Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234504
Eleonora D'Andrea, P. Ducange, F. Marcelloni
In this paper, we present an approach to monitor the Italian public opinion from tweets analysis, with reference to the vaccination topic. This topic has recently become controversial, due to the disinformation about the alleged connection between autism and vaccines. Further, the Italian Ministry of Health has noticed a drop in vaccination rates, enhancing the risk of reemergence of eradicated diseases. Thus, a system to monitor the negative public opinion about vaccines could become very important for decision making. The proposed approach i) fetches vaccine-related tweets, ii) applies a text elaboration on the tweets, and iii) performs a binary classification aimed at discriminating negative opinions tweets (i.e., not in favor of vaccination) from the rest of tweets. By employing the Simple Logistic classifier, we achieved a 75.5% average accuracy. Finally, we monitor the trend over time of public opinion about vaccination decision making in a free, real-time and quick fashion.
{"title":"Monitoring negative opinion about vaccines from tweets analysis","authors":"Eleonora D'Andrea, P. Ducange, F. Marcelloni","doi":"10.1109/ICRCICN.2017.8234504","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234504","url":null,"abstract":"In this paper, we present an approach to monitor the Italian public opinion from tweets analysis, with reference to the vaccination topic. This topic has recently become controversial, due to the disinformation about the alleged connection between autism and vaccines. Further, the Italian Ministry of Health has noticed a drop in vaccination rates, enhancing the risk of reemergence of eradicated diseases. Thus, a system to monitor the negative public opinion about vaccines could become very important for decision making. The proposed approach i) fetches vaccine-related tweets, ii) applies a text elaboration on the tweets, and iii) performs a binary classification aimed at discriminating negative opinions tweets (i.e., not in favor of vaccination) from the rest of tweets. By employing the Simple Logistic classifier, we achieved a 75.5% average accuracy. Finally, we monitor the trend over time of public opinion about vaccination decision making in a free, real-time and quick fashion.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115967776","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234523
Piyali De, Sandip Dey
Millions of users harvest their personal information (photo, video, status) on different online social networks (OSNs). Hence, these rich repositories of sensitive information attract the eyes of adversary to launch variety of cyber attacks on OSN. Here we have identified all crucial threats on social network that may lead to severe risks. In this paper, we have formalized possible social network risks into two types depending upon the origin of risk. Risks are presented here along with impact factor and control measures to mitigate the outcome of the security threats. The relationship between risks and basic security properties has been presented in tabular format. Finally we provide three case studies on identity theft.
{"title":"Security risk assessment in online social networking: A detailed survey","authors":"Piyali De, Sandip Dey","doi":"10.1109/ICRCICN.2017.8234523","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234523","url":null,"abstract":"Millions of users harvest their personal information (photo, video, status) on different online social networks (OSNs). Hence, these rich repositories of sensitive information attract the eyes of adversary to launch variety of cyber attacks on OSN. Here we have identified all crucial threats on social network that may lead to severe risks. In this paper, we have formalized possible social network risks into two types depending upon the origin of risk. Risks are presented here along with impact factor and control measures to mitigate the outcome of the security threats. The relationship between risks and basic security properties has been presented in tabular format. Finally we provide three case studies on identity theft.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115080217","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234497
Akm Ashiquzzaman, A. Tushar, S. Dutta, Farzana Mohsin
Handwritten character recognition is an essential part of optical character recognition domain. Bangla handwritten compound character recognition is a complex task that is challenging due to extensive size of and sheer diversity within the alphabet. The current work proposes a novel method of recognition of compound characters in Bangla language using deep convolutional neural networks (DCNN) and efficient greedy layer-wise training approach. Introduction of dropout technology mitigates data overfitting and Exponential Linear Unit (ELU) is introduced to tackle the vanishing gradient problem during training. ELU is a special rectified linear unit which provides sustainability against the vanishing as well as exploding gradients. Furthermore, dropout influences network elements to learn diverse representation of data, which contributes to generalization of model. The model is tested on CMATERdb 3.1.3.3 data set of compound characters, and the performance is found to outperform existing state-of-the-art methods of Bangla handwritten complex character recognition.
手写体字符识别是光学字符识别领域的重要组成部分。孟加拉语手写复合字识别是一项复杂的任务，由于其庞大的规模和绝对的多样性，具有挑战性。本文提出了一种基于深度卷积神经网络(DCNN)和高效贪婪分层训练方法的孟加拉语复合字识别新方法。引入dropout技术减轻了数据的过拟合，并引入指数线性单元(Exponential Linear Unit, ELU)来解决训练过程中梯度消失的问题。ELU是一种特殊的整流线性单元，它提供了对消失和爆炸梯度的可持续性。此外，辍学影响网络元素学习数据的多样化表示，有助于模型的泛化。在CMATERdb 3.1.3.3复合字数据集上对该模型进行了测试，结果表明该模型的性能优于现有的孟加拉文手写复合字识别方法。
{"title":"An efficient method for improving classification accuracy of handwritten Bangla compound characters using DCNN with dropout and ELU","authors":"Akm Ashiquzzaman, A. Tushar, S. Dutta, Farzana Mohsin","doi":"10.1109/ICRCICN.2017.8234497","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234497","url":null,"abstract":"Handwritten character recognition is an essential part of optical character recognition domain. Bangla handwritten compound character recognition is a complex task that is challenging due to extensive size of and sheer diversity within the alphabet. The current work proposes a novel method of recognition of compound characters in Bangla language using deep convolutional neural networks (DCNN) and efficient greedy layer-wise training approach. Introduction of dropout technology mitigates data overfitting and Exponential Linear Unit (ELU) is introduced to tackle the vanishing gradient problem during training. ELU is a special rectified linear unit which provides sustainability against the vanishing as well as exploding gradients. Furthermore, dropout influences network elements to learn diverse representation of data, which contributes to generalization of model. The model is tested on CMATERdb 3.1.3.3 data set of compound characters, and the performance is found to outperform existing state-of-the-art methods of Bangla handwritten complex character recognition.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128437165","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234525
Priyanka Das, A. Das
The present work proposes an unsupervised approach for recognising relations between named entities from a large corpora based on crime in Indian states and union territories. Initially, named entities have been identified from the extracted crime corpus and certain pair of entities have been chosen that facilitates the crime analysis. Then the entity pairs with their intermediate context words have been represented as a shallow parse tree for relation instance. From the parse trees, only the head words (in each entity pair) reflecting the main meaning of the phrases has been considered for measuring a semantic similarity using a web search engine that retrieves the page count of those particular words and their conjunctives. The derived page count is used for measuring the Simpson Coefficient between the pairs and based on this similarity score, an agglomerative hierarchical clustering technique has been applied that makes several clusters of entity pairs of same relationship. The resultant clusters also have been characterised with the most frequent head word present in the group. This proposed method shows a simple similarity measure technique for relation extraction from crime data providing better accuracy than other existing methods.
{"title":"Relation recognition among named entities from a crime corpus using a web-based semantic similarity measurement","authors":"Priyanka Das, A. Das","doi":"10.1109/ICRCICN.2017.8234525","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234525","url":null,"abstract":"The present work proposes an unsupervised approach for recognising relations between named entities from a large corpora based on crime in Indian states and union territories. Initially, named entities have been identified from the extracted crime corpus and certain pair of entities have been chosen that facilitates the crime analysis. Then the entity pairs with their intermediate context words have been represented as a shallow parse tree for relation instance. From the parse trees, only the head words (in each entity pair) reflecting the main meaning of the phrases has been considered for measuring a semantic similarity using a web search engine that retrieves the page count of those particular words and their conjunctives. The derived page count is used for measuring the Simpson Coefficient between the pairs and based on this similarity score, an agglomerative hierarchical clustering technique has been applied that makes several clusters of entity pairs of same relationship. The resultant clusters also have been characterised with the most frequent head word present in the group. This proposed method shows a simple similarity measure technique for relation extraction from crime data providing better accuracy than other existing methods.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125932783","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234479
A. Dey, S. Chowdhury, Manas Ghosh
In Face recognition, a combination of neural network (NN), known as an ensemble of neural network, often outperforms individual ones. This paper is aiming to present a support vector machines (SVM)-ensemble-based efficient face recognition system. The training samples are randomly chosen by means of bootstrap technique to train the different SVM independently. These SVM's are combined together to generate the ensemble SVM. The proposed method then makes a collective decision by aggregating them. It may be noted that, the performance of the practical SVM is far from the theoretical SVM as the implementations are based on approximated algorithms. The performance of the real SVM can be uplifted by using the proposed ensemble SVM with bagging (bootstrap aggregating). Finally, the proposed method takes the collective decision by aggregating the training samples. The proposed method is validated on AT&T, FERET face databases to show its supremacy over the single SVM-based methods.
{"title":"Face recognition using ensemble support vector machine","authors":"A. Dey, S. Chowdhury, Manas Ghosh","doi":"10.1109/ICRCICN.2017.8234479","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234479","url":null,"abstract":"In Face recognition, a combination of neural network (NN), known as an ensemble of neural network, often outperforms individual ones. This paper is aiming to present a support vector machines (SVM)-ensemble-based efficient face recognition system. The training samples are randomly chosen by means of bootstrap technique to train the different SVM independently. These SVM's are combined together to generate the ensemble SVM. The proposed method then makes a collective decision by aggregating them. It may be noted that, the performance of the practical SVM is far from the theoretical SVM as the implementations are based on approximated algorithms. The performance of the real SVM can be uplifted by using the proposed ensemble SVM with bagging (bootstrap aggregating). Finally, the proposed method takes the collective decision by aggregating the training samples. The proposed method is validated on AT&T, FERET face databases to show its supremacy over the single SVM-based methods.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128898743","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234488
Rajdeep Ray, Payel Majumder, M. H. Khondekar, K. Ghosh, A. Bhattacharjee
The temperature and dew point records of seven weather stations, located in India have been scrutinized under the self-organized criticality regime. The data models an almost ideal scaling behaviour as of the type of t/fα noise; with α≈1. This scaling behaviour strongly suggests the presence of self-organized criticality (SOC) behind both the signals. To draw more insight into the detailed dynamics and to strengthen the explanation of such behaviour, causal relationship exploiting singularity spectrum analysis (SSA), multivariate singularity spectrum analysis (MSSA) and Correlation of Probability of Recurrence (CPR) based on recurrence plot have been studied in between the two signals for all the stations. The results reveal sufficient causal relationship and high correlation of probability of recurrence for strong support behind such critical dynamical systems.
{"title":"Self-organized criticality, causality and correlation of probability of recurrence between daily mean temperature and dew point across India","authors":"Rajdeep Ray, Payel Majumder, M. H. Khondekar, K. Ghosh, A. Bhattacharjee","doi":"10.1109/ICRCICN.2017.8234488","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234488","url":null,"abstract":"The temperature and dew point records of seven weather stations, located in India have been scrutinized under the self-organized criticality regime. The data models an almost ideal scaling behaviour as of the type of t/fα noise; with α≈1. This scaling behaviour strongly suggests the presence of self-organized criticality (SOC) behind both the signals. To draw more insight into the detailed dynamics and to strengthen the explanation of such behaviour, causal relationship exploiting singularity spectrum analysis (SSA), multivariate singularity spectrum analysis (MSSA) and Correlation of Probability of Recurrence (CPR) based on recurrence plot have been studied in between the two signals for all the stations. The results reveal sufficient causal relationship and high correlation of probability of recurrence for strong support behind such critical dynamical systems.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132082083","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234524
Soumitra Sasmal, Indrajit Pan
Cloud computing technology is gaining rapid growth in popularity. Mostly it is web based service and some private modules are built on intranet or virtual private network. Network security is a major concern for both cloud users and hosts of data centers. In addition to this, clients are also concerned about the infrastructure and service quality offered by cloud service providers. Cloud service providers equally need to monitor clients' activities and applications running in the client terminals. This paper presents a mutual auditing framework in client-server mode. This interactive framework will keep track of different service modules from both end and will establish a mutual agreement of trust. Proposed framework has been tested on CloudSim and Xen hypervisor under different scale, scope and complexity. Experimental results show effectiveness and efficacy of the framework.
{"title":"Mutual auditing framework for service level security auditing in cloud","authors":"Soumitra Sasmal, Indrajit Pan","doi":"10.1109/ICRCICN.2017.8234524","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234524","url":null,"abstract":"Cloud computing technology is gaining rapid growth in popularity. Mostly it is web based service and some private modules are built on intranet or virtual private network. Network security is a major concern for both cloud users and hosts of data centers. In addition to this, clients are also concerned about the infrastructure and service quality offered by cloud service providers. Cloud service providers equally need to monitor clients' activities and applications running in the client terminals. This paper presents a mutual auditing framework in client-server mode. This interactive framework will keep track of different service modules from both end and will establish a mutual agreement of trust. Proposed framework has been tested on CloudSim and Xen hypervisor under different scale, scope and complexity. Experimental results show effectiveness and efficacy of the framework.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133247129","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234530
Sujan Sarkar, Jishan Mehedi
Adders are the main components in digital designs not only in additions but also in filter designing, multiplexing, and division. The circuit performance depends on the design of base adder. The demand of high-performance VLSI (very large scale integration) systems is increasingly rapidly for used in small and portable devices. The speed of operation is depends on the delay of the basic adder and it is a very important parameter for high performance. There are so many research works have been so far done on the adder to reduce the delay of it. This paper have done comparative study of various parallel adders and proposed a hybrid adder circuit to improve the delay. Carry Save Adder (CSA) and Carry Skip Adder (CSkA) have been incorporated to improve propagation delay. The result shows the effectiveness for propagation delay improvement.
{"title":"Design of hybrid (CSA-CSkA) adder for improvement of propagation delay","authors":"Sujan Sarkar, Jishan Mehedi","doi":"10.1109/ICRCICN.2017.8234530","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234530","url":null,"abstract":"Adders are the main components in digital designs not only in additions but also in filter designing, multiplexing, and division. The circuit performance depends on the design of base adder. The demand of high-performance VLSI (very large scale integration) systems is increasingly rapidly for used in small and portable devices. The speed of operation is depends on the delay of the basic adder and it is a very important parameter for high performance. There are so many research works have been so far done on the adder to reduce the delay of it. This paper have done comparative study of various parallel adders and proposed a hybrid adder circuit to improve the delay. Carry Save Adder (CSA) and Carry Skip Adder (CSkA) have been incorporated to improve propagation delay. The result shows the effectiveness for propagation delay improvement.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116968769","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234487
Nilotpal Das, M. Chakraborty
Filtering of ECG signal is an essential step in ECG signal processing and analysis. Since ECG is a low amplitude biosignal, it is easily corrupted by external noises, and by the presence of other biosignals. The aim of filtering is to remove unwanted noise while preserving important characteristics of the signal. Various filtering techniques are available thus choosing the right filter for ECG denoising is a problem. This study aims to solve that problem by quantifying and comparing performance of various FIR and IIR filters, based on their SNR values, in both diagnostic and monitoring mode. For this purpose, ECG signals are collected from MIT-BIH Physionet Database. This study identifies the order at which filters perform best, thus order optimization for each filter design has been done. Readers will be able to understand the variation of filter performance with filter order and choose the best filter for ECG signal denoising.
{"title":"Performance analysis of FIR and IIR filters for ECG signal denoising based on SNR","authors":"Nilotpal Das, M. Chakraborty","doi":"10.1109/ICRCICN.2017.8234487","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234487","url":null,"abstract":"Filtering of ECG signal is an essential step in ECG signal processing and analysis. Since ECG is a low amplitude biosignal, it is easily corrupted by external noises, and by the presence of other biosignals. The aim of filtering is to remove unwanted noise while preserving important characteristics of the signal. Various filtering techniques are available thus choosing the right filter for ECG denoising is a problem. This study aims to solve that problem by quantifying and comparing performance of various FIR and IIR filters, based on their SNR values, in both diagnostic and monitoring mode. For this purpose, ECG signals are collected from MIT-BIH Physionet Database. This study identifies the order at which filters perform best, thus order optimization for each filter design has been done. Readers will be able to understand the variation of filter performance with filter order and choose the best filter for ECG signal denoising.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129446375","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234535
Jishan Mehedi, Souvik Paul, R. Upadhyay
This paper proposed a Boolean expression simplification method with reduced complexity. To avoid bulky size of the truth table for multiple input combinational circuits, a reduced truth table is proposed here and this methodology is able to reduce its present size. This paper discusses about the generation of minterms from the SOP form of the Boolean expression. Here any input system is described as SOP form and represented by a matrix. This paper shows the effectiveness for reducing the computational complexity for Boolean expression simplification.
{"title":"Modified minterms genarations algorithm using weighted sum method","authors":"Jishan Mehedi, Souvik Paul, R. Upadhyay","doi":"10.1109/ICRCICN.2017.8234535","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234535","url":null,"abstract":"This paper proposed a Boolean expression simplification method with reduced complexity. To avoid bulky size of the truth table for multiple input combinational circuits, a reduced truth table is proposed here and this methodology is able to reduce its present size. This paper discusses about the generation of minterms from the SOP form of the Boolean expression. Here any input system is described as SOP form and represented by a matrix. This paper shows the effectiveness for reducing the computational complexity for Boolean expression simplification.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129490463","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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