Arc faults in electrical systems pose significant safety risks, and their early detection is crucial for preventing fires and other hazards. Traditional methods for arc fault detection in power systems often rely on conventional signal processing techniques, which may lack robustness and accuracy, especially in noisy environments. In this study, we propose a novel approach for arc fault detection using Mel-frequency cepstral coefficients (MFCCs) extracted from current signals generated by both arc and non-arc faults. MFCCs have been widely used in speech and audio processing due to their ability to capture relevant spectral features. In this paper we aim to investigate how MFCCs can differentiate between arc and non-arc faults in electrical systems. By analyzing the MFCC features extracted from current waveforms during both fault and non-fault conditions, to identify unique patterns and characteristics associated with arc faults.
{"title":"Analysis and Classification of Arcing Signals by Using MFCC","authors":"Ratnakar Nutenki, Aditya Thatipudi, Anil Kumar Perikala, Harshita Medida","doi":"10.1109/ICAECT60202.2024.10468692","DOIUrl":"https://doi.org/10.1109/ICAECT60202.2024.10468692","url":null,"abstract":"Arc faults in electrical systems pose significant safety risks, and their early detection is crucial for preventing fires and other hazards. Traditional methods for arc fault detection in power systems often rely on conventional signal processing techniques, which may lack robustness and accuracy, especially in noisy environments. In this study, we propose a novel approach for arc fault detection using Mel-frequency cepstral coefficients (MFCCs) extracted from current signals generated by both arc and non-arc faults. MFCCs have been widely used in speech and audio processing due to their ability to capture relevant spectral features. In this paper we aim to investigate how MFCCs can differentiate between arc and non-arc faults in electrical systems. By analyzing the MFCC features extracted from current waveforms during both fault and non-fault conditions, to identify unique patterns and characteristics associated with arc faults.","PeriodicalId":518900,"journal":{"name":"2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"32 4","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531571","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}
Outliers are the uncommon data points which deviate from the majority of the Data from a dataset. Presence of Outliers can affect the model’s performance, leading to incorrect data analysis. Hence, identifying and eliminating Outliers is a crucial pre-processing step. This research paper suggests a method for removing outliers that takes standard deviation into account. Standard Deviation is a statistical measure which measures the dispersion within the dataset. In the proposed algorithm, the first step is to calculate Standard Deviations of all the features within the Dataset. Next, the feature with highest Standard Deviation is chosen. After normalization of this column, individual Standardized values for the data points are calculated from the standardized Median. Furthermore, these values are arranged in the ascending order. Selecting closest left 85% and right 85% values from the Standardized Median. For the remaining features, only those observations are selected which are corresponding to the above selected range of data points. To check the efficacy of this algorithm, it is implemented on 5 Standard datasets - Iris Species, Pima Diabetes Dataset, College Dataset, Seattle Weather, Water Quality Dataset. After elimination of Outliers, the proposed algorithm aims to form dense clusters. When compared with K-means clustering, for all the 5 datasets, it gives a better Silhouette Score. The highest score of 0.7 is for Iris Species and the highest difference of 0.49 between the Silhouette score of K-means and the proposed algorithm is for Water Quality Dataset.
{"title":"A Novel approach for formation of Dense Clusters by Outlier Elimination and Standard Deviation","authors":"Pushkar Joglekar, Tejaswini Katale, Aishwarya Katale, Surabhi Deshpande, Aarya Nirgude, Aakash Chotrani","doi":"10.1109/ICAECT60202.2024.10468952","DOIUrl":"https://doi.org/10.1109/ICAECT60202.2024.10468952","url":null,"abstract":"Outliers are the uncommon data points which deviate from the majority of the Data from a dataset. Presence of Outliers can affect the model’s performance, leading to incorrect data analysis. Hence, identifying and eliminating Outliers is a crucial pre-processing step. This research paper suggests a method for removing outliers that takes standard deviation into account. Standard Deviation is a statistical measure which measures the dispersion within the dataset. In the proposed algorithm, the first step is to calculate Standard Deviations of all the features within the Dataset. Next, the feature with highest Standard Deviation is chosen. After normalization of this column, individual Standardized values for the data points are calculated from the standardized Median. Furthermore, these values are arranged in the ascending order. Selecting closest left 85% and right 85% values from the Standardized Median. For the remaining features, only those observations are selected which are corresponding to the above selected range of data points. To check the efficacy of this algorithm, it is implemented on 5 Standard datasets - Iris Species, Pima Diabetes Dataset, College Dataset, Seattle Weather, Water Quality Dataset. After elimination of Outliers, the proposed algorithm aims to form dense clusters. When compared with K-means clustering, for all the 5 datasets, it gives a better Silhouette Score. The highest score of 0.7 is for Iris Species and the highest difference of 0.49 between the Silhouette score of K-means and the proposed algorithm is for Water Quality Dataset.","PeriodicalId":518900,"journal":{"name":"2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"29 4","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531572","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}
Within the framework of a rapidly expanding worldwide population and the critical need to guarantee food security, precision agriculture has arisen as a crucial area of study and advancement. In the scope of this field, our research aims to make a significant impact by enhancing the evaluation of onion smut disease severity through an innovative multiclassification framework. The present study presents a new hybrid model that combines the strengths of Convolutional Neural Networks (CNN) and Random Forest (RF). This model integrates the feature extraction capabilities of deep learning (DL) with the classification robustness of ensemble learning, resulting in a synergistic approach. The combination of many elements leads to the development of a model that not only exceeds current benchmarks but also establishes a notable standard, demonstrating an outstanding overall accuracy rate of 96.38%. The significance of our model extends beyond its exceptional accuracy. The feature interpretability of this confers a significant advantage, as it enables a comprehensive comprehension of the various aspects that contribute to the severity of the condition. The availability of interpretability in this context provides farmers and agricultural specialists with a powerful tool that can significantly enhance their ability to make informed decisions based on data when it comes to managing diseases. Our research represents a groundbreaking advancement in the field of multiclass categorization in the context of agriculture. The historical constraints given by the complexity and diversity of crops and illnesses have been significant. However, our hybrid approach presents a scalable alternative that surpasses the limitations of traditional onion farming. Not only does it offer the potential for improved disease evaluation, but it also establishes a precedent for addressing multiclass classification jobs in the agricultural domain on a wider scale.
{"title":"Bridging the Gap in Precision Agriculture: A CNN-Random Forest Fusion for Disease Classification","authors":"Arshleen Kaur, Vinay Kukreja, Sushant Chamoli, Siddhant Thapliyal, Rishabh Sharma","doi":"10.1109/ICAECT60202.2024.10469081","DOIUrl":"https://doi.org/10.1109/ICAECT60202.2024.10469081","url":null,"abstract":"Within the framework of a rapidly expanding worldwide population and the critical need to guarantee food security, precision agriculture has arisen as a crucial area of study and advancement. In the scope of this field, our research aims to make a significant impact by enhancing the evaluation of onion smut disease severity through an innovative multiclassification framework. The present study presents a new hybrid model that combines the strengths of Convolutional Neural Networks (CNN) and Random Forest (RF). This model integrates the feature extraction capabilities of deep learning (DL) with the classification robustness of ensemble learning, resulting in a synergistic approach. The combination of many elements leads to the development of a model that not only exceeds current benchmarks but also establishes a notable standard, demonstrating an outstanding overall accuracy rate of 96.38%. The significance of our model extends beyond its exceptional accuracy. The feature interpretability of this confers a significant advantage, as it enables a comprehensive comprehension of the various aspects that contribute to the severity of the condition. The availability of interpretability in this context provides farmers and agricultural specialists with a powerful tool that can significantly enhance their ability to make informed decisions based on data when it comes to managing diseases. Our research represents a groundbreaking advancement in the field of multiclass categorization in the context of agriculture. The historical constraints given by the complexity and diversity of crops and illnesses have been significant. However, our hybrid approach presents a scalable alternative that surpasses the limitations of traditional onion farming. Not only does it offer the potential for improved disease evaluation, but it also establishes a precedent for addressing multiclass classification jobs in the agricultural domain on a wider scale.","PeriodicalId":518900,"journal":{"name":"2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"59 2","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531564","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 : 2024-01-11DOI: 10.1109/ICAECT60202.2024.10469200
Ravi Kumar, Veena Sharma, Vineet Kumar
In this study, a modelling and comparative analysis of an optimally tuned PID (Proportional Integral Derivative) controller for DC motor control is conducted. The research aims to find the ideal set of PID parameters that yield optimal performance in terms of speed control and response characteristics for a DC motor. The study likely explores various methods for the tuning of PID controller, such as Ziegler Nichols, Auto-tuning, and Particle SwarmOptimization, and evaluates their effectiveness in achieving the desired control objectives. The comparative analysis of these methods allows for a comprehensive assessment of their advantages and disadvantages, ultimately providing valuable insights for engineering students and professionals seeking to enhance their understanding and application of PID-based control systems in industrial processes.
{"title":"Modelling and Comparative Analysis of Optimally Tuned PID Controllers in DC Motor Systems","authors":"Ravi Kumar, Veena Sharma, Vineet Kumar","doi":"10.1109/ICAECT60202.2024.10469200","DOIUrl":"https://doi.org/10.1109/ICAECT60202.2024.10469200","url":null,"abstract":"In this study, a modelling and comparative analysis of an optimally tuned PID (Proportional Integral Derivative) controller for DC motor control is conducted. The research aims to find the ideal set of PID parameters that yield optimal performance in terms of speed control and response characteristics for a DC motor. The study likely explores various methods for the tuning of PID controller, such as Ziegler Nichols, Auto-tuning, and Particle SwarmOptimization, and evaluates their effectiveness in achieving the desired control objectives. The comparative analysis of these methods allows for a comprehensive assessment of their advantages and disadvantages, ultimately providing valuable insights for engineering students and professionals seeking to enhance their understanding and application of PID-based control systems in industrial processes.","PeriodicalId":518900,"journal":{"name":"2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"22 3","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531575","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 : 2024-01-11DOI: 10.1109/ICAECT60202.2024.10469544
Jahnavi Katragadda, Mubeena Shaik, Seetha Ramanjaneyulu B
Due to the difficulties faced with wire-based signaling systems, wireless communication based signaling systems are gaining importance worldwide. While the cellular communication based systems like GSM-R and LTE-R were proposed in majority of the cases, the non-cellular communication systems that are based on adhoc networks and wireless sensor networks have also become equally important. In this later case, routing protocols like AODV are needed to forward the data packets when destination is not reachable directly from the source. In this context, if some improvements are made to these protocols to make it more suitable to these environments, it can help to have better performance. These aspects are studied and analyzed in this work by carrying out the simulation studies for the model network considered for railway signaling application. Results suggest that making use of GPS and keeping the alternate route on hand can improve the performance considerably. Omnet++ simulator with INET framework is used to carry out the simulations.
{"title":"Analysis Of Data Forwarding Issues In Wireless Networks Aimed For Railway Signaling Systems","authors":"Jahnavi Katragadda, Mubeena Shaik, Seetha Ramanjaneyulu B","doi":"10.1109/ICAECT60202.2024.10469544","DOIUrl":"https://doi.org/10.1109/ICAECT60202.2024.10469544","url":null,"abstract":"Due to the difficulties faced with wire-based signaling systems, wireless communication based signaling systems are gaining importance worldwide. While the cellular communication based systems like GSM-R and LTE-R were proposed in majority of the cases, the non-cellular communication systems that are based on adhoc networks and wireless sensor networks have also become equally important. In this later case, routing protocols like AODV are needed to forward the data packets when destination is not reachable directly from the source. In this context, if some improvements are made to these protocols to make it more suitable to these environments, it can help to have better performance. These aspects are studied and analyzed in this work by carrying out the simulation studies for the model network considered for railway signaling application. Results suggest that making use of GPS and keeping the alternate route on hand can improve the performance considerably. Omnet++ simulator with INET framework is used to carry out the simulations.","PeriodicalId":518900,"journal":{"name":"2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"8 10","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531735","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 : 2024-01-11DOI: 10.1109/ICAECT60202.2024.10468881
Jeslet Joy, Amalda Theresa John, Angel Anna Alex, Adityakrishna S Nair, P.R Sreesh, Anto Manuel
This project focuses on the implementation of an elderly fall detection system using millimeter-wave radar technology, prioritizing privacy preservation within indoor environments. By harnessing mm-wave radar, our system offers technical advantages over camera-based solutions. Radar operates in the radio frequency spectrum, ensuring privacy, as it does not capture visual data, addressing concerns regarding surveillance and consent. Technical aspects encompass mm-wave radar sensor deployment, signal processing algorithm development for fall detection, and real-time data analysis integration. Radar mitigates issues posed by low lighting, occlusions, and line-of- sight limitations common in camera-based systems. Additionally, machine learning enhances fall detection accuracy, reducing false alarms while maintaining high sensitivity.
{"title":"Elderly Fall Detection System Using mm-Wave Radar Sensor","authors":"Jeslet Joy, Amalda Theresa John, Angel Anna Alex, Adityakrishna S Nair, P.R Sreesh, Anto Manuel","doi":"10.1109/ICAECT60202.2024.10468881","DOIUrl":"https://doi.org/10.1109/ICAECT60202.2024.10468881","url":null,"abstract":"This project focuses on the implementation of an elderly fall detection system using millimeter-wave radar technology, prioritizing privacy preservation within indoor environments. By harnessing mm-wave radar, our system offers technical advantages over camera-based solutions. Radar operates in the radio frequency spectrum, ensuring privacy, as it does not capture visual data, addressing concerns regarding surveillance and consent. Technical aspects encompass mm-wave radar sensor deployment, signal processing algorithm development for fall detection, and real-time data analysis integration. Radar mitigates issues posed by low lighting, occlusions, and line-of- sight limitations common in camera-based systems. Additionally, machine learning enhances fall detection accuracy, reducing false alarms while maintaining high sensitivity.","PeriodicalId":518900,"journal":{"name":"2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"46 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531738","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 : 2024-01-11DOI: 10.1109/ICAECT60202.2024.10469485
Kasula Vaishnavi, Gummalla Sreya, Kishor Kumar Reddy, Anisha P R
Air Pollution is the contamination of air due to the presence of substances in the atmosphere that are major issue to human life as well as to the other living organisms. Air quality is the result of the composite interactions of many elements, including the chemical reactions, meteorological parameters, and emissions from natural & anthropogenic (man-kind). The study implies that the forecasting performance differs across diverse regions & cities in India. Utilization of the Random Forest algorithm to anticipate the air quality index bucket is done in multiple locations across India annually, depending on air pollutants like PM2.5, PM10, NOx, CO, SO2, O3, NH3, and NO2.
{"title":"Machine Learning for Air Quality Prediction: Random Forest Classifier","authors":"Kasula Vaishnavi, Gummalla Sreya, Kishor Kumar Reddy, Anisha P R","doi":"10.1109/ICAECT60202.2024.10469485","DOIUrl":"https://doi.org/10.1109/ICAECT60202.2024.10469485","url":null,"abstract":"Air Pollution is the contamination of air due to the presence of substances in the atmosphere that are major issue to human life as well as to the other living organisms. Air quality is the result of the composite interactions of many elements, including the chemical reactions, meteorological parameters, and emissions from natural & anthropogenic (man-kind). The study implies that the forecasting performance differs across diverse regions & cities in India. Utilization of the Random Forest algorithm to anticipate the air quality index bucket is done in multiple locations across India annually, depending on air pollutants like PM2.5, PM10, NOx, CO, SO2, O3, NH3, and NO2.","PeriodicalId":518900,"journal":{"name":"2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"18 9","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531576","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}
Music genres, with their diverse sonic landscapes and distinct characteristics, have been a subject of profound interest in audio analysis. This research investigates the application of digital image processing in the field of music genre classification, utilizing Mel spectrogram images obtained from audio files. This study employs a sequential approach to analyze the 'GTZAN Dataset,' which consists of 10,000 documented Mel spectrogram images that represent ten distinct music genres. The dataset was partitioned in a systematic manner into three separate segments. This partitioning allowed for thorough training and evaluation of the model, with a distribution ratio of 60% for training, 20% for validation, and 20% for testing. The sequential model, which is based on deep learning tenets effectively captures complex genre-specific characteristics from Mel spectrograms in order to achieve accurate music genre categorization. By utilizing a dataset consisting of 6,000 training photos and 2,000 validation photos, the model's parameters underwent refinement. Subsequently, an evaluation was conducted on a distinct set of 2,000 test photographs, which unveiled a remarkable accuracy rate of 94%. During the course of the research, performance metrics such as accuracy and loss graphs were employed to monitor the learning progress of the model during the training phase. Moreover, the examination of the confusion matrix in the testing phase provided insight into the effectiveness of the model, resulting in notable performance measurements. This confirms the model's strength in accurately categorizing music genres. This research makes a substantial contribution towards the advancement of autonomous systems that possess the ability to accurately classify music genres by utilizing spectrogram representations. The model's accuracy of 94% serves as evidence of its effectiveness, indicating its possible applications in systems for recommendations, music indexing, and content organization. This emphasizes its significant contribution to the field of audio content analysis and classification approaches.
{"title":"Sonic Signatures: Sequential Model-driven Music Genre Classification with Mel Spectograms","authors":"Rudresh Pillai, Neha Sharma, Deepak Upadhyay, Sarishma Dangi, Rupesh Gupta","doi":"10.1109/ICAECT60202.2024.10468856","DOIUrl":"https://doi.org/10.1109/ICAECT60202.2024.10468856","url":null,"abstract":"Music genres, with their diverse sonic landscapes and distinct characteristics, have been a subject of profound interest in audio analysis. This research investigates the application of digital image processing in the field of music genre classification, utilizing Mel spectrogram images obtained from audio files. This study employs a sequential approach to analyze the 'GTZAN Dataset,' which consists of 10,000 documented Mel spectrogram images that represent ten distinct music genres. The dataset was partitioned in a systematic manner into three separate segments. This partitioning allowed for thorough training and evaluation of the model, with a distribution ratio of 60% for training, 20% for validation, and 20% for testing. The sequential model, which is based on deep learning tenets effectively captures complex genre-specific characteristics from Mel spectrograms in order to achieve accurate music genre categorization. By utilizing a dataset consisting of 6,000 training photos and 2,000 validation photos, the model's parameters underwent refinement. Subsequently, an evaluation was conducted on a distinct set of 2,000 test photographs, which unveiled a remarkable accuracy rate of 94%. During the course of the research, performance metrics such as accuracy and loss graphs were employed to monitor the learning progress of the model during the training phase. Moreover, the examination of the confusion matrix in the testing phase provided insight into the effectiveness of the model, resulting in notable performance measurements. This confirms the model's strength in accurately categorizing music genres. This research makes a substantial contribution towards the advancement of autonomous systems that possess the ability to accurately classify music genres by utilizing spectrogram representations. The model's accuracy of 94% serves as evidence of its effectiveness, indicating its possible applications in systems for recommendations, music indexing, and content organization. This emphasizes its significant contribution to the field of audio content analysis and classification approaches.","PeriodicalId":518900,"journal":{"name":"2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"44 2","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531568","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 : 2024-01-11DOI: 10.1109/ICAECT60202.2024.10468978
Kireet Joshi, V. K. Gupta, Paras Jain, Anurag Shukla, Monika Bharti, Himanshu Jindal
The disease prediction system predicts the disease by taking symptoms from the user and predict using machine learning algorithms that whether the user has disease or not. The proposed model supports the user-friendly interface and is easy to handle and performs operations accordingly. It is built to help the people at early stage to check the presence of disease, producing the results with an accuracy of almost 86% for Parkinson's disease, 97% for Gestational disease and 85% for cardiovascular disease. Our methodology is performing better in comparison of existing methods, where we have developed one algorithm for the same. The dataset of various patients related to this disease is taken from Kaggle websites. We represented our results with various diagrams and charts as well.
{"title":"Disease Prediction System in Human Beings using Machine Learning Approaches","authors":"Kireet Joshi, V. K. Gupta, Paras Jain, Anurag Shukla, Monika Bharti, Himanshu Jindal","doi":"10.1109/ICAECT60202.2024.10468978","DOIUrl":"https://doi.org/10.1109/ICAECT60202.2024.10468978","url":null,"abstract":"The disease prediction system predicts the disease by taking symptoms from the user and predict using machine learning algorithms that whether the user has disease or not. The proposed model supports the user-friendly interface and is easy to handle and performs operations accordingly. It is built to help the people at early stage to check the presence of disease, producing the results with an accuracy of almost 86% for Parkinson's disease, 97% for Gestational disease and 85% for cardiovascular disease. Our methodology is performing better in comparison of existing methods, where we have developed one algorithm for the same. The dataset of various patients related to this disease is taken from Kaggle websites. We represented our results with various diagrams and charts as well.","PeriodicalId":518900,"journal":{"name":"2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"36 12","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531569","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 : 2024-01-11DOI: 10.1109/ICAECT60202.2024.10468808
R. Agarwal, Himanshu Kumar, Himanshu Mishra
A backward-to-forward continuous beam scanning leaky wave antenna (LWA) in substrate integrated waveguide (SIW) technology using delay lines is demonstrated in this study. A periodical H-shaped grooves on the top surface of SIW are etched to provide the slow-wave effect. Delay lines is introduced to change the group delay profile, increasing the SR. According to simulations, the presented LWA scans a broad angle in a limited bandwidth. Within the frequency range of 11.6 GHz to 12.3 GHz, scanning angle ranging from -41° to +31° (overall scanning angle of 72°) with scanning rate of 90°/GHz. This antenna has maximum gain of 14.53 dBi at 11.9 GHz and 92.5% efficiency, which is acceptable considering the antenna’s compact size.
{"title":"Enhanced scanning rate for SIW-LWA with continuous beam steering using delay lines","authors":"R. Agarwal, Himanshu Kumar, Himanshu Mishra","doi":"10.1109/ICAECT60202.2024.10468808","DOIUrl":"https://doi.org/10.1109/ICAECT60202.2024.10468808","url":null,"abstract":"A backward-to-forward continuous beam scanning leaky wave antenna (LWA) in substrate integrated waveguide (SIW) technology using delay lines is demonstrated in this study. A periodical H-shaped grooves on the top surface of SIW are etched to provide the slow-wave effect. Delay lines is introduced to change the group delay profile, increasing the SR. According to simulations, the presented LWA scans a broad angle in a limited bandwidth. Within the frequency range of 11.6 GHz to 12.3 GHz, scanning angle ranging from -41° to +31° (overall scanning angle of 72°) with scanning rate of 90°/GHz. This antenna has maximum gain of 14.53 dBi at 11.9 GHz and 92.5% efficiency, which is acceptable considering the antenna’s compact size.","PeriodicalId":518900,"journal":{"name":"2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"34 5","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531570","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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