Pub Date : 2019-04-01DOI: 10.1109/ICIICT1.2019.8741358
K. Ramya, D. Vennila, P. Rajapriyan
Fossil fuels such as diesel, petrol etc are currently used as the main source of energy in supplying our society. However, these sources cause serious damage to our climate and global system by emitting greenhouse gasses. This will cause the major drawbacks to increase the environmental pollution and also the maximum utilization of vehicles increases the cost of the fuel. This indirectly increases the cost of living. Only 10% of vehicle is used as electric vehicles and they are depending on conventional energy sources. In order to cope with global change a fundamental change towards an energy system based on renewable is needed. Our project focuses on harvesting the energy from renewable energy resources such as solar, wind , electromagnetic sources . Centralized electricity systems are being integrated by a multitude of small distributed generation clusters, called Nano grids, capable of ensuring full utilization of renewable energy. Renewable energy plays an vital role in harvesting such a sources there by protecting the environment. With this innovative technology give a great change in reduce the cost of the fuel. The FPGA controls the panel using MPPT algorithm and provides the maximum power. Using multiple panels we can run smaller vehicles.
{"title":"Design and Implementation of Low Power Nanogrid with Intelligent Solar PV Utilization","authors":"K. Ramya, D. Vennila, P. Rajapriyan","doi":"10.1109/ICIICT1.2019.8741358","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741358","url":null,"abstract":"Fossil fuels such as diesel, petrol etc are currently used as the main source of energy in supplying our society. However, these sources cause serious damage to our climate and global system by emitting greenhouse gasses. This will cause the major drawbacks to increase the environmental pollution and also the maximum utilization of vehicles increases the cost of the fuel. This indirectly increases the cost of living. Only 10% of vehicle is used as electric vehicles and they are depending on conventional energy sources. In order to cope with global change a fundamental change towards an energy system based on renewable is needed. Our project focuses on harvesting the energy from renewable energy resources such as solar, wind , electromagnetic sources . Centralized electricity systems are being integrated by a multitude of small distributed generation clusters, called Nano grids, capable of ensuring full utilization of renewable energy. Renewable energy plays an vital role in harvesting such a sources there by protecting the environment. With this innovative technology give a great change in reduce the cost of the fuel. The FPGA controls the panel using MPPT algorithm and provides the maximum power. Using multiple panels we can run smaller vehicles.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116866325","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/ICIICT1.2019.8741475
J. Ranjani, A. Sheela, K. Meena
Python is a simple, dominant and well-organized interpreted language. Python is used to develop the very high performance scientific related application and it is used to develop an application for Numeric computation, it is frequently used. Using python we can develop a high performance applications Python often utilizes external libraries to perform scientific computing. The most important libraries used are NumPy, SciPy and Matplotlib to perform scientific and numeric applications. The Python libraries are open-source tag-on modules, which do additional frequent mathematical and numerical routines in pre-compiled, high-speed tasks. These two modules are providing the functionality that meets, or perhaps exceeds, that related with common commercial software like MATLAB. The python package NumPy (Numeric Python) is used for manipulating large arrays and matrices of numeric data. The Scientific Python (SciPy) extends the functionality of NumPy with a considerable collection of valuable algorithms, like minimization, Fourier transformation, regression, and other applied mathematical techniques.Matrix Laboratory (MATLAB) is a multiple programming paradigm. it is used for numerical computing environment and it is a proprietary programming language. The following various operations are performed by the MATLAB, operations are plotting of functions and data, matrix manipulations, python use an interface for user to interact with an application, some type of algorithms are used for implementation purpose and another type of programming languages are used to create interfacing with programs. The combination of the following three packages NumPy, SciPy and Matplotlib/Pylab are alternatively used as a MATLAB, so this three packages give the alternative to MATLAB, MATLAB environment is best suit for scientific works. Python provide a alternative to MATLAB.
{"title":"Combination of NumPy, SciPy and Matplotlib/Pylab -a good alternative methodology to MATLAB - A Comparative analysis","authors":"J. Ranjani, A. Sheela, K. Meena","doi":"10.1109/ICIICT1.2019.8741475","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741475","url":null,"abstract":"Python is a simple, dominant and well-organized interpreted language. Python is used to develop the very high performance scientific related application and it is used to develop an application for Numeric computation, it is frequently used. Using python we can develop a high performance applications Python often utilizes external libraries to perform scientific computing. The most important libraries used are NumPy, SciPy and Matplotlib to perform scientific and numeric applications. The Python libraries are open-source tag-on modules, which do additional frequent mathematical and numerical routines in pre-compiled, high-speed tasks. These two modules are providing the functionality that meets, or perhaps exceeds, that related with common commercial software like MATLAB. The python package NumPy (Numeric Python) is used for manipulating large arrays and matrices of numeric data. The Scientific Python (SciPy) extends the functionality of NumPy with a considerable collection of valuable algorithms, like minimization, Fourier transformation, regression, and other applied mathematical techniques.Matrix Laboratory (MATLAB) is a multiple programming paradigm. it is used for numerical computing environment and it is a proprietary programming language. The following various operations are performed by the MATLAB, operations are plotting of functions and data, matrix manipulations, python use an interface for user to interact with an application, some type of algorithms are used for implementation purpose and another type of programming languages are used to create interfacing with programs. The combination of the following three packages NumPy, SciPy and Matplotlib/Pylab are alternatively used as a MATLAB, so this three packages give the alternative to MATLAB, MATLAB environment is best suit for scientific works. Python provide a alternative to MATLAB.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115831269","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/ICIICT1.2019.8741423
Nitin Asthana, R. Bahl
This project aims at providing smart solutions to monitor poisonous sewage gases and works on a system of live sewage level detection and monitoring. Whenever, a certain threshold is crossed, an alert is sent to the observer who is examining the conditions from a remote location. The information is then forwarded along with different gas ppm values indicating whether it is safe for the worker to clean or work in that environment or not. The remotely placed IoT monitoring equipment and IoT platform are integrated to create proposed system. This requires calibration of gas sensors for industrial purposes and determining the correct threshold levels for septic plants and facilities. The hardware is designed such that it shall send a prior alert to the sewage worker to ensure their safety, if damaging gaseous constituents increase in concentration over time. Various types of sensors are utilized to monitor parameters present in sewage like gas, temperature etc. When the threshold value is lesser than the sensed values, this system alerts the sewage worker/cleaner by sending SMS and call alerts by analyzing concentrations of different toxic gases and graphing out their results for real-time monitoring thereby aiding in protection from hazardous diseases and hence serves a social cause as well. In the proposed system, sample values for sensors have been recorded and plotted on ThingSpeak analysis tool. Carbon monoxide and methane sensors charted values up-to 2.3 and 60 ppm respectively, and this breached threshold and GSM module was utilized for sending alert to mobile number fed in the code.
{"title":"IoT Device For Sewage Gas Monitoring And Alert System","authors":"Nitin Asthana, R. Bahl","doi":"10.1109/ICIICT1.2019.8741423","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741423","url":null,"abstract":"This project aims at providing smart solutions to monitor poisonous sewage gases and works on a system of live sewage level detection and monitoring. Whenever, a certain threshold is crossed, an alert is sent to the observer who is examining the conditions from a remote location. The information is then forwarded along with different gas ppm values indicating whether it is safe for the worker to clean or work in that environment or not. The remotely placed IoT monitoring equipment and IoT platform are integrated to create proposed system. This requires calibration of gas sensors for industrial purposes and determining the correct threshold levels for septic plants and facilities. The hardware is designed such that it shall send a prior alert to the sewage worker to ensure their safety, if damaging gaseous constituents increase in concentration over time. Various types of sensors are utilized to monitor parameters present in sewage like gas, temperature etc. When the threshold value is lesser than the sensed values, this system alerts the sewage worker/cleaner by sending SMS and call alerts by analyzing concentrations of different toxic gases and graphing out their results for real-time monitoring thereby aiding in protection from hazardous diseases and hence serves a social cause as well. In the proposed system, sample values for sensors have been recorded and plotted on ThingSpeak analysis tool. Carbon monoxide and methane sensors charted values up-to 2.3 and 60 ppm respectively, and this breached threshold and GSM module was utilized for sending alert to mobile number fed in the code.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121646488","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/ICIICT1.2019.8741466
Uma Priyadarsini, M. Nalini
The rapid growth in multimedia services and the enormous offers of video contents in online social systems, clients experience issues in getting their interests. In this way, different customized suggestion frameworks have been proposed. Also, none of them has considered both the security of clients’ unique situations (e. g., economic well being, ages and leisure activities) and video benefit sellers’ vaults, which are to a great degree touchy and of huge business esteem. To deal with these issues, it’s been proposed a cloud-helped differential private video suggestion framework in light of circulated web based learning. In our project we proposed the new optimization technique for recommendation. The video recommendation is based on user’s behaviour (user’s interest) and also using the pattern mining for video tag search recommendation. We have search option as sub category search and global search in our application. Facing massive multimedia services and contents in the Internet is based the content provider. Cutting-edge that collection of providers we need to find out the irrelevant content promoters.
{"title":"Transient Factor-Mindful Video Affective Analysis- A Proposal for Internet Based Application","authors":"Uma Priyadarsini, M. Nalini","doi":"10.1109/ICIICT1.2019.8741466","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741466","url":null,"abstract":"The rapid growth in multimedia services and the enormous offers of video contents in online social systems, clients experience issues in getting their interests. In this way, different customized suggestion frameworks have been proposed. Also, none of them has considered both the security of clients’ unique situations (e. g., economic well being, ages and leisure activities) and video benefit sellers’ vaults, which are to a great degree touchy and of huge business esteem. To deal with these issues, it’s been proposed a cloud-helped differential private video suggestion framework in light of circulated web based learning. In our project we proposed the new optimization technique for recommendation. The video recommendation is based on user’s behaviour (user’s interest) and also using the pattern mining for video tag search recommendation. We have search option as sub category search and global search in our application. Facing massive multimedia services and contents in the Internet is based the content provider. Cutting-edge that collection of providers we need to find out the irrelevant content promoters.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115864490","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/ICIICT1.2019.8741507
R. Vijayalakshmi, G. Premalatha, K. Dhivya
In today’s world Internet of Things plays an important role in many applications such as smart home, smart transportation system etc. Vehicle theft is the major problem faced by the people in the society. The statistical survey shows vehicles which get stolen out of 4 only 1 get recovered. Present systems use key and remote to lock the vehicle. At many places CCTV cameras are present which are used to locate the stolen vehicle. But CCTV cameras are not present at all places. Control of vehicle and knowledge of their location even after theft can help recovery of the stolen vehicle fastly. The proposed system helps to find the vehicle location using GPS and the vehicle speed is gradually reduced by reducing speed of ignition motor. It helps to find the vehicle immediately after knowing it’s stolen. As GPS system is used, the location of the vehicle is also known. The command sent from the smartphone goes to the GSM system which is interfaced with controller which reduces the speed of ignition motor and immobilizes it. The system developed is reliable, low cost and user friendly which can help in the recovery of vehicle if it gets stolen.
{"title":"IOT based Wireless Controlled Smart Transportation System","authors":"R. Vijayalakshmi, G. Premalatha, K. Dhivya","doi":"10.1109/ICIICT1.2019.8741507","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741507","url":null,"abstract":"In today’s world Internet of Things plays an important role in many applications such as smart home, smart transportation system etc. Vehicle theft is the major problem faced by the people in the society. The statistical survey shows vehicles which get stolen out of 4 only 1 get recovered. Present systems use key and remote to lock the vehicle. At many places CCTV cameras are present which are used to locate the stolen vehicle. But CCTV cameras are not present at all places. Control of vehicle and knowledge of their location even after theft can help recovery of the stolen vehicle fastly. The proposed system helps to find the vehicle location using GPS and the vehicle speed is gradually reduced by reducing speed of ignition motor. It helps to find the vehicle immediately after knowing it’s stolen. As GPS system is used, the location of the vehicle is also known. The command sent from the smartphone goes to the GSM system which is interfaced with controller which reduces the speed of ignition motor and immobilizes it. The system developed is reliable, low cost and user friendly which can help in the recovery of vehicle if it gets stolen.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116027542","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/ICIICT1.2019.8741421
V. Ceronmani Sharmila, Kiran Kumar R, Sundaram R, Samyuktha D, H. R
Credit card fraud transaction detection system is a method used for determining the fraudulent transactions that take place every once in a while. The project uses a test data set of around 27,000 credit card transactions which have been taken from Caltech (Kaggle). The project comprises of primarily 2 major algorithms and uses anomaly detection as a method to classify the fraudulent transactions. The Local Outlier Factor (LoF) defines the various parameters that have to be used in determining the criteria for fraudulent transactions. It then checks upon the different transactions for the various parameters present in the given LoF. This factor then gives each transaction a score based on the various transactions that have or will have taken place. These scores can range from 0 - 1. Each transaction is thus given a score which is based on the various parameters given in the LoF. The second part of the project is isolation forest algorithms which is an algorithm that isolates the transaction which have a high rate of anomaly detected in them. Thus, these transactions are isolated and then checked with various parameters to be labelled as either fraudulent or real transactions. The algorithm also uses charts to check for spikes in the average transaction. We also uses technique like data visualization in order to show the output in more understandable ways which may include histograms, graphs and matrix .Through these two algorithms and with help of data visualization technique we can detect the fraudulent transactions from correct transaction and obtain results in quick time, Since these algorithms are much more time efficient than other machine learning algorithms in this type of tasks.
{"title":"Credit Card Fraud Detection Using Anomaly Techniques","authors":"V. Ceronmani Sharmila, Kiran Kumar R, Sundaram R, Samyuktha D, H. R","doi":"10.1109/ICIICT1.2019.8741421","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741421","url":null,"abstract":"Credit card fraud transaction detection system is a method used for determining the fraudulent transactions that take place every once in a while. The project uses a test data set of around 27,000 credit card transactions which have been taken from Caltech (Kaggle). The project comprises of primarily 2 major algorithms and uses anomaly detection as a method to classify the fraudulent transactions. The Local Outlier Factor (LoF) defines the various parameters that have to be used in determining the criteria for fraudulent transactions. It then checks upon the different transactions for the various parameters present in the given LoF. This factor then gives each transaction a score based on the various transactions that have or will have taken place. These scores can range from 0 - 1. Each transaction is thus given a score which is based on the various parameters given in the LoF. The second part of the project is isolation forest algorithms which is an algorithm that isolates the transaction which have a high rate of anomaly detected in them. Thus, these transactions are isolated and then checked with various parameters to be labelled as either fraudulent or real transactions. The algorithm also uses charts to check for spikes in the average transaction. We also uses technique like data visualization in order to show the output in more understandable ways which may include histograms, graphs and matrix .Through these two algorithms and with help of data visualization technique we can detect the fraudulent transactions from correct transaction and obtain results in quick time, Since these algorithms are much more time efficient than other machine learning algorithms in this type of tasks.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121755756","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/ICIICT1.2019.8741363
Udaya Mouni Boppana, Ranjana P, Upendra C, K. Priya
Obstructive Apnea is a breathing based sleeping problem, in which throat tissues drops back in the direction of air passages as well as obstructs the air flow, partly or completely during the rest. Because of lack of air movement in blood, oxygen levels will drop suddenly in boosting high blood pressure as well as strains cardiovascular system, leads to boost the danger of Cardiovascular diseases, Stroke, Excessive Weight, Diabetes Mellitus, High blood pressure, Myocardial infarction (MI) etc. where MI is a cardiovascular disease occurs when the circulation of blood, decreases or quits to a part of the heart, which it brings about heart damage. One of commonly detected method for sleeping conditions is Polysomnography (PSG), which is a lot pricier as well as eats much effort, as a result of these reasons in a lot of the cases sleep problems were undiagnosed. To get over the drawbacks of Polysomnography in an affordable way and in less initiative, existing system uses ECG signals in order to identify OSA. ECG is mostly used to diagnose cardiovascular disease, in order to find MI from ECG where as in traditional standard system assessment of ECG signals via visual analysis can be done by physicians or medical professionals is not effective and time consuming. This paper proposes a system to conquer the disadvantage of standard system, by translating the exact ECG by utilizing machine perception and to identify the problems in ECG, mainly concentrates on OSA as well as Myocardial infraction. Machine perception is the capability of a system in order to interpret the data based on exactly how human beings detects and connects the world around them. It permits the system to collect the information based upon equipment vision with better accuracy as well as to provide it in a form which is more comfortable to the user. Computer vision mostly focuses on acquiring, processing, analyzing, and recognizing images. Here the input information is absorbed kind of images as opposed to signals for accurate ECG interpretation. This can be done by using wavelet changes and Auto Regression (AR) approaches and should able to distinguish between regular ECG and also uncommon ECG and it can be done by using improvised classification Algorithm based on integration of both K-Medoid and also improvised KNN classifier is used to reduce the computation complexity and also enhance the precision by using the hyper tuning parameters.
{"title":"Electrocardiogram Data Capturing System By Using Machine Perception","authors":"Udaya Mouni Boppana, Ranjana P, Upendra C, K. Priya","doi":"10.1109/ICIICT1.2019.8741363","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741363","url":null,"abstract":"Obstructive Apnea is a breathing based sleeping problem, in which throat tissues drops back in the direction of air passages as well as obstructs the air flow, partly or completely during the rest. Because of lack of air movement in blood, oxygen levels will drop suddenly in boosting high blood pressure as well as strains cardiovascular system, leads to boost the danger of Cardiovascular diseases, Stroke, Excessive Weight, Diabetes Mellitus, High blood pressure, Myocardial infarction (MI) etc. where MI is a cardiovascular disease occurs when the circulation of blood, decreases or quits to a part of the heart, which it brings about heart damage. One of commonly detected method for sleeping conditions is Polysomnography (PSG), which is a lot pricier as well as eats much effort, as a result of these reasons in a lot of the cases sleep problems were undiagnosed. To get over the drawbacks of Polysomnography in an affordable way and in less initiative, existing system uses ECG signals in order to identify OSA. ECG is mostly used to diagnose cardiovascular disease, in order to find MI from ECG where as in traditional standard system assessment of ECG signals via visual analysis can be done by physicians or medical professionals is not effective and time consuming. This paper proposes a system to conquer the disadvantage of standard system, by translating the exact ECG by utilizing machine perception and to identify the problems in ECG, mainly concentrates on OSA as well as Myocardial infraction. Machine perception is the capability of a system in order to interpret the data based on exactly how human beings detects and connects the world around them. It permits the system to collect the information based upon equipment vision with better accuracy as well as to provide it in a form which is more comfortable to the user. Computer vision mostly focuses on acquiring, processing, analyzing, and recognizing images. Here the input information is absorbed kind of images as opposed to signals for accurate ECG interpretation. This can be done by using wavelet changes and Auto Regression (AR) approaches and should able to distinguish between regular ECG and also uncommon ECG and it can be done by using improvised classification Algorithm based on integration of both K-Medoid and also improvised KNN classifier is used to reduce the computation complexity and also enhance the precision by using the hyper tuning parameters.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124697738","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/ICIICT1.2019.8741452
B. Baiju, John Aravindhar
The problem of diabetic prediction has been well studied in this paper. The disease predictions have been explored using various methods of data mining. The use of medical data set on the prediction of diabetic mellitus has been analyzed. This paper performs a detailed survey on disease prediction using data mining approaches based on diabetic data set. The presence of disease has been identified using the appearance of various symptoms. However, the methods use different features and produces varying accuracy. The result of prediction differs with the methods/measures/ features being used. Towards diabetic prediction, a Disease Influence Measure (DIM) based diabetic prediction has been presented. The method preprocesses the input data set and removes the noisy records. In the second stage, the method estimates disease influence measure (DIM) based on the features of input data point. Based on the DIM value, the method performs diabetic prediction. Different approaches of disease prediction have been considered and their performance in disease prediction has been compared. The analysis result has been presented in detail towards the development.
{"title":"Disease Influence Measure Based Diabetic Prediction with Medical Data Set Using Data Mining","authors":"B. Baiju, John Aravindhar","doi":"10.1109/ICIICT1.2019.8741452","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741452","url":null,"abstract":"The problem of diabetic prediction has been well studied in this paper. The disease predictions have been explored using various methods of data mining. The use of medical data set on the prediction of diabetic mellitus has been analyzed. This paper performs a detailed survey on disease prediction using data mining approaches based on diabetic data set. The presence of disease has been identified using the appearance of various symptoms. However, the methods use different features and produces varying accuracy. The result of prediction differs with the methods/measures/ features being used. Towards diabetic prediction, a Disease Influence Measure (DIM) based diabetic prediction has been presented. The method preprocesses the input data set and removes the noisy records. In the second stage, the method estimates disease influence measure (DIM) based on the features of input data point. Based on the DIM value, the method performs diabetic prediction. Different approaches of disease prediction have been considered and their performance in disease prediction has been compared. The analysis result has been presented in detail towards the development.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129879301","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/ICIICT1.2019.8741444
Samatha P Salim, A. James, C. Saravanan
Handwritten character recognition demands great importance in the field of bank cheque processing, tax returns, etc. Deep learning techniques for recognition of handwritten characters have surpassed the traditional techniques involving handcrafted feature extraction. Although it achieves around 0.95 recognition rate, some misclassification does exist. This is because the classifier function at the output layer performs classification that does not consider the parameter adjustments using the low and mid-level features. This paper proposes an efficient method of recognition of handwritten Malayalam characters, including compound characters and signs, using deep layered graph neural network, Residual network enhanced by multi-scaled features from lower and middle layers.
{"title":"Proposed method to Malayalam Handwritten Character Recognition using Residual Network enhanced by multi-scaled features","authors":"Samatha P Salim, A. James, C. Saravanan","doi":"10.1109/ICIICT1.2019.8741444","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741444","url":null,"abstract":"Handwritten character recognition demands great importance in the field of bank cheque processing, tax returns, etc. Deep learning techniques for recognition of handwritten characters have surpassed the traditional techniques involving handcrafted feature extraction. Although it achieves around 0.95 recognition rate, some misclassification does exist. This is because the classifier function at the output layer performs classification that does not consider the parameter adjustments using the low and mid-level features. This paper proposes an efficient method of recognition of handwritten Malayalam characters, including compound characters and signs, using deep layered graph neural network, Residual network enhanced by multi-scaled features from lower and middle layers.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130038051","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/ICIICT1.2019.8741474
R. V, Ajmal K T
DC-AC conversion with three phase single ended primary inductance inverter and boost converter is a advanced design when comparing with conventional topologies .Here only used four-switches and three-phase SEPIC inverter .This pro- posed inverter is designed to reduce the fare, difficulty and switching losses .Converter offer a design for the inverter with the combination of single-ended primary-inductance with DC- DC converter which can be upgrade the phase voltage than traditional FSTP single ended primary inductance inverter . For boost up the output voltage inserting a boost converter (DC-DC) between input side and the four switch three phase single-ended primary-inductance inverter . Constant current control strategy is used with the proposed topology . Working principle , circuit diagram and simulation diagrams also introducing in this paper .
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