Pub Date : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201760
Rakesh Rosan Prusty, R. Mallick, P. Nayak, Sairam Mishra
Fault detection and classification in transmission lines is a crucial task for engineers to maintain reliability and safe operation of electrical power systems. This article proposes a new technique based on statistical features and Boosted Decision Tree (BDT) to identify the fault and classify it. The essential statistical features are calculated from fault currents with 10 different types of faults, then BDT is applied to identify and classify the faults. Experimental results show that the proposed technique can identify and classify transmission line faults accurately. The proposed BDT is compared with other competitive machine learning classifiers to justify the improved performance.
{"title":"Fault Detection AND Classification in Transmission Lines using Boosted Decision Tree","authors":"Rakesh Rosan Prusty, R. Mallick, P. Nayak, Sairam Mishra","doi":"10.1109/APSIT58554.2023.10201760","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201760","url":null,"abstract":"Fault detection and classification in transmission lines is a crucial task for engineers to maintain reliability and safe operation of electrical power systems. This article proposes a new technique based on statistical features and Boosted Decision Tree (BDT) to identify the fault and classify it. The essential statistical features are calculated from fault currents with 10 different types of faults, then BDT is applied to identify and classify the faults. Experimental results show that the proposed technique can identify and classify transmission line faults accurately. The proposed BDT is compared with other competitive machine learning classifiers to justify the improved performance.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117201709","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201759
A. Mishra, P. K. Nanda, Debiprasanna Das, A. Patra, Narayan Nahak, Lalit M. Sathapathy
The main cause of supply voltage distortion is due to the extensive use of power electronics based loads in almost all types of residential, commercial and industrial customer. The power electronics based loads draw harmonic current from the source. In order to reduce the harmonic currents entering into the distribution systems Passive filters are widely used. To present the complete design procedure of Shunt Passive Filter this article has been written again in this article a 5th, 7th single tuned and a 11th and 13th double tuned shunt passive filter is connected in a 3Φ three wire power system supplying power to a nonlinear load in order to reduce the harmonics and to compensate the reactive power. The performance parameters are obtained showing the better response after the connection Passive filter.
{"title":"Design and Analysis of Shunt Passive Filter for Harmonic and Reactive Power Compensation","authors":"A. Mishra, P. K. Nanda, Debiprasanna Das, A. Patra, Narayan Nahak, Lalit M. Sathapathy","doi":"10.1109/APSIT58554.2023.10201759","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201759","url":null,"abstract":"The main cause of supply voltage distortion is due to the extensive use of power electronics based loads in almost all types of residential, commercial and industrial customer. The power electronics based loads draw harmonic current from the source. In order to reduce the harmonic currents entering into the distribution systems Passive filters are widely used. To present the complete design procedure of Shunt Passive Filter this article has been written again in this article a 5th, 7th single tuned and a 11th and 13th double tuned shunt passive filter is connected in a 3Φ three wire power system supplying power to a nonlinear load in order to reduce the harmonics and to compensate the reactive power. The performance parameters are obtained showing the better response after the connection Passive filter.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115329564","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}
Electric Vehicles (EVs) performance highly depends on the battery capacity. Lithium-ion batteries have become an integral part in EV's because of their high-power density, extended lifespan, and adaptability to a variety of temperatures. There is a tremendous quantity of heat produced throughout the charging and discharging operation. To guarantee optimal operation, the battery's temperature must be monitored. This document gives information on the various Battery Thermal Management Systems (BTMS) available for battery protection. To prevent the battery from overheating, it is important to have the efficient thermal management system.
{"title":"EV's Battery Thermal Management analysis using various cooling techniques- A Case study","authors":"S. Mishra, Priyanka Priyadarshini Padhi, Sudheshna G, Priyanka D, Lokeswar Rao K, Adilakshmi K, Manojna Ch","doi":"10.1109/APSIT58554.2023.10201743","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201743","url":null,"abstract":"Electric Vehicles (EVs) performance highly depends on the battery capacity. Lithium-ion batteries have become an integral part in EV's because of their high-power density, extended lifespan, and adaptability to a variety of temperatures. There is a tremendous quantity of heat produced throughout the charging and discharging operation. To guarantee optimal operation, the battery's temperature must be monitored. This document gives information on the various Battery Thermal Management Systems (BTMS) available for battery protection. To prevent the battery from overheating, it is important to have the efficient thermal management system.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126883542","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}
The exponential growth of digital data has resulted in an unprecedented amount of information being generated on a daily basis. It has become increasingly difficult to keep up with the sheer volume of information, and manual text summarization has become a tedious and time-consuming task. As a result, text summarization has grown in significance as a field of study in natural language processing. This study offers a text summarizing method that identifies a text's key sentences using partition-based clustering and similarity metrics. The sentence similarity score is computed using Euclidian Distance (Euc), Cosine Similarity (Cos), and Jaccard Similarity (Jac). The proposed model uses possible combinations of clustering and similarity algorithms and is validated over the Document Understanding Conferences (DUC) dataset. The proposed model combination of K-Mean clustering with cosine similarity shows significantly better results than the other summarizers. Overall, this paper provides an efficient and effective way to generate text summaries that capture the essential information in a given text.
{"title":"PCTS: Partition Based Clustering for Text Summarization","authors":"Subhransu Dash, Tanuj Mohanty, Sri Rijul Das, Ankit Mohanty, Rasmita Rautray","doi":"10.1109/APSIT58554.2023.10201655","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201655","url":null,"abstract":"The exponential growth of digital data has resulted in an unprecedented amount of information being generated on a daily basis. It has become increasingly difficult to keep up with the sheer volume of information, and manual text summarization has become a tedious and time-consuming task. As a result, text summarization has grown in significance as a field of study in natural language processing. This study offers a text summarizing method that identifies a text's key sentences using partition-based clustering and similarity metrics. The sentence similarity score is computed using Euclidian Distance (Euc), Cosine Similarity (Cos), and Jaccard Similarity (Jac). The proposed model uses possible combinations of clustering and similarity algorithms and is validated over the Document Understanding Conferences (DUC) dataset. The proposed model combination of K-Mean clustering with cosine similarity shows significantly better results than the other summarizers. Overall, this paper provides an efficient and effective way to generate text summaries that capture the essential information in a given text.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125991008","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201674
S. Shilaskar, Dyuti Bobby, Atharva Dusane, S. Bhatlawande
With the spotlight on emotional intelligence development in machines, advancements in the field of human computer interactions have gained importance. Emotion identification is particularly important in today's world, where people have developed social behavior masking abilities. This paper explores a fusion of EEG (electroencephalogram), EMG (electromyography) and ECG (electrocardiography) to detect human emotions such as pain, happiness, and disgust. This work becomes prominent in the use of affective computing methods for developing optimized human computer interactions. Systems built using this approach can adapt to users' emotional states providing a refined, personalized approach. Furthermore, this effort can aid in the development of apparatus that can be used in cases where people are unable to physically show emotion, such as facial paralysis. The proposed method is unique in that it combines all three - EEG, ECG, and EMG.
{"title":"Fusion of EEG, EMG, and ECG Signals for Accurate Recognition of Pain, Happiness, and Disgust","authors":"S. Shilaskar, Dyuti Bobby, Atharva Dusane, S. Bhatlawande","doi":"10.1109/APSIT58554.2023.10201674","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201674","url":null,"abstract":"With the spotlight on emotional intelligence development in machines, advancements in the field of human computer interactions have gained importance. Emotion identification is particularly important in today's world, where people have developed social behavior masking abilities. This paper explores a fusion of EEG (electroencephalogram), EMG (electromyography) and ECG (electrocardiography) to detect human emotions such as pain, happiness, and disgust. This work becomes prominent in the use of affective computing methods for developing optimized human computer interactions. Systems built using this approach can adapt to users' emotional states providing a refined, personalized approach. Furthermore, this effort can aid in the development of apparatus that can be used in cases where people are unable to physically show emotion, such as facial paralysis. The proposed method is unique in that it combines all three - EEG, ECG, and EMG.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131523328","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}
In recent times, the use of roadways as a mode of transportation has seen a surge in global popularity. Maintaining well-structured roads is crucial to economic growth and social development, especially in developing countries like India. However, the maintenance of roads is becoming an emerging concern due to poor road conditions and potholes. Potholes are a major road infrastructure problem, causing vehicle damage and posing a risk to road safety. This research proposes an IoT-based pothole-tracking system that uses a deep-learning based object detection mechanism and ultrasonic sensors to detect and track potholes on roads. It aims to contribute to developing effective solutions for improving road safety and maintenance, while also addressing its practicality and cost considerations for its implementation. The results of the proposed work on a dataset of potholes demonstrate its effectiveness in detecting potholes.
{"title":"SmartPave: An Advanced IoT-Based System for Real-Time Pothole Detection, Tracking, and Maintenance","authors":"Sahel Bej, Swarnava Roy, Debjit Daw, Alok Paul, Shubhojit Saha, Satyabrata Maity, Nimisha Ghosh","doi":"10.1109/APSIT58554.2023.10201720","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201720","url":null,"abstract":"In recent times, the use of roadways as a mode of transportation has seen a surge in global popularity. Maintaining well-structured roads is crucial to economic growth and social development, especially in developing countries like India. However, the maintenance of roads is becoming an emerging concern due to poor road conditions and potholes. Potholes are a major road infrastructure problem, causing vehicle damage and posing a risk to road safety. This research proposes an IoT-based pothole-tracking system that uses a deep-learning based object detection mechanism and ultrasonic sensors to detect and track potholes on roads. It aims to contribute to developing effective solutions for improving road safety and maintenance, while also addressing its practicality and cost considerations for its implementation. The results of the proposed work on a dataset of potholes demonstrate its effectiveness in detecting potholes.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131617770","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201678
S. Dash, A. Sahoo, Shruti Ray, Tamanna Samantaray, N. Nayak
This manuscript presents a novel teamwork optimization algorithm based simultaneous allocation of active devices (biomass DGs and DSTATCOMs) for three different voltage dependent loading scenarios namely constant power, constant current, and constant impedance in power distribution network. Both the placements and sizes of these active devices are optimized concurrently for different numbers of devices in a multi-objective framework that includes real power loss reduction, reactive power loss reduction, voltage deviation reduction, and voltage stability index enhancement. The proposed technique has been successfully validated on a standard 33 bus distribution network, and simulation results show that the performance of the power distribution network improves significantly in the presence of optimally allocated biomass DGs and DSTATCOMs for the studied loading scenarios.
{"title":"A Novel Teamwork Optimization Algorithm for Simultaneous Optimal Placement and Sizing of Biomass DGs and DSTATCOMs Considering Voltage Dependent Load Models","authors":"S. Dash, A. Sahoo, Shruti Ray, Tamanna Samantaray, N. Nayak","doi":"10.1109/APSIT58554.2023.10201678","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201678","url":null,"abstract":"This manuscript presents a novel teamwork optimization algorithm based simultaneous allocation of active devices (biomass DGs and DSTATCOMs) for three different voltage dependent loading scenarios namely constant power, constant current, and constant impedance in power distribution network. Both the placements and sizes of these active devices are optimized concurrently for different numbers of devices in a multi-objective framework that includes real power loss reduction, reactive power loss reduction, voltage deviation reduction, and voltage stability index enhancement. The proposed technique has been successfully validated on a standard 33 bus distribution network, and simulation results show that the performance of the power distribution network improves significantly in the presence of optimally allocated biomass DGs and DSTATCOMs for the studied loading scenarios.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130518036","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201726
Sandeep Soumya Sekhar Mishra, P. Dutta, Gayatri Nayak, A. Tripathy, P. Kishore, S. Barisal
In Software Engineering, the faults present in software are the most critical issues, since they produce many incorrect and unreliable results. For developing reliable software, these faults must be resolved. In this project, a fault counter model is designed to predict the number of faulty modules present in a software project. There are four contributions in this work. The first contribution is to collect the dataset. The collected dataset contains numerous high-ranged and null values. In the second contribution, data pre-processing techniques like standard scaling and null-value removal are applied. The third contribution is to apply feature selection techniques to remove the least important features from the dataset. The fourth contribution is to predict the number of faults present in software projects using the Bagging Technique. The proposed model achieves a 0.55 R2_ Score.
{"title":"Designing Fault-Counter for Object-Oriented Software using Bagging Technique","authors":"Sandeep Soumya Sekhar Mishra, P. Dutta, Gayatri Nayak, A. Tripathy, P. Kishore, S. Barisal","doi":"10.1109/APSIT58554.2023.10201726","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201726","url":null,"abstract":"In Software Engineering, the faults present in software are the most critical issues, since they produce many incorrect and unreliable results. For developing reliable software, these faults must be resolved. In this project, a fault counter model is designed to predict the number of faulty modules present in a software project. There are four contributions in this work. The first contribution is to collect the dataset. The collected dataset contains numerous high-ranged and null values. In the second contribution, data pre-processing techniques like standard scaling and null-value removal are applied. The third contribution is to apply feature selection techniques to remove the least important features from the dataset. The fourth contribution is to predict the number of faults present in software projects using the Bagging Technique. The proposed model achieves a 0.55 R2_ Score.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130621931","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201657
D. Gupta, Ananya Komal Singh, Naman Gupta, D. Vishwakarma
Human Action Recognition is quite popular among researchers and scientists and is considered one of the most active applications in the field of computer vision. It is quite useful in modern era applications like healthcare, surveillance, sports and many more fields. Deep Learning has provided an upliftment to predict human actions in an easiest way possible. This paper proposes a combined CNN & RNN human action recognition model named SDL-Net, which generates skeletal representations using Part Affinity Fields (PAFs) and generates skeletal gait energy images. It also captures sequential patterns to generate sequential data as well. Experiments are conducted on Kinect Activity Recognition Dataset (KARD) and it shows the efficiency and effectiveness by achieving desirable results.
{"title":"SDL-Net: A Combined CNN & RNN Human Activity Recognition Model","authors":"D. Gupta, Ananya Komal Singh, Naman Gupta, D. Vishwakarma","doi":"10.1109/APSIT58554.2023.10201657","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201657","url":null,"abstract":"Human Action Recognition is quite popular among researchers and scientists and is considered one of the most active applications in the field of computer vision. It is quite useful in modern era applications like healthcare, surveillance, sports and many more fields. Deep Learning has provided an upliftment to predict human actions in an easiest way possible. This paper proposes a combined CNN & RNN human action recognition model named SDL-Net, which generates skeletal representations using Part Affinity Fields (PAFs) and generates skeletal gait energy images. It also captures sequential patterns to generate sequential data as well. Experiments are conducted on Kinect Activity Recognition Dataset (KARD) and it shows the efficiency and effectiveness by achieving desirable results.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133512767","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}
Multimodal intelligence-based systems for medical analytics and decision-making are crucial in the healthcare industry. One of the most common types of cancer is liver cancer, and early detection is essential for successful treatment. The severity of irregular tumor forms varies depending on the malignancy stage and the tumor type. Identifying the liver and subsequent tumor segmentation are the two primary stages of tumor segmentation in the liver. In addition to detecting cancers from publically available data of liver scans, this research offers a novel deep learning-based segmentation with a grey wolf Optimization-Extreme Learning Model approach that exhibits excellent efficiency in results. To improve the efficacy of the liver tumor detection system, this work applies the GWO-ELM classifier and Haar wavelet transform. It uses one of the most widely used feature extractions. The GWO-ELM acts like a Support Vector Machine with a Neural Network structure and can solve multi and binary classification problems. In contrast, the Haar wavelet transform can extract the most pertinent features with low dimensionality. As a result, the GWO-ELM classifier and Haar wavelet transform characteristics are used to provide a useful method for classifying and extracting features from liver tumors. According to the results, the proposed GWO-ELM model performed very well, achieving an accuracy of 99.41 % for a multi-class dataset. This reveals that the GWO-ELM and Haar wavelet transform is a robust classifier for identifying liver tumors and might be used to handle various types of image data.
{"title":"Liver Tumor Detection and Classification Using GWO-ELM Model","authors":"Workeneh Geleta Negassa, Satyasis Mishra, Haymanot Derebe Bizuneh","doi":"10.1109/APSIT58554.2023.10201687","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201687","url":null,"abstract":"Multimodal intelligence-based systems for medical analytics and decision-making are crucial in the healthcare industry. One of the most common types of cancer is liver cancer, and early detection is essential for successful treatment. The severity of irregular tumor forms varies depending on the malignancy stage and the tumor type. Identifying the liver and subsequent tumor segmentation are the two primary stages of tumor segmentation in the liver. In addition to detecting cancers from publically available data of liver scans, this research offers a novel deep learning-based segmentation with a grey wolf Optimization-Extreme Learning Model approach that exhibits excellent efficiency in results. To improve the efficacy of the liver tumor detection system, this work applies the GWO-ELM classifier and Haar wavelet transform. It uses one of the most widely used feature extractions. The GWO-ELM acts like a Support Vector Machine with a Neural Network structure and can solve multi and binary classification problems. In contrast, the Haar wavelet transform can extract the most pertinent features with low dimensionality. As a result, the GWO-ELM classifier and Haar wavelet transform characteristics are used to provide a useful method for classifying and extracting features from liver tumors. According to the results, the proposed GWO-ELM model performed very well, achieving an accuracy of 99.41 % for a multi-class dataset. This reveals that the GWO-ELM and Haar wavelet transform is a robust classifier for identifying liver tumors and might be used to handle various types of image data.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129836924","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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