Pub Date : 2022-11-30DOI: 10.55524/ijircst.2022.10.6.26
Dr. L.Rama Prasad Reddy, S. Rao, R. Madhuri, S. Krishna, A. Srinivasrao, Y. Venkatesh, K. Reddy
Concrete is one of the oldest and most widely used building materials in the world, mostly because it is inexpensive and readily available. In all areas of contemporary construction, concrete has become a key component of structures. It is challenging to name another building material that is as versatile as concrete. When strength, durability, impermeability, fire resistance, and absorption resistance are needed, concrete is the ideal material to use. This study's main goal is to compare plain M30 grade concrete to basalt fiber concrete in terms of compressive, flexural, and splitting tensile strength. Basalt fiber is a substance created from the incredibly tiny basalt fibers that naturally occur in volcanic rocks that are the result of frozen lava. In the aerospace and automobile industries, it is utilized as a fire-resistant textile. Fibers are typically added to concrete to strengthen its structural stability. Due to its remarkable qualities, such as resistance to corrosion and low thermal conductivity, basalt fiber is currently among the fibers that is gaining more prominence. Additionally, it increases the concrete's toughness, flexural strength, and tensile strength. Important concrete constructions like nuclear power stations, roads, and bridges can employ it to prolong their lifespan. The variable factors taken into account in this study were M30 grade concrete cubes, cylinders, and beams, which were cast and cured in portable water for 28 days. The cubes' dimensions were 150 x 150 x 150 mm, the cylinders' dimensions were 150 mm (dia) x 300 mm (depth), and the beams' dimensions were 500 x 100 x 100 mm. Then, at 7, 14, and 28 days, the specimens were examined for split tensile strength, flexural strength, and compression strength using ordinary concrete with and without basalt fiber.
{"title":"A Study on Strength Characteristics of Concrete by Addition of Basalt Fiber","authors":"Dr. L.Rama Prasad Reddy, S. Rao, R. Madhuri, S. Krishna, A. Srinivasrao, Y. Venkatesh, K. Reddy","doi":"10.55524/ijircst.2022.10.6.26","DOIUrl":"https://doi.org/10.55524/ijircst.2022.10.6.26","url":null,"abstract":"Concrete is one of the oldest and most widely used building materials in the world, mostly because it is inexpensive and readily available. In all areas of contemporary construction, concrete has become a key component of structures. It is challenging to name another building material that is as versatile as concrete. When strength, durability, impermeability, fire resistance, and absorption resistance are needed, concrete is the ideal material to use. This study's main goal is to compare plain M30 grade concrete to basalt fiber concrete in terms of compressive, flexural, and splitting tensile strength. Basalt fiber is a substance created from the incredibly tiny basalt fibers that naturally occur in volcanic rocks that are the result of frozen lava. In the aerospace and automobile industries, it is utilized as a fire-resistant textile. Fibers are typically added to concrete to strengthen its structural stability. Due to its remarkable qualities, such as resistance to corrosion and low thermal conductivity, basalt fiber is currently among the fibers that is gaining more prominence. Additionally, it increases the concrete's toughness, flexural strength, and tensile strength. Important concrete constructions like nuclear power stations, roads, and bridges can employ it to prolong their lifespan. The variable factors taken into account in this study were M30 grade concrete cubes, cylinders, and beams, which were cast and cured in portable water for 28 days. The cubes' dimensions were 150 x 150 x 150 mm, the cylinders' dimensions were 150 mm (dia) x 300 mm (depth), and the beams' dimensions were 500 x 100 x 100 mm. Then, at 7, 14, and 28 days, the specimens were examined for split tensile strength, flexural strength, and compression strength using ordinary concrete with and without basalt fiber.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114680989","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 : 2022-09-30DOI: 10.55524/ijircst.2022.10.5.23
V. Sivaprasad, M. Anusha, Y. S. Reddy, V. Venugopal, Sriram Vishwanath, M. Selvam
Composite materials are created by carefully combining two or more components to get a beneficial result. a system of materials made up of two or more physically distinct phases that, when combined, create aggregate properties that are distinct from those of their individual components. The fibre’s function is to give the product strength, bind the filaments together into a matrix, and shield the fibres from the elements. On a weight-to-weight ratio, composites are a class of materials that are stronger and more rigid than any other traditional engineering material. We employ in daily life. Other extremely intriguing options for further weight reduction include altering the volume proportion of fibre and resin in the component and aligning the orientation of the fibre along the direction of load. To create the hybrid natural fibre composites, the current experimental investigation intends to. Samples of a variety of jute, basalt, and polyester hybrid natural fibres will be created utilising the hand layup process, where the weight fraction of the fibre matrix is at various percentages and the stacking of the plies is alternated. Composites' void content rises as both the fibre loading and the fibre length increase. Composites with a 25wt% fibre loading demonstrate a better hardness value as far as the influence of fibre loading is concerned. Tensile strength diminishes after 25%, hence tensile modulus is appropriate for average weight percentage of fibre loading, or 25wt%.
{"title":"Fabrication and Investigation on Basalt Fiber and Jute Fiber Reinforced Hybrid Composites","authors":"V. Sivaprasad, M. Anusha, Y. S. Reddy, V. Venugopal, Sriram Vishwanath, M. Selvam","doi":"10.55524/ijircst.2022.10.5.23","DOIUrl":"https://doi.org/10.55524/ijircst.2022.10.5.23","url":null,"abstract":"Composite materials are created by carefully combining two or more components to get a beneficial result. a system of materials made up of two or more physically distinct phases that, when combined, create aggregate properties that are distinct from those of their individual components. The fibre’s function is to give the product strength, bind the filaments together into a matrix, and shield the fibres from the elements. On a weight-to-weight ratio, composites are a class of materials that are stronger and more rigid than any other traditional engineering material. We employ in daily life. Other extremely intriguing options for further weight reduction include altering the volume proportion of fibre and resin in the component and aligning the orientation of the fibre along the direction of load. To create the hybrid natural fibre composites, the current experimental investigation intends to. Samples of a variety of jute, basalt, and polyester hybrid natural fibres will be created utilising the hand layup process, where the weight fraction of the fibre matrix is at various percentages and the stacking of the plies is alternated. Composites' void content rises as both the fibre loading and the fibre length increase. Composites with a 25wt% fibre loading demonstrate a better hardness value as far as the influence of fibre loading is concerned. Tensile strength diminishes after 25%, hence tensile modulus is appropriate for average weight percentage of fibre loading, or 25wt%.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125658422","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 : 2022-09-30DOI: 10.55524/ijircst.2022.10.5.25
Adusumalli Manikanta, Birudula Nageswara Rao, K. Reddy, I.Manikanta Sai Charan, M. Mahesh, N.Siva Shankar, P. Kiran
Because there are more used car tyres produced each year, disposing of them has become a significant environmental issue on a global scale. Utilizing used tyres will reduce the effect on the environment and increase resource preservation. The stabilisation of soils using CRP (5%, 10%, 15%) is discussed in this article. The conduct and effectiveness of the stabilised soil were evaluated using the soil properties, compaction, California bearing ratio (CBR), and direct shear test. When soil and CRP are combined, it is seen that the maximum dry density and ideal moisture content decline as the percentage of crumb rubber in the soil increases. Bearing capacity and tensile strength are barely affected by blending. Nevertheless, the numbers stayed within reasonable bounds.
{"title":"Soil Stabilization Using Crumb Rubber Powder","authors":"Adusumalli Manikanta, Birudula Nageswara Rao, K. Reddy, I.Manikanta Sai Charan, M. Mahesh, N.Siva Shankar, P. Kiran","doi":"10.55524/ijircst.2022.10.5.25","DOIUrl":"https://doi.org/10.55524/ijircst.2022.10.5.25","url":null,"abstract":"Because there are more used car tyres produced each year, disposing of them has become a significant environmental issue on a global scale. Utilizing used tyres will reduce the effect on the environment and increase resource preservation. The stabilisation of soils using CRP (5%, 10%, 15%) is discussed in this article. The conduct and effectiveness of the stabilised soil were evaluated using the soil properties, compaction, California bearing ratio (CBR), and direct shear test. When soil and CRP are combined, it is seen that the maximum dry density and ideal moisture content decline as the percentage of crumb rubber in the soil increases. Bearing capacity and tensile strength are barely affected by blending. Nevertheless, the numbers stayed within reasonable bounds.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129403418","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 : 2022-09-30DOI: 10.55524/ijircst.2022.10.5.26
K. Edukondalu, Adusumalli Manikanta, D. Divya, Sk. Sulthan Sharif, G. N. Kumar, I. Srihari, K. Rakesh
Plastic waste generation and disposal contribute significantly to pollution and global warming. The properties and strength of bituminous mixtures are both enhanced by the inclusion of plastic detritus. Also, it will be a fix for other pavement issues including potholes, corrugation, ruts, and so forth. It was discovered that bitumen mixtures used in flexible pavements work well with plastic as a binder. By preventing cracks and rainwater infiltration, which would otherwise contribute to the development of potholes, this efficient method helps pavements tolerate greater temperatures. For India's hot and extremely humid climate, where temperatures regularly exceed 50°C and torrential rains cause havoc and leave the majority of the roads with large potholes, plastic roads would be a godsend. Bitumen is used as a binder in the traditional road construction process. Such bitumen can be altered with leftover plastic bits to create a bitumen mix that can be applied as the top coat of flexible pavement. This modified bitumen made from discarded plastic exhibits enhanced adhesion, stability, density, and water resistance.
{"title":"Use of Waste Plastic Materials in Flexible Pavements","authors":"K. Edukondalu, Adusumalli Manikanta, D. Divya, Sk. Sulthan Sharif, G. N. Kumar, I. Srihari, K. Rakesh","doi":"10.55524/ijircst.2022.10.5.26","DOIUrl":"https://doi.org/10.55524/ijircst.2022.10.5.26","url":null,"abstract":"Plastic waste generation and disposal contribute significantly to pollution and global warming. The properties and strength of bituminous mixtures are both enhanced by the inclusion of plastic detritus. Also, it will be a fix for other pavement issues including potholes, corrugation, ruts, and so forth. It was discovered that bitumen mixtures used in flexible pavements work well with plastic as a binder. By preventing cracks and rainwater infiltration, which would otherwise contribute to the development of potholes, this efficient method helps pavements tolerate greater temperatures. For India's hot and extremely humid climate, where temperatures regularly exceed 50°C and torrential rains cause havoc and leave the majority of the roads with large potholes, plastic roads would be a godsend. Bitumen is used as a binder in the traditional road construction process. Such bitumen can be altered with leftover plastic bits to create a bitumen mix that can be applied as the top coat of flexible pavement. This modified bitumen made from discarded plastic exhibits enhanced adhesion, stability, density, and water resistance.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133623009","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 : 2022-05-31DOI: 10.55524/ijircst.2022.10.3.66
G. Krishna, E. R. Reddy, K. Prakash, G. Johnson, Dr. Pattan Hussian Basha, V. G. Krishna
The process of anticipating the stock market is one that is both difficult and time-consuming. On the other hand, advancements in stock market projection have begun to incorporate these methods of evaluating stock market data since the introduction of Machine Learning and its various algorithms. This has occurred since the beginning of the 21st century. We found that the Long-Short Term Memory (LSTM) technique was the most effective when predicting stock values by using historical data. This was determined by analyzing the performance of the various algorithms in this endeavor. Because the algorithm has been taught using a massive accumulation of historical data and has been selected after being tested on a sample of data, it is going to be an excellent instrument for dealers and purchasers to utilize when they are investing in the stock market. According to the findings of this research, the machine learning model is superior to other machine learning models in terms of its ability to effectively predict market price.
{"title":"Stock Price Prediction Using Python in Machine Learning","authors":"G. Krishna, E. R. Reddy, K. Prakash, G. Johnson, Dr. Pattan Hussian Basha, V. G. Krishna","doi":"10.55524/ijircst.2022.10.3.66","DOIUrl":"https://doi.org/10.55524/ijircst.2022.10.3.66","url":null,"abstract":"The process of anticipating the stock market is one that is both difficult and time-consuming. On the other hand, advancements in stock market projection have begun to incorporate these methods of evaluating stock market data since the introduction of Machine Learning and its various algorithms. This has occurred since the beginning of the 21st century. We found that the Long-Short Term Memory (LSTM) technique was the most effective when predicting stock values by using historical data. This was determined by analyzing the performance of the various algorithms in this endeavor. Because the algorithm has been taught using a massive accumulation of historical data and has been selected after being tested on a sample of data, it is going to be an excellent instrument for dealers and purchasers to utilize when they are investing in the stock market. According to the findings of this research, the machine learning model is superior to other machine learning models in terms of its ability to effectively predict market price.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124982449","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 : 2022-05-29DOI: 10.55524/ijircst.2022.10.3.65
Sk. Mohammed Jubear, D. P. K. Reddy, G. Subramanyam, Sk. Farooq, T. Sreenivasulu, N. S. Rao
This paper focuses on the development of a robust speech emotion recognition system using a combination of different speech features with feature optimization techniques and speech de-noising technique to acquire improved emotion classification accuracy, decreasing the system complexity and obtain noise robustness. Additionally, we create original methods for SER to merge features. We employ feature optimization methods that are based on the feature transformation and feature selection machine learning techniques in order to build SER. The following is a list of the upcoming events. A neural network can use either of these two techniques. As more feelings are taken into account, the feature fusion-acquired SER accuracy falls short of expectations, and the plague of dimensionality starts to spread due to the addition of speech features, which makes the SER system work harder to complete its task. This is due to the SER system becoming more complicated when voice elements are added. Therefore, it is crucial to create a SER system that is more trustworthy, has the most practical features, and uses the least amount of computing power possible. By using strategies that maximize current features, it is possible to streamline the feature selection process by reducing the total number of accessible choices to a more reasonable level. This piece employs a method known as Semi-Non Negative Matrix Factorization to lessen the amount of processing trash that the SER system generates. (Semi-NMF). This approach can be used to change traits that are capable of learning on their own.
{"title":"A Review on Speech Emotion Recognition Using Machine Learning","authors":"Sk. Mohammed Jubear, D. P. K. Reddy, G. Subramanyam, Sk. Farooq, T. Sreenivasulu, N. S. Rao","doi":"10.55524/ijircst.2022.10.3.65","DOIUrl":"https://doi.org/10.55524/ijircst.2022.10.3.65","url":null,"abstract":"This paper focuses on the development of a robust speech emotion recognition system using a combination of different speech features with feature optimization techniques and speech de-noising technique to acquire improved emotion classification accuracy, decreasing the system complexity and obtain noise robustness. Additionally, we create original methods for SER to merge features. We employ feature optimization methods that are based on the feature transformation and feature selection machine learning techniques in order to build SER. The following is a list of the upcoming events. A neural network can use either of these two techniques. As more feelings are taken into account, the feature fusion-acquired SER accuracy falls short of expectations, and the plague of dimensionality starts to spread due to the addition of speech features, which makes the SER system work harder to complete its task. This is due to the SER system becoming more complicated when voice elements are added. Therefore, it is crucial to create a SER system that is more trustworthy, has the most practical features, and uses the least amount of computing power possible. By using strategies that maximize current features, it is possible to streamline the feature selection process by reducing the total number of accessible choices to a more reasonable level. This piece employs a method known as Semi-Non Negative Matrix Factorization to lessen the amount of processing trash that the SER system generates. (Semi-NMF). This approach can be used to change traits that are capable of learning on their own.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127962234","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 : 2022-05-27DOI: 10.55524/ijircst.2022.10.3.64
Gorantla Lavanya, Bobbala Naga Sunitha, Konkala Sai Kalpana, Ravinutala V P SaiViswanadh Sarma, B. Sravani, N. -
Banks and other financial institutions compete for customers by providing a wide range of services and products. Most banks, however, make the vast majority of their money from their credit portfolio. Loans accepted by borrowers might lead to interest charges. The loan portfolio, and customers' repayment habits in particular, can have a substantial impact on a bank's bottom line. The financial institution's Non-Performing Assets can be reduced if it can accurately predict which borrowers are likely to default on their loans. Therefore, there is substantial scholarly value in exploring the prediction of loan endorsement. In order to make accurate predictions, it is crucial to use Machine Learning methods. Based on a person's past loan qualification history, this research uses a machine learning methodology to predict the person's likelihood of consistently making loan repayments. The primary aim of this research is to foretell how likely it is that a given individual will be granted a loan.
{"title":"Loan Eligibility Prediction Using Machine Learning","authors":"Gorantla Lavanya, Bobbala Naga Sunitha, Konkala Sai Kalpana, Ravinutala V P SaiViswanadh Sarma, B. Sravani, N. -","doi":"10.55524/ijircst.2022.10.3.64","DOIUrl":"https://doi.org/10.55524/ijircst.2022.10.3.64","url":null,"abstract":"Banks and other financial institutions compete for customers by providing a wide range of services and products. Most banks, however, make the vast majority of their money from their credit portfolio. Loans accepted by borrowers might lead to interest charges. The loan portfolio, and customers' repayment habits in particular, can have a substantial impact on a bank's bottom line. The financial institution's Non-Performing Assets can be reduced if it can accurately predict which borrowers are likely to default on their loans. Therefore, there is substantial scholarly value in exploring the prediction of loan endorsement. In order to make accurate predictions, it is crucial to use Machine Learning methods. Based on a person's past loan qualification history, this research uses a machine learning methodology to predict the person's likelihood of consistently making loan repayments. The primary aim of this research is to foretell how likely it is that a given individual will be granted a loan.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121115638","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 : 2022-05-26DOI: 10.55524/ijircst.2022.10.3.62
V. G. Kumar, K. Damodar, B. Ganesh, B. Aravind, V. S. Kiran
The article presents the analysis and design of MIMO monopoly Antenna along with split ring resonator to get frequency notch characteristic in the wide band. Frequency notch characteristics are achieved by keeping the split ring resonators on one side of the substrate and on the back of the substrate at deficient ground structure a complementary split Ring resonator with respect to microstrip feeding. Between 2.5-9.5GHz and 12.548-20GHz the dual notch band characteristics are acquired. The inspected conformal characteristics of the antenna hold eminent unceasing reflection coefficient characteristics at different angles in the overall band. Analyzed the unit cell of the SRR and also examined the antenna impedance and radiation characteristics of the model.
{"title":"Triple Band Mime Antenna for Modern Commercial Applications","authors":"V. G. Kumar, K. Damodar, B. Ganesh, B. Aravind, V. S. Kiran","doi":"10.55524/ijircst.2022.10.3.62","DOIUrl":"https://doi.org/10.55524/ijircst.2022.10.3.62","url":null,"abstract":"The article presents the analysis and design of MIMO monopoly Antenna along with split ring resonator to get frequency notch characteristic in the wide band. Frequency notch characteristics are achieved by keeping the split ring resonators on one side of the substrate and on the back of the substrate at deficient ground structure a complementary split Ring resonator with respect to microstrip feeding. Between 2.5-9.5GHz and 12.548-20GHz the dual notch band characteristics are acquired. The inspected conformal characteristics of the antenna hold eminent unceasing reflection coefficient characteristics at different angles in the overall band. Analyzed the unit cell of the SRR and also examined the antenna impedance and radiation characteristics of the model.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123245214","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 : 2022-05-26DOI: 10.55524/ijircst.2022.10.3.63
B. Nikitha, Addanki Sudha Maheswari, Dudekula Shameena, Bandaru Poojasri, H. Kauser, G. S. Rao
We use and study a wide range of machine learning methods to predict and trade in the daily crypto currency market. We teach the algorithms to make daily market predictions based on how the 100 cryptocurrencies with the most market value change in price. Based on our research, all of the used models are able to make estimates that are statistically sound, with the average accuracy of all crypto currencies falling between 52.9% and 54.1%. When these accurate numbers are based on the 10% most confident expectations for each class and day, they go up to somewhere between 57.5% and 59.5%. A well-known case study in the field of data science looks at how people try to figure out how much different digital currencies are worth. Stock prices and the prices of cryptocurrencies are based on more than just the amount of buy and sell orders. At the moment, the government's financial policies about digital currencies affect how the prices of these things change. People's views about a crypto currency or a star who directly or indirectly backs a crypto currency can also cause a big rise in buying and selling of that currency. This study looks at the trustworthiness of the three most famous coins on the market today: bitcoin, how well buying strategies for ethereum and litecoin that are based on machine learning work. The models are checked and tested with both good and bad market situations. This lets us figure out how accurate the forecasts are in light of any changes in how the market feels between the proof and test times.
{"title":"Crypto Currency Price Prediction with Machine Learning Using Python","authors":"B. Nikitha, Addanki Sudha Maheswari, Dudekula Shameena, Bandaru Poojasri, H. Kauser, G. S. Rao","doi":"10.55524/ijircst.2022.10.3.63","DOIUrl":"https://doi.org/10.55524/ijircst.2022.10.3.63","url":null,"abstract":"We use and study a wide range of machine learning methods to predict and trade in the daily crypto currency market. We teach the algorithms to make daily market predictions based on how the 100 cryptocurrencies with the most market value change in price. Based on our research, all of the used models are able to make estimates that are statistically sound, with the average accuracy of all crypto currencies falling between 52.9% and 54.1%. When these accurate numbers are based on the 10% most confident expectations for each class and day, they go up to somewhere between 57.5% and 59.5%. A well-known case study in the field of data science looks at how people try to figure out how much different digital currencies are worth. Stock prices and the prices of cryptocurrencies are based on more than just the amount of buy and sell orders. At the moment, the government's financial policies about digital currencies affect how the prices of these things change. People's views about a crypto currency or a star who directly or indirectly backs a crypto currency can also cause a big rise in buying and selling of that currency. This study looks at the trustworthiness of the three most famous coins on the market today: bitcoin, how well buying strategies for ethereum and litecoin that are based on machine learning work. The models are checked and tested with both good and bad market situations. This lets us figure out how accurate the forecasts are in light of any changes in how the market feels between the proof and test times.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126815470","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 : 2022-03-31DOI: 10.55524/ijircst.2022.10.2.116
P. Edukondalu, Macharla Dileep Kumar, Punugoti Naga Chandu, Idupogula Salman Raju, S. Ashik, B. Chandra, B. Nagaraju, G. Murthy, Dr. Rajaselvan C, Dr. Jeyakumar K
In order to manage the speed of brushless DC (BLDC) motors, this research presents a novel hybrid control method that simultaneously regulates the DC bus voltage of the inverter and the BLDC motor reference current. A fractional-order PID (FOPID) controller manages the BLDC motor reference current, and a fuzzy logic controller manages the inverter DC bus voltage. A modified harmony search (HS) metaheuristic technique is developed for adjusting the FOPID controller parameters. Three separate working scenarios—no load, varying load, and varying speed—are used to test the motor's capabilities. Run the proposed controller at a high speed to verify its efficacy. The suggested hybrid control method has also been put to the test. weighed against FOPID and fuzzy-based speed control techniques The outcomes demonstrate the effectiveness of the suggested control. approach enables more accurate speed control over a wide region.
{"title":"A New Approach of BLDC Motor Using Fuzzy Fractional Order PID","authors":"P. Edukondalu, Macharla Dileep Kumar, Punugoti Naga Chandu, Idupogula Salman Raju, S. Ashik, B. Chandra, B. Nagaraju, G. Murthy, Dr. Rajaselvan C, Dr. Jeyakumar K","doi":"10.55524/ijircst.2022.10.2.116","DOIUrl":"https://doi.org/10.55524/ijircst.2022.10.2.116","url":null,"abstract":"In order to manage the speed of brushless DC (BLDC) motors, this research presents a novel hybrid control method that simultaneously regulates the DC bus voltage of the inverter and the BLDC motor reference current. A fractional-order PID (FOPID) controller manages the BLDC motor reference current, and a fuzzy logic controller manages the inverter DC bus voltage. A modified harmony search (HS) metaheuristic technique is developed for adjusting the FOPID controller parameters. Three separate working scenarios—no load, varying load, and varying speed—are used to test the motor's capabilities. Run the proposed controller at a high speed to verify its efficacy. The suggested hybrid control method has also been put to the test. weighed against FOPID and fuzzy-based speed control techniques The outcomes demonstrate the effectiveness of the suggested control. approach enables more accurate speed control over a wide region.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116352102","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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