Few diseases are hard to detect and life-threatening as well, and Pulmonary Fibrosis (PF) is one of them. PF is a chronic disorder that leads to progressive scarring of the lungs, and we can say that PF is Idiopathic Pulmonary Fibrosis (IPF) because the cause of the disease is unknown. 50,000 fresh cases per year are diagnosed with PF, which is likely to increase. With machine learning and deep learning, we can predict the lung function decline of a patient suffering from IPF. This prediction will improve the medication process and will increase the longevity of the patient. Early detection of IPF is crucial as it increases the morbidity and mortality rate and healthcare costs. We have predicted IPF in the early stages using forced vital capacity (FVC) records of different patients. FVC is the amount of air that we can exhale from our lungs after taking a deep breath. We have created a Multiple-Quantile Regression model to detect a decline in lung function using CNN. With this approach, the cross-validation accuracy of prediction is 92 percent.
{"title":"Analysis of Idiopathic Pulmonary Fibrosis through Machine Learning Techniques","authors":"Upasana Chutia, Anand Shanker Tewari, Jyoti Prakash Singh","doi":"10.1109/ICSCC51209.2021.9528243","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528243","url":null,"abstract":"Few diseases are hard to detect and life-threatening as well, and Pulmonary Fibrosis (PF) is one of them. PF is a chronic disorder that leads to progressive scarring of the lungs, and we can say that PF is Idiopathic Pulmonary Fibrosis (IPF) because the cause of the disease is unknown. 50,000 fresh cases per year are diagnosed with PF, which is likely to increase. With machine learning and deep learning, we can predict the lung function decline of a patient suffering from IPF. This prediction will improve the medication process and will increase the longevity of the patient. Early detection of IPF is crucial as it increases the morbidity and mortality rate and healthcare costs. We have predicted IPF in the early stages using forced vital capacity (FVC) records of different patients. FVC is the amount of air that we can exhale from our lungs after taking a deep breath. We have created a Multiple-Quantile Regression model to detect a decline in lung function using CNN. With this approach, the cross-validation accuracy of prediction is 92 percent.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121092789","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 : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528174
Ajith Vijayan, Venugopalan Kurupath, J. Das
There is an exponential increase for the global electricity demand during the last decade owing to overall development, especially in the industrial sector. Demand side management (DSM) is a critical function of a grid that encourages users to make decisions about their energy usage and enables energy suppliers minimize peak demand and reshape the profile of load. Energy demand could be minimized at specific time intervals using grid control algorithms like DSM. It is planning, implementing, and monitoring activities of electrical utilities which encourage consumers to modify their level and pattern of electricity usage, ensuring stability on the electricity grid and balance the electrical demand throughout the year. This paper presents a load shifting demand side management which transfers low priority consumer loads from peak to off peak periods, which can reduce peak demand and thereby cost. Simulations are carried out for a residential infrastructure. The results show that significant cost savings are achievable with the proposed optimization strategy.
{"title":"Residential Demand Side Management Using Artificial Intelligence","authors":"Ajith Vijayan, Venugopalan Kurupath, J. Das","doi":"10.1109/ICSCC51209.2021.9528174","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528174","url":null,"abstract":"There is an exponential increase for the global electricity demand during the last decade owing to overall development, especially in the industrial sector. Demand side management (DSM) is a critical function of a grid that encourages users to make decisions about their energy usage and enables energy suppliers minimize peak demand and reshape the profile of load. Energy demand could be minimized at specific time intervals using grid control algorithms like DSM. It is planning, implementing, and monitoring activities of electrical utilities which encourage consumers to modify their level and pattern of electricity usage, ensuring stability on the electricity grid and balance the electrical demand throughout the year. This paper presents a load shifting demand side management which transfers low priority consumer loads from peak to off peak periods, which can reduce peak demand and thereby cost. Simulations are carried out for a residential infrastructure. The results show that significant cost savings are achievable with the proposed optimization strategy.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129633177","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 : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528158
R. Rathna, U. V. Anbazhagu, L. Mary Gladence, V. Anu, J. Sybi Cynthia
In the current scenario, getting good drinking water or getting good quality water for domestic purpose is highly essential to maintain good health. By exploiting the water scarcity problem many private tanker water suppliers are providing water for very high cost. Even though the quality of water becomes questionable, many people are availing only this facility to fill their tanks in India, as there is no alternative. The proposed system uses a PH (Potential of Hydrogen) sensor and a temperature sensor to assess the water quality; a relay driver and solenoid valve to communicate with central controller ESP32 (ESP represents the company Espressif Systems) about the water quality and water level in tank; the collected data is sent through the cloud (analysing the PH levels of the collected data) to the mobile number of the user. The sensor setup can be controlled by the android application. All the components used are very simple reactive machines category, coming under type II of Artificial Intelligence. This system applies a very simple logic as intelligence to detect the PH level and water level in residential flats.
{"title":"An Intelligent Monitoring System for Water Quality Management using Internet of Things","authors":"R. Rathna, U. V. Anbazhagu, L. Mary Gladence, V. Anu, J. Sybi Cynthia","doi":"10.1109/ICSCC51209.2021.9528158","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528158","url":null,"abstract":"In the current scenario, getting good drinking water or getting good quality water for domestic purpose is highly essential to maintain good health. By exploiting the water scarcity problem many private tanker water suppliers are providing water for very high cost. Even though the quality of water becomes questionable, many people are availing only this facility to fill their tanks in India, as there is no alternative. The proposed system uses a PH (Potential of Hydrogen) sensor and a temperature sensor to assess the water quality; a relay driver and solenoid valve to communicate with central controller ESP32 (ESP represents the company Espressif Systems) about the water quality and water level in tank; the collected data is sent through the cloud (analysing the PH levels of the collected data) to the mobile number of the user. The sensor setup can be controlled by the android application. All the components used are very simple reactive machines category, coming under type II of Artificial Intelligence. This system applies a very simple logic as intelligence to detect the PH level and water level in residential flats.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130310106","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 : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528172
N. G, Vishnupriya Vijayan, R. Jose
Automatic modulation classification is used to identify the modulation scheme of the received signal, without prior knowledge of system parameters. In this work, we compare the performance of modulation classification in additive white gaussian noise channel using a conventional method and a deep learning-based method. Firstly, we classified the modulation schemes using a likelihood-based classifier. Another classifier is also implemented by exploiting the estimated probability density function. Next, a feature-based learning technique using a feedforward neural network was executed. We have analyzed this for digital modulation schemes like BPSK, QPSK, and 16-QAM. The performance of each modulation classification technique in different signal-to-noise ratios is tabulated.
{"title":"Performance Analysis of Modulation Classification Using Machine learning","authors":"N. G, Vishnupriya Vijayan, R. Jose","doi":"10.1109/ICSCC51209.2021.9528172","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528172","url":null,"abstract":"Automatic modulation classification is used to identify the modulation scheme of the received signal, without prior knowledge of system parameters. In this work, we compare the performance of modulation classification in additive white gaussian noise channel using a conventional method and a deep learning-based method. Firstly, we classified the modulation schemes using a likelihood-based classifier. Another classifier is also implemented by exploiting the estimated probability density function. Next, a feature-based learning technique using a feedforward neural network was executed. We have analyzed this for digital modulation schemes like BPSK, QPSK, and 16-QAM. The performance of each modulation classification technique in different signal-to-noise ratios is tabulated.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"13 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130519407","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 : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528249
Josna Philomina
Network on chip (NoC) is the communication infrastructure used in multicores which has been subject to a surfeit of security threats like degrading the system performance, changing the system functionality or leaking sensitive information. Because of the globalization of the advanced semiconductor industry, many third-party venders take part in the hardware design of system. As a result, a malicious circuit, called Hardware Trojans (HT) can be added anywhere into the NoC design and thus making the hardware untrusted. In this paper, a detailed study on the taxonomy of hardware trojans, its detection and prevention mechanisms are presented. Two case studies on HT-assisted Denial of service attacks and its analysis in the performance of network on Chip architecture is also presented in this paper.
{"title":"A Study on the Effect of Hardware Trojans in the Performance of Network on Chip Architectures","authors":"Josna Philomina","doi":"10.1109/ICSCC51209.2021.9528249","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528249","url":null,"abstract":"Network on chip (NoC) is the communication infrastructure used in multicores which has been subject to a surfeit of security threats like degrading the system performance, changing the system functionality or leaking sensitive information. Because of the globalization of the advanced semiconductor industry, many third-party venders take part in the hardware design of system. As a result, a malicious circuit, called Hardware Trojans (HT) can be added anywhere into the NoC design and thus making the hardware untrusted. In this paper, a detailed study on the taxonomy of hardware trojans, its detection and prevention mechanisms are presented. Two case studies on HT-assisted Denial of service attacks and its analysis in the performance of network on Chip architecture is also presented in this paper.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129133779","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 : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528240
M. Mishra, U. C. Pati
Among various brain disorders, Autism Spectrum Disorder (ASD) is very different of its kind. It generally occurs at a very early age of children. It becomes difficult for even parents to identify an abnormality in their child due to its early occurrence. This paper presents the machine learning approach for the detection of ASD using surface morphometric features of T1 weighted structural Magnetic Resonance Imaging (sMRI). It also compares the classification evaluation of the utilized machine learning models based on left hemispheric surface and right hemispheric surface morphometric features of the brain. This work utilizes the Decision Tree (DT) and Random Forest (RF) for learning and classification purposes. Classification evaluation validates the better performance of RF in comparison to DT towards the classification between the controls and patients suffering from ASD.
{"title":"Autism Spectrum Disorder Detection using Surface Morphometric Feature of sMRI in Machine Learning","authors":"M. Mishra, U. C. Pati","doi":"10.1109/ICSCC51209.2021.9528240","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528240","url":null,"abstract":"Among various brain disorders, Autism Spectrum Disorder (ASD) is very different of its kind. It generally occurs at a very early age of children. It becomes difficult for even parents to identify an abnormality in their child due to its early occurrence. This paper presents the machine learning approach for the detection of ASD using surface morphometric features of T1 weighted structural Magnetic Resonance Imaging (sMRI). It also compares the classification evaluation of the utilized machine learning models based on left hemispheric surface and right hemispheric surface morphometric features of the brain. This work utilizes the Decision Tree (DT) and Random Forest (RF) for learning and classification purposes. Classification evaluation validates the better performance of RF in comparison to DT towards the classification between the controls and patients suffering from ASD.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122238518","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 : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528195
R. Kankale, S. Paraskar, S. Jadhao
This paper introduces a new method for detecting and classifying power quality disturbances (PQDs) in emerging power system with Distributed Generation (DG). The Space Phasor Model (SPM) and Normalized Cross-Correlation (NCC) based image pattern (template) matching algorithm is proposed to detect and classify the PQDs. An emerging power system with DG system is simulated in MATLAB Simulink environment. In this paper, seven PQDs namely voltage sag, voltage swell, voltage interruption, oscillatory transients, voltage flicker, voltage notch, and voltage harmonics with notch which are caused by the DG operating conditions, and other causes are considered under study. The space phasor models represented in the complex plane are obtained for each case of PQDs using the three-phase voltage signals. The NCC-based image pattern matching technique is used to convert these space phasor models into template and matching images for the detection and classification of PQDs. The graphical results show that the proposed algorithm accurately detects and classifies the PQD provided in the template image by finding its exact match and position in the matching image.
{"title":"Classification of Power Quality Disturbances in Emerging Power System with Distributed Generation Using Space Phasor Model and Normalized Cross Correlation","authors":"R. Kankale, S. Paraskar, S. Jadhao","doi":"10.1109/ICSCC51209.2021.9528195","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528195","url":null,"abstract":"This paper introduces a new method for detecting and classifying power quality disturbances (PQDs) in emerging power system with Distributed Generation (DG). The Space Phasor Model (SPM) and Normalized Cross-Correlation (NCC) based image pattern (template) matching algorithm is proposed to detect and classify the PQDs. An emerging power system with DG system is simulated in MATLAB Simulink environment. In this paper, seven PQDs namely voltage sag, voltage swell, voltage interruption, oscillatory transients, voltage flicker, voltage notch, and voltage harmonics with notch which are caused by the DG operating conditions, and other causes are considered under study. The space phasor models represented in the complex plane are obtained for each case of PQDs using the three-phase voltage signals. The NCC-based image pattern matching technique is used to convert these space phasor models into template and matching images for the detection and classification of PQDs. The graphical results show that the proposed algorithm accurately detects and classifies the PQD provided in the template image by finding its exact match and position in the matching image.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115375000","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 : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528177
S. Krishnapriya, R. Komaragiri, K. J. Suja
Microcoils provide significant applications in biomedical microdevices. Lab-on-chip systems make use of electromagnetic microactuators for controlling fluid flow within microscopic devices. Microcoils are inevitable components in electromagnetic microactuators. The effect of the geometrical parameters of a coplanar microcoil on the electromagnetic force of a microactuator is analysed and presented in this work. An optimized coil geometry is also presented to produce required electromagnetic force for the microactuating applications.
{"title":"Effect of Geometrical Parameters of Nonspiral microcoils on the Magnetic field Generation for Microactuating Applications","authors":"S. Krishnapriya, R. Komaragiri, K. J. Suja","doi":"10.1109/ICSCC51209.2021.9528177","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528177","url":null,"abstract":"Microcoils provide significant applications in biomedical microdevices. Lab-on-chip systems make use of electromagnetic microactuators for controlling fluid flow within microscopic devices. Microcoils are inevitable components in electromagnetic microactuators. The effect of the geometrical parameters of a coplanar microcoil on the electromagnetic force of a microactuator is analysed and presented in this work. An optimized coil geometry is also presented to produce required electromagnetic force for the microactuating applications.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124017392","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 : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528098
Praveen Mohandas, A. R, Antony John, Midhun K. Madhu, Gylson Thomas, Venugopalan Kurupath
Major focus of this paper is in the development and testing of a prototype of Electrocardiogram (ECG) machine intended for automatic analysis of cardiovascular diseases by applying artificial intelligence. The objective of the work is in cardiac screening of school children at rural areas, in order to detect the cardiac diseases at its early stages. This work has focused to differentiate ECG signals of people into arrhythmia affected, congestive heart failure, and normal sinus rhythm. For feature extraction from ECG signal, wavelet time scattering methodology has been used and a Support Vector Machine (SVM) classifier is employed to accurately distinguish between ECG signals, which were carried out in MATLAB toolbox. A hardware system of interfaced ARDUINO UNO and ECG sensor AD8232 has been developed and the entire system is tested on group members and predictions were made accurately. Testing with school children is pending due to concerns about COVID-19 safety issues.
{"title":"Automated cardiac condition diagnosis using AI based ECG analysis system for school children","authors":"Praveen Mohandas, A. R, Antony John, Midhun K. Madhu, Gylson Thomas, Venugopalan Kurupath","doi":"10.1109/ICSCC51209.2021.9528098","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528098","url":null,"abstract":"Major focus of this paper is in the development and testing of a prototype of Electrocardiogram (ECG) machine intended for automatic analysis of cardiovascular diseases by applying artificial intelligence. The objective of the work is in cardiac screening of school children at rural areas, in order to detect the cardiac diseases at its early stages. This work has focused to differentiate ECG signals of people into arrhythmia affected, congestive heart failure, and normal sinus rhythm. For feature extraction from ECG signal, wavelet time scattering methodology has been used and a Support Vector Machine (SVM) classifier is employed to accurately distinguish between ECG signals, which were carried out in MATLAB toolbox. A hardware system of interfaced ARDUINO UNO and ECG sensor AD8232 has been developed and the entire system is tested on group members and predictions were made accurately. Testing with school children is pending due to concerns about COVID-19 safety issues.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124719130","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 : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528292
Paras Nath Singh
A very novel predicament for quantitative data science has been generated by the abundance of large, well-cured data sets in biological and social science, coupled with an extraordinary increase in computational ability. This is the possibility of sophisticated studies combined with remedial understanding. Analytics for intelligent systems should cover architecture of hardware platforms and application of software methods, technique and tools. It is anticipated that adapting dynamic memory information, processing parametric values of large data sheets with optimization, would be faster. The field of Big-Data Analytics under recent trends of Data Science studies various means of pre-processing, analyzing and filtering from huge and semi-structured data sets from different sources which are complex to be handled by traditional data processing systems. In addition to extracting and aggregating data from various main performance measures, this proposal also forecasts potential values for these KPIs (Key Performance Indicators) and alerts them when unfavorable values are about to occur. As AI and ML are implemented through different platforms and sectors including chat-bots, robotics, social media, healthcare, self-driven automobile and space exploration, large companies are investing in these fields, and the demand for ML and AI experts is growing accordingly. Python is becoming the most popular language for AI (Artificial Intelligence and Machine Learning) due to its rich supported tools. This proposed applications "I-Care" (Intelligent Care) provide recommendations to improve Quality of Service of Big-data analytics. So, the proposed paper examines the methodology and requirements, architecture, modeling and analytics with implementation and describes the architectural design and the results obtained by the pilot application using Python and its powerful tools like Pandas and Scikit-Learn.
{"title":"\"I-Care\" - Big-data Analytics for Intelligent Systems","authors":"Paras Nath Singh","doi":"10.1109/ICSCC51209.2021.9528292","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528292","url":null,"abstract":"A very novel predicament for quantitative data science has been generated by the abundance of large, well-cured data sets in biological and social science, coupled with an extraordinary increase in computational ability. This is the possibility of sophisticated studies combined with remedial understanding. Analytics for intelligent systems should cover architecture of hardware platforms and application of software methods, technique and tools. It is anticipated that adapting dynamic memory information, processing parametric values of large data sheets with optimization, would be faster. The field of Big-Data Analytics under recent trends of Data Science studies various means of pre-processing, analyzing and filtering from huge and semi-structured data sets from different sources which are complex to be handled by traditional data processing systems. In addition to extracting and aggregating data from various main performance measures, this proposal also forecasts potential values for these KPIs (Key Performance Indicators) and alerts them when unfavorable values are about to occur. As AI and ML are implemented through different platforms and sectors including chat-bots, robotics, social media, healthcare, self-driven automobile and space exploration, large companies are investing in these fields, and the demand for ML and AI experts is growing accordingly. Python is becoming the most popular language for AI (Artificial Intelligence and Machine Learning) due to its rich supported tools. This proposed applications \"I-Care\" (Intelligent Care) provide recommendations to improve Quality of Service of Big-data analytics. So, the proposed paper examines the methodology and requirements, architecture, modeling and analytics with implementation and describes the architectural design and the results obtained by the pilot application using Python and its powerful tools like Pandas and Scikit-Learn.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130026114","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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