2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)最新文献

{"title":"IoT based Health Care Monitoring Kit","authors":"Anand D. Acharya, Shital Patil","doi":"10.1109/ICCMC48092.2020.ICCMC-00068","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00068","url":null,"abstract":"This paper describes the design and implementation of an IoT-based smart doctor kit for a critical medical condition that can provide a versatile connection to IOT data that can help emergency health services such as Intensive Care Units (ICU).In recent technology, IoT gives base where the user can access all information regarding health from anywhere. Some of the Example where IOT are used such as Connected Car Smart Home, and Health Monitoring System. Now in recent days, the healthcare control system is necessary to regularly monitor the patient’s physiological parameters. Heart Beat, body temp, ECG and Respiration are the physiological parameters of the body. The process of measuring these body parameters are called Health monitoring. Different sensors are used to monitor this data and this data is continuously monitored and send towards the internet server or on a mobile app. main advantage of health monitoring is that it reduces human error. The proposed model allows users to achieve better health-related risks and minimizes healthcare costs by collecting, recording, testing and distributing large data in real-time and perfectly. The idea behind this paper is to reduce the patient’s worry about visiting a doctor every time. The time not only of patients but also of doctors is saved with the aid of this project proposal, so doctors can also help patients as much as possible in critical condition.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"54 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":"127858082","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}

{"title":"A Hybrid Secure Keyword Search Scheme in Encrypted Graph for Social Media Database","authors":"R. Arthy, E. Daniel, T.G. Maran, M. Praveen","doi":"10.1109/ICCMC48092.2020.ICCMC-000186","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000186","url":null,"abstract":"Privacy preservation is a challenging task with the huge amount of data that are available in social media. The data those are stored in the distributed environment or in cloud environment need to ensure confidentiality to data. In addition, representing the voluminous data is graph will be convenient to perform keyword search. The proposed work initially reads the data corresponding to social media and converts that into a graph. In order to prevent the data from the active attacks Advanced Encryption Standard algorithm is used to perform graph encryption. Later, search operation is done using two algorithms: kNK keyword search algorithm and top k nearest keyword search algorithm. The first scheme is used to fetch all the data corresponding to the keyword. The second scheme is used to fetch the nearest neighbor. This scheme increases the efficiency of the search process. Here shortest path algorithm is used to find the minimum distance. Now, based on the minimum value the results are produced. The proposed algorithm shows high performance for graph generation and searching and moderate performance for graph encryption.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"112 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":"124121781","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}

{"title":"Wildfire forecast within the districts of Kerala using Fuzzy and ANFIS","authors":"Abhijith Jayakumar, Anandhu Shaji, Nitha L","doi":"10.1109/ICCMC48092.2020.ICCMC-000123","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000123","url":null,"abstract":"Wildfire is an unbounded catastrophe that affects the serenity of biodiversity. Thus wildfire prediction helps to maintain the resource conservation and recovery management. This paper depicts the prediction of wildfire prevailing in 2 districts of Kerala (Idukki, Wayanad). This paper is implemented using ANFIS (Adaptive Neuro-Fuzzy-Inference System) and fuzzy clustering. Objective of this paper is to predict wildfire. Fuzzy C-means (FCM) of fuzzy clustering is used to obtain the clustered output followed by the classification using ANFIS. It enables an experimental environment on which human knowledge (rules) and learning algorithms to be combined. Based on the output value generated from ANFIS classification the objective is predicted.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"59 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":"114347551","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}

{"title":"Privacy Preserving Data Publishing of Multiple Sensitive Attributes by using Various Anonymization Techniques","authors":"Jasmma N Vanasiwala, Nirali R. Nanavati","doi":"10.1109/ICCMC48092.2020.ICCMC-0000155","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-0000155","url":null,"abstract":"The enhancement in digital era speeds up the process of aggregating the massive amount of information from various sectors of governments, diverse sections of healthcare unit, multiple organizations as well as from individuals. This aggregated data’s release is essential for the betterment of researchers, varied occupations, and individuals etc. This gives rise for necessitate releasing and exchanging of assembled data. However, when information is in native form, it carries some crucial sensitive facts about human beings and/or organizations. If such information is disclosed, personal and/or organizational privacy may be threatened. Therefore, Privacy Preserving Data Publishing (PPDP) comes up with tools and techniques which describe how to publish valuable facts along with its privacy protection. Thus, it is inevitable to alter the data before its release with the aim to persist its privacy without jeopardize its utility. This is achieved by varied anonymization schemes. In point of fact, datasets comprise of distinct kinds of Multiple Sensitive Attributes (MSAs) (which can be numerical and/or categorical). Anonymization done for only Single Sensitive Attribute is not having any importance in practical scenarios. On that account, it is significant that, while operating the highly dimensioned data, the association amidst these MSAs is sustained along with the efficient privacy preservation of Mixed (numerical as well as categorical) MSAs. This paper concentrates mainly on analysing different schemes proposed in literature for PPDP of MSAs.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"27 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":"125746020","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}

{"title":"Review on Candidate Feature Extraction and Categorization for Unstructured Text Document","authors":"P. P. Shelke, Aditya A Pardeshi","doi":"10.1109/ICCMC48092.2020.ICCMC-00017","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00017","url":null,"abstract":"Word is a primary unit in the sentences, which contains some extra information. This extra information is crucial in the candidate feature categorization progression. To gain such information the established techniques mine the candidate feature via n gram and noun phrase based approaches, but such approaches ignore the grammatical structure, which laid in huge quantity of insubstantial features. This paper inspects various mechanisms for feature mining and various issues are explored. A system is propounded which is based on tree structure for the candidate feature mining and branches of the tree are extracted using part-of-speech (POS) labelling for candidate phrase. To avoided redundant phrases, filtering is recommended. Finally, machine learning is used for the progression of feature categorization.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"240 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":"127142788","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}

{"title":"Weed Identification using Convolutional Neural Network and Convolutional Neural Network Architectures","authors":"E. Gothai, P. Natesan, S. Aishwariya, T. Aarthy, G. Brijpal Singh","doi":"10.1109/ICCMC48092.2020.ICCMC-000178","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000178","url":null,"abstract":"In order to overcome this threat imposed by weeds in agriculture, a measure is taken to identify the weeds that grow along with the seedlings with the help of deep learning (DL) technique. Convolutional neural network (CNN), a class of DL render a good way to identify the weeds that harm the plant’s growth. Aiming at achieving a greater accuracy, the models such as four convolution layered, six convolution layered, eight convolution layered and thirteen convolution layered architecture were built. Comparatively, eight convolution layered architecture resulted with 97.83% as training accuracy and 96.53% of validation accuracy than the VGG-16 model resulted with. The use of CNN architectures paved way to reach training accuracy of 96.27% and validation accuracy with 91.67% in ZFNet and 97.63% as training accuracy and 92.62% of validation accuracy in ALEXNET. Therefore, by the use of this technology and suggested method there is a lot of possibilities to avoid the manual field work of identifying the weeds. Our results suggest that more of datasets can be used and fine-tuning of parameters can be done.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"64 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":"129965432","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}

{"title":"Hybrid Model for Lung Nodule Segmentation based on Support Vector Machine and k-Nearest Neighbor","authors":"Srishti Sharma, Prasenjeet Fulzele, I. Sreedevi","doi":"10.1109/ICCMC48092.2020.ICCMC-00034","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00034","url":null,"abstract":"The synergy of recently developed diagnostic radiology and machine learning algorithms has assured far reaching implications for the healthcare industry. At present, radiologists have access to top notch computer aided diagnostic (CAD) systems to create a consequence of the amplifying use and substantial applications of AI tools built right on the top of simple machine learning algorithms. This article proposes a model that extracts lung nodules from a 2 dimensional computed tomography (CT) slice by utilizing synthetic minority over-sampling technique (S MOTE) along with support vector machine (SVM) and k-nearest neighbor (K-NN) on a dataset of SPIE-AAPM Lung CT Challenge, 2015. Morphological transformations were performed on the 2D CT slices to achieve lung segmentation. Shape and textural features were retrieved into a vector to represent the region of interests (ROIs) from the lungs. Further, SMOTE was applied to resolve the issue of an imbalanced training data set which had very few samples of positive class in comparison with the samples of negative class. This ensured unbiased training of the classifiers and higher sensitivity towards the positive class. In the proposed work, two binary classifiers are combined in order to get an efficient model that exploited the individuality of both the classifiers. First, SVM and k-NN are trained separately on the balanced training dataset and then the outputs of both the classifiers are combined using simple sum rule to make the final prediction based on the collective scores for each data sample. Consequently, the resultant predictions depend on the collective performance of both classifiers for enhancing the overall efficiency of the model. The proposed hybrid model of SVM-k-NN outperforms the individual models with a sensitivity of 94.45% and G-Mean value of 94.19%. The model concentrates on accurately predicting the presence of a nodule and not for misclassifying a positive sample as it may lead to a huge loss to the patient.CCS CONCEPTS• Diagnostic radiology • computer aided diagnostic system (CAD) • machine learning","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"1 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":"130174885","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}

{"title":"The Challenges for Context – Oriented Data Accumulation with Privacy Preserving in Wireless Sensor Networks","authors":"T. Babu, V. Jayalakshmi","doi":"10.1109/ICCMC48092.2020.ICCMC-000160","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000160","url":null,"abstract":"Wireless Sensor Networks (WSNs) plays a vital role in our everyday lives. In WSNs the data are to be sensed between one node to another set of nodes in the network for the purpose of achieving transmission. At the time of transmitting sensed data in the Wireless Networks it may utilize large amount of energy (like power consumption, payload, etc.) for any operation. Accumulating data plays vital role in conserving energy in the network framed using wireless sensors. Accumulation of the data is a procedure which was mainly designed to minimize the overhead in the communication as well as control energy utilization in sensor nodes during the process of data collection. A data aggregation protocol plays a firewall for protecting data among the elements of wireless transmission. Enhancing the lifetime of wireless networks is a challenging issue. In this paper we analyse the challenges for privacy preserving in protocols of data accumulation (aggregation). initially the accumulation protocol is based on various metrics like energy consumption, accuracy of data, authentication of data and confidentiality of data. Here we also identify various resolvable issues for enhancing quality of preserving privacy in aggregation protocols.","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":"122355054","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}

{"title":"Machine Learning Applications Using Waikato Environment for Knowledge Analysis","authors":"Swarnali Daw, Rohini Basak","doi":"10.1109/ICCMC48092.2020.ICCMC-00065","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00065","url":null,"abstract":"Data is the most important word of the present world. As nowadays’ data is growing rapidly in every second, handling of data has become a great challenge. Data mining basically extracts knowledge from large amount of data and is used to obtain rules or patterns from the existing data. As Machine Learning (ML) is introduced, it applies new algorithms on the pattern of data and from past experience. ML is used so that the machine can handle the data more efficiently. Many algorithms are used for this purpose. WEKA-knowledge analysis based on the Waikato environment is introduced as a data mining platform and it has the facility to use machine learning algorithms with reference to data mining.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"1 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":"130585939","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}

{"title":"Cardiovascular Disease Forecast using Machine Learning Paradigms","authors":"Saiful Islam, N. Jahan, Mst. Eshita Khatun","doi":"10.1109/ICCMC48092.2020.ICCMC-00091","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00091","url":null,"abstract":"In this recent era, Cardiovascular disease (CVD) propagation rate has been intensifying the cause of death worldwide among the non-communicable disease. In particular the south asian countries have a tremendous risk of cardiovascular disease at an early age than any other ethnic group. Most often it’s challenging for medical practitioners to predict cardiovascular disease as it requires experience and knowledge which is a complex task to accomplish. This health industry has enormous amounts of data which is useful for making effective conclusions using their hidden information. So, using appropriate results and making effective decisions on data, some superior data analysis techniques are used, for example Naive Bayes, Decision Tree. By using some properties like (age, gender, bp, stress, etc) it can be predicted the chances of cardiovascular disease. In this study, we collected 301 sample data with 12 clinical attributes. Logistic regression, Decision tree, SVM, and Naive bayes classification algorithms have been applied to predict heart disease. In this case, logistic regression provided 86.25% accuracy. However, we also compared the UCI dataset based results with our model.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"25 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":"125405888","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}