Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587620
Souparnika Jadhav, K. N. Nagesh
In communication systems, distortion and noise are the main reason to decrease the reliability of the system, these distortions occurs because of the hardware deficiencies. In Wireless ad hoc network, connectivity and coverage area the two biggest issues. Therefore, this paper gives the practical analysis of the hardware deficiencies on hoping transmission, which amplifies and sends the better signal for the static and dynamic gain of the nodes and determine the localization of isolation nodes, improve connectivity and coverage area by reducing Nakagami-m fading. The outage possibility obtained in practical is by the signal to noise with the distortion noise, where these achieve the hardware deficiencies in the initial node and the relay node. In the same way, we consider the ergodic capability, in which hops are not dependent but it also assigns the ‘Nakagami-m’fading effect in the communication system. In this, the SNR and the signal to noise with distortion is more, which are continuous, where these are inversely proportional for the amount of hardware deficiencies. Theoretical transceivers will not satisfy the requirements in practically therefore in this paper, we provide some of the basic rules which is very helpful for the better communication in practical manner while focusing on ‘Nakagami-m’ fading effect in improving connectivity and coverage are in wireless ad-hoc networks.
{"title":"Impairment Impact on the Wireless Communication System","authors":"Souparnika Jadhav, K. N. Nagesh","doi":"10.1109/GCAT52182.2021.9587620","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587620","url":null,"abstract":"In communication systems, distortion and noise are the main reason to decrease the reliability of the system, these distortions occurs because of the hardware deficiencies. In Wireless ad hoc network, connectivity and coverage area the two biggest issues. Therefore, this paper gives the practical analysis of the hardware deficiencies on hoping transmission, which amplifies and sends the better signal for the static and dynamic gain of the nodes and determine the localization of isolation nodes, improve connectivity and coverage area by reducing Nakagami-m fading. The outage possibility obtained in practical is by the signal to noise with the distortion noise, where these achieve the hardware deficiencies in the initial node and the relay node. In the same way, we consider the ergodic capability, in which hops are not dependent but it also assigns the ‘Nakagami-m’fading effect in the communication system. In this, the SNR and the signal to noise with distortion is more, which are continuous, where these are inversely proportional for the amount of hardware deficiencies. Theoretical transceivers will not satisfy the requirements in practically therefore in this paper, we provide some of the basic rules which is very helpful for the better communication in practical manner while focusing on ‘Nakagami-m’ fading effect in improving connectivity and coverage are in wireless ad-hoc networks.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114896632","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587582
D. Vishwakarma, Amandeep Singh, A. Kushwaha, Ayush K. Sharma
Rainfall prediction is a routine part of meteorological observations. An essential work of this department includes keeping daily record of rainfall characteristics, variations, intensity, etc. for a particular region. Although, in our general life, we may not pay much attention to our day-to-day weather conditions, for instance, sunrise, humidity, rainfall, air pressure and others, but in terms of climatology, such weather events or their fluctuation can leave a great impact on the habitat, if they remain consistent in long run. For this reason, advance methods are being implemented to develop accurate weather prediction tools. Also, several researches are done in the area of climatology to confirm a presumed connection between our Earth's weather and unconventional, new unusual phenomenon that is noticed within the climate influencing atmospheric range. Our research is planned to analyze such a phenomenon. In our study, we constructed a Machine Learning Based Rainfall prediction Model with Moon's phases included as a feature to observe the its importance level in rainfall prediction as well as compare its value with other influencing factors. We have chosen Machine Learning approach to achieve desired accuracy, speed and efficiency than any contemporary manual engineering processes done for data analysis. We have incorporated two predictive algorithms, namely, Logistic Regression and Random Forest to enhance our Model's predictive potentiality. Since, lowering of computational speed, making erroneous calculations, increasing system processing risk are some of the difficulties of Machine Learning implementation with large dataset, we have utilized Feature Selection Technique to overcome them.
{"title":"Comparative Study on Influence of Moon's Phases in Rainfall Prediction","authors":"D. Vishwakarma, Amandeep Singh, A. Kushwaha, Ayush K. Sharma","doi":"10.1109/GCAT52182.2021.9587582","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587582","url":null,"abstract":"Rainfall prediction is a routine part of meteorological observations. An essential work of this department includes keeping daily record of rainfall characteristics, variations, intensity, etc. for a particular region. Although, in our general life, we may not pay much attention to our day-to-day weather conditions, for instance, sunrise, humidity, rainfall, air pressure and others, but in terms of climatology, such weather events or their fluctuation can leave a great impact on the habitat, if they remain consistent in long run. For this reason, advance methods are being implemented to develop accurate weather prediction tools. Also, several researches are done in the area of climatology to confirm a presumed connection between our Earth's weather and unconventional, new unusual phenomenon that is noticed within the climate influencing atmospheric range. Our research is planned to analyze such a phenomenon. In our study, we constructed a Machine Learning Based Rainfall prediction Model with Moon's phases included as a feature to observe the its importance level in rainfall prediction as well as compare its value with other influencing factors. We have chosen Machine Learning approach to achieve desired accuracy, speed and efficiency than any contemporary manual engineering processes done for data analysis. We have incorporated two predictive algorithms, namely, Logistic Regression and Random Forest to enhance our Model's predictive potentiality. Since, lowering of computational speed, making erroneous calculations, increasing system processing risk are some of the difficulties of Machine Learning implementation with large dataset, we have utilized Feature Selection Technique to overcome them.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114634335","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587650
Radha Krishna Guntur, Kr Ramakrishnan, V. K. Mittal
Automatic accent classification using a database developed with both L1 and L2 language data has been proposed. Speech samples were collected from native Indian speakers speaking in their mother tongue namely Kannada, Tamil, or Telugu, and from non-native English speakers with one of the above as the first language. The vocal tract characteristics were used in the present study. The MFCC features extracted from both native speech and non-native speech were extensively analyzed. Performance validation in Regional Nativity Identification has been investigated using both native South Indian speech, and non-native English speech by the compatriots of the linguistic groups. Detecting regional identity using MFCC features with GMMUBM / i-vector modeling has been proposed. The challenges of second language speech recognition have been addressed by leveraging native, and non-native speech, which produced an SVM classification score of 86.1%, and the Area Under Curve (AUC) is found to be well above 90% for all three languages.
{"title":"Automatic Classification of Foreign Language Accent","authors":"Radha Krishna Guntur, Kr Ramakrishnan, V. K. Mittal","doi":"10.1109/GCAT52182.2021.9587650","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587650","url":null,"abstract":"Automatic accent classification using a database developed with both L1 and L2 language data has been proposed. Speech samples were collected from native Indian speakers speaking in their mother tongue namely Kannada, Tamil, or Telugu, and from non-native English speakers with one of the above as the first language. The vocal tract characteristics were used in the present study. The MFCC features extracted from both native speech and non-native speech were extensively analyzed. Performance validation in Regional Nativity Identification has been investigated using both native South Indian speech, and non-native English speech by the compatriots of the linguistic groups. Detecting regional identity using MFCC features with GMMUBM / i-vector modeling has been proposed. The challenges of second language speech recognition have been addressed by leveraging native, and non-native speech, which produced an SVM classification score of 86.1%, and the Area Under Curve (AUC) is found to be well above 90% for all three languages.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127437046","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587480
R. Deepalakshmi, J. Amudha
In video established eye tracking methods, there are both mechanical and electrical based approaches existing. With the emerging spread of gaze tracking technology in the recent years and its significance in daily life routine, the data content acquired from the eye behavior tracing turn into important. Several research works were proposed to track the behavior of gaze while playing videos. Tracking an eye gaze while playing a dynamic videos consisting of numerous frames is a complex problem which needs excessive computational efforts. To handle such a complex task, this research proposes Reinforcement Learning (RL) based gaze behavior prediction model. These techniques are found to be invasive in nature and for visual attention behavior analysis applications, these invasive eye tracking system is not applicable. Hence the non-invasive eye tracking could be developed by determining the point of gaze based on observed image processing techniques. Some of the prevailing techniques include artificial intelligence, deep learning, and reinforcement learning and so on. Though quite a few research works has been admitted in this research area, there are several challenges existing so far. The suggested learning techniques are found to be computationally complex and time consuming. This current research work intends to propose a deep convolutional reinforcement learning (DC-RL) model for predicting the visual attention behavior of a person over dynamic scenes.
{"title":"A Reinforcement Learning based Eye-Gaze Behavior Tracking","authors":"R. Deepalakshmi, J. Amudha","doi":"10.1109/GCAT52182.2021.9587480","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587480","url":null,"abstract":"In video established eye tracking methods, there are both mechanical and electrical based approaches existing. With the emerging spread of gaze tracking technology in the recent years and its significance in daily life routine, the data content acquired from the eye behavior tracing turn into important. Several research works were proposed to track the behavior of gaze while playing videos. Tracking an eye gaze while playing a dynamic videos consisting of numerous frames is a complex problem which needs excessive computational efforts. To handle such a complex task, this research proposes Reinforcement Learning (RL) based gaze behavior prediction model. These techniques are found to be invasive in nature and for visual attention behavior analysis applications, these invasive eye tracking system is not applicable. Hence the non-invasive eye tracking could be developed by determining the point of gaze based on observed image processing techniques. Some of the prevailing techniques include artificial intelligence, deep learning, and reinforcement learning and so on. Though quite a few research works has been admitted in this research area, there are several challenges existing so far. The suggested learning techniques are found to be computationally complex and time consuming. This current research work intends to propose a deep convolutional reinforcement learning (DC-RL) model for predicting the visual attention behavior of a person over dynamic scenes.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124874073","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587790
Rajesh Sudi, Vibha Biligere, Tejaswini R, T. P, T. M
In recent years, lifestyle related illness has become more pronounced and demand for technology that enables easy and quick checking of diseases is increasing. Breath analysis is a very promising field of research work having great potential for diagnosis of diseases in non-invasive way for analyzing the volatile organic compounds (VOC’s) and its concentrations in exhaled human breath and also has potential for the early detection and progress monitoring of several diseases. As far as detection of Diabetes Mellitus is concerned, glucose level is calculated by invasive methods which is quite painful, time consuming and tormenting to some people, hence there has been a constant demand for the development of non-invasive, sensitive sensor system that offers fast and real-time electronic readout of blood glucose level. Responding to this we have prototyped a design of handy and non-invasive instrument which investigates the potential of breath signal analysis as a way for blood glucose monitoring with the help of an acetone gas sensor through which results can be actualized. Acetone is not only an effective biomarker of Diabetes Mellitus but also proved to be a rapid, patient compliant viable alternative to the conventional methods of blood glucose determination.
{"title":"Analysis of Volatile Organic Compounds in Exhaled Breath for Detection of Diabetes Mellitus","authors":"Rajesh Sudi, Vibha Biligere, Tejaswini R, T. P, T. M","doi":"10.1109/GCAT52182.2021.9587790","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587790","url":null,"abstract":"In recent years, lifestyle related illness has become more pronounced and demand for technology that enables easy and quick checking of diseases is increasing. Breath analysis is a very promising field of research work having great potential for diagnosis of diseases in non-invasive way for analyzing the volatile organic compounds (VOC’s) and its concentrations in exhaled human breath and also has potential for the early detection and progress monitoring of several diseases. As far as detection of Diabetes Mellitus is concerned, glucose level is calculated by invasive methods which is quite painful, time consuming and tormenting to some people, hence there has been a constant demand for the development of non-invasive, sensitive sensor system that offers fast and real-time electronic readout of blood glucose level. Responding to this we have prototyped a design of handy and non-invasive instrument which investigates the potential of breath signal analysis as a way for blood glucose monitoring with the help of an acetone gas sensor through which results can be actualized. Acetone is not only an effective biomarker of Diabetes Mellitus but also proved to be a rapid, patient compliant viable alternative to the conventional methods of blood glucose determination.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"335 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122135764","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587481
Sanjay Razdan, Himanshu Gupta, A. Seth
Cloud computing has enabled organizations to get rid of the infrastructural cost and increase the service availability. However, the risks associated with the openness and resource sharing of the cloud presents serious security challenges. Intrusion Detection System acts as a monitoring and alerting system against the security breaches. However, such a system needs to be efficient and generate least false alarms. This paper reviews the Intrusion Detection Systems proposed during the year 2015-2020 and evaluates their performance based on Accuracy, Detection Rate and False Positive Rate. This work also highlights the average performance of Intrusion Detection Systems during the period of study and method that resulted in best performance.
{"title":"Performance of Network Intrusion Detection Systems in Cloud Computing: A Review","authors":"Sanjay Razdan, Himanshu Gupta, A. Seth","doi":"10.1109/GCAT52182.2021.9587481","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587481","url":null,"abstract":"Cloud computing has enabled organizations to get rid of the infrastructural cost and increase the service availability. However, the risks associated with the openness and resource sharing of the cloud presents serious security challenges. Intrusion Detection System acts as a monitoring and alerting system against the security breaches. However, such a system needs to be efficient and generate least false alarms. This paper reviews the Intrusion Detection Systems proposed during the year 2015-2020 and evaluates their performance based on Accuracy, Detection Rate and False Positive Rate. This work also highlights the average performance of Intrusion Detection Systems during the period of study and method that resulted in best performance.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123139511","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587621
K. Akshatha, Subhrajyoti Biswas, A. K. Karunakar, B. Satish Shenoy
With the increase in the traffic on roadways, traffic monitoring is the major need we have at this moment. Using UAVs for traffic monitoring has numerous advantages such as broader field of view, higher mobility, no effect on detected traffic, etc., however, variation in camera orientation, UAV height, cluttered background imposes challenges to this aerial object detection. To provide a UAV-based traffic monitoring solution, we have proposed a car detection system for UAV images using deep learning approaches. We compared the performance of the anchorless Fully Convolutional One Stage (FCOS) object detection algorithm with the popular YOLOv3 algorithm. The performance analysis of these models based on mean Average Precision (mAP) indicates that FCOS yields better results over YOLOv3, whereas in terms of computation speed YOLOv3 performed better.
随着道路交通量的增加，交通监控是我们目前的主要需求。利用无人机进行交通监控具有视场更宽、机动性更高、对被检测交通不产生影响等诸多优点，但摄像机方向、无人机高度、背景杂乱等因素的变化给这种空中目标检测带来了挑战。为了提供基于无人机的交通监控解决方案，我们提出了一种使用深度学习方法的无人机图像汽车检测系统。我们比较了无锚定的全卷积单阶段(FCOS)目标检测算法与流行的YOLOv3算法的性能。基于mean Average Precision (mAP)对这些模型的性能分析表明，FCOS的计算结果优于YOLOv3，而在计算速度方面，YOLOv3表现更好。
{"title":"Anchored versus Anchorless Detector for Car Detection in Aerial Imagery","authors":"K. Akshatha, Subhrajyoti Biswas, A. K. Karunakar, B. Satish Shenoy","doi":"10.1109/GCAT52182.2021.9587621","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587621","url":null,"abstract":"With the increase in the traffic on roadways, traffic monitoring is the major need we have at this moment. Using UAVs for traffic monitoring has numerous advantages such as broader field of view, higher mobility, no effect on detected traffic, etc., however, variation in camera orientation, UAV height, cluttered background imposes challenges to this aerial object detection. To provide a UAV-based traffic monitoring solution, we have proposed a car detection system for UAV images using deep learning approaches. We compared the performance of the anchorless Fully Convolutional One Stage (FCOS) object detection algorithm with the popular YOLOv3 algorithm. The performance analysis of these models based on mean Average Precision (mAP) indicates that FCOS yields better results over YOLOv3, whereas in terms of computation speed YOLOv3 performed better.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131891910","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587507
Vaddi Sowmya Sree, Chaitna Sri Koganti, Srinivas K Kalyana, P. Anudeep
Artificial intelligence (AI) is a broad field of computer science that focuses on designing smart machines capable of performing tasks typically requiring human intelligence. Despite the fact that security solutions are growing progressively modern and stable, cyberattacks are still evolving and are at their extreme. The main reason is that conventional methods of malware detection fail. Cyber attackers are actively developing new ways to prevent defence programmes from infecting malware networks and servers. Most anti-malware and antivirus applications currently use signature-based detection to identify attacks, which is unsuccessful in detecting new threats. This is where Artificial Intelligence is most handy. The standardised models for threatened hunting and performance quantification from the start of hazard hunting to the end still allow methodological rigour and completeness to be studied remain undefined. The organised practise of hazard hunts seeks to disclose the presence of TTP in the field of detection that has not already been detected. In this study, a realistic and comprehensive model is outlined to detect attackers in six stages: aim, scale, equipment, planning, execution and input. This study describes Threat Hunting in an ecosystem as the constructive, analyst-driven scanning mechanism for attackers TTP. The model has been checked for real-world data sets using a variety of threats. The effectiveness and practicality of this research have been shown with and without a blueprint through danger hunts. In addition, the article presents an analysis of the concept of threat hunting based on data from Ukrainian electricity grid attacks in an online environment to highlight the effects of this model on threat hunting in a simulated environment. The findings of this analysis include an effective and repetitive way to search for and quantify honesty, coverage and rigour.
{"title":"Artificial Intelligence Based Predictive Threat Hunting In The Field of Cyber Security","authors":"Vaddi Sowmya Sree, Chaitna Sri Koganti, Srinivas K Kalyana, P. Anudeep","doi":"10.1109/GCAT52182.2021.9587507","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587507","url":null,"abstract":"Artificial intelligence (AI) is a broad field of computer science that focuses on designing smart machines capable of performing tasks typically requiring human intelligence. Despite the fact that security solutions are growing progressively modern and stable, cyberattacks are still evolving and are at their extreme. The main reason is that conventional methods of malware detection fail. Cyber attackers are actively developing new ways to prevent defence programmes from infecting malware networks and servers. Most anti-malware and antivirus applications currently use signature-based detection to identify attacks, which is unsuccessful in detecting new threats. This is where Artificial Intelligence is most handy. The standardised models for threatened hunting and performance quantification from the start of hazard hunting to the end still allow methodological rigour and completeness to be studied remain undefined. The organised practise of hazard hunts seeks to disclose the presence of TTP in the field of detection that has not already been detected. In this study, a realistic and comprehensive model is outlined to detect attackers in six stages: aim, scale, equipment, planning, execution and input. This study describes Threat Hunting in an ecosystem as the constructive, analyst-driven scanning mechanism for attackers TTP. The model has been checked for real-world data sets using a variety of threats. The effectiveness and practicality of this research have been shown with and without a blueprint through danger hunts. In addition, the article presents an analysis of the concept of threat hunting based on data from Ukrainian electricity grid attacks in an online environment to highlight the effects of this model on threat hunting in a simulated environment. The findings of this analysis include an effective and repetitive way to search for and quantify honesty, coverage and rigour.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"382 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131762319","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587887
Anju Singh, S. Vaishnavi, Ajay Andhiwal, A. V. Nirmal
Low Dropout Regulators (LDO) are extensively used in power conditioning and distribution systems that need a low noise & stable voltage supplies independent of load, input voltage variations, temperature, and time. This article details the development of thick film technology-based miniature LDO hybrid providing excellent electrical performance with required thermal management attained by implementing high thermal conductive GlidCop base metal package & Copper cored pin materials for applications in space power systems. Advance attachment materials used at various segments of hybrid i.e., silicon die component to substrate, substrate to package and heat sink interface material for isometric heat transfer, are also highlighted. Hybrid realization of LDO faced various challenges as traditional packaging approach results in instable performance with extremely unmanageable thermal deals. Fabrication challenges and issues faced while designing functional & Burn-in test jigs are also addressed. Glidcop package qualification and hybrid realization process qualification details have also been presented in this article.
{"title":"Novel Process for Miniature Low Dropout Voltage Regulator Hybrid for Aerospace Applications","authors":"Anju Singh, S. Vaishnavi, Ajay Andhiwal, A. V. Nirmal","doi":"10.1109/GCAT52182.2021.9587887","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587887","url":null,"abstract":"Low Dropout Regulators (LDO) are extensively used in power conditioning and distribution systems that need a low noise & stable voltage supplies independent of load, input voltage variations, temperature, and time. This article details the development of thick film technology-based miniature LDO hybrid providing excellent electrical performance with required thermal management attained by implementing high thermal conductive GlidCop base metal package & Copper cored pin materials for applications in space power systems. Advance attachment materials used at various segments of hybrid i.e., silicon die component to substrate, substrate to package and heat sink interface material for isometric heat transfer, are also highlighted. Hybrid realization of LDO faced various challenges as traditional packaging approach results in instable performance with extremely unmanageable thermal deals. Fabrication challenges and issues faced while designing functional & Burn-in test jigs are also addressed. Glidcop package qualification and hybrid realization process qualification details have also been presented in this article.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"178 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120899833","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587830
Anupriya Jain, Muskan Agarwal, V. Shubha Rao
Burnout is a mental state caused by excessive stress that results in emotional instability and a reduction in an individual’s performance capacity. Burnout is defined as, among other things, a fear of failure, a sense of powerlessness, and a sense of performance pressure. It has more to do with dealing with one’s conscience than with dealing with society. Tiredness, a lack of sleep, a lack of inspiration and productivity, concentration issues, frequent headaches, and other factors all contribute to burnout. Burnout isn’t just a problem for people in the business world; it can affect anyone, including students, stay-at-home moms, teachers, and others. Many people are affected by this, and they are unaware that they are “burned out,” therefore the symptoms go untreated, which can be problematic in the long term. It is possible to create a model that allows people to assess themselves using a curated set of criteria (factors) and estimate the rate of burnout. This paper gives an overview of various regression models offered in the existing literature for predicting employee burnout, and the best performing model is selected through a comparison based on different evaluation techniques.
{"title":"Employee Burnout Prediction: A Supervised Learning Approach","authors":"Anupriya Jain, Muskan Agarwal, V. Shubha Rao","doi":"10.1109/GCAT52182.2021.9587830","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587830","url":null,"abstract":"Burnout is a mental state caused by excessive stress that results in emotional instability and a reduction in an individual’s performance capacity. Burnout is defined as, among other things, a fear of failure, a sense of powerlessness, and a sense of performance pressure. It has more to do with dealing with one’s conscience than with dealing with society. Tiredness, a lack of sleep, a lack of inspiration and productivity, concentration issues, frequent headaches, and other factors all contribute to burnout. Burnout isn’t just a problem for people in the business world; it can affect anyone, including students, stay-at-home moms, teachers, and others. Many people are affected by this, and they are unaware that they are “burned out,” therefore the symptoms go untreated, which can be problematic in the long term. It is possible to create a model that allows people to assess themselves using a curated set of criteria (factors) and estimate the rate of burnout. This paper gives an overview of various regression models offered in the existing literature for predicting employee burnout, and the best performing model is selected through a comparison based on different evaluation techniques.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120952090","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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