendu, P. Lakshmi, L. Nitha","doi":"10.1109/ICCMC48092.2020.ICCMC-000169","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000169","url":null,"abstract":"In India, the crime rate is increasing each day. In the current situation, recent technological influence, effects of social media and modern approaches help the offenders to achieve their crimes. Both analysis and prediction of crime is a systematized method that classifies and examines the crime patterns. There exist various clustering algorithms for crime analysis and pattern prediction but they do not reveal all the requirements. Among these, K means algorithm provides a better way for predicting the results. The proposed research work mainly focused on predicting the region with higher crime rates and age groups with more or less criminal tendencies. We propose an optimized K means algorithm to lower the time complexity and improve efficiency in the result.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"433 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":"123571871","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-000157
Xinming Gao, Gaoteng Yuan, Mengjiao Zhang
With electric cars, large-scale development, in order to make the electric vehicles charging more convenient and efficient, public charging piles began to be used on a large scale. However, traditional fault detection methods are still used in charging piles, which makes the detection efficiency low. This paper proposes an error detection procedure of charging pile founded on ELM method. Different from the traditional charging pile fault detection model, this method constructs data for common features of the charging pile and establishes a classification prediction frame work that relies on the Extreme Learning Machine (ELM) algorithm. Experimental results evinces that the frame works accuracy is 83%, with a high efficiency, strong practicability, and is easy to popularize.
{"title":"Fault Detection of Electric Vehicle Charging Piles Based on Extreme Learning Machine Algorithm","authors":"Xinming Gao, Gaoteng Yuan, Mengjiao Zhang","doi":"10.1109/ICCMC48092.2020.ICCMC-000157","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000157","url":null,"abstract":"With electric cars, large-scale development, in order to make the electric vehicles charging more convenient and efficient, public charging piles began to be used on a large scale. However, traditional fault detection methods are still used in charging piles, which makes the detection efficiency low. This paper proposes an error detection procedure of charging pile founded on ELM method. Different from the traditional charging pile fault detection model, this method constructs data for common features of the charging pile and establishes a classification prediction frame work that relies on the Extreme Learning Machine (ELM) algorithm. Experimental results evinces that the frame works accuracy is 83%, with a high efficiency, strong practicability, and is easy to popularize.","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":"121605689","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-00014
A. Mathew
The fifth-generation (5G) of mobile communications is currently being rolled out, with service providers adopting it in their networks. 5G enables the connection of multiple devices and their communication over the Internet, giving it the capability to support the Internet of Things (IoT). One of the technologies behind its support of multiple and diverse devices is the network slicing technology which helps in managing the logical networks comprising of different user equipment. The objective of the present study is to explore the network slicing technology and investigate the security concerns that it introduces to 5G. The study adopts the systematic review, which is a qualitative methodology, for gathering and analyzing data. The review comprises of the analysis of various literature sources obtained from Google Scholar, IEEE, and the Cochrane Library. The data analysis process is thematic in nature, focusing on security challenges, their solutions, and the merits of network slicing. The literature findings indicate that network slicing has two major challenges, namely security and the implementation of 5G radio access network (RAN) to accommodate slicing. The potential solutions to these challenges include network isolation through slicing, cryptography, authentication, and manual slice allocation to different devices. The principle conclusions derived from the proposed study are that the network slicing is suitable for 5G and the solutions remain effective in minimizing the security risks it poses to the network.
{"title":"Network Slicing in 5G and the Security Concerns","authors":"A. Mathew","doi":"10.1109/ICCMC48092.2020.ICCMC-00014","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00014","url":null,"abstract":"The fifth-generation (5G) of mobile communications is currently being rolled out, with service providers adopting it in their networks. 5G enables the connection of multiple devices and their communication over the Internet, giving it the capability to support the Internet of Things (IoT). One of the technologies behind its support of multiple and diverse devices is the network slicing technology which helps in managing the logical networks comprising of different user equipment. The objective of the present study is to explore the network slicing technology and investigate the security concerns that it introduces to 5G. The study adopts the systematic review, which is a qualitative methodology, for gathering and analyzing data. The review comprises of the analysis of various literature sources obtained from Google Scholar, IEEE, and the Cochrane Library. The data analysis process is thematic in nature, focusing on security challenges, their solutions, and the merits of network slicing. The literature findings indicate that network slicing has two major challenges, namely security and the implementation of 5G radio access network (RAN) to accommodate slicing. The potential solutions to these challenges include network isolation through slicing, cryptography, authentication, and manual slice allocation to different devices. The principle conclusions derived from the proposed study are that the network slicing is suitable for 5G and the solutions remain effective in minimizing the security risks it poses to the network.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"46 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":"121570526","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-000183
R. Kamalraj, E. S. Madhan, K. Ghamya, V. Bhargavi
Child security in the school campus is most important in building a good society. In and around the world the children are abused and killed also in sometimes by the people those who are not in good attitude in the school campus. To track and resolve such issues an enhanced security feature system is required. Hence in this paper an enhanced version of security system for children is proposed by using ‘Wearable Sensors’. In this proposed method two wearable sensors nodes such as ‘Staff Node’ and ‘Student Node’ are paired by using ‘Bluetooth’ communication technology and Smart Watch technology is also used to communicate the Security Center or Processing Node for tracking them about their location and whether the two nodes are moved away from the classroom. If the child node is not moving for a long period then it may be notified by the center and they will inform the security officers near to the place. This proposed method may satisfy the need of school management about the staff movements with students and the behavior of students to avoid unexpected issues.
{"title":"Enhance Safety and Security System for Children in School Campus by using Wearable Sensors","authors":"R. Kamalraj, E. S. Madhan, K. Ghamya, V. Bhargavi","doi":"10.1109/ICCMC48092.2020.ICCMC-000183","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000183","url":null,"abstract":"Child security in the school campus is most important in building a good society. In and around the world the children are abused and killed also in sometimes by the people those who are not in good attitude in the school campus. To track and resolve such issues an enhanced security feature system is required. Hence in this paper an enhanced version of security system for children is proposed by using ‘Wearable Sensors’. In this proposed method two wearable sensors nodes such as ‘Staff Node’ and ‘Student Node’ are paired by using ‘Bluetooth’ communication technology and Smart Watch technology is also used to communicate the Security Center or Processing Node for tracking them about their location and whether the two nodes are moved away from the classroom. If the child node is not moving for a long period then it may be notified by the center and they will inform the security officers near to the place. This proposed method may satisfy the need of school management about the staff movements with students and the behavior of students to avoid unexpected issues.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"695 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":"126241554","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-00024
R. Mante, Reshma Khan
In the past couple of decades, the most emerging domain across the globe is Information Technology [IT]. It has made a significant contribution in cracking the criminal case and preparing the strong cases in the court of justice. IT technologies have become a vital tool for examination and gathering of the digital evidences. As crime rate is increasing day-by-day, in order to reduce the occurrence of crime and bring the culprit of the crime to justice, the computer forensics plays an indispensable role. With the recent technological developments, it has become more powerful and portable. Large amount of data has created stored and accessed everyday. Many technologically advanced devices are acting as repositories for the collected personal information. Such devices remain portable and accessible with a single click or even to the voice command. To have such large-scale information is important to get the conviction for the culprits but at the same time, investigating agencies has to maintain the privacy of the recovered data to make it admissible in the court. This gives rise to the security mechanism for such data. Collection, analysis, investigation and reporting of the digital data come under forensic science, which is a part of digital forensics domain. In this paper, we have presented a review on the existing researches available in the field of video forensic analysis.
{"title":"A Survey on Video-based Evidence Analysis and Digital Forensic","authors":"R. Mante, Reshma Khan","doi":"10.1109/ICCMC48092.2020.ICCMC-00024","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00024","url":null,"abstract":"In the past couple of decades, the most emerging domain across the globe is Information Technology [IT]. It has made a significant contribution in cracking the criminal case and preparing the strong cases in the court of justice. IT technologies have become a vital tool for examination and gathering of the digital evidences. As crime rate is increasing day-by-day, in order to reduce the occurrence of crime and bring the culprit of the crime to justice, the computer forensics plays an indispensable role. With the recent technological developments, it has become more powerful and portable. Large amount of data has created stored and accessed everyday. Many technologically advanced devices are acting as repositories for the collected personal information. Such devices remain portable and accessible with a single click or even to the voice command. To have such large-scale information is important to get the conviction for the culprits but at the same time, investigating agencies has to maintain the privacy of the recovered data to make it admissible in the court. This gives rise to the security mechanism for such data. Collection, analysis, investigation and reporting of the digital data come under forensic science, which is a part of digital forensics domain. In this paper, we have presented a review on the existing researches available in the field of video forensic analysis.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"153 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":"127276437","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-000120
M. Mohan, Lekshmi Priya T, L. S. Nair
Cancer statistics all around the globe are rising day by day, out of which breast cancer is the dominating one in women. Mammography is used to detect the presence of cancerous cells and computer-aided detection technologies are used to get more accurate results. There are different image processing techniques which are applied for cancer detection in mammograms. In this work, a combined methodology is used for enhancing the contrast of the mammogram which includes contrast limited histogram equalization (CLAHE), morphological gradient and fourth order nonlinear complex diffusion based unsharpening. The enhanced mammogram is then segmented using fuzzy c-means. An analysis is done on each of the following cases with Fuzzy-c means segmentation: CLAHE enhanced, morphologicall gradient enhanced, fourth order nonlinear complex diffusion enhanced and combined enhanced image using the above methods.
{"title":"Fuzzy c-means Segmentation on Enhanced Mammograms Using CLAHE and Fourth Order Complex Diffusion","authors":"M. Mohan, Lekshmi Priya T, L. S. Nair","doi":"10.1109/ICCMC48092.2020.ICCMC-000120","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000120","url":null,"abstract":"Cancer statistics all around the globe are rising day by day, out of which breast cancer is the dominating one in women. Mammography is used to detect the presence of cancerous cells and computer-aided detection technologies are used to get more accurate results. There are different image processing techniques which are applied for cancer detection in mammograms. In this work, a combined methodology is used for enhancing the contrast of the mammogram which includes contrast limited histogram equalization (CLAHE), morphological gradient and fourth order nonlinear complex diffusion based unsharpening. The enhanced mammogram is then segmented using fuzzy c-means. An analysis is done on each of the following cases with Fuzzy-c means segmentation: CLAHE enhanced, morphologicall gradient enhanced, fourth order nonlinear complex diffusion enhanced and combined enhanced image using the above methods.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"28 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":"127681704","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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