Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987937
Amit Gupta, Abhishek Kumar, M. Mamatha, Shravani Kalkonda
Microstrip patch antenna is adopted considering domestic along with utilization, popularly for mobile as it is light weight, simple to build and low cost. The proposed antenna consists of six dipoles on single common feed, FR-4 Epoxy whose proportionate dielectric function is 4.4 and destruction tangent is 0.02 is used for proposed design. The dimensions for the substrate are 15.1794mm x 18.25mm x 1.5 mm. It is intended to be operated in 1 GHz–75GHz i.e., from L band to V band with a maximum return loss of −43.67 dB and with a maximum Gain of 5.72dB. For the same design, Rogers whose approximate permittivity is 2.2 and casualty tangent is 0.0009 and Arlon whose contingent permittivity is 6.15 and catastrophe tangent is 0.03 used as substrate materials for the optimal characteristics. Patch aerial potential characteristics are in same manner with resonant frequencies, return loss, gain, bandwidth, VSWR, directivity are taken into account for the analysis of proposed antenna. In Rogers material, maximum return loss of −23.51dB with a maximum gain of 8.66dB and in Arlon material, maximum return loss of −34.64dB with a maximum gain of 9.82dB are measured from HFSS software. The newly generated antenna can therefore, be helpful for multiple wide band utilization depending on the particular substrate material.
{"title":"Design and Simulation of Microstrip Patch Antenna for Next Generation Communication Applications","authors":"Amit Gupta, Abhishek Kumar, M. Mamatha, Shravani Kalkonda","doi":"10.1109/ICSSIT46314.2019.8987937","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987937","url":null,"abstract":"Microstrip patch antenna is adopted considering domestic along with utilization, popularly for mobile as it is light weight, simple to build and low cost. The proposed antenna consists of six dipoles on single common feed, FR-4 Epoxy whose proportionate dielectric function is 4.4 and destruction tangent is 0.02 is used for proposed design. The dimensions for the substrate are 15.1794mm x 18.25mm x 1.5 mm. It is intended to be operated in 1 GHz–75GHz i.e., from L band to V band with a maximum return loss of −43.67 dB and with a maximum Gain of 5.72dB. For the same design, Rogers whose approximate permittivity is 2.2 and casualty tangent is 0.0009 and Arlon whose contingent permittivity is 6.15 and catastrophe tangent is 0.03 used as substrate materials for the optimal characteristics. Patch aerial potential characteristics are in same manner with resonant frequencies, return loss, gain, bandwidth, VSWR, directivity are taken into account for the analysis of proposed antenna. In Rogers material, maximum return loss of −23.51dB with a maximum gain of 8.66dB and in Arlon material, maximum return loss of −34.64dB with a maximum gain of 9.82dB are measured from HFSS software. The newly generated antenna can therefore, be helpful for multiple wide band utilization depending on the particular substrate material.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125955655","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 : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987741
S. Barath, Madhumitha V, Kusuma S, K. Navya, B. Meghana
Many detection methods for the identification of Alzheimer's disease (AD) had been proposed in the past several decades. As there is no heal for AD to reverse its advancement, it is of key significance for early diagnosis and supervising of AD at its early introductory stage, i.e., mild cognitive impairment (MCI). New applications and methodologies are required for analyzing and to provide immediate early stage treating. Different biomarkers and clinical signs are used to assess the progression of AD depending on the patient's condition and disease stage. The current technology aims to help the drug in the treatment and care of patients with symptoms and biological properties. These parameters will assist in prior medication, and prevention could be ascertained in order to prevent the disease in reaching further stages.
{"title":"Detection and Analysis of Alzheimer's Disease from Medical Images: A Survey","authors":"S. Barath, Madhumitha V, Kusuma S, K. Navya, B. Meghana","doi":"10.1109/ICSSIT46314.2019.8987741","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987741","url":null,"abstract":"Many detection methods for the identification of Alzheimer's disease (AD) had been proposed in the past several decades. As there is no heal for AD to reverse its advancement, it is of key significance for early diagnosis and supervising of AD at its early introductory stage, i.e., mild cognitive impairment (MCI). New applications and methodologies are required for analyzing and to provide immediate early stage treating. Different biomarkers and clinical signs are used to assess the progression of AD depending on the patient's condition and disease stage. The current technology aims to help the drug in the treatment and care of patients with symptoms and biological properties. These parameters will assist in prior medication, and prevention could be ascertained in order to prevent the disease in reaching further stages.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128845488","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 : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987808
Ankush Kumar, H. Monga, V. Singh
In this research, PI controller is planned for controlling the level of single-tank system by using sine-cosine algorithm (SCA). Integral-square-error (ISE) of unit step be abated for obtaining best controller parameters. Alpha and Beta tables are used for calculating the ISE. Sine-cosine algorithm is used for abating the ISE to find the best parameters of PI controller. Results attained using SCA specify that the performance of the PI controlled structure can be improved significantly. Statistical analysis and time domain simulations are given to confirm the proposed controller.
{"title":"Design of Proportional Integral Controller for Level Control of Single-Tank System Using Sine-Cosine Algorithm","authors":"Ankush Kumar, H. Monga, V. Singh","doi":"10.1109/ICSSIT46314.2019.8987808","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987808","url":null,"abstract":"In this research, PI controller is planned for controlling the level of single-tank system by using sine-cosine algorithm (SCA). Integral-square-error (ISE) of unit step be abated for obtaining best controller parameters. Alpha and Beta tables are used for calculating the ISE. Sine-cosine algorithm is used for abating the ISE to find the best parameters of PI controller. Results attained using SCA specify that the performance of the PI controlled structure can be improved significantly. Statistical analysis and time domain simulations are given to confirm the proposed controller.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125642661","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 : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987919
M. Tamilselvi, S. Karthikeyan
Facial expressions assume an important part in our everyday collaborations, and late generation has seen an awesome measure of exploring methods for dependable facial impressions identification frameworks. Different innovations of Facial Expression Recognition have been tested by analysts in the course of recent years. Changes in facial expression turn into a troublesome undertaking in perceiving faces. In this we dissect regional facial transformations and utilize various straightforward attributes to shape a compelling classifier. Finally, here exhibited an approach which utilizing an Active Appearance Model and Support Vector Machines. Active Appearance Model (AAM) is used to pull out the unique facial key points and also to consolidate their regional structure attributes to design a classifier. After extracting facial features, these facial coordinates are fed into a Support Vector Machine and the prepared framework classifies the expressions into six classifications specifically like Anger, Fear, Normal, S ad, Disgust and Happy. This framework accomplishes robust and superior expression classification which shows improved results than the existing methods by leading experiments.
{"title":"Feature Extraction and Facial Expression Recognition using Support Vector Machine","authors":"M. Tamilselvi, S. Karthikeyan","doi":"10.1109/ICSSIT46314.2019.8987919","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987919","url":null,"abstract":"Facial expressions assume an important part in our everyday collaborations, and late generation has seen an awesome measure of exploring methods for dependable facial impressions identification frameworks. Different innovations of Facial Expression Recognition have been tested by analysts in the course of recent years. Changes in facial expression turn into a troublesome undertaking in perceiving faces. In this we dissect regional facial transformations and utilize various straightforward attributes to shape a compelling classifier. Finally, here exhibited an approach which utilizing an Active Appearance Model and Support Vector Machines. Active Appearance Model (AAM) is used to pull out the unique facial key points and also to consolidate their regional structure attributes to design a classifier. After extracting facial features, these facial coordinates are fed into a Support Vector Machine and the prepared framework classifies the expressions into six classifications specifically like Anger, Fear, Normal, S ad, Disgust and Happy. This framework accomplishes robust and superior expression classification which shows improved results than the existing methods by leading experiments.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121316338","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 : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987583
P. Sreenivasulu, S. Varadharajan
Nowadays, there is an increase in the volume of data produced and stored in the medical field. Therefore for the efficient handling of these large data there needs the compression technique to re-explore by considering the algorithm's complexity. In this research work, a narrative medical image compression approach is implanted by means of intelligent techniques and is composed of three main stages like Segmentation, Image compression, and Image decompression. From the start, the division procedure is started by parting the picture's Region of Interest (ROI) and Non-ROI areas by Modified Region Growing (MRG) calculation. Further, for ROI regions, Discrete Cosine Transform (DCT) model and SPHIT encoding method are deployed for compression, whereas the Non-ROI region uses the Discrete Wavelet Transform (DWT) and Merge-based Huffman encoding (MHE) methods for doing compression process. Mainly, this research work employs the optimization concept for the optimal selection of filter coefficients from DWT and DCT approaches. For this purpose, a new Improvised Steering angle and Gear-based ROA (ISG-ROA) is proposed, which is the modification of Rider Optimization Algorithm (ROA). To the last, decompression process is handled by reversing the compression process using the same optimized coefficients. The filter coefficient is adapted to finalize the result with reduced compression Ratio (CR).
{"title":"Medical Image Compression Using DCT based MRG Algorithem","authors":"P. Sreenivasulu, S. Varadharajan","doi":"10.1109/ICSSIT46314.2019.8987583","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987583","url":null,"abstract":"Nowadays, there is an increase in the volume of data produced and stored in the medical field. Therefore for the efficient handling of these large data there needs the compression technique to re-explore by considering the algorithm's complexity. In this research work, a narrative medical image compression approach is implanted by means of intelligent techniques and is composed of three main stages like Segmentation, Image compression, and Image decompression. From the start, the division procedure is started by parting the picture's Region of Interest (ROI) and Non-ROI areas by Modified Region Growing (MRG) calculation. Further, for ROI regions, Discrete Cosine Transform (DCT) model and SPHIT encoding method are deployed for compression, whereas the Non-ROI region uses the Discrete Wavelet Transform (DWT) and Merge-based Huffman encoding (MHE) methods for doing compression process. Mainly, this research work employs the optimization concept for the optimal selection of filter coefficients from DWT and DCT approaches. For this purpose, a new Improvised Steering angle and Gear-based ROA (ISG-ROA) is proposed, which is the modification of Rider Optimization Algorithm (ROA). To the last, decompression process is handled by reversing the compression process using the same optimized coefficients. The filter coefficient is adapted to finalize the result with reduced compression Ratio (CR).","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"529 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127061891","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 : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987777
V. K. Daliya, T. K. Ramesh
An IoT based healthcare system promises the implementation of high-quality healthcare services in a time bound and accurate manner. But the varieties of data coming from various sources will make the system more heterogeneous and hence it is challenging to process them further. These data coming from sensors are usually collected from the sensor's web and stored in Electronic Health Records (EHR). Data in EHR consists of each patients' details with respect to his hospital visits, previous treatment history, medication used, medical history etc. An error free and understandable data handling process enhances data interoperability among various EHRs, which use different ways of representing data. To handle these multiple types of data stored in different EHRs, data interoperability enhancement techniques such as semantic and syntactic methods play major roles. But, Syntactic method fails in tapping the meaning of the data while semantic method does not consider the format of the data. These shortcomings are overcome by the proposed hybrid method which can tap the meaning of data from heterogeneous sources while bringing uniformity for the data format as well. The proposed technique is analyzed in healthcare domain and is proven to be more efficient than using each method separately.
{"title":"Data Interoperability Enhancement of Electronic Health Record data using a hybrid model","authors":"V. K. Daliya, T. K. Ramesh","doi":"10.1109/ICSSIT46314.2019.8987777","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987777","url":null,"abstract":"An IoT based healthcare system promises the implementation of high-quality healthcare services in a time bound and accurate manner. But the varieties of data coming from various sources will make the system more heterogeneous and hence it is challenging to process them further. These data coming from sensors are usually collected from the sensor's web and stored in Electronic Health Records (EHR). Data in EHR consists of each patients' details with respect to his hospital visits, previous treatment history, medication used, medical history etc. An error free and understandable data handling process enhances data interoperability among various EHRs, which use different ways of representing data. To handle these multiple types of data stored in different EHRs, data interoperability enhancement techniques such as semantic and syntactic methods play major roles. But, Syntactic method fails in tapping the meaning of the data while semantic method does not consider the format of the data. These shortcomings are overcome by the proposed hybrid method which can tap the meaning of data from heterogeneous sources while bringing uniformity for the data format as well. The proposed technique is analyzed in healthcare domain and is proven to be more efficient than using each method separately.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126815628","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 : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987789
Reshali Crystal Rebello, Vasudeva Pai, K. Pai
Wireless/Remote Sensor Networks (WSNs) contains sensor hubs and a base station. The role of sensor nodes is to acquire the data from the surrounding in which they are placed and then report the data to base station or sink. While the data gathering, data processing, data reporting and maintaining, it requires a lot of security measures for the data as well as motes(nodes) to be well protected from attacks. Intrusion detection systems (IDs) is a way to detect any anomalies or attacks in the network and also helps to tackle it. The paper focuses on the comparison of the types of intrusion detection systems used against the various attacks in WSNs.
{"title":"A Review: Intrusion Detection Systems in Remote Sensor Network","authors":"Reshali Crystal Rebello, Vasudeva Pai, K. Pai","doi":"10.1109/ICSSIT46314.2019.8987789","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987789","url":null,"abstract":"Wireless/Remote Sensor Networks (WSNs) contains sensor hubs and a base station. The role of sensor nodes is to acquire the data from the surrounding in which they are placed and then report the data to base station or sink. While the data gathering, data processing, data reporting and maintaining, it requires a lot of security measures for the data as well as motes(nodes) to be well protected from attacks. Intrusion detection systems (IDs) is a way to detect any anomalies or attacks in the network and also helps to tackle it. The paper focuses on the comparison of the types of intrusion detection systems used against the various attacks in WSNs.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126252117","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 : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987946
G. P. Sarmila, N. Gnanmbigai, P. Dinadayalan
Cloud Computing (CC) has become an appealing computing criterion in both academic and business establishments. Fault tolerance is the key challenge faced by the CSP to provide guaranteed service to its users. Prior works proposed various algorithms for guaranteeing fault tolerance using job scheduling by assigning deadlines via time sliding (TS) and bandwidth scaling (BS). Job scheduling has proven to be an effective method to reduce fault occurrence and to address scalable user requests by balancing the incoming load. This paper proposes Hexagonal Chebyshev Gaussian and Discrete Time Organized Map-based (HCG-DTOM) job scheduling method which is an adaptive fault tolerance method based on Self organizing map. The HCG-DTOM method involves four steps. They are Hexagonal Lattice Structure Initialization model that performs initialization of cloud users, jobs to be assigned, virtual machines and job scheduler. Second, the virtual manager checks resource availability for a given set of input jobs using Chebyshev Discriminant Competitive model. Third, scheduling is performed by the job scheduler via Gaussian Neighbourhood Cooperative model. Finally, the resources are updated with the corresponding jobs for the appropriate cloud users are performed using the Discrete Time Adaptation model.
{"title":"Self Scheduling Based on Hexagonal Chebysev Gaussian and Discrete Time Organized Mapping in Cloud","authors":"G. P. Sarmila, N. Gnanmbigai, P. Dinadayalan","doi":"10.1109/ICSSIT46314.2019.8987946","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987946","url":null,"abstract":"Cloud Computing (CC) has become an appealing computing criterion in both academic and business establishments. Fault tolerance is the key challenge faced by the CSP to provide guaranteed service to its users. Prior works proposed various algorithms for guaranteeing fault tolerance using job scheduling by assigning deadlines via time sliding (TS) and bandwidth scaling (BS). Job scheduling has proven to be an effective method to reduce fault occurrence and to address scalable user requests by balancing the incoming load. This paper proposes Hexagonal Chebyshev Gaussian and Discrete Time Organized Map-based (HCG-DTOM) job scheduling method which is an adaptive fault tolerance method based on Self organizing map. The HCG-DTOM method involves four steps. They are Hexagonal Lattice Structure Initialization model that performs initialization of cloud users, jobs to be assigned, virtual machines and job scheduler. Second, the virtual manager checks resource availability for a given set of input jobs using Chebyshev Discriminant Competitive model. Third, scheduling is performed by the job scheduler via Gaussian Neighbourhood Cooperative model. Finally, the resources are updated with the corresponding jobs for the appropriate cloud users are performed using the Discrete Time Adaptation model.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127422445","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 : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987872
R. Singh, P. Suryavanshi, Ritesh Gandhi, Musharraf Hussain Mulla
This project “Signaling and Vehicle Crossing with Smart Intelligent System (SVCSIS)” aims at developing a fully functional computerized system to maintaining and alerting the oncoming vehicles, thus by reducing the road accidents. Vehicle accidents are considered one of the most destructive phenomena. Though there are many different reasons behind Vehicle accidents, most accidents occur due to driver's unawareness and uncontrolled speed.to overcome this problem we have designed the system which can reduce the accidents in the prone areas by alerting the drivers with the presence of oncoming vehicles. The system will make use of sensor-based LEDs to detect the oncoming vehicle on the main road which is 3-Path or T-shape. Detection is done by using the Photo electronic laser sensors. The whole system is based on the Raspberry pi which can be controlled or monitored automatically or manually.
{"title":"Signaling and Vehicle Crossing with Smart Intelligent System (SVCSIS)","authors":"R. Singh, P. Suryavanshi, Ritesh Gandhi, Musharraf Hussain Mulla","doi":"10.1109/ICSSIT46314.2019.8987872","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987872","url":null,"abstract":"This project “Signaling and Vehicle Crossing with Smart Intelligent System (SVCSIS)” aims at developing a fully functional computerized system to maintaining and alerting the oncoming vehicles, thus by reducing the road accidents. Vehicle accidents are considered one of the most destructive phenomena. Though there are many different reasons behind Vehicle accidents, most accidents occur due to driver's unawareness and uncontrolled speed.to overcome this problem we have designed the system which can reduce the accidents in the prone areas by alerting the drivers with the presence of oncoming vehicles. The system will make use of sensor-based LEDs to detect the oncoming vehicle on the main road which is 3-Path or T-shape. Detection is done by using the Photo electronic laser sensors. The whole system is based on the Raspberry pi which can be controlled or monitored automatically or manually.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127468343","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 : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987882
P. Saranya, Dr. P. Asha
Massive amount of data in different forms need to be handled in any healthcare applications. Type of data, size of data, data security and other features has more significance in handling the data. The term big data refers to data with certain characteristics, volume, velocity, value, veracity and variability. Such big data need to be stored, processed, and analyzed for required results. Medical data has more complexity in predicting the results from it, which will have more significance in patient's treatment. Because of its significance, there is need of developing efficient and better performing algorithms, techniques and tools to analyze medical big data. Whereas, the traditional algorithms are not capable for analyzing such complex data. Machine learning algorithms well fit for these kinds of data and analytics. In this Keywords: Big data, Health care, disease prediction, SVM, CNN survey paper, we discussed about characteristic of big data, features of big data, how to represent big data, different types of machine learning algorithms used in big data analytics. We discussed about big data analytics in major healthcare areas like EHR maintenance, disease diagnose, prediction of emergency condition of patients, etc.,. Also stated different machine algorithms usage in disease diagnose and patient's data analysis and discussed about importance of various machine learning algorithms. Here, we have highlighted the areas where big data analytics have been applied in healthcare sectors. It describes the characteristics and features of big data, importance of big data analytics in healthcare sectors, various machine learning algorithms used in big data analytics and their efficiency.
{"title":"Survey on Big Data Analytics in Health Care","authors":"P. Saranya, Dr. P. Asha","doi":"10.1109/ICSSIT46314.2019.8987882","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987882","url":null,"abstract":"Massive amount of data in different forms need to be handled in any healthcare applications. Type of data, size of data, data security and other features has more significance in handling the data. The term big data refers to data with certain characteristics, volume, velocity, value, veracity and variability. Such big data need to be stored, processed, and analyzed for required results. Medical data has more complexity in predicting the results from it, which will have more significance in patient's treatment. Because of its significance, there is need of developing efficient and better performing algorithms, techniques and tools to analyze medical big data. Whereas, the traditional algorithms are not capable for analyzing such complex data. Machine learning algorithms well fit for these kinds of data and analytics. In this Keywords: Big data, Health care, disease prediction, SVM, CNN survey paper, we discussed about characteristic of big data, features of big data, how to represent big data, different types of machine learning algorithms used in big data analytics. We discussed about big data analytics in major healthcare areas like EHR maintenance, disease diagnose, prediction of emergency condition of patients, etc.,. Also stated different machine algorithms usage in disease diagnose and patient's data analysis and discussed about importance of various machine learning algorithms. Here, we have highlighted the areas where big data analytics have been applied in healthcare sectors. It describes the characteristics and features of big data, importance of big data analytics in healthcare sectors, various machine learning algorithms used in big data analytics and their efficiency.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127338475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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