Pub Date : 2019-04-01DOI: 10.1109/ICOEI.2019.8862553
G. Hemanth, M. Janardhan, L. Sujihelen
Nowadays, brain tumor detection has turned upas a general causality in the realm of health care. Brain tumor can be denoted as a malformed mass of tissue wherein the cells multiply abruptly and ceaselessly, that is there is no control over the growth of the cells. The process of Image segmentation is adopted for extracting abnormal tumor region within the brain. In the MRI (magnetic resonance image), segmentation of brain tissue holds very significant in order to identify the presence of outlines concerning the brain tumor. There is abundance of hidden information in stored in the Health care sector. With appropriate use of accurate data mining classification techniques, early prediction of any disease can be effectively performed. In the medical field, the techniques of ML (machine learning) and Data mining holds a significant stand. Majority of which is adopted effectively. The research examines list of risk factors that are being traced out in brain tumor surveillance systems. Also the method proposed assures to be highly efficient and precise for brain tumor detection, classification and segmentation. To achieve this precise automatic or semi-automatic methods are needed. The research proposes an automatic segmentation method that relies upon CNN (Convolution Neural Networks), determining small 3 × 3 kernels. By incorporating this single technique, segmentation and classification is accomplished. CNN (a ML technique) from NN (Neural Networks)wherein it has layer based for results classification. Various levels involved in the proposed mechanisms are: 1. Data collection, 2. Pre-processing, 3. Average filtering, 4. segmentation, 5. feature extraction, 6. CNN via classification and identification. By utilizing the DM (data mining) techniques, significant relations and patterns from the data can be extracted. The techniques of ML (machine learning) and Data mining are being effectively employed for brain tumor detection and prevention at an early stage.
{"title":"Design and Implementing Brain Tumor Detection Using Machine Learning Approach","authors":"G. Hemanth, M. Janardhan, L. Sujihelen","doi":"10.1109/ICOEI.2019.8862553","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862553","url":null,"abstract":"Nowadays, brain tumor detection has turned upas a general causality in the realm of health care. Brain tumor can be denoted as a malformed mass of tissue wherein the cells multiply abruptly and ceaselessly, that is there is no control over the growth of the cells. The process of Image segmentation is adopted for extracting abnormal tumor region within the brain. In the MRI (magnetic resonance image), segmentation of brain tissue holds very significant in order to identify the presence of outlines concerning the brain tumor. There is abundance of hidden information in stored in the Health care sector. With appropriate use of accurate data mining classification techniques, early prediction of any disease can be effectively performed. In the medical field, the techniques of ML (machine learning) and Data mining holds a significant stand. Majority of which is adopted effectively. The research examines list of risk factors that are being traced out in brain tumor surveillance systems. Also the method proposed assures to be highly efficient and precise for brain tumor detection, classification and segmentation. To achieve this precise automatic or semi-automatic methods are needed. The research proposes an automatic segmentation method that relies upon CNN (Convolution Neural Networks), determining small 3 × 3 kernels. By incorporating this single technique, segmentation and classification is accomplished. CNN (a ML technique) from NN (Neural Networks)wherein it has layer based for results classification. Various levels involved in the proposed mechanisms are: 1. Data collection, 2. Pre-processing, 3. Average filtering, 4. segmentation, 5. feature extraction, 6. CNN via classification and identification. By utilizing the DM (data mining) techniques, significant relations and patterns from the data can be extracted. The techniques of ML (machine learning) and Data mining are being effectively employed for brain tumor detection and prevention at an early stage.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133085114","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862700
Shambhavi S. Salelkar, Palhavi Kerkar
A Rectangular microsrip patch antenna with a semicircular slot is fabricated which is desirable for ISM band applications. The main objective of this antenna is to have less loss and substantial gain. The resonant frequency of this antenna which is at ISM band is 5.8 GHz. Three antennas are been designed. A patch antenna, an array of 2×1 and 2×2 is plotted on the FR4 substrate with the relative permittivity of 4.4. An amount of air gap is kept between the ground plane and the substrate. The software that is used for designing this antenna is IE3D. The designed antenna at 5.8 GHz provides the outcome that gives return loss of −16 dB, −23 dB and −39.9 dB, gain of 5.4 dB, 7.2 dB and 8.9 db, VSWR of 1.02, 1.1 and 1.2 respectively. The size of the antenna is very compact and hence it is easy to fabricate. WiFi, W-LAN, Bluetooth are the applications of ISM Band that is provide by this antenna.
{"title":"Patch Antenna for ISM Band Application","authors":"Shambhavi S. Salelkar, Palhavi Kerkar","doi":"10.1109/ICOEI.2019.8862700","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862700","url":null,"abstract":"A Rectangular microsrip patch antenna with a semicircular slot is fabricated which is desirable for ISM band applications. The main objective of this antenna is to have less loss and substantial gain. The resonant frequency of this antenna which is at ISM band is 5.8 GHz. Three antennas are been designed. A patch antenna, an array of 2×1 and 2×2 is plotted on the FR4 substrate with the relative permittivity of 4.4. An amount of air gap is kept between the ground plane and the substrate. The software that is used for designing this antenna is IE3D. The designed antenna at 5.8 GHz provides the outcome that gives return loss of −16 dB, −23 dB and −39.9 dB, gain of 5.4 dB, 7.2 dB and 8.9 db, VSWR of 1.02, 1.1 and 1.2 respectively. The size of the antenna is very compact and hence it is easy to fabricate. WiFi, W-LAN, Bluetooth are the applications of ISM Band that is provide by this antenna.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133150865","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862552
Moumita Chanda, M. Biswas
Agriculture is the culture of land and rearing of plants to supply food to nourish and enhance life. Different types of plants are farmed every year based on environmental conditions and it is one of the main economic sources in India. These plants are prone to many diseases which hinders normal growth of the plants; these diseases are caused by seasonal changes, environmental variations, and cultivation procedures. To protect the plants from such damages, diseases need to be identified and properly diagnosed on time. Hence, innovation of feasible and powerful methods for identification and classification of plant diseases is an urgent need. There are lots of classifiers which are good in the classification of plant diseases: Back-propagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) but only using these methods do not make the best tradeoff between time and accuracy. So to remove this constraint, in this paper we have given an image processing solution to distinguish and classify plant diseases efficiently and accurately. In our proposed method, for classification first, we use back-propagation algorithm to get the weights of neural network (NN) connections and then we optimize these weights using Particle Swarm Optimization (PSO) to come out of the problems like local optima and overfitting which are very common in conventional NN training methods. We have used images of leaves affected by different bacterial and fungal diseases: Alternaria Alternata, Anthracnose, Bacterial Blight and Cercospora Leaf Spot in our experiment and our proposed method achieves 96.2% accuracy.
{"title":"Plant disease identification and classification using Back-Propagation Neural Network with Particle Swarm Optimization","authors":"Moumita Chanda, M. Biswas","doi":"10.1109/ICOEI.2019.8862552","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862552","url":null,"abstract":"Agriculture is the culture of land and rearing of plants to supply food to nourish and enhance life. Different types of plants are farmed every year based on environmental conditions and it is one of the main economic sources in India. These plants are prone to many diseases which hinders normal growth of the plants; these diseases are caused by seasonal changes, environmental variations, and cultivation procedures. To protect the plants from such damages, diseases need to be identified and properly diagnosed on time. Hence, innovation of feasible and powerful methods for identification and classification of plant diseases is an urgent need. There are lots of classifiers which are good in the classification of plant diseases: Back-propagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) but only using these methods do not make the best tradeoff between time and accuracy. So to remove this constraint, in this paper we have given an image processing solution to distinguish and classify plant diseases efficiently and accurately. In our proposed method, for classification first, we use back-propagation algorithm to get the weights of neural network (NN) connections and then we optimize these weights using Particle Swarm Optimization (PSO) to come out of the problems like local optima and overfitting which are very common in conventional NN training methods. We have used images of leaves affected by different bacterial and fungal diseases: Alternaria Alternata, Anthracnose, Bacterial Blight and Cercospora Leaf Spot in our experiment and our proposed method achieves 96.2% accuracy.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115880981","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862721
Pooja Ghule, Mansi Kambli
Nowadays people are very concerned about the environment because of the rapid changes in the environment which will harm to human health. Hence it is necessary to monitor environment where the people spend more time like at home, office, industry, any working area in real time and long term manner. Using internet of things we can control system as well as we can access system remotely using IoT. It first take information with help of different sensors and transfer sensors values on thingspeak directly, from which can be accessed at anytime and anywhere. Literature survey is done on use of wireless sensors, Cloud and Internet of things, and connection between devices with sensors and network connection will read sensor value which can be further monitored from the internet with the help of thingspeak. Monitoring environment is done through website & controlled manually and automatically by detecting sensor values. We can controlled it manually through website and it can automatically controlled by sensing values. The main Objective design of cloud storage environment is used to store data and to process the data. Internet of things allows physical devices or things which are not computer system, that only act very smartly and makes collaborations decision which are beneficial for different applications. That application allow things to capture value of devices. They transfer “things from being passively computing” and makes an individually decisions in active manner and communicate and collaborate to form single difficult decision. IoT technologies of computing, embedded sensors, communication protocol and internet protocol for communication allow internet of things to provide significant which impose number of challenges and introduces standards which require to specialize and communication
{"title":"Web Based Environment Monitoring System Using IOT","authors":"Pooja Ghule, Mansi Kambli","doi":"10.1109/ICOEI.2019.8862721","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862721","url":null,"abstract":"Nowadays people are very concerned about the environment because of the rapid changes in the environment which will harm to human health. Hence it is necessary to monitor environment where the people spend more time like at home, office, industry, any working area in real time and long term manner. Using internet of things we can control system as well as we can access system remotely using IoT. It first take information with help of different sensors and transfer sensors values on thingspeak directly, from which can be accessed at anytime and anywhere. Literature survey is done on use of wireless sensors, Cloud and Internet of things, and connection between devices with sensors and network connection will read sensor value which can be further monitored from the internet with the help of thingspeak. Monitoring environment is done through website & controlled manually and automatically by detecting sensor values. We can controlled it manually through website and it can automatically controlled by sensing values. The main Objective design of cloud storage environment is used to store data and to process the data. Internet of things allows physical devices or things which are not computer system, that only act very smartly and makes collaborations decision which are beneficial for different applications. That application allow things to capture value of devices. They transfer “things from being passively computing” and makes an individually decisions in active manner and communicate and collaborate to form single difficult decision. IoT technologies of computing, embedded sensors, communication protocol and internet protocol for communication allow internet of things to provide significant which impose number of challenges and introduces standards which require to specialize and communication","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115100233","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862601
C. J. Mariya, K. A. Nyni
This paper mainly focuses on various feature selection methods that is followed for achieving accurate diagnosis of neuromuscular diseases such as Amyotrophic Lateral Sclerosis (ALS) and Myopathy. Since both of these has similarity in the Electromyography (EMG) waveform of normal patients, this will create more difficulties in terms of diagnosis. Hence, proper feature selection is the essential part in the diagnosis. Two feature selection methods were adopted for evaluation. In the first method, time domain and frequency domain features are taken from each frame of EMG signal and in the second method, Discrete Wavelet Transform (DWT) features like maximum DWT coefficient and mean value of high energy DWT coefficients were analysed. For the purpose of classification, the Multi-Support Vector Machine (MSVM) classifier is employed.
{"title":"Review on Feature Extraction Methods in Neuromuscular Disease Diagnosis","authors":"C. J. Mariya, K. A. Nyni","doi":"10.1109/ICOEI.2019.8862601","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862601","url":null,"abstract":"This paper mainly focuses on various feature selection methods that is followed for achieving accurate diagnosis of neuromuscular diseases such as Amyotrophic Lateral Sclerosis (ALS) and Myopathy. Since both of these has similarity in the Electromyography (EMG) waveform of normal patients, this will create more difficulties in terms of diagnosis. Hence, proper feature selection is the essential part in the diagnosis. Two feature selection methods were adopted for evaluation. In the first method, time domain and frequency domain features are taken from each frame of EMG signal and in the second method, Discrete Wavelet Transform (DWT) features like maximum DWT coefficient and mean value of high energy DWT coefficients were analysed. For the purpose of classification, the Multi-Support Vector Machine (MSVM) classifier is employed.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114511096","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862594
V. Harshini, Shreevani Danai, H R Usha, Manjunath R. Kounte
The world is moving towards progress, to achieve the desired progress, the world should have a healthy population and health records are the projections of an individual's health over time. The centralised approach of maintaining the health records lead to data breaches. According to 2017 Ponemon Cost of Data Breach Study, the cost of the data breach for healthcare organizations estimated to be $380 per record. According to 2016 Breach Barometer Report, 27,314,647 patient records were affected. So we moved towards institution-driven approach of record maintenance, which didn't make much difference with the previously existing one. Since the patient have no control over the data, the chances of data being misused is high. So we need a patient-centered approach which is completely decentralised, which can identify data thefts, prevent data manipulation, and patient has the right in access control. Blockchain Technology serves as a best solution to address all the problems and fulfill the needs. Blockchain being a decentralised and distributed ledger it can also impact on billing, record sharing, medical research, identify thefts and financial data crimes in days to come. Implementation of smart contracts in health care can simplify things even better. Where invoking, record creation and validation will be done on Blockchain. This paper highlights on the patient-driven model of record maintenance using Blockchain technology where smart contracts can be incorporated in future days making it more potential in data exchange. Finding its huge scope, hoping that more researches will be carried out and practically implemented.
{"title":"Health Record Management through Blockchain Technology","authors":"V. Harshini, Shreevani Danai, H R Usha, Manjunath R. Kounte","doi":"10.1109/ICOEI.2019.8862594","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862594","url":null,"abstract":"The world is moving towards progress, to achieve the desired progress, the world should have a healthy population and health records are the projections of an individual's health over time. The centralised approach of maintaining the health records lead to data breaches. According to 2017 Ponemon Cost of Data Breach Study, the cost of the data breach for healthcare organizations estimated to be $380 per record. According to 2016 Breach Barometer Report, 27,314,647 patient records were affected. So we moved towards institution-driven approach of record maintenance, which didn't make much difference with the previously existing one. Since the patient have no control over the data, the chances of data being misused is high. So we need a patient-centered approach which is completely decentralised, which can identify data thefts, prevent data manipulation, and patient has the right in access control. Blockchain Technology serves as a best solution to address all the problems and fulfill the needs. Blockchain being a decentralised and distributed ledger it can also impact on billing, record sharing, medical research, identify thefts and financial data crimes in days to come. Implementation of smart contracts in health care can simplify things even better. Where invoking, record creation and validation will be done on Blockchain. This paper highlights on the patient-driven model of record maintenance using Blockchain technology where smart contracts can be incorporated in future days making it more potential in data exchange. Finding its huge scope, hoping that more researches will be carried out and practically implemented.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116237961","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862696
T. Bhagya, K. Anand, D. S. Kanchana, Ajai A S Remya
Image segmentation plays a vital role in medical image processing. Different pre-processing methods yield different results. The pre-processing methods such as histogram stretching with erosion and dilation, average filter and median filter along with histogram stretching is applied to the four different segmentation algorithms which are Otsu's thresholding, Watershed based segmentation, Canny edge detection and K-mean clustering. These algorithms are used to segment Acute Lymphoblastic Leukemia datasets and the parameters such as precision, accuracy and sensitivity of the results are calculated so as to find a better algorithm which is suitable for segmentation of the leukemic cells.
{"title":"Analysis of Image Segmentation Algorithms for the Effective Detection of Leukemic Cells","authors":"T. Bhagya, K. Anand, D. S. Kanchana, Ajai A S Remya","doi":"10.1109/ICOEI.2019.8862696","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862696","url":null,"abstract":"Image segmentation plays a vital role in medical image processing. Different pre-processing methods yield different results. The pre-processing methods such as histogram stretching with erosion and dilation, average filter and median filter along with histogram stretching is applied to the four different segmentation algorithms which are Otsu's thresholding, Watershed based segmentation, Canny edge detection and K-mean clustering. These algorithms are used to segment Acute Lymphoblastic Leukemia datasets and the parameters such as precision, accuracy and sensitivity of the results are calculated so as to find a better algorithm which is suitable for segmentation of the leukemic cells.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129501372","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862529
B. Ajith, S. Adlinge, Sudin Dinesh, U. Rajeev, E. S. Padmakumar
Airport runway detection and tracking can play an important role in landing an aircraft. In some situations the runway may not be visible to pilot due to adverse weather condition. Considering the case of Unmanned aerial vehicles, the runway detection and tracking algorithm is one of its essential part which enable them to position itself and land safely. This paper explains an algorithm which will track the runway when it is visible using a camera. The algorithm is based on identification of runway colour and runway characteristics. This method ensures the detection of runway accurately. Algorithm detects the runway boundaries by selecting the appropriate hough lines using runway characteristics and runway colour. Once the runway is detected it tracks the runway using feature matching techniques. In tracking phase the algorithm will track the runway and it will find out the accurate runway boundary and threshold stripes. This algorithm can be used to assist pilot during landing and it can be also used to detect runways in UAVs.
{"title":"Robust Method to Detect and Track the Runway during Aircraft Landing Using Colour segmentation and Runway features","authors":"B. Ajith, S. Adlinge, Sudin Dinesh, U. Rajeev, E. S. Padmakumar","doi":"10.1109/ICOEI.2019.8862529","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862529","url":null,"abstract":"Airport runway detection and tracking can play an important role in landing an aircraft. In some situations the runway may not be visible to pilot due to adverse weather condition. Considering the case of Unmanned aerial vehicles, the runway detection and tracking algorithm is one of its essential part which enable them to position itself and land safely. This paper explains an algorithm which will track the runway when it is visible using a camera. The algorithm is based on identification of runway colour and runway characteristics. This method ensures the detection of runway accurately. Algorithm detects the runway boundaries by selecting the appropriate hough lines using runway characteristics and runway colour. Once the runway is detected it tracks the runway using feature matching techniques. In tracking phase the algorithm will track the runway and it will find out the accurate runway boundary and threshold stripes. This algorithm can be used to assist pilot during landing and it can be also used to detect runways in UAVs.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128481584","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862697
G. Jaspher Willsie Kathrine, P. M. Praise, A. Amrutha Rose, Eligious C Kalaivani
Phishing is a treacherous effort to steal private data from users like address, aadhar number, PAN card details, credit/debit card details, bank account details, password for online shopping sites, etc. Pinching or phishing of private information on the web has caused havoc on a majority of users due to the lack of internet security. Phishing attacks make use of fake emails or websites, intended to fool users into revealing personal or financial information by posing as the trusted bank/shopping site. The various types of phishing attacks and the recent approaches to prevent the attacks are discussed. A framework to detect and prevent phishing attacks is also proposed. A combination of supervised and unsupervised machine learning techniques is used to detect known and unknown attacks.
{"title":"Variants of phishing attacks and their detection techniques","authors":"G. Jaspher Willsie Kathrine, P. M. Praise, A. Amrutha Rose, Eligious C Kalaivani","doi":"10.1109/ICOEI.2019.8862697","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862697","url":null,"abstract":"Phishing is a treacherous effort to steal private data from users like address, aadhar number, PAN card details, credit/debit card details, bank account details, password for online shopping sites, etc. Pinching or phishing of private information on the web has caused havoc on a majority of users due to the lack of internet security. Phishing attacks make use of fake emails or websites, intended to fool users into revealing personal or financial information by posing as the trusted bank/shopping site. The various types of phishing attacks and the recent approaches to prevent the attacks are discussed. A framework to detect and prevent phishing attacks is also proposed. A combination of supervised and unsupervised machine learning techniques is used to detect known and unknown attacks.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127277379","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862591
Mitul Sheth, Pinal Rupani
The Global Sensing enabled by Wireless Sensor Network (WSN) cut crosswise over numerous zones of current living. This provides the potentiality to compute, and understand the environmental indicators. In today's digital world, a person expects Automatization which makes the task easy, comfortable, fast and efficient. The idea is to advance our traditional system to a Smart Automated System for supplying water in home gardening, farms fields, etc. In this system, we use soil wetness detector, temperature detector and humidity detector that are mounted at the root space of the plants. The values recognize by the system are conveyed to the base station. The target is to fetch data and sync those values with internet using Wifi. It notifies the user as the water level goes down below the set point. This paper shows that making use of NodeMCU we can do observing of circuit diagrams using wireless technology and shows the result using Blynk App. As it detects low wetness and warm temperature, a message is passed between NodeMCU and Blynk App and it automatically starts the motor in home gardening, farm, etc.
{"title":"Smart Gardening Automation using IoT With BLYNK App","authors":"Mitul Sheth, Pinal Rupani","doi":"10.1109/ICOEI.2019.8862591","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862591","url":null,"abstract":"The Global Sensing enabled by Wireless Sensor Network (WSN) cut crosswise over numerous zones of current living. This provides the potentiality to compute, and understand the environmental indicators. In today's digital world, a person expects Automatization which makes the task easy, comfortable, fast and efficient. The idea is to advance our traditional system to a Smart Automated System for supplying water in home gardening, farms fields, etc. In this system, we use soil wetness detector, temperature detector and humidity detector that are mounted at the root space of the plants. The values recognize by the system are conveyed to the base station. The target is to fetch data and sync those values with internet using Wifi. It notifies the user as the water level goes down below the set point. This paper shows that making use of NodeMCU we can do observing of circuit diagrams using wireless technology and shows the result using Blynk App. As it detects low wetness and warm temperature, a message is passed between NodeMCU and Blynk App and it automatically starts the motor in home gardening, farm, etc.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121327541","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：