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A Generic and Robust System for Automated Detection of Different Classes of Arrhythmia

Procedia computer Science 2020年 167卷 1801-1810页

作者： Vandana Singh U. Srinivasulu Reddy G. Manju Bhargavia M.Tech (Data Analytics) National Institute of Technology Tiruchirappalli 620015 India Assistant Professor Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli 620015 India Post-Doc Department of Physics National Institute of Technology Tiruchirappalli 620015 India

Cardiovascular Arrhythmias (irregular beat) are related to the sudden death, can be characterized into two kinds, life-threatening (dangerous) and non-life-threatening. In clinical research and diagnosis analysis electrocardiography (ECG) plays an important role. The Advancement of Medical Instrumentation (AAMI) suggests, all the heartbeat of MIT-BIH dataset into four classes, namely, normal or bundle branch block (N), supraventricular ectopic (S), ventricular ectopic (V) and fusion of ventricular and normal (F). The objective of this paper is the classification of arrhythmias on the ECG as per AAMI standards. First beat detection and then on a given window size beat segmentation is performed. Then, features computed from ECG signals are RR (separation between two successive heartbeat pulses), local binary pattern(LBP), morphological changes, wavelet, high order statistics, and several amplitude values; Pre-RR, Post-RR, Local RR and Global RR. The main reason is to computing many morphological features to comparison analysis with the model when trained and tested on single features vs combining all features together. Model gives highest performance which is 90.8% accuracy using LBP as single feature compared with other features. Combined all important features together and optimal features (total 104 ) are given as an input to Single SVM classifier as well as ensembles of SVM which maps the component feature vectors to the particular class name. Principal Component Analysis (PCA) technique is also applied to the model which reduces the total number of features from 104 to 30 features. The Single SVM system can accurately classify four beat types and has an overall classification rate of 94.05% without PCA and using PCA 92.84% while the Ensemble SVM classifies and with an accuracy of 92.96%

关键词： Electrocardiogram (ECG) signal MIT-BIH dataset Wavelet Transform Support Vector Machines OVO

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Machine learning approach for detection of keratoconus

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IOP Conference Series: Materials Science and Engineering 2021年第1期1055卷

作者： S Shanthi K Nirmaladevi M Pyingkodi K Dharanesh T Gowthaman B Harsavardan Associate Professor Department of Computer Science and Engineering Kongu Engineering College Erode India Assistant Professor Department of Computer Applications Kongu Engineering College Erode India UG Student Department of Computer Science and Engineering Kongu Engineering College Erode India

Keratoconus is a progressive eye disease and it should be detected in early stage, to avert probable refractive surgery that could develop ecstasies. In this the authors proposes a new computer aided diagnosis model based on Support Vector Machine (SVM) learning to detect the early stage of keratoconus using the available topographic, pachymetric and aberrometry parameters of patients with keratoconus, subclinical keratoconus and normal corneas. The proposed SVM produces 91.8% accuracy with 94.2% sensitivity, 97.5% specificity for classification of early keratoconus from normal; 100% accuracy with 100%, 100% of sensitivity and specificity respectively for classification of early keratoconus from subclinical keratoconus.

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LRFM model for customer purchase behaviour using K-Means algorithm

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IOP Conference Series: Materials Science and Engineering 2021年第1期1055卷

作者： C Jamunadevi S Tamil Selvan M Govindarajan C Saravanan B R Janaki Raman Assistant Professor Department of CT-PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India Associate Professor Department of Computer Applications Velalar College of Engineering and Technology Erode India Assistant Professor Department of Information Technology Velalar College of Engineering and Technology Erode India UG Scholar Department of Computer Technology-PG Kongu Engineering College Perundurai Erode India

The COVID-19 pandemics have a major collision on every aspect of life, including how people shop for their requirements. As the pandemic has reshaped life as we know, it's also initiated many trends – but the biggest of these trends may be online shopping. The shift toward online shopping was happening before the pandemic, but according to new statistics from IBM, the COIVD-19 has accelerated consumers shift toward online shopping by 5 years. The chief idea of the article is to inspect if the situation is approaching people to purchase things online and the continuation of shopping things online even after the end of pandemic. The information for the article has been gathered by circulating the survey on social networks. The questionnaire is comprised of 12 different questions, and 615 people responded to it. This work is based on LRFM (Length, Recency, Frequency, and Monetary) replica and separation of data based on the questionnaire using K-Means algorithm. Silhouette analysis helps to decide the extent of division among clusters. The results of the survey has a termination that people are fond of purchasing products online through the lockdown and people too agreed that the rate of online shopping will increase in the future when this pandemic is over.

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An Automated Optimize Utilization of Water and Crop Monitoring in Agriculture Using IoT

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IOP Conference Series: Materials Science and Engineering 2021年第1期1131卷

作者： Sanwta Ram Dogiwal Pankaj Dadheech Ankit Kumar Linesh Raja Abhishek Kumar Mahender Kumar Beniwal Associate Professor Department of Information Technology Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Associate Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Applications Manipal University Jaipur Rajasthan India Assistant Professor Department of Computer Science & IT Jain (Deemed to be University) Bangalore India

AI Solution for Farmers is an agricultural based project, created to help farmers and to help them in increasing their productivity. Since the technologies are running the world, why agriculture must be deprived of it. Agriculture is one of the most important areas that have major impacts on the economy and society of a country. Technological developments serve as tools to share knowledge and practices of agricultural products and make more satisfactory lives for farmers, traders, policymakers, and the overall society. It is evident that knowledge has become a crucial component in production, society, food security, health, poverty, and other millennium development goals. The use of technology may provide a better approach to solve the problems arising from sowing the seed to harvest the crop. The new technologies like Machine Learning and Data Science may be great help to have an eye on the deciding factors to the growth of crop.

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A hybrid artificial neural network classifier based on feature selection using binary dragonfly optimization for breast cancer detection

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IOP Conference Series: Materials Science and Engineering 2021年第1期1055卷

作者： S Parvathavarthini D Deepa Assistant Professor (Sr. Grade) School of Computer Technology and Applications Kongu Engineering College Erode Tamilnadu INDIA Data Scientist Rubixe Technologies private Limited Karnataka

Medical image analysis has become a challenging task as it contributes to disease diagnosis. Breast cancer has been the prominent reason for death among women. While analysing mammogram images, there is a need for clear differentiation of between benign and malignant tissues. Also, early detection of breast masses lead to prediction of breast cancer at the initial stage and minimizes risk of death. In this work, the image is preprocessed using Median filter and is segmented using Fuzzy C Means clustering. Fuzzy C-Means clustering algorithm helps in extracting the region of interest by allocating pixels with similar characteristics into a single group. A pixel may be present in various clusters with different membership values. The belongingness of a pixel to a cluster is decided by the highest membership value. Then the statistical, texture and shape features are extracted from the image. Since there may be many features that are less relevant for classification process, prominent features are selected with the help of Binary Dragonfly Optimization Algorithm and the selected features are fed into a Feed Forward Neural Network trained with Back Propagation Learning to classify the mass as benign or malignant. Experiments are conducted over 320 images from mini-MIAS database out of which 200 ROIs are used in training and 120 ROIs are used in testing phase. The region of interest from given mammogram images are extracted successfully and classified with an accuracy of 98.75%.

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MAR_Sybil: Cooperative RSU Based Detection and Prevention of Sybil Attacks in Routing Process of VANET

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Journal of Physics: Conference Series 2020年第1期1427卷

作者： Mahabaleshwar Kabbur V. Arul Kumar Research Scholar School of Computer Science & Applications REVA University Bengaluru Assistant Professor School of Computer Science & Applications REVA University Bengaluru

VANET is a next generation communication technology where vehicles create an autonomous network with assistance of RSU (Road Side Units). VANET provides legitimate information to the users on the road, in order to increase the road and user's safety. It provides useful information to the vehicles about directions, location mapping, premises, etc. The fast propagation of emergency and local warning messages to the approaching vehicles will be helpful for preventing secondary accidents. If security of the network is not guaranteed, several attacks may occur, thereby alert messages may not reach to the RSUs on time or message may get spoof due to attack on it. Major attacks on emergency messages such as timing attack, spoofing attack, DoS attack, Sybil attack, etc. would be considered to mitigate and make the communication and infrastructure more secure. One of the major emergency message attack is Sybil, which is used by selfish message propagators to claim authenticity of their message by propagating with multiple identities. By deceiving other nodes with their false support using multiple identifies, they can achieve various purpose in network like vehicle re-routing, speed shift etc. This work proposes a RSU cooperative detection mechanism involving triangulation and fake propagation by RSU to identify the Sybil attacks in the VANET. The proposed methodology secures fast communication emergency messages to prevent secondary accidents by validating the message sources, nodes will accept only the messages from RSU which are digitally signed and source validated against Sybil nodes. The performance evaluation is made with existing methodology and effectiveness of the proposed methodology results demonstrated in article. The spatial location and trajectory of attacker is learnt effectively with RSU cooperation in the proposed work.

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Smart Villages: IoT Technology Based Transformation

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Journal of Physics: Conference Series 2021年第1期2070卷

作者： Pankaj Mudholkar Megha Mudholkar B S Puneeth Kumar V Dankan Gowda S. Srinivasulu Raju Assistant Professor Department of Master of Computer Applications. Thakur Institute of Management Studies Career Development and Research Mumbai Maharashtra (State) INDIA Assistant Professor Department of Computer Science. St. Joseph's College (Autonomous) Bangalore Karnataka (State) INDIA Assistant Professor Department of Electronics and Communication Engineering. B.M.S. Institute of Technology and Management Bangalore Karnataka (State) INDIA Department of Electronics and Instrumentation Engineering. Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Andhra Pradesh(State) INDIA

In recent years, large-scale urbanisation has been on the rise with cities being the hubs for growth, but rural business continues to play an important role in any country's overall development. A recent report reveals that almost 69 percent of India's population resides in their villages, accounting for almost 50 percent of the nation's GDP. This agricultural region varies from small towns with less than 500 residents to small towns. There are also similar situations in western nations. Despite their economic contribution, smaller villages seem to earn fewer in terms of infrastructural expenditure. The major problems addressed in these small towns include shortage of adequate public transit, emergency care and limited knowledge on federal subsidies for rural areas. Our vision is to expand the Smart city to Smart village by allowing use of recent technical advances and giving more attention to the problems in rural areas. The approach in this chapter is to bring IoT technology to Villages by literally showing a network of linked sensors and knowledge dissemination devices, controlling energy use and ensuring infrastructure protection. It gives a wide-ranging vision of enhancing the standard of living in villages and encourages them to meet the essential needs of domestic villagers.

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Analysis of Cryptography Encryption for Network Security

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IOP Conference Series: Materials Science and Engineering 2020年第2期981卷

作者： V. Esther Jyothi Dr. BDCN Prasad Dr Ramesh Kumar Mojjada Assistant Professor Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Professor Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Assistant Professor Dept of ECM Koneru Lakshamaiah Educational Foundation Vaddeswaram

In order to secure network and data transmission via a wireless network, cryptography and network encryption is being used. Providing data protection is one of the key aspects of wireless network data transmission. There are sensors in the wireless networks; they are linked to the base station. The need for protection of the wireless network sensor is very critical, and encryption and network security are necessary. Network security includes security for the terminal system as well as for the whole network system. Network security is one of the main concerns as the world transitions into the digital world. Security of the network provides security for administrator managed data. Increasing communication technology also requires safe communication which is met through various encryption techniques such as cryptography, digital signatures, watermarking, steganography and other applications. Cryptography is a technique of encryption used to protect the network, as various networks are related and admire attacks and intrusions. In this paper we discuss the cryptography with its aims, forms and algorithms. Intrusion and computer protection technologies are also used in attack forms.

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Monitoring and control of temperature, humidity using machine learning

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AIP Conference Proceedings 2021年第1期2378卷

作者： Arvind Kumar Shukla Ashish Gupta A. Arunraja C. Arunkumar Madhuvappan 1Associate professor School of Computer Science and Applications IFTM University Lodhipur Rajput Moradabad India 2Professor & Head Department of English Govt. Girls College Sadar Betul. (M.P) 460001 India 3Assistant Professor Department of ECE Sri Ramakrishna Engineering College Coimbatore India 4Assistant Professor/ECE Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation - Deemed to be University (VMRFDU) Salem India

In this paper, In Recent Year Machine learning plays important role in analyzing the various time of weather condition all around the world especially Indian Subcontinent. Data is available in the government website to analyze the data for few long years. This should be matched with UCI technique for the machine learning and obtain the condition of different level of data repository. Temperature and humidity level condition of different parameters were analyzed and taken as example of weather condition to monitor the weather condition. We need to design a model to fit the different condition and need to extrapolating the needs of information, and optimize the technique using some algorithm and variation should be targeted and value should be analyzed.

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A Novel Bio-Inspired Framework for CO 2 Emission Forecast in India

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Procedia computer Science 2018年 125卷 367-375页

作者： A. Sangeetha T. Amudha Research Scholar Department of Computer Applications Bharathiar University Coimbatore 641046 India Assistant Professor Department of Computer Applications Bharathiar University Coimbatore 641046 India

Greenhouse gases (GHG) emitted from the combustion of fossil fuels lead to erratic climatic change, and creates severe environmental problem worldwide. GHG emissions from diverse sources have harmful effects on the quality of air, water, soil and living organisms. Carbon-di-oxide (CO 2 ) is one among the GHG which plays a major role in polluting the air, hence the estimation and forecasting of CO 2 emission has become essential for energy planning and ecological strategy decisions. The objective of this research work is to estimate and forecast CO 2 emission in India from various sources of energy consumption. Multiple linear Regression model and PSO algorithm based on nonlinear model were used for CO 2 emission estimation. The obtained results have shown that India’s CO 2 emission has alarmingly increased over the past decade. The results reveal that PSO model could obtain a highly accurate estimation compared to MLR model. From the outcome of PSO estimation, the future projection of CO 2 emission in India was carried out for the years from 2017 to 2030 using artificial neural network. The prediction results also emphasize that necessary steps must be taken straight away to reduce CO 2 emission across the country, as its impulsive increase in India poses extreme threat to nature and environment.

关键词： Air pollution Artificial Neural Network Bio-Inspired Computing CO 2 Emission Estimation CO 2 Emission Forecasting Particle Swarm Optimization

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