Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836363
Refat Uddin Rafi, Famin Rahman Rakib, M. Alim
This work utilizes the Angelov model via experimental validation to estimate the performance of GaN HEMT. We changed the two variables (a and λ) and then used Angelov model to explain how to match the experimental results of the I- V characteristics curve and highlight their impacts on the saturation and the linear regions. We also look at how the output conductance and transconductance behave, and compare the data from the simulation and the data from the experiment. The measurements and simulations closely matched the DC findings of the GaN HEMT.
{"title":"Performance Projection of GaN HEMT: Experimental Verification Using Angelov Model","authors":"Refat Uddin Rafi, Famin Rahman Rakib, M. Alim","doi":"10.1109/icaeee54957.2022.9836363","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836363","url":null,"abstract":"This work utilizes the Angelov model via experimental validation to estimate the performance of GaN HEMT. We changed the two variables (a and λ) and then used Angelov model to explain how to match the experimental results of the I- V characteristics curve and highlight their impacts on the saturation and the linear regions. We also look at how the output conductance and transconductance behave, and compare the data from the simulation and the data from the experiment. The measurements and simulations closely matched the DC findings of the GaN HEMT.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122671038","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836352
Humaira Binte Harun, Md. Saiful Islam, Mohammad Hanif
Wireless Sensor Node (WSN) is made up of a large number of microsensor nodes that collect data and deliver it to base station based on some pre-defined instructions. In order to improve the system performance, LEACH protocol is usually employed to select the optimal Cluster head. In this study, genetic algorithm (GA) is utilized for optimal cluster head selection in the LEACH protocol. The suggested GA-based LEACH technique was compared to the performance of the conventional LEACH and I-LEACH protocols, which were previously implemented. According to the findings, the proposed GA-based LEACH protocol outperforms the traditional LEACH and I-LEACH methods in terms of throughput, lifetime and energy dissipation. The GA-based LEACH can send over 350% more data packets than the traditional LEACH technique. Furthermore, as compared to I-LEACH, the sensor nodes' lifetime in the GA-based LEACH network increases by around 150%, and by nearly 300% when compared to traditional LEACH.
{"title":"Genetic Algorithm for Efficient Cluster Head Selection in LEACH protocol of Wireless Sensor Network","authors":"Humaira Binte Harun, Md. Saiful Islam, Mohammad Hanif","doi":"10.1109/icaeee54957.2022.9836352","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836352","url":null,"abstract":"Wireless Sensor Node (WSN) is made up of a large number of microsensor nodes that collect data and deliver it to base station based on some pre-defined instructions. In order to improve the system performance, LEACH protocol is usually employed to select the optimal Cluster head. In this study, genetic algorithm (GA) is utilized for optimal cluster head selection in the LEACH protocol. The suggested GA-based LEACH technique was compared to the performance of the conventional LEACH and I-LEACH protocols, which were previously implemented. According to the findings, the proposed GA-based LEACH protocol outperforms the traditional LEACH and I-LEACH methods in terms of throughput, lifetime and energy dissipation. The GA-based LEACH can send over 350% more data packets than the traditional LEACH technique. Furthermore, as compared to I-LEACH, the sensor nodes' lifetime in the GA-based LEACH network increases by around 150%, and by nearly 300% when compared to traditional LEACH.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127528237","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836408
Nitun Kumar Podder, P. C. Shill, Humayan Kabir Rana, Subir Saha, A. Mimi, Nahnun Nahar Corniya, Nandita Paul, Tarun Kumar Saha
COVID-19 is an infectious illness concerning coronavirus that is transmitted through droplets propagated by an infected person exhales, coughs, or sneezes. People affected by coronavirus have a risk to occur respiratory diseases (RDs). The longevity of COVID-19 may appear a vital risk of manifesting RDs. To address these issues, we explored transcriptomic data to identify the genetic effects of COVID-19 on the development of RDs such as Bronchitis (BC), Asthma (AT), Lung cancer (LC), and Pulmonary Edema (PE). We explored GEO datasets from NCBI for COVID-19, BC, AT, LC, PE case, and control subjects. We identified COVID-19 is associated with RDs by sharing 16, 19, 27, and 59 commonly DEGs accordingly. By using these genes we performed some bioinformatics analysis and constructed diseasome networks, identified functional and ontological pathways. We formed PPIs networks and PDIs network. On the basis of PPIs and PDIs, we have identified hub proteins and constructed hub proteins network. We have successfully developed a quantitative model to identify the genetic effects of COVID-19 on the progression of RDs. We also validated our investigations through gold-benchmark datasets. Our results are an effective resource to mark out the most important influences on the development of RDs for COVID-19.
{"title":"Network-based Approach to Identify Pathways and Macromolecule Interactions that Mediate Influences of COVID-19 on the Progression of Respiratory System Diseases","authors":"Nitun Kumar Podder, P. C. Shill, Humayan Kabir Rana, Subir Saha, A. Mimi, Nahnun Nahar Corniya, Nandita Paul, Tarun Kumar Saha","doi":"10.1109/icaeee54957.2022.9836408","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836408","url":null,"abstract":"COVID-19 is an infectious illness concerning coronavirus that is transmitted through droplets propagated by an infected person exhales, coughs, or sneezes. People affected by coronavirus have a risk to occur respiratory diseases (RDs). The longevity of COVID-19 may appear a vital risk of manifesting RDs. To address these issues, we explored transcriptomic data to identify the genetic effects of COVID-19 on the development of RDs such as Bronchitis (BC), Asthma (AT), Lung cancer (LC), and Pulmonary Edema (PE). We explored GEO datasets from NCBI for COVID-19, BC, AT, LC, PE case, and control subjects. We identified COVID-19 is associated with RDs by sharing 16, 19, 27, and 59 commonly DEGs accordingly. By using these genes we performed some bioinformatics analysis and constructed diseasome networks, identified functional and ontological pathways. We formed PPIs networks and PDIs network. On the basis of PPIs and PDIs, we have identified hub proteins and constructed hub proteins network. We have successfully developed a quantitative model to identify the genetic effects of COVID-19 on the progression of RDs. We also validated our investigations through gold-benchmark datasets. Our results are an effective resource to mark out the most important influences on the development of RDs for COVID-19.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131309895","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836435
Mohammad Sulaiman Redoy, Ruma
The objective of this paper is to design an optimum controller for an interconnected power system based on conventional Proportional-Integral-Derivative (PID) controller and Particle Swarm Optimization (PSO) optimization technique for better Load Frequency Control (LFC) in the event of changing load. This proposed PID-PSO controller provides better solutions in terms of rising time, settling time, minimum overshoot and tie-line error. In this proposed simulation model, two power areas with multi-source generation units are connected via a tie-line connection. Parameters of the PID controller are tuned using the PSO algorithm for optimum output. The system was designed and simulated using MATLAB software using SIMULINK. The results are presented in terms of frequency deviation, tie line error, and settling time.
{"title":"Load Frequency Control of an Inter Connected Power System Using PSO Based PID Controller","authors":"Mohammad Sulaiman Redoy, Ruma","doi":"10.1109/icaeee54957.2022.9836435","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836435","url":null,"abstract":"The objective of this paper is to design an optimum controller for an interconnected power system based on conventional Proportional-Integral-Derivative (PID) controller and Particle Swarm Optimization (PSO) optimization technique for better Load Frequency Control (LFC) in the event of changing load. This proposed PID-PSO controller provides better solutions in terms of rising time, settling time, minimum overshoot and tie-line error. In this proposed simulation model, two power areas with multi-source generation units are connected via a tie-line connection. Parameters of the PID controller are tuned using the PSO algorithm for optimum output. The system was designed and simulated using MATLAB software using SIMULINK. The results are presented in terms of frequency deviation, tie line error, and settling time.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130705017","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836484
Md. Raghib Iftekhar, Md. Golam Rabbani, Adnan Hosen, Md. Saiful Islam, Md. Suruz Mian, Sheikh Rashel Al Ahmed
Lead-free double perovskites show much potential as optoelectronic materials because they are stable and non-toxic. We have conducted a numerical simulation to investigate non-toxic and inorganic Cs2AgBiBr6 absorber based-photovoltaic (PV) device with NiOx hole transport layer (HTL). Herein, the Solar Cell Capacitance Simulator in One Dimensional (SCAPS-1D) has been used to design the cell structure of Ni/NiOx/Cs2AgBiBr6/TiO2/FTO/Al. The optimum thickness of the Cs2AgBiBr6 perovskite layer is found to be 600 nm. Varuous physical parameters of the designed cell on the PV outputs have been explored. To understand the stability of the proposed perovskite solar cell (PSC), effect of functioning temperature on the PV efficiency is also analyzed. Maximum power conversion efficiency (PCE) of 25.38% is achieved with open-circuit voltage (Voc) of 1.33V, short-circuit current (Jsc) of 21.46 mA/cm2, and fill factor (FF) of 88.53% at the optimized device configuration. Therefore, these findings will give useful guidance in the replacement of frequently used detrimental Pb-based perovskite with an environmentally safe and highly efficient inorganic PSC. This research advances the development of flexible perovskite with a simple fabrication procedure and great device performance.
{"title":"Simulating the electrical characteristics of a highly efficient Cs2AgBiBr6-based perovskite solar cell with NiOx hole transport layer","authors":"Md. Raghib Iftekhar, Md. Golam Rabbani, Adnan Hosen, Md. Saiful Islam, Md. Suruz Mian, Sheikh Rashel Al Ahmed","doi":"10.1109/icaeee54957.2022.9836484","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836484","url":null,"abstract":"Lead-free double perovskites show much potential as optoelectronic materials because they are stable and non-toxic. We have conducted a numerical simulation to investigate non-toxic and inorganic Cs2AgBiBr6 absorber based-photovoltaic (PV) device with NiOx hole transport layer (HTL). Herein, the Solar Cell Capacitance Simulator in One Dimensional (SCAPS-1D) has been used to design the cell structure of Ni/NiOx/Cs2AgBiBr6/TiO2/FTO/Al. The optimum thickness of the Cs2AgBiBr6 perovskite layer is found to be 600 nm. Varuous physical parameters of the designed cell on the PV outputs have been explored. To understand the stability of the proposed perovskite solar cell (PSC), effect of functioning temperature on the PV efficiency is also analyzed. Maximum power conversion efficiency (PCE) of 25.38% is achieved with open-circuit voltage (Voc) of 1.33V, short-circuit current (Jsc) of 21.46 mA/cm2, and fill factor (FF) of 88.53% at the optimized device configuration. Therefore, these findings will give useful guidance in the replacement of frequently used detrimental Pb-based perovskite with an environmentally safe and highly efficient inorganic PSC. This research advances the development of flexible perovskite with a simple fabrication procedure and great device performance.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129211767","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836411
Abdullah Al Mamun, K. Ahmed, S. Chowdhury
This paper aims to study the power quality disturbances that might occur in a Photovoltaic (PV) connected distribution grid. Recently, there has been rapid increase in grid-connected PV plants to meet the ever-increasing peak consumer demands. Grid-connected PV sources as well as non-linear loads introduce Power Quality issues. The PQ issues such as voltage sag, voltage swell, transients, total harmonic distortions, etc. might have an adverse effect on the reliable operation of power plant and distribution network. The first step in preparing a reliable grid system is to understand the types of quality issues and their effects. In this paper, the PQ issues that occur in the low voltage and medium voltage distribution grid due to the PV penetration and nonlinear loads are studied. To assess the quality issues, a MATLAB/SIMULINK model has been developed where a PV source has been integrated into the distribution network. Voltage variation caused by faults, total harmonic distortions due to PV penetration, and transient stability issues due to nonlinear loads have been simulated and analyzed.
{"title":"Detection of Power Quality Disturbances in Distribution Grid Network with Photovoltaic Penetration","authors":"Abdullah Al Mamun, K. Ahmed, S. Chowdhury","doi":"10.1109/icaeee54957.2022.9836411","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836411","url":null,"abstract":"This paper aims to study the power quality disturbances that might occur in a Photovoltaic (PV) connected distribution grid. Recently, there has been rapid increase in grid-connected PV plants to meet the ever-increasing peak consumer demands. Grid-connected PV sources as well as non-linear loads introduce Power Quality issues. The PQ issues such as voltage sag, voltage swell, transients, total harmonic distortions, etc. might have an adverse effect on the reliable operation of power plant and distribution network. The first step in preparing a reliable grid system is to understand the types of quality issues and their effects. In this paper, the PQ issues that occur in the low voltage and medium voltage distribution grid due to the PV penetration and nonlinear loads are studied. To assess the quality issues, a MATLAB/SIMULINK model has been developed where a PV source has been integrated into the distribution network. Voltage variation caused by faults, total harmonic distortions due to PV penetration, and transient stability issues due to nonlinear loads have been simulated and analyzed.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132568375","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836343
Ifat Arin, Md. Nahiduzzaman, Md Jahirul Islam, M. R. Kaysir
Lab-on-fiber (LOF) technology has diverse applications in different types of sensing and actuating nano-systems. Radiation dosimeter based on LOF can be effectively used to monitor radiation dose, where the optical properties are quantified due to the variation of radiation dose. In this work, we investigate the performance of an optical fiber tip having a patterned PMMA layer which is covered by a thin Au overlay. We observe the shifts in resonant frequency of the reflection spectra according to the variation of PMMA layer. In contrast, PMMA thickness is changed by the dose rate and correspondingly shifts the resonant frequency of the reflection spectra. In this analysis, design frequency ranges from 210 THz to 214 THz (wavelength 1402 nm to 1430nm). With the increase of the PMMA layer thickness, the peak-to-peak difference (in dB) of the reflection spectra S11, S21 decreases, and the minima and maxima point shift in the left-hand side (frequency decreasing in manner). This analysis would help designers to tune important design parameters to enhance the performance of the dosimeter.
{"title":"Effect of the PMMA Layer Thickness on the Performance of Lab-on-fiber Radiation Dosimeter","authors":"Ifat Arin, Md. Nahiduzzaman, Md Jahirul Islam, M. R. Kaysir","doi":"10.1109/icaeee54957.2022.9836343","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836343","url":null,"abstract":"Lab-on-fiber (LOF) technology has diverse applications in different types of sensing and actuating nano-systems. Radiation dosimeter based on LOF can be effectively used to monitor radiation dose, where the optical properties are quantified due to the variation of radiation dose. In this work, we investigate the performance of an optical fiber tip having a patterned PMMA layer which is covered by a thin Au overlay. We observe the shifts in resonant frequency of the reflection spectra according to the variation of PMMA layer. In contrast, PMMA thickness is changed by the dose rate and correspondingly shifts the resonant frequency of the reflection spectra. In this analysis, design frequency ranges from 210 THz to 214 THz (wavelength 1402 nm to 1430nm). With the increase of the PMMA layer thickness, the peak-to-peak difference (in dB) of the reflection spectra S11, S21 decreases, and the minima and maxima point shift in the left-hand side (frequency decreasing in manner). This analysis would help designers to tune important design parameters to enhance the performance of the dosimeter.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126764714","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836434
Towkir Ahmed, M. Alam, R. Paul, M. T. Hasan, Raqeebir Rab
Music genre classification is extremely important for both music recommendation and acquisition of music data, as well as for music discovery. There have already been a vast amount of researches conducted on the classification of music genres using various machine learning algorithms. Despite the fact that Bangla music is extremely diverse in terms of its own style, there has been little notable work done to date to categorize song genres in Bangla music using machine learning approaches. There are numerous varieties and modes of Bangla music, all of which may be categorised into different classes by their musical compositions. The dataset we use contains six different Bangla music genres. There are several unique attributes for each song which is included in the dataset, including zero crossing value, delta, chroma frequency, spectral roll-off, spectral bandwidth, and many others. Several machine learning models, as well as a deep learning technique, are proposed in this paper for classi-fying Bangla musics into multi-class classification. To train the supervised learning models, we used dimentionality reduction and feature scaling to increase the performance. Finally, our models are evaluated using f'l-score, recall, accuracy and precision. As can be observed, the implemented deep neural network model was able to reach an accuracy of 77.68 percent.
{"title":"Machine Learning and Deep Learning Techniques For Genre Classification of Bangla Music","authors":"Towkir Ahmed, M. Alam, R. Paul, M. T. Hasan, Raqeebir Rab","doi":"10.1109/icaeee54957.2022.9836434","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836434","url":null,"abstract":"Music genre classification is extremely important for both music recommendation and acquisition of music data, as well as for music discovery. There have already been a vast amount of researches conducted on the classification of music genres using various machine learning algorithms. Despite the fact that Bangla music is extremely diverse in terms of its own style, there has been little notable work done to date to categorize song genres in Bangla music using machine learning approaches. There are numerous varieties and modes of Bangla music, all of which may be categorised into different classes by their musical compositions. The dataset we use contains six different Bangla music genres. There are several unique attributes for each song which is included in the dataset, including zero crossing value, delta, chroma frequency, spectral roll-off, spectral bandwidth, and many others. Several machine learning models, as well as a deep learning technique, are proposed in this paper for classi-fying Bangla musics into multi-class classification. To train the supervised learning models, we used dimentionality reduction and feature scaling to increase the performance. Finally, our models are evaluated using f'l-score, recall, accuracy and precision. As can be observed, the implemented deep neural network model was able to reach an accuracy of 77.68 percent.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131109046","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836370
Robi Paul
The aggressive reduction of FET devices predicted in Moore's law has escorted us to an exponential decrease in device performance. Shifting from existing FET devices to Tunneling Field-Effect Transistor (TFET) has demonstrated higher performance while maintaining a significantly lower transistor gate size. It offers a steep subthreshold swing slope with a substantially lower leakage current, resulting in competitively lower power absorption from ordinary FETs. However, to increase the control over the TFET device even further, a slight variation in a design known as the Double Gate Tunneling Field-Effect Transistor (DG- TFET) is implicated. In this study, I have investigated and adjusted the performance of an N-type DG-TFET by altering several parameters such as device materials, high-k dielectric as oxide layers, and oxide thickness. In the end, Tungsten Ditelluride (WTe2) a 2-D material is used as the device material, while Niobium pentoxide (Nb2O5) is used as the high-k dielectric material according to the optimization process of the DG-TFET. The device has achieved a subthreshold swing of 18.37 mv/Dec and an Ion/Ioff of 1011. Finally, I have also conducted a comparative analysis between DG-TFET and a Single Gate Tunneling Field-Effect Transistor (SG-TFET) device with identical specifications.
{"title":"Performance Investigation and Optimization of 2-D Material based Double Gate Tunneling Field-Effect Transistor (DG-TFET)","authors":"Robi Paul","doi":"10.1109/icaeee54957.2022.9836370","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836370","url":null,"abstract":"The aggressive reduction of FET devices predicted in Moore's law has escorted us to an exponential decrease in device performance. Shifting from existing FET devices to Tunneling Field-Effect Transistor (TFET) has demonstrated higher performance while maintaining a significantly lower transistor gate size. It offers a steep subthreshold swing slope with a substantially lower leakage current, resulting in competitively lower power absorption from ordinary FETs. However, to increase the control over the TFET device even further, a slight variation in a design known as the Double Gate Tunneling Field-Effect Transistor (DG- TFET) is implicated. In this study, I have investigated and adjusted the performance of an N-type DG-TFET by altering several parameters such as device materials, high-k dielectric as oxide layers, and oxide thickness. In the end, Tungsten Ditelluride (WTe2) a 2-D material is used as the device material, while Niobium pentoxide (Nb2O5) is used as the high-k dielectric material according to the optimization process of the DG-TFET. The device has achieved a subthreshold swing of 18.37 mv/Dec and an Ion/Ioff of 1011. Finally, I have also conducted a comparative analysis between DG-TFET and a Single Gate Tunneling Field-Effect Transistor (SG-TFET) device with identical specifications.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129667911","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836523
Ummey Hany Ainan, Md. Nur-E-Arefin
Bank performance is defined as the reflection of the way by which the assets of the bank are utilized in a form which enables it to accomplice its targets. Economic development highly depends on the functionalities of the banks. In past statistical approach is used to predict bank performance. Nowadays Machine Learning (ML) approaches are used in banking sector for better accuracy. In this work three famous Machine Learning classifiers named Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are used to find out the bank performance. The dataset used in this work are consist of 50 Turkish banks, 30 American banks and 20 European banks. The data have 24 performance indicators that measures performance from the year of 2010 to 2020. CAMEL technique is applied in this dataset in order to find ratings of the banks. In this study Genetic Algorithm (GA) plays a vital role. GA is used as optimizer and feature selector. At the end the models are evaluated with and without feature selection as well as with and without optimization. In this study SVM with optimization but without feature selection provides best accuracy among all the models which is 97.06% test accuracy. On the other hand, LR provides 80.21% test accuracy with feature selection but without optimization which is lowest in the whole study.
{"title":"Prediction of Bank Performance Using Machine Learning Classifiers Optimized by Genetic Algorithm","authors":"Ummey Hany Ainan, Md. Nur-E-Arefin","doi":"10.1109/icaeee54957.2022.9836523","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836523","url":null,"abstract":"Bank performance is defined as the reflection of the way by which the assets of the bank are utilized in a form which enables it to accomplice its targets. Economic development highly depends on the functionalities of the banks. In past statistical approach is used to predict bank performance. Nowadays Machine Learning (ML) approaches are used in banking sector for better accuracy. In this work three famous Machine Learning classifiers named Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are used to find out the bank performance. The dataset used in this work are consist of 50 Turkish banks, 30 American banks and 20 European banks. The data have 24 performance indicators that measures performance from the year of 2010 to 2020. CAMEL technique is applied in this dataset in order to find ratings of the banks. In this study Genetic Algorithm (GA) plays a vital role. GA is used as optimizer and feature selector. At the end the models are evaluated with and without feature selection as well as with and without optimization. In this study SVM with optimization but without feature selection provides best accuracy among all the models which is 97.06% test accuracy. On the other hand, LR provides 80.21% test accuracy with feature selection but without optimization which is lowest in the whole study.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047883","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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