Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587771
Deepanshu Garg, Nishu Bansal
The PEN testing permits a tester to verify the nonfunctional as well as functional aspects of a model in such a way that it can judge that how much a target is vulnerable to the intrusion attacks as well as security. It also helps to check its defense mechanisms in case any of the attack occurs. In this research paper review on the work proposed by the various researchers in the area of Penetration (PEN) testing is discussed. Various phases related to the PEN testing are reviewed in detail. In addition to these numerous tools used in PEN testing are also discussed.
{"title":"A Systematic Review on Penetration Testing","authors":"Deepanshu Garg, Nishu Bansal","doi":"10.1109/GCAT52182.2021.9587771","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587771","url":null,"abstract":"The PEN testing permits a tester to verify the nonfunctional as well as functional aspects of a model in such a way that it can judge that how much a target is vulnerable to the intrusion attacks as well as security. It also helps to check its defense mechanisms in case any of the attack occurs. In this research paper review on the work proposed by the various researchers in the area of Penetration (PEN) testing is discussed. Various phases related to the PEN testing are reviewed in detail. In addition to these numerous tools used in PEN testing are also discussed.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"42 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":"115639025","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/GCAT52182.2021.9587873
Sanjay Kumar, A. Singh, A. Kalam, D. Singh
Due to increasing demand for consumption of electrical power along with the difficulties in expansion of available networks for transmission. Considerably, transmission line is the most relevant part of the power system. The requirement of power along with its allegiance has observed to be exponentially growing over the advanced technical era and the key objective of a transmission line is to pass the electric power from the source to destination of distribution network. The term fault analysis is very challenging in power system engineering to deduct the fault in short time from transmission line as well as re-establish the power system as earlier as possible on very less interruption. The main aim for this study is that fault detection and diagnostics for preventing the loss of electricity is still a key issue of research, and the problem has yet to be solved. Thus, utilizing an intelligent control switch such as the IEC-61850 (International Electro Technical Commission) based on the GOOSE (Generic Object Oriented Substation Event) protocol, a real-time modelling and testing of transmission line error protection and communication is designed. Because transmission line error cannot be avoided in an electrical power system, we employ the GOOSE protocol for communication to convey the detected fault in the transmission line via the remote protection relay. The simulation result is performed by using SVM to train the system and ANN is utilized to classify the occurrence of faults in different types in order to get the satisfactory outcome.
{"title":"Improved Fault Prediction using Hybrid Machine Learning Techniques","authors":"Sanjay Kumar, A. Singh, A. Kalam, D. Singh","doi":"10.1109/GCAT52182.2021.9587873","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587873","url":null,"abstract":"Due to increasing demand for consumption of electrical power along with the difficulties in expansion of available networks for transmission. Considerably, transmission line is the most relevant part of the power system. The requirement of power along with its allegiance has observed to be exponentially growing over the advanced technical era and the key objective of a transmission line is to pass the electric power from the source to destination of distribution network. The term fault analysis is very challenging in power system engineering to deduct the fault in short time from transmission line as well as re-establish the power system as earlier as possible on very less interruption. The main aim for this study is that fault detection and diagnostics for preventing the loss of electricity is still a key issue of research, and the problem has yet to be solved. Thus, utilizing an intelligent control switch such as the IEC-61850 (International Electro Technical Commission) based on the GOOSE (Generic Object Oriented Substation Event) protocol, a real-time modelling and testing of transmission line error protection and communication is designed. Because transmission line error cannot be avoided in an electrical power system, we employ the GOOSE protocol for communication to convey the detected fault in the transmission line via the remote protection relay. The simulation result is performed by using SVM to train the system and ANN is utilized to classify the occurrence of faults in different types in order to get the satisfactory outcome.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"17 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":"117092747","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/GCAT52182.2021.9587774
Sujit Kumar
Rotating bearings are one of the widely used components in machinery systems. Bearings are the main reason for the occurrence of faults in rotating machinery systems. Accurate and quick bearings faults detection is important for machinery systems. Nowadays, Deep learning comes up as a very effective artificial intelligence technique. CNN or Convolution neural network is a class of deep neural networks that are used for the diagnosis of faults. Another technique is the support vector machine technique which is a supervised machine learning model which is effectively used for fault classification. In this study, Convolution neural network (CNN) and support vector machine (SVM) algorithm is proposed for fault detection and classification. For the classification of rolling bearing faults, Firstly, vibration signals are converted into time-domain signals and normalization has also been done for achieving better result. A new model is generated for fault classification based on Convolution neural networks and SVM algorithm. To find out the bearing fault and to classify them in real-time a training model can be used. Comparative analysis is done and experimental results show that the CNN model classify with 100% accuracy. To show the effectiveness of the proposed algorithm, the performance is compared with existing literature works. Better results are obtained from the algorithm of CNN than the existing work.
{"title":"Motor Bearing Faults Detection and Classification based on Convolutional Neural Network and Support Vector Machine: A Comparative Study","authors":"Sujit Kumar","doi":"10.1109/GCAT52182.2021.9587774","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587774","url":null,"abstract":"Rotating bearings are one of the widely used components in machinery systems. Bearings are the main reason for the occurrence of faults in rotating machinery systems. Accurate and quick bearings faults detection is important for machinery systems. Nowadays, Deep learning comes up as a very effective artificial intelligence technique. CNN or Convolution neural network is a class of deep neural networks that are used for the diagnosis of faults. Another technique is the support vector machine technique which is a supervised machine learning model which is effectively used for fault classification. In this study, Convolution neural network (CNN) and support vector machine (SVM) algorithm is proposed for fault detection and classification. For the classification of rolling bearing faults, Firstly, vibration signals are converted into time-domain signals and normalization has also been done for achieving better result. A new model is generated for fault classification based on Convolution neural networks and SVM algorithm. To find out the bearing fault and to classify them in real-time a training model can be used. Comparative analysis is done and experimental results show that the CNN model classify with 100% accuracy. To show the effectiveness of the proposed algorithm, the performance is compared with existing literature works. Better results are obtained from the algorithm of CNN than the existing work.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"24 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":"123406652","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/GCAT52182.2021.9587619
L. R. Chandran, Nikhil Jayagopal, L. S. Lal, Chaithanya Narayanan, S. Deepak, Harikrishnan V
Power consumption schedule can shift the loads from peak hours and redistribute them across a day based on user time preferences. This can indirectly help the utility to improve the load curve. It also helps the residential user to reduce the total electric bill as well. In India, electric billing has fixed energy charges based on the unit the user consumes. Demand-side management can introduce dynamic pricing, so the cost of power consumption is reduced. It motivates the consumer to schedule their load. This paper proposes to minimize the cost of electrical energy by optimal scheduling of home appliances in residential homes using mixed-integer linear programming. It reduces the peak-to-average ratio resulting in a reduction of stress on utility. The paper also discusses the benefit of time of use pricing and real-time pricing over a flat tariff system in the Indian electricity market.
{"title":"Impact of Dynamic Pricing in Residential Load Scheduling and Energy Management","authors":"L. R. Chandran, Nikhil Jayagopal, L. S. Lal, Chaithanya Narayanan, S. Deepak, Harikrishnan V","doi":"10.1109/GCAT52182.2021.9587619","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587619","url":null,"abstract":"Power consumption schedule can shift the loads from peak hours and redistribute them across a day based on user time preferences. This can indirectly help the utility to improve the load curve. It also helps the residential user to reduce the total electric bill as well. In India, electric billing has fixed energy charges based on the unit the user consumes. Demand-side management can introduce dynamic pricing, so the cost of power consumption is reduced. It motivates the consumer to schedule their load. This paper proposes to minimize the cost of electrical energy by optimal scheduling of home appliances in residential homes using mixed-integer linear programming. It reduces the peak-to-average ratio resulting in a reduction of stress on utility. The paper also discusses the benefit of time of use pricing and real-time pricing over a flat tariff system in the Indian electricity market.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"32 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":"123722786","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/GCAT52182.2021.9587617
Amardeep A. Shirolkar, S. Sankpal
In cooperative spectrum sensing in cognitive radio network for the detection of primary user (PU), the detection in classical methods solely depend on signal power and threshold. The selection of threshold is important issue which defines the level of accuracy of detection of PU. This paper focuses on machine learning based prediction of presence of PU based on recorded data training which also shows solution for the problem of various signal strength confusing issues. The model is tested using support vector machine (SVM) based linear binary classifier for combinations of recorded signal strengths from simulated experimental data. The deep learning based method is also tested using recurrent neural network configured using long short term memory (LSTM) and gated recurrent unit (GRU) layers in the model. The performance is compared for the accuracy of PU detection and deep learning approach shows better performance.
{"title":"Deep Learning Based Performance of Cooperative Sensing in Cognitive Radio Network","authors":"Amardeep A. Shirolkar, S. Sankpal","doi":"10.1109/GCAT52182.2021.9587617","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587617","url":null,"abstract":"In cooperative spectrum sensing in cognitive radio network for the detection of primary user (PU), the detection in classical methods solely depend on signal power and threshold. The selection of threshold is important issue which defines the level of accuracy of detection of PU. This paper focuses on machine learning based prediction of presence of PU based on recorded data training which also shows solution for the problem of various signal strength confusing issues. The model is tested using support vector machine (SVM) based linear binary classifier for combinations of recorded signal strengths from simulated experimental data. The deep learning based method is also tested using recurrent neural network configured using long short term memory (LSTM) and gated recurrent unit (GRU) layers in the model. The performance is compared for the accuracy of PU detection and deep learning approach shows better performance.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"73 6 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":"123748928","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/GCAT52182.2021.9587474
Samiksha Sharma, Anchal Pokharana
Data security refers to safeguard or protect the data. Data security is an essential prerequisite of the world today because of internet that provides a medium for communication between different communities of the world. Communication can be through a wireless media or wired, that requires security of data while transmitting through it because such mediums are susceptible to different external threats, so there’s a strong need of data security for those channels. Cryptography is an art to write code for solving such data threats related problems for communications over an unsecure channel. Cryptography provides diverse set of services to the data such as authentication, privacy, integrity and nonrepudiation through a wide range of techniques. It is broadly classified into two categories named as symmetric and asymmetric cryptography; symmetric technique is fast and uses the same key for encrypting the data and converting the cipher back to its original text using decryption process whereas asymmetric requires different key pair for encrypting and decrypting data. This paper presents a comparative analysis of two hybrid models AES-ECC and AES-ECDH implemented for a client server system. AES is a symmetric technique and is first implemented with ECC for a client server system. After first implementation for further enhancement in the security of data communication between client and server, AES is again implemented with another asymmetric technique ECDH commonly known as a key agreement protocol and a variant of Diffie-Hellman combined with elliptic curve cryptography that adds up more security by establishing a shared secret after a successful key agreement between client and server. After implementing, both the models are analyzed on the basis of various parameters. This paper thus presents the comparison between AES-ECC and AES-ECDH on the basis of various metrics that signify the performance, effectiveness, strength and weakness of an algorithm and also the paper will verify which hybrid technique will be more superior in providing the security and effective delivery of confidential information for a client server communication system.
{"title":"Comparative Analysis of AES-ECC and AES-ECDH Hybrid Models for a Client-Server System","authors":"Samiksha Sharma, Anchal Pokharana","doi":"10.1109/GCAT52182.2021.9587474","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587474","url":null,"abstract":"Data security refers to safeguard or protect the data. Data security is an essential prerequisite of the world today because of internet that provides a medium for communication between different communities of the world. Communication can be through a wireless media or wired, that requires security of data while transmitting through it because such mediums are susceptible to different external threats, so there’s a strong need of data security for those channels. Cryptography is an art to write code for solving such data threats related problems for communications over an unsecure channel. Cryptography provides diverse set of services to the data such as authentication, privacy, integrity and nonrepudiation through a wide range of techniques. It is broadly classified into two categories named as symmetric and asymmetric cryptography; symmetric technique is fast and uses the same key for encrypting the data and converting the cipher back to its original text using decryption process whereas asymmetric requires different key pair for encrypting and decrypting data. This paper presents a comparative analysis of two hybrid models AES-ECC and AES-ECDH implemented for a client server system. AES is a symmetric technique and is first implemented with ECC for a client server system. After first implementation for further enhancement in the security of data communication between client and server, AES is again implemented with another asymmetric technique ECDH commonly known as a key agreement protocol and a variant of Diffie-Hellman combined with elliptic curve cryptography that adds up more security by establishing a shared secret after a successful key agreement between client and server. After implementing, both the models are analyzed on the basis of various parameters. This paper thus presents the comparison between AES-ECC and AES-ECDH on the basis of various metrics that signify the performance, effectiveness, strength and weakness of an algorithm and also the paper will verify which hybrid technique will be more superior in providing the security and effective delivery of confidential information for a client server communication system.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"52 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":"125558376","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/GCAT52182.2021.9587876
Liyanage A.N, A.G.A. Madhushanka D, U.M. Uduwara L, W.M.S. D Jayarathne, S. Siriwardana, Shyam Reyal, W.K. Mithsara M
At present, it has become challenging for university students to manage their workload such assignments, projects etc. among their day-to-day tasks and personal chores. It has become hard to spend time efficiently on tasks that should be prioritized, and to decide what the best way to spend their remaining time is. Even though integration methods and multi-functional Time Management Tools (TMTs) such as Trello and Asana exist, finding, following, and implementing them is time consuming and monotonous. ScheduleME is a smart digital personal assistant, which will be in a form of a mobile app that collects and stores all the tasks the student must do, prioritize them according to their importance, schedule them intelligently across the student’s remaining time considering his/her existing academic and personal timetables and daily routines. A user-friendly and comprehensible mobile app is designed where the right amount of information is presented to the user without important details that user could conFigure and override and not show too much information such that the user becomes overwhelmed. This overcomes the weakness found in many time-management and to do list apps. (e.g. - Trello, Microsoft Tasks, Todoist) where the user must enter all the details of the tasks manually and set the priority manually. The main emphasis of our suggested system is four primary components. They are Data engineering, Intelligent task breakdown and scheduling, Personalized task scheduling and User-centered interaction design. Aside from that, this system employs a variety of technologies and algorithms to improve the research’s accuracy and efficiency.
目前，大学生在日常工作和个人琐事中管理好自己的工作量已经成为一项挑战。很难有效地把时间花在应该优先考虑的任务上，也很难决定如何最好地利用剩下的时间。尽管存在集成方法和多功能时间管理工具(tmt)，如Trello和Asana，但查找、跟踪和实现它们既耗时又单调。ScheduleME是一款智能数字个人助理，它将以移动应用程序的形式收集和存储学生必须完成的所有任务，根据其重要性对其进行优先排序，并根据学生现有的学术和个人时间表以及日常生活，在学生的剩余时间内智能地安排它们。一款用户友好且易于理解的手机应用应该向用户呈现适量的信息，而不是用户可以配置和覆盖的重要细节，而不是显示过多的信息，这样用户就会感到不知所措。这克服了许多时间管理和待办事项列表应用程序的弱点。(例如- Trello, Microsoft Tasks, Todoist)，用户必须手动输入任务的所有细节并手动设置优先级。我们建议的系统主要强调四个主要组成部分。它们是数据工程、智能任务分解和调度、个性化任务调度和以用户为中心的交互设计。除此之外，该系统还采用了多种技术和算法来提高研究的准确性和效率。
{"title":"ScheduleME - Smart Digital Personal Assistant for Automatic Priority Based Task Scheduling and Time Management","authors":"Liyanage A.N, A.G.A. Madhushanka D, U.M. Uduwara L, W.M.S. D Jayarathne, S. Siriwardana, Shyam Reyal, W.K. Mithsara M","doi":"10.1109/GCAT52182.2021.9587876","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587876","url":null,"abstract":"At present, it has become challenging for university students to manage their workload such assignments, projects etc. among their day-to-day tasks and personal chores. It has become hard to spend time efficiently on tasks that should be prioritized, and to decide what the best way to spend their remaining time is. Even though integration methods and multi-functional Time Management Tools (TMTs) such as Trello and Asana exist, finding, following, and implementing them is time consuming and monotonous. ScheduleME is a smart digital personal assistant, which will be in a form of a mobile app that collects and stores all the tasks the student must do, prioritize them according to their importance, schedule them intelligently across the student’s remaining time considering his/her existing academic and personal timetables and daily routines. A user-friendly and comprehensible mobile app is designed where the right amount of information is presented to the user without important details that user could conFigure and override and not show too much information such that the user becomes overwhelmed. This overcomes the weakness found in many time-management and to do list apps. (e.g. - Trello, Microsoft Tasks, Todoist) where the user must enter all the details of the tasks manually and set the priority manually. The main emphasis of our suggested system is four primary components. They are Data engineering, Intelligent task breakdown and scheduling, Personalized task scheduling and User-centered interaction design. Aside from that, this system employs a variety of technologies and algorithms to improve the research’s accuracy and efficiency.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"33 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":"116404616","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/GCAT52182.2021.9587723
Apsana Khatoon, N. Srivastava
Low noise amplifier is a first building block of receiver system. Basically, it amplifies the strength of the output signal without adding much noise at output. For 5G communication, system requires high speed data for faster connectivity with sufficient strength without much disturbance. Here, in this paper, a cascoded low noise amplifier is proposed which is implemented and analysed on 45nm CMOS technology using cadence virtuoso software. This circuit consists of two stages and both stages are cascode stage. This circuit is working at 8GHz frequency with 1.4Vsupply voltage and gives S11< -10dB, S22 < -10dB and power gain of 27.57 dB with 2.31dB noise figure.
{"title":"Design of Cascode Low Noise Amplifier for 5G Application on 45nm CMOS Technology","authors":"Apsana Khatoon, N. Srivastava","doi":"10.1109/GCAT52182.2021.9587723","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587723","url":null,"abstract":"Low noise amplifier is a first building block of receiver system. Basically, it amplifies the strength of the output signal without adding much noise at output. For 5G communication, system requires high speed data for faster connectivity with sufficient strength without much disturbance. Here, in this paper, a cascoded low noise amplifier is proposed which is implemented and analysed on 45nm CMOS technology using cadence virtuoso software. This circuit consists of two stages and both stages are cascode stage. This circuit is working at 8GHz frequency with 1.4Vsupply voltage and gives S11< -10dB, S22 < -10dB and power gain of 27.57 dB with 2.31dB noise figure.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"43 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":"122698662","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/GCAT52182.2021.9587713
Shivam Prajapati, Y. Upadhyay, Aviral Chharia, Bikramjit Sharma
Electric Vehicles (EVs) have gained immense attention in recent years due to their numerous advantages as a green alternative to their fuel-based counterparts. Four-wheeler EVs are often expensive and not affordable by many people, but a high demand for two-wheeler EVs is being witnessed in the Indian market segment. Due to the novelty of the technology, many buyers in emerging EV markets lack a clear understanding of EV selection compared to their fuel-based equivalents, which have been on the market for decades. Therefore, customers often face difficulties in selecting models for purchase. Moreover, multiple features in EV models further make it challenging to develop appropriate criteria for building a recommendation system. Thus, there is a present need for a robust recommendation system that can rank the best alternative EV. This paper presents a novel hybrid Fuzzy AHP-TOPSIS approach for the ideal selection of two-wheeler EVs, explicitly targeting the Indian Market Segment. In this study, six criteria are selected to judge among eight popular EV alternatives. The Analytical Hierarchy Process (AHP) is employed to find the Fuzzy relative weights of each criterion, while TOPSIS is used to select one of the best alternatives among various similar options. The study would also help to aid low-performing EVs in determining their benchmarks.
{"title":"A novel hybrid Fuzzy AHP-TOPSIS Approach towards Enhanced multi-criteria Feature-based EV Recommender System","authors":"Shivam Prajapati, Y. Upadhyay, Aviral Chharia, Bikramjit Sharma","doi":"10.1109/GCAT52182.2021.9587713","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587713","url":null,"abstract":"Electric Vehicles (EVs) have gained immense attention in recent years due to their numerous advantages as a green alternative to their fuel-based counterparts. Four-wheeler EVs are often expensive and not affordable by many people, but a high demand for two-wheeler EVs is being witnessed in the Indian market segment. Due to the novelty of the technology, many buyers in emerging EV markets lack a clear understanding of EV selection compared to their fuel-based equivalents, which have been on the market for decades. Therefore, customers often face difficulties in selecting models for purchase. Moreover, multiple features in EV models further make it challenging to develop appropriate criteria for building a recommendation system. Thus, there is a present need for a robust recommendation system that can rank the best alternative EV. This paper presents a novel hybrid Fuzzy AHP-TOPSIS approach for the ideal selection of two-wheeler EVs, explicitly targeting the Indian Market Segment. In this study, six criteria are selected to judge among eight popular EV alternatives. The Analytical Hierarchy Process (AHP) is employed to find the Fuzzy relative weights of each criterion, while TOPSIS is used to select one of the best alternatives among various similar options. The study would also help to aid low-performing EVs in determining their benchmarks.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"38 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":"129683923","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/GCAT52182.2021.9587494
Moksh Jadhav, Shivani Bhamare, V. Chauhan, S. Rao, Nibha Desai, S. Subramaniam
Delicate and quantifiable analysis of protein is essential for disease diagnosis, drug screening and large-scaled study of proteins. Taking in consideration the recent trends and research in biomolecule analysis; Nanowires, configured as Field-Effect Transistors have emerged as a very successful and efficient platform to detect proteins and other species efficaciously, owing to its high sensitivity. Here we attempt to optimize the parameters of a nanowire-based biosensor in order to improve the overall efficiency of the biosensor, in order to provide a more insightful output. Using an open-source tool available on NanoHub which enables us to vary the physical parameters and analyze the corresponding output after revamping the parameters. Our endeavor is concerned with realizing the best characteristics that would give us the best performance, that is, minimum settling time, maximum selectivity and maximum sensitivity. A sensor is best defined by its ability to discriminate the response from the adjacent inputs, ability to detect the tiniest changes in input and present the fluctuation in input in the least amount of time as possible; better termed as selectivity, sensitivity and settling time respectively. The objective of this project is to have the highest sensitivity and selectivity, whilst keeping the settling time as low as possible.
{"title":"Design Optimization and Implementation of Nanowire Based Biosensors","authors":"Moksh Jadhav, Shivani Bhamare, V. Chauhan, S. Rao, Nibha Desai, S. Subramaniam","doi":"10.1109/GCAT52182.2021.9587494","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587494","url":null,"abstract":"Delicate and quantifiable analysis of protein is essential for disease diagnosis, drug screening and large-scaled study of proteins. Taking in consideration the recent trends and research in biomolecule analysis; Nanowires, configured as Field-Effect Transistors have emerged as a very successful and efficient platform to detect proteins and other species efficaciously, owing to its high sensitivity. Here we attempt to optimize the parameters of a nanowire-based biosensor in order to improve the overall efficiency of the biosensor, in order to provide a more insightful output. Using an open-source tool available on NanoHub which enables us to vary the physical parameters and analyze the corresponding output after revamping the parameters. Our endeavor is concerned with realizing the best characteristics that would give us the best performance, that is, minimum settling time, maximum selectivity and maximum sensitivity. A sensor is best defined by its ability to discriminate the response from the adjacent inputs, ability to detect the tiniest changes in input and present the fluctuation in input in the least amount of time as possible; better termed as selectivity, sensitivity and settling time respectively. The objective of this project is to have the highest sensitivity and selectivity, whilst keeping the settling time as low as possible.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"74 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":"129765888","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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