Pub Date : 2023-06-22DOI: 10.51983/ajes-2023.12.1.3694
Divya Dewangan, Smita Selot, Sreejit Panicker
Suicide is the matter of purposely causing one’s death and suicidal ideation refers to thoughts or preoccupations with ending one’s own life. Studies have explored verbal and written communications related to suicide, including analyzing suicide notes, online discussions, and social media posts to identify linguistic and content markers that may help in early detection and intervention. The primary purpose of this study is to detect signs of risk of suicide/self-harm in social media users by investigating several frequency-based featuring and prediction-based featuring methods along with different baseline machine learning classifiers. The algorithms applied for analysis are Decision Tree, K-Nearest Neighbors, Random Forest, Multinomial Naïve Bayes, and SVM. Our experimental results showed that the best performance is obtained by the FastText embedding with SVM model having the highest accuracy of 93.76% which outperforms other baselines. The aim of this work is to learn the significance of analysis and do a comparative study of algorithms to find the best suited algorithm.
{"title":"The Accuracy Analysis of Different Machine Learning Classifiers for Detecting Suicidal Ideation and Content","authors":"Divya Dewangan, Smita Selot, Sreejit Panicker","doi":"10.51983/ajes-2023.12.1.3694","DOIUrl":"https://doi.org/10.51983/ajes-2023.12.1.3694","url":null,"abstract":"Suicide is the matter of purposely causing one’s death and suicidal ideation refers to thoughts or preoccupations with ending one’s own life. Studies have explored verbal and written communications related to suicide, including analyzing suicide notes, online discussions, and social media posts to identify linguistic and content markers that may help in early detection and intervention. The primary purpose of this study is to detect signs of risk of suicide/self-harm in social media users by investigating several frequency-based featuring and prediction-based featuring methods along with different baseline machine learning classifiers. The algorithms applied for analysis are Decision Tree, K-Nearest Neighbors, Random Forest, Multinomial Naïve Bayes, and SVM. Our experimental results showed that the best performance is obtained by the FastText embedding with SVM model having the highest accuracy of 93.76% which outperforms other baselines. The aim of this work is to learn the significance of analysis and do a comparative study of algorithms to find the best suited algorithm.","PeriodicalId":365290,"journal":{"name":"Asian Journal of Electrical Sciences","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132849226","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 : 2023-06-22DOI: 10.51983/ajes-2023.12.1.3681
A. Husainy, Bhushan S Kumbhar, Prajwal S. Chavan, Ajeem A. Attar, Hemant A. Patil
The Cold chain/Refrigeration plays an important role for preserving vaccines as per the vaccine storage and guidelines prescribed by World Health Organizations and it is better than most of other preservation methods. The primary goal of adopting phase change material is to increase performance, cooling duration, capacity for storage, and to sustain the steady cooling effect for a longer length of time during power outages. In experimental set up it is decoded to use Inorganic and Eutectic phase change materials which is encapsulated in plastic containers inside the vaccine storage. The project will involve collecting data from sensors installed in cabinet and using machine learning algorithms to analyse the data and generate insights. The results of the project can be used to inform best practices for vaccine storage and contribute to the development of more efficient and effective cabinet management systems. This project explores the use of machine learning techniques to optimize the storage of vaccines in cabinet. Proper storage is critical to maintain vaccine efficacy, and therefore it is important to ensure that the temperature and humidity are kept within specific ranges. Machine learning models can be trained to predict the temperature and humidity levels in the cold rooms and to detect anomalies that may indicate a potential problem. This can help improve the monitoring and maintenance of the cabinet and reduce the risk of vaccine spoilage. Machine Learning algorithms like Regression and Classification are used on web-app because they provide us with continuous as well as discrete value as an output. Libraries like pandas, Numpy, sklearn, matplotlib are used to predict along with visualization because of which it will be possible to forecast the actual aspects like temperature and humidity. User Interface has also been developed which acquires input from any user and displays the data according to user’s inputs. The project will involve collecting data from sensors installed in cabinet and using machine learning algorithms to analyse the data and generate insights. The results of the project can be used to inform best practices for vaccine storage and contribute to the development of more efficient and effective cabinet management systems.
{"title":"Analysing and Optimizing the Refrigeration System Using Machine Learning Algorithm","authors":"A. Husainy, Bhushan S Kumbhar, Prajwal S. Chavan, Ajeem A. Attar, Hemant A. Patil","doi":"10.51983/ajes-2023.12.1.3681","DOIUrl":"https://doi.org/10.51983/ajes-2023.12.1.3681","url":null,"abstract":"The Cold chain/Refrigeration plays an important role for preserving vaccines as per the vaccine storage and guidelines prescribed by World Health Organizations and it is better than most of other preservation methods. The primary goal of adopting phase change material is to increase performance, cooling duration, capacity for storage, and to sustain the steady cooling effect for a longer length of time during power outages. In experimental set up it is decoded to use Inorganic and Eutectic phase change materials which is encapsulated in plastic containers inside the vaccine storage. The project will involve collecting data from sensors installed in cabinet and using machine learning algorithms to analyse the data and generate insights. The results of the project can be used to inform best practices for vaccine storage and contribute to the development of more efficient and effective cabinet management systems. This project explores the use of machine learning techniques to optimize the storage of vaccines in cabinet. Proper storage is critical to maintain vaccine efficacy, and therefore it is important to ensure that the temperature and humidity are kept within specific ranges. Machine learning models can be trained to predict the temperature and humidity levels in the cold rooms and to detect anomalies that may indicate a potential problem. This can help improve the monitoring and maintenance of the cabinet and reduce the risk of vaccine spoilage. Machine Learning algorithms like Regression and Classification are used on web-app because they provide us with continuous as well as discrete value as an output. Libraries like pandas, Numpy, sklearn, matplotlib are used to predict along with visualization because of which it will be possible to forecast the actual aspects like temperature and humidity. User Interface has also been developed which acquires input from any user and displays the data according to user’s inputs. The project will involve collecting data from sensors installed in cabinet and using machine learning algorithms to analyse the data and generate insights. The results of the project can be used to inform best practices for vaccine storage and contribute to the development of more efficient and effective cabinet management systems.","PeriodicalId":365290,"journal":{"name":"Asian Journal of Electrical Sciences","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132044397","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 : 2023-06-22DOI: 10.51983/ajes-2023.12.1.3684
A. Husainy, Sairam A. Patil, Atharva S. Sinfal, Vasim M. Mujawar, Chandrashekhar S. Sinfal
The implementation of machine learning in a chiller system provides several benefits. It can improve energy efficiency by optimizing chiller operation based on predicted load requirements. It can enhance system reliability and reduce maintenance costs by detecting and diagnosing faults in advance. Furthermore, it can enable data-driven decision-making, enabling operators to make informed choices based on accurate predictions and insights. This implementation aims to leverage machine learning techniques to optimize the performance and energy efficiency of a chiller system. Chiller systems are widely used in various industries for cooling purposes, and their efficient operation is critical to reducing energy consumption and operational costs. By employing machine learning algorithms, this implementation aims to analyze historical data, understand patterns, and develop predictive models to optimize chiller system performance. The implementation process involves several steps. First, historical data from the chiller system, including sensor measurements, operating parameters and energy consumption, is collected and preprocessed. The data is then split into training and testing sets. Next, suitable machine learning algorithms, such as regression, classification, or time-series forecasting models, are selected based on the specific goals and requirements of the chiller system. Overall, this implementation demonstrates the potential of machine learning to optimize chiller system performance, reduce energy consumption, and improve operational efficiency. By leveraging historical data and advanced analytics, machine learning can play a crucial role in transforming traditional chiller systems into intelligent, adaptive, and energy-efficient cooling solutions.
{"title":"Parameter Optimization of Refrigeration Chiller by Machine Learning","authors":"A. Husainy, Sairam A. Patil, Atharva S. Sinfal, Vasim M. Mujawar, Chandrashekhar S. Sinfal","doi":"10.51983/ajes-2023.12.1.3684","DOIUrl":"https://doi.org/10.51983/ajes-2023.12.1.3684","url":null,"abstract":"The implementation of machine learning in a chiller system provides several benefits. It can improve energy efficiency by optimizing chiller operation based on predicted load requirements. It can enhance system reliability and reduce maintenance costs by detecting and diagnosing faults in advance. Furthermore, it can enable data-driven decision-making, enabling operators to make informed choices based on accurate predictions and insights. This implementation aims to leverage machine learning techniques to optimize the performance and energy efficiency of a chiller system. Chiller systems are widely used in various industries for cooling purposes, and their efficient operation is critical to reducing energy consumption and operational costs. By employing machine learning algorithms, this implementation aims to analyze historical data, understand patterns, and develop predictive models to optimize chiller system performance. The implementation process involves several steps. First, historical data from the chiller system, including sensor measurements, operating parameters and energy consumption, is collected and preprocessed. The data is then split into training and testing sets. Next, suitable machine learning algorithms, such as regression, classification, or time-series forecasting models, are selected based on the specific goals and requirements of the chiller system. Overall, this implementation demonstrates the potential of machine learning to optimize chiller system performance, reduce energy consumption, and improve operational efficiency. By leveraging historical data and advanced analytics, machine learning can play a crucial role in transforming traditional chiller systems into intelligent, adaptive, and energy-efficient cooling solutions.","PeriodicalId":365290,"journal":{"name":"Asian Journal of Electrical Sciences","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128237325","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 : 2023-06-22DOI: 10.51983/ajes-2023.12.1.3676
Y. S. Mohammed, B. Kwembe
Modern electric power networks are progressively in demand for advanced protection systems due to the changing structures of the systems. In distribution power systems, emerging scenarios need some compelling protection efforts. In the last few decades, a lot of innovations such as Advanced Metering Infrastructure (AMI) have made incursions into the electric power sector. The development of AMI has fostered the integration of smart meter systems in microgrids and distribution networks with the capability to permit communication between electricity customers and utility service companies. Control and monitoring of information with regard to energy consumption is a phenomenal task that requires advanced information and communication technologies including some other essential parameters in real-time. Smart meters integrated into microgrids enable the utility to develop an electric power business efficiently. Therefore, this paper presents a study on the experimental on overview and investigation of the energy consumption pattern and data validation of smart meters installed in a smart grid project deployed by PowerGen in Nigeria. In addition, a comprehensive review of the security threats and control measures in microgrids was also presented. The results obtained show that utilities can increase their situational responses in a timely manner to the occurrence of abnormalities in the power grid to provide better monitoring and control services to energy customers.
{"title":"Power System Security Protection in Microgrids Based on Advanced Metering Infrastructure","authors":"Y. S. Mohammed, B. Kwembe","doi":"10.51983/ajes-2023.12.1.3676","DOIUrl":"https://doi.org/10.51983/ajes-2023.12.1.3676","url":null,"abstract":"Modern electric power networks are progressively in demand for advanced protection systems due to the changing structures of the systems. In distribution power systems, emerging scenarios need some compelling protection efforts. In the last few decades, a lot of innovations such as Advanced Metering Infrastructure (AMI) have made incursions into the electric power sector. The development of AMI has fostered the integration of smart meter systems in microgrids and distribution networks with the capability to permit communication between electricity customers and utility service companies. Control and monitoring of information with regard to energy consumption is a phenomenal task that requires advanced information and communication technologies including some other essential parameters in real-time. Smart meters integrated into microgrids enable the utility to develop an electric power business efficiently. Therefore, this paper presents a study on the experimental on overview and investigation of the energy consumption pattern and data validation of smart meters installed in a smart grid project deployed by PowerGen in Nigeria. In addition, a comprehensive review of the security threats and control measures in microgrids was also presented. The results obtained show that utilities can increase their situational responses in a timely manner to the occurrence of abnormalities in the power grid to provide better monitoring and control services to energy customers.","PeriodicalId":365290,"journal":{"name":"Asian Journal of Electrical Sciences","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359135","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 : 2023-06-14DOI: 10.51983/ajes-2023.12.1.3667
Sukla Basu
This paper presents a compact novel shaped micro strip line fed four element Multiple Input Multiple Output (MIMO) antenna for wireless application at 2.4 GHz. Four vertically oriented identical rectangular patch antennas are used to form a hollow cylinder of rectangular cross section. The direction of radiation of each antenna is thus oriented at 900 with respect to its two adjacent antennas providing good isolation between adjacent antennas in this compact MIMO system. Each antenna element has the dimensions of 0.30λx0.38λx0.012λ. The whole MIMO antenna has the outer dimensions of 0.38λx0.38λx0.30λ. The uniqueness of the proposed MIMO antenna is that the inner space of the hollow cylindrical structure can be used for hosting the associated signal processing unit of the whole system. Performance parameters of the proposed MIMO antenna are investigated using Computer Simulation Technology (CST). Envelope Correlation Coefficient (ECC) less than 0.033 and almost 10dB Diversity Gain (DG) are obtained at 2.4 GHz for this simple and compact antenna system. Ratio of Mean Effective Gain (MEG) of any two antennas is less than 0.02dB. Performance parameters of the proposed MIMO antenna system are compared with similar types of antennas found in recent literature.
{"title":"A Novel Shaped Four Port MIMO Antenna for Wireless Communication","authors":"Sukla Basu","doi":"10.51983/ajes-2023.12.1.3667","DOIUrl":"https://doi.org/10.51983/ajes-2023.12.1.3667","url":null,"abstract":"This paper presents a compact novel shaped micro strip line fed four element Multiple Input Multiple Output (MIMO) antenna for wireless application at 2.4 GHz. Four vertically oriented identical rectangular patch antennas are used to form a hollow cylinder of rectangular cross section. The direction of radiation of each antenna is thus oriented at 900 with respect to its two adjacent antennas providing good isolation between adjacent antennas in this compact MIMO system. Each antenna element has the dimensions of 0.30λx0.38λx0.012λ. The whole MIMO antenna has the outer dimensions of 0.38λx0.38λx0.30λ. The uniqueness of the proposed MIMO antenna is that the inner space of the hollow cylindrical structure can be used for hosting the associated signal processing unit of the whole system. Performance parameters of the proposed MIMO antenna are investigated using Computer Simulation Technology (CST). Envelope Correlation Coefficient (ECC) less than 0.033 and almost 10dB Diversity Gain (DG) are obtained at 2.4 GHz for this simple and compact antenna system. Ratio of Mean Effective Gain (MEG) of any two antennas is less than 0.02dB. Performance parameters of the proposed MIMO antenna system are compared with similar types of antennas found in recent literature.","PeriodicalId":365290,"journal":{"name":"Asian Journal of Electrical Sciences","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124727800","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 : 2023-06-12DOI: 10.51983/ajes-2023.12.1.3659
Sunil Bhutada, C. Rakshit, G. Vaishnavi, V. Varshitha
In today’s technological era, chatbots not only mimic human conversation but can also perform sophisticated jobs like purchasing theatre tickets. RASA is a free and open-source programme that may be used to run various versions of the NLU and DIET models. It has the capacity for natural language processing, reinforcement learning, and API/database integration. nervous system. This project incorporates a number of rasa’s fundamental principles to allow for user interaction in its execution of tasks. Using the Rasa platform, we developed a chatbot specifically for universities. You can use this chatbot to obtain student information, view grades, and verify attendance. By integrating with Telegram, users no longer need to download and run a separate programme to use this bot. This method is far more interesting than the standard web page.
{"title":"Chatra Paryesana: A Student Enquiry System","authors":"Sunil Bhutada, C. Rakshit, G. Vaishnavi, V. Varshitha","doi":"10.51983/ajes-2023.12.1.3659","DOIUrl":"https://doi.org/10.51983/ajes-2023.12.1.3659","url":null,"abstract":"In today’s technological era, chatbots not only mimic human conversation but can also perform sophisticated jobs like purchasing theatre tickets. RASA is a free and open-source programme that may be used to run various versions of the NLU and DIET models. It has the capacity for natural language processing, reinforcement learning, and API/database integration. nervous system. This project incorporates a number of rasa’s fundamental principles to allow for user interaction in its execution of tasks. Using the Rasa platform, we developed a chatbot specifically for universities. You can use this chatbot to obtain student information, view grades, and verify attendance. By integrating with Telegram, users no longer need to download and run a separate programme to use this bot. This method is far more interesting than the standard web page.","PeriodicalId":365290,"journal":{"name":"Asian Journal of Electrical Sciences","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134583787","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 : 2023-06-12DOI: 10.51983/ajes-2023.12.1.3665
M. Nurul Islam
The objective of the present work is to pursue and characterize the physical properties of inorganic transparent conducting oxide thin layer films made by spin-coating technique/spray pyrolysis technique and compare these properties with other sensitizing agents. It isexpected that this method will lead to the development of a new platform for the contribution of inorganic TCOs’ physical properties. I also hope that this technique will be helpful to enhance the efficiency and potency of solar cells and other optoelectronic devices.
{"title":"Fabrication and Characterization of Manganese Based Thin Films in Optoelectronic Applications","authors":"M. Nurul Islam","doi":"10.51983/ajes-2023.12.1.3665","DOIUrl":"https://doi.org/10.51983/ajes-2023.12.1.3665","url":null,"abstract":"The objective of the present work is to pursue and characterize the physical properties of inorganic transparent conducting oxide thin layer films made by spin-coating technique/spray pyrolysis technique and compare these properties with other sensitizing agents. It isexpected that this method will lead to the development of a new platform for the contribution of inorganic TCOs’ physical properties. I also hope that this technique will be helpful to enhance the efficiency and potency of solar cells and other optoelectronic devices.","PeriodicalId":365290,"journal":{"name":"Asian Journal of Electrical Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129430613","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 : 2023-04-22DOI: 10.51983/ajes-2023.12.1.3572
J. Kaur, Rahul Prashar
Microgrids are believed to be the befitting green solution to the ever-rising energy crisis in the developing nations. The resource rich nations are rapidly shifting their dependence on microgrids to meet the reduced carbon footprint target at a global level. This paper addresses this issue and presents a detailed technical review on microgrid integration in competitive electricity market scenario. An in-depth coverage of the need for and importance of microgrid deployments across India is presented in this work. The relevant literature that highlights the significance of various energy storage systems is reviewed. The authors present the perspective of microgrid integration in a competitive market scenario covering both day ahead and real time market scenarios thereby discussing the concept of maximizing social benefit in a grid integrated microgrid framework. This work also focuses on microgrid optimization techniques and outlines a detailed comparison showing merits and demerits of all the techniques implemented by the researchers so far. The authors highlight the fact that green energy holds the potential to not only outgrow the fossil fuels but it shall do so by fetching the requisite economic benefit to the investors as well.
{"title":"Microgrids in Competitive Market Environment: An Indian Perspective","authors":"J. Kaur, Rahul Prashar","doi":"10.51983/ajes-2023.12.1.3572","DOIUrl":"https://doi.org/10.51983/ajes-2023.12.1.3572","url":null,"abstract":"Microgrids are believed to be the befitting green solution to the ever-rising energy crisis in the developing nations. The resource rich nations are rapidly shifting their dependence on microgrids to meet the reduced carbon footprint target at a global level. This paper addresses this issue and presents a detailed technical review on microgrid integration in competitive electricity market scenario. An in-depth coverage of the need for and importance of microgrid deployments across India is presented in this work. The relevant literature that highlights the significance of various energy storage systems is reviewed. The authors present the perspective of microgrid integration in a competitive market scenario covering both day ahead and real time market scenarios thereby discussing the concept of maximizing social benefit in a grid integrated microgrid framework. This work also focuses on microgrid optimization techniques and outlines a detailed comparison showing merits and demerits of all the techniques implemented by the researchers so far. The authors highlight the fact that green energy holds the potential to not only outgrow the fossil fuels but it shall do so by fetching the requisite economic benefit to the investors as well.","PeriodicalId":365290,"journal":{"name":"Asian Journal of Electrical Sciences","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115114559","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 : 2022-12-15DOI: 10.51983/ajes-2022.11.2.3524
P. Nandha Kumar
Multipliers are the basic building blocks in various digital signal processing applications such as convolution, correlation, and filters. However, conventional array multipliers, vedic multipliers were resulted in higher area, power, delay consumptions. Therefore, this work is focused on design and implementation of variable width Radix-4 booth multiplier using Data Scaling Technology (DST). The radix-4 modified booth encoding was used in the production of these incomplete items. In accumulation, the bits of the fractional products are added in a parallel manner with decreased stages using a multi-stage carry propagation adder (MSCPA). The simulation demonstrated that the suggested DST-Radix-4 booth multiplier (DST-R4BM) resulted in higher performance in comparison to traditional multiplies in terms of area, delay, and power.
{"title":"Design of Accuracy Based Fixed-Width Booth Multipliers Using Data Scaling Technology","authors":"P. Nandha Kumar","doi":"10.51983/ajes-2022.11.2.3524","DOIUrl":"https://doi.org/10.51983/ajes-2022.11.2.3524","url":null,"abstract":"Multipliers are the basic building blocks in various digital signal processing applications such as convolution, correlation, and filters. However, conventional array multipliers, vedic multipliers were resulted in higher area, power, delay consumptions. Therefore, this work is focused on design and implementation of variable width Radix-4 booth multiplier using Data Scaling Technology (DST). The radix-4 modified booth encoding was used in the production of these incomplete items. In accumulation, the bits of the fractional products are added in a parallel manner with decreased stages using a multi-stage carry propagation adder (MSCPA). The simulation demonstrated that the suggested DST-Radix-4 booth multiplier (DST-R4BM) resulted in higher performance in comparison to traditional multiplies in terms of area, delay, and power.","PeriodicalId":365290,"journal":{"name":"Asian Journal of Electrical Sciences","volume":"10 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124528303","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 : 2022-12-15DOI: 10.51983/ajes-2022.11.2.3528
J. Pradeep, N. Erick Jeffery
Diabetes, a most prevalent disease that affects peoples of all ages and is caused by inadequate insulin production, which raises blood sugar levels. Several diseases can develop throughout the body if treatable conditions are not addressed. Diabetes causes diabetic retinopathy (DR), an asymptomatic eye condition which affects retinal blood vessels. In this paper, numerous automated diagnosis systems have been created using Deep Learning technique. Deep Learning (DL) performs autonomous feature extraction, which enables it to produce more accurate and promising results, particularly for medical data. The most often used deep learning methods for processing medical image data are convolutional neural networks (CNN). This research analyses and discusses several Deep Learning-based Convolutional Neural Network, Support Vector Machine (SVM) models for diagnosing and classifying diabetic retinopathy with further literature reviews and classification in order to acquire a better knowledge of the condition.
{"title":"A Review of the Research to Detect Diabetic Retinopathy Using Deep Learning","authors":"J. Pradeep, N. Erick Jeffery","doi":"10.51983/ajes-2022.11.2.3528","DOIUrl":"https://doi.org/10.51983/ajes-2022.11.2.3528","url":null,"abstract":"Diabetes, a most prevalent disease that affects peoples of all ages and is caused by inadequate insulin production, which raises blood sugar levels. Several diseases can develop throughout the body if treatable conditions are not addressed. Diabetes causes diabetic retinopathy (DR), an asymptomatic eye condition which affects retinal blood vessels. In this paper, numerous automated diagnosis systems have been created using Deep Learning technique. Deep Learning (DL) performs autonomous feature extraction, which enables it to produce more accurate and promising results, particularly for medical data. The most often used deep learning methods for processing medical image data are convolutional neural networks (CNN). This research analyses and discusses several Deep Learning-based Convolutional Neural Network, Support Vector Machine (SVM) models for diagnosing and classifying diabetic retinopathy with further literature reviews and classification in order to acquire a better knowledge of the condition.","PeriodicalId":365290,"journal":{"name":"Asian Journal of Electrical Sciences","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126579904","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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