Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243348
Bindu Madhuri Cheekati, Roje Spandana Rajeti
Present years are the exciting times for recognition of handwritten characters in the fields of Image Processing, Pattern Recognition, and Computer Vision. Recognizing handwritten characters using deep convolutional neural networks is a new era. There are various techniques available for handwritten recognition of characters, depending on hand-designed features. The proposed work is based on a systematic method to recognize both offline and online Telugu handwritten characters with residual learning framework called ResNet. A residual learning network is a concept of deeper neural networks where the training of the data is more effective. ResNet enables building very deep networks by addressing the vanishing gradient problem that occurs in deep convolutional neural networks. This paper deals in developing a fast, reliable Telugu handwritten ResNet for both online and offline character recognition and also improves the classification performance. The model is evaluated with IIITS-Telugu Handwriting Database; HP Labs database (Telugu) India and achieved very promising results. The Proposed residual net (ResNet-50) achieves 2.37% error on the ResNet-18 & 34 test set.
{"title":"Telugu handwritten character recognition using deep residual learning","authors":"Bindu Madhuri Cheekati, Roje Spandana Rajeti","doi":"10.1109/I-SMAC49090.2020.9243348","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243348","url":null,"abstract":"Present years are the exciting times for recognition of handwritten characters in the fields of Image Processing, Pattern Recognition, and Computer Vision. Recognizing handwritten characters using deep convolutional neural networks is a new era. There are various techniques available for handwritten recognition of characters, depending on hand-designed features. The proposed work is based on a systematic method to recognize both offline and online Telugu handwritten characters with residual learning framework called ResNet. A residual learning network is a concept of deeper neural networks where the training of the data is more effective. ResNet enables building very deep networks by addressing the vanishing gradient problem that occurs in deep convolutional neural networks. This paper deals in developing a fast, reliable Telugu handwritten ResNet for both online and offline character recognition and also improves the classification performance. The model is evaluated with IIITS-Telugu Handwriting Database; HP Labs database (Telugu) India and achieved very promising results. The Proposed residual net (ResNet-50) achieves 2.37% error on the ResNet-18 & 34 test set.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"28 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132027153","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243600
R. Reshma, V. Sathiyavathi, T. Sindhu, K. Selvakumar, L. Sairamesh
Agriculture aided by IoT is called Smart Agriculture and it gives rise to precision farming. Soil Monitoring combined with Internet of Things (IoT) technology aids in the enhancement of agriculture by increasing yield through gauging the exact soil characteristics such as Moisture, Temperature, Humidity, PH, and Nutrition content/Fertility. This data is then gathered in cloud storage and with the appropriate data operations; it enabled us to optimize farming strategies and helped create a trend analysis. This, then, allows us to precisely utilize resources and steer the farming methods in prudent ways to optimize yield. The proposed IoT system is composed of pH sensors, Humidity and temperature sensors, Soil moisture sensors, soil nutrient sensors (NPK) probes, microcontroller/microprocessor equipped with WiFi and Cloud storage. When the sensors are implemented, they measure the corresponding characteristics and transmit time-stamped live data to the cloud server. These sensors work together and provide wholesome data to the analyst. For the recommending system, the SVM and Decision Tree algorithm is proposed to get the crop suitable for the given soil data and helps to enhance the growth using an optimized farming process.
{"title":"IoT based Classification Techniques for Soil Content Analysis and Crop Yield Prediction","authors":"R. Reshma, V. Sathiyavathi, T. Sindhu, K. Selvakumar, L. Sairamesh","doi":"10.1109/I-SMAC49090.2020.9243600","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243600","url":null,"abstract":"Agriculture aided by IoT is called Smart Agriculture and it gives rise to precision farming. Soil Monitoring combined with Internet of Things (IoT) technology aids in the enhancement of agriculture by increasing yield through gauging the exact soil characteristics such as Moisture, Temperature, Humidity, PH, and Nutrition content/Fertility. This data is then gathered in cloud storage and with the appropriate data operations; it enabled us to optimize farming strategies and helped create a trend analysis. This, then, allows us to precisely utilize resources and steer the farming methods in prudent ways to optimize yield. The proposed IoT system is composed of pH sensors, Humidity and temperature sensors, Soil moisture sensors, soil nutrient sensors (NPK) probes, microcontroller/microprocessor equipped with WiFi and Cloud storage. When the sensors are implemented, they measure the corresponding characteristics and transmit time-stamped live data to the cloud server. These sensors work together and provide wholesome data to the analyst. For the recommending system, the SVM and Decision Tree algorithm is proposed to get the crop suitable for the given soil data and helps to enhance the growth using an optimized farming process.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132510850","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243326
Lokesh Singh, P. Gupta, R. Katarya, Pragyat Jayvant
As the innovation age encourages individuals to communicate their emotions via web-based media locales like Facebook, Instagram, Twitter, and so on. Numerous individuals share their musings and thoughts step by step in twitter similar to other tweets. This is considered as an exciting and attractive way to express ourselves as inspections are increasing bit by bit, due to which rundown of surveys are necessary for the job where summed up of text is required to give helpful data from the huge number of surveys. It is exceptionally hard for an individual to extricate helpful information or sum up it from the extremely enormous record. This paper focuses on comparison and analysis of different sentiment analysis techniques. It gives a far-reaching review about the recent and past examinations on sentiment analysis, challenges, and approaches for future angles.
{"title":"Twitter data in Emotional Analysis - A study","authors":"Lokesh Singh, P. Gupta, R. Katarya, Pragyat Jayvant","doi":"10.1109/I-SMAC49090.2020.9243326","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243326","url":null,"abstract":"As the innovation age encourages individuals to communicate their emotions via web-based media locales like Facebook, Instagram, Twitter, and so on. Numerous individuals share their musings and thoughts step by step in twitter similar to other tweets. This is considered as an exciting and attractive way to express ourselves as inspections are increasing bit by bit, due to which rundown of surveys are necessary for the job where summed up of text is required to give helpful data from the huge number of surveys. It is exceptionally hard for an individual to extricate helpful information or sum up it from the extremely enormous record. This paper focuses on comparison and analysis of different sentiment analysis techniques. It gives a far-reaching review about the recent and past examinations on sentiment analysis, challenges, and approaches for future angles.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692373","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243358
Shipra Singh, Kaptan Singh, A. Saxena
With the evolution of technology, usage of the Internet of Things (IoT) has grown immensely. IoT is now getting used in homes, traffic controls, mode of commute, and many more. IoT brings different physical entities together virtually. IoT is revolutionizing how different devices communicate with each other. With IoT, in the future, the transformation of the physical devices into smart devices with additional capabilities, are increased in ease of usage. Due to the advancement of wireless communication over the internet exposed to these devices and objects including several threats and security vulnerabilities. This paper discusses different issues related to the security and privacy of the Internet of Things along with proposed countermeasures.
{"title":"Security Domain, Threats, Privacy issues in the Internet of Things (IoT): A Survey","authors":"Shipra Singh, Kaptan Singh, A. Saxena","doi":"10.1109/I-SMAC49090.2020.9243358","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243358","url":null,"abstract":"With the evolution of technology, usage of the Internet of Things (IoT) has grown immensely. IoT is now getting used in homes, traffic controls, mode of commute, and many more. IoT brings different physical entities together virtually. IoT is revolutionizing how different devices communicate with each other. With IoT, in the future, the transformation of the physical devices into smart devices with additional capabilities, are increased in ease of usage. Due to the advancement of wireless communication over the internet exposed to these devices and objects including several threats and security vulnerabilities. This paper discusses different issues related to the security and privacy of the Internet of Things along with proposed countermeasures.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133216422","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243328
J. Divya, S. Shivagami
With the expansion of distributed computing, security of sensitive client information is emerging as a significant challenge. This paper proposes a secured cloud engineering with an equipment security module that separates cloud client information from conceivably malignant special areas or cloud chairmen. Further, the equipment security module gives basic security usefulness within a safely disconnected execution condition with just limited interfaces presented to weak administration frameworks or then again to cloud directors. Such limitation forestalls cloud directors from influencing the security of visitor instances [7]. The proposed building not simply makes preparations for wide attack vectors yet furthermore achieves a hardware security module [12]. This paper talks about the equipment and programming of the proposed cloud design along with its security and presents its exhibition results.
{"title":"A study of Secure cryptographic based Hardware security module in a cloud environment","authors":"J. Divya, S. Shivagami","doi":"10.1109/I-SMAC49090.2020.9243328","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243328","url":null,"abstract":"With the expansion of distributed computing, security of sensitive client information is emerging as a significant challenge. This paper proposes a secured cloud engineering with an equipment security module that separates cloud client information from conceivably malignant special areas or cloud chairmen. Further, the equipment security module gives basic security usefulness within a safely disconnected execution condition with just limited interfaces presented to weak administration frameworks or then again to cloud directors. Such limitation forestalls cloud directors from influencing the security of visitor instances [7]. The proposed building not simply makes preparations for wide attack vectors yet furthermore achieves a hardware security module [12]. This paper talks about the equipment and programming of the proposed cloud design along with its security and presents its exhibition results.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"71 28","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120941556","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243545
Praveen Kumar Sadineni
Today we are living in a digital world where most of the activities performed are online. Fraud transactions are ever growing since the growth of ecommerce applications. Millions of transactions are happening around every second everyday giving us the benefit of enjoying financial services through credit and debit cards. Fraud transactions are allowing illegal users to misuse the money of genuine users causing them financial loss. Accessibility of credit card transactions data, techniques used by the frauds, identifying scams in the bulk data which is getting produced very quickly, imbalanced data are some of the major challenges involved in detecting fraudulent credit card transactions. Hence, we need powerful techniques to identify fraudulent transactions. The current paper deals with various machine learning techniques such as Artificial Neural Network (ANN), Decision Trees, Support Vector Machine (SVM), Logistic Regression and Random Forest to detect fraudulent transactions. Performance analysis of these techniques is done using Accuracy, Precision and False alarm rate metrics. Dataset used to carry out the experiment is taken from Kaggle data repository. The experiment shows that Radom Forest could achieve an accuracy of 99.21%, Decision Tree 98.47%. Logistic Regression 95.55%, SVM 95.16% and ANN 99.92%.
今天，我们生活在一个数字世界，大多数活动都是在网上进行的。随着电子商务应用的发展，欺诈交易越来越多。每时每刻都有数以百万计的交易发生，这让我们可以通过信用卡和借记卡享受金融服务。欺诈交易允许非法用户滥用真正用户的钱，给他们造成经济损失。信用卡交易数据的可访问性、欺诈者使用的技术、在快速生成的大量数据中识别骗局、不平衡数据是检测欺诈性信用卡交易所涉及的一些主要挑战。因此，我们需要强大的技术来识别欺诈性交易。本文涉及各种机器学习技术，如人工神经网络(ANN)，决策树，支持向量机(SVM)，逻辑回归和随机森林来检测欺诈交易。这些技术的性能分析是使用准确度、精度和虚警率指标来完成的。用于实验的数据集取自Kaggle数据库。实验表明，随机森林的准确率为99.21%，决策树的准确率为98.47%。Logistic回归95.55%，SVM 95.16%， ANN 99.92%。
{"title":"Detection of Fraudulent Transactions in Credit Card using Machine Learning Algorithms","authors":"Praveen Kumar Sadineni","doi":"10.1109/I-SMAC49090.2020.9243545","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243545","url":null,"abstract":"Today we are living in a digital world where most of the activities performed are online. Fraud transactions are ever growing since the growth of ecommerce applications. Millions of transactions are happening around every second everyday giving us the benefit of enjoying financial services through credit and debit cards. Fraud transactions are allowing illegal users to misuse the money of genuine users causing them financial loss. Accessibility of credit card transactions data, techniques used by the frauds, identifying scams in the bulk data which is getting produced very quickly, imbalanced data are some of the major challenges involved in detecting fraudulent credit card transactions. Hence, we need powerful techniques to identify fraudulent transactions. The current paper deals with various machine learning techniques such as Artificial Neural Network (ANN), Decision Trees, Support Vector Machine (SVM), Logistic Regression and Random Forest to detect fraudulent transactions. Performance analysis of these techniques is done using Accuracy, Precision and False alarm rate metrics. Dataset used to carry out the experiment is taken from Kaggle data repository. The experiment shows that Radom Forest could achieve an accuracy of 99.21%, Decision Tree 98.47%. Logistic Regression 95.55%, SVM 95.16% and ANN 99.92%.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120941111","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243431
Komal P. Kansara, Bintu Kadhiwala
In today's world, trillions of persons are providing their data to social network data provider for connecting, interacting and data sharing with other users. The data provider may utilize these collected data for analysis purpose. Alternatively, multiple data providers prefer collaboration to attain enhanced analysis outcomes from the collected collaborated data. For such collaboration, the data providers do not share their data directly due to privacy issues instead they share the collected data with the trusted data publisher. The data publisher combines these collected data and subsequently publishes the data. Data collected at trusted data publisher site from multiple providers contain individuals' information that may be sensitive. Hence, the privacy of individuals may be compromised if it is published by the publisher in its original form. As a consequence, in literature, various non-cryptographic approaches are discussed for privacy-preserving collaborative social network data publishing. The motive of this paper is to emphasize the evaluation of these existing approaches with the help of different parameters.
{"title":"Non-cryptographic Approaches for Collaborative Social Network Data Publishing - A Survey","authors":"Komal P. Kansara, Bintu Kadhiwala","doi":"10.1109/I-SMAC49090.2020.9243431","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243431","url":null,"abstract":"In today's world, trillions of persons are providing their data to social network data provider for connecting, interacting and data sharing with other users. The data provider may utilize these collected data for analysis purpose. Alternatively, multiple data providers prefer collaboration to attain enhanced analysis outcomes from the collected collaborated data. For such collaboration, the data providers do not share their data directly due to privacy issues instead they share the collected data with the trusted data publisher. The data publisher combines these collected data and subsequently publishes the data. Data collected at trusted data publisher site from multiple providers contain individuals' information that may be sensitive. Hence, the privacy of individuals may be compromised if it is published by the publisher in its original form. As a consequence, in literature, various non-cryptographic approaches are discussed for privacy-preserving collaborative social network data publishing. The motive of this paper is to emphasize the evaluation of these existing approaches with the help of different parameters.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115534699","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243472
V. Prakasam, N. Reddy
This paper uses coaxial probe feed method to present, design, and simulate elliptical microstrip patch antenna at ISM band. This paper processes an innovative elliptical microstrip patch (MSPA) antenna at standard ISM frequency band ranges from 2.4 GHz to 2.5 GHz. The planned and simulated EMSPA operating frequency is 2.4 GHz to 2.5 GHz and 4.2, 4.4, 4.6 & 4.8 FR4 substrate, this selected frequency increases efficiency in terms of S11 and reasonable gain value. In this study, coaxial probes feed the proposed antenna fixed on an FR-4 substrate material which has 4.2, 4.4, 4.6 & 4.8 dielectric constant, substratum thickness is 6.6 mm. The intension of the proposed antenna is that to determine the higher gain, less S11 at different operating frequencies that are 2.35 GHz, 2.4 GHz, 2.45GHz and 2.5 GHz, which is the ISM band range. The high-performance systems such as rockets, ships, missiles and satellites use elliptical microstrip patch antennas. Antennas with optimal measurements of elliptical microstrip patches act as circularly polarized wave radiators. Various simulation antenna design software is available, such as FEKO, IE3D, CST, HFSS, Antenna Magus and MATLAB. Here, using MATLAB simulation software tool, the EMSPA is designed and simulated and also estimate the performance characteristics, such as s-parameter, vswr, EH fields, radiation pattern, current distribution, gain, elevation and azimuthal radiation pattern.
{"title":"Design and Simulation of Elliptical Micro strip Patch Antenna with Coaxial Probe Feeding for Satellites Applications Using Matlab","authors":"V. Prakasam, N. Reddy","doi":"10.1109/I-SMAC49090.2020.9243472","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243472","url":null,"abstract":"This paper uses coaxial probe feed method to present, design, and simulate elliptical microstrip patch antenna at ISM band. This paper processes an innovative elliptical microstrip patch (MSPA) antenna at standard ISM frequency band ranges from 2.4 GHz to 2.5 GHz. The planned and simulated EMSPA operating frequency is 2.4 GHz to 2.5 GHz and 4.2, 4.4, 4.6 & 4.8 FR4 substrate, this selected frequency increases efficiency in terms of S11 and reasonable gain value. In this study, coaxial probes feed the proposed antenna fixed on an FR-4 substrate material which has 4.2, 4.4, 4.6 & 4.8 dielectric constant, substratum thickness is 6.6 mm. The intension of the proposed antenna is that to determine the higher gain, less S11 at different operating frequencies that are 2.35 GHz, 2.4 GHz, 2.45GHz and 2.5 GHz, which is the ISM band range. The high-performance systems such as rockets, ships, missiles and satellites use elliptical microstrip patch antennas. Antennas with optimal measurements of elliptical microstrip patches act as circularly polarized wave radiators. Various simulation antenna design software is available, such as FEKO, IE3D, CST, HFSS, Antenna Magus and MATLAB. Here, using MATLAB simulation software tool, the EMSPA is designed and simulated and also estimate the performance characteristics, such as s-parameter, vswr, EH fields, radiation pattern, current distribution, gain, elevation and azimuthal radiation pattern.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114827329","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243531
Gundlapalle Raiesh, Boda Saroia, Manian Dhivya, A. B. Gurulakshmi
Histopathological examination of tissue models is basic for the conclusion and reviewing of colon malignancy. In any case, the technique is subjective and prompts imperative intra/bury spectator distinction in the examination as it predominantly relies upon the graphical evaluation of histopathologists. Thus, a tried and true PC supported technique, which can naturally group harmful and ordinary colon tests are required; however, automating this strategy is demanding because of the nearness of exceptions. In this paper, a productive technique for identifying colon disease from biopsy tests which comprise of four imperative stages. DB-SCAN estimation to distinguish colon tumor from biopsy tests is presented in this paper. In the proposed approach, from the outset, the colon biopsy tests are preprocessed using DB-SCAN configuration to make a set of redundant localities in which groups or clusters are formed. At that point, the exceptions inside the bunched areas are created as a tree structure in light of the choice tree in which the anomalies are hubs, and the connection between hubs are delivered based on data about exceptions. At that point, entropy-based exception score calculation will be done on every hub of the tree. The Information picks up technique is utilized to figure the score for the exceptions. At long last, score based grouping is accomplished to order the ordinary or harmful cells. Experimental trials exhibit, the proposed strategy has better outcomes contrasted to existing strategies. It furthermore acclaims that the proposed procedure is adequate for the colon tumor identification process. The proposed strategy is executed on Matlab working platform and the investigations exhibit that the proposed technique has high accomplished high grouping precision contrasted and different strategies.
{"title":"DB-Scan Algorithm based Colon Cancer Detection And Stratification Analysis","authors":"Gundlapalle Raiesh, Boda Saroia, Manian Dhivya, A. B. Gurulakshmi","doi":"10.1109/I-SMAC49090.2020.9243531","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243531","url":null,"abstract":"Histopathological examination of tissue models is basic for the conclusion and reviewing of colon malignancy. In any case, the technique is subjective and prompts imperative intra/bury spectator distinction in the examination as it predominantly relies upon the graphical evaluation of histopathologists. Thus, a tried and true PC supported technique, which can naturally group harmful and ordinary colon tests are required; however, automating this strategy is demanding because of the nearness of exceptions. In this paper, a productive technique for identifying colon disease from biopsy tests which comprise of four imperative stages. DB-SCAN estimation to distinguish colon tumor from biopsy tests is presented in this paper. In the proposed approach, from the outset, the colon biopsy tests are preprocessed using DB-SCAN configuration to make a set of redundant localities in which groups or clusters are formed. At that point, the exceptions inside the bunched areas are created as a tree structure in light of the choice tree in which the anomalies are hubs, and the connection between hubs are delivered based on data about exceptions. At that point, entropy-based exception score calculation will be done on every hub of the tree. The Information picks up technique is utilized to figure the score for the exceptions. At long last, score based grouping is accomplished to order the ordinary or harmful cells. Experimental trials exhibit, the proposed strategy has better outcomes contrasted to existing strategies. It furthermore acclaims that the proposed procedure is adequate for the colon tumor identification process. The proposed strategy is executed on Matlab working platform and the investigations exhibit that the proposed technique has high accomplished high grouping precision contrasted and different strategies.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125385971","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243512
Shwetha N, M. Priyatham
Digital communication has become an important part of our lives and technology has been undergoing advancements. The main two problems faced in digital communication is noise and inter-symbol interference (ISI). The IS I is induced due to channel characteristics, which is time-varying and unknown. Hence an adaptive channel equalizer is used to inverse the effect channel had on the signal to get back the initial information. There are many adaptive algorithms to update the coefficients of equalizers, evolutionary algorithms are used in this paper to do so. The two algorithms used before are particle swarm optimization (PSO) and conventional differential evolution (DE). The newest algorithm is the Evolutionary Programming Least Mean Square Algorithm (EPLMS) this gives a better solution faster.
{"title":"Performance Analysis of Self Adaptive Equalizers using EPLMS Algorithm","authors":"Shwetha N, M. Priyatham","doi":"10.1109/I-SMAC49090.2020.9243512","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243512","url":null,"abstract":"Digital communication has become an important part of our lives and technology has been undergoing advancements. The main two problems faced in digital communication is noise and inter-symbol interference (ISI). The IS I is induced due to channel characteristics, which is time-varying and unknown. Hence an adaptive channel equalizer is used to inverse the effect channel had on the signal to get back the initial information. There are many adaptive algorithms to update the coefficients of equalizers, evolutionary algorithms are used in this paper to do so. The two algorithms used before are particle swarm optimization (PSO) and conventional differential evolution (DE). The newest algorithm is the Evolutionary Programming Least Mean Square Algorithm (EPLMS) this gives a better solution faster.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121954224","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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