Pub Date : 2023-05-26DOI: 10.1109/INCET57972.2023.10170301
Xikang Du
The energy-saving marine garbage collection algorithm and system based on machine vision is a system that provides real-time information of marine garbage collection. The system can be used to measure the amount of garbage in water, calculate the percentage of garbage collected by automatic mechanism, and predict its return rate. It also contributes to making all ocean related actions more efficient and effective. It is based on machine vision technology. The algorithm can identify marine debris and other objects in the water, including ships, buoys and fishing nets. The system will help reduce marine litter by up to 90 per cent. The main goal of the algorithm is to reduce the amount of garbage dumped into the ocean. This will also help to save energy by reducing the amount of energy used to treat such wastes.
{"title":"Energy saving Ocean Garbage Collection Return Algorithm and System Based on Machine Vision","authors":"Xikang Du","doi":"10.1109/INCET57972.2023.10170301","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170301","url":null,"abstract":"The energy-saving marine garbage collection algorithm and system based on machine vision is a system that provides real-time information of marine garbage collection. The system can be used to measure the amount of garbage in water, calculate the percentage of garbage collected by automatic mechanism, and predict its return rate. It also contributes to making all ocean related actions more efficient and effective. It is based on machine vision technology. The algorithm can identify marine debris and other objects in the water, including ships, buoys and fishing nets. The system will help reduce marine litter by up to 90 per cent. The main goal of the algorithm is to reduce the amount of garbage dumped into the ocean. This will also help to save energy by reducing the amount of energy used to treat such wastes.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129884263","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170375
N. A, Shoney Sebastian
Determining patterns in monolithic systems to help improve the overall system development and maintenance has become quite commonplace. However, recognizing the patterns that have emerged (or are emerging) in cloud computing - especially with respect to microservices, is challenging. Although numerous patterns have been proposed through extensive research and implementation, the quality assessment tools that are currently available fall short when it comes to accurately recognizing patterns in microservices. It has been identified that a completely autonomous tool for the identification and classification of patterns in microservices has not been developed so far. Moreover, classification of services is an approach that has not been considered by researchers that are working in this field. This paper aims to perform a detailed systematic literature review that can help to explore the various possibilities of identifying and classifying the patterns in microservices. The article also briefly lists out a set of tools that is used in the industry for the implementation of patterns in microservices.
{"title":"A Systematic Review on the Identification and Classification of Patterns in Microservices","authors":"N. A, Shoney Sebastian","doi":"10.1109/INCET57972.2023.10170375","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170375","url":null,"abstract":"Determining patterns in monolithic systems to help improve the overall system development and maintenance has become quite commonplace. However, recognizing the patterns that have emerged (or are emerging) in cloud computing - especially with respect to microservices, is challenging. Although numerous patterns have been proposed through extensive research and implementation, the quality assessment tools that are currently available fall short when it comes to accurately recognizing patterns in microservices. It has been identified that a completely autonomous tool for the identification and classification of patterns in microservices has not been developed so far. Moreover, classification of services is an approach that has not been considered by researchers that are working in this field. This paper aims to perform a detailed systematic literature review that can help to explore the various possibilities of identifying and classifying the patterns in microservices. The article also briefly lists out a set of tools that is used in the industry for the implementation of patterns in microservices.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122138217","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170346
Nehul Gupta, Vedangi Thakur, Vaishnavi Patil, Tamanna Vishnoi, K. Bhangale
Speech Emotion Recognition (SER) offers a wide range of potential uses, including strengthening human-computer interaction in virtual reality and gaming settings, enhancing the detection and tracking of mental health disorders, and enhancing the precision of speech based assistants and chat bots. It faces the challenge of cross corpus SER, intonation variations, dialects variations and prosodic changes in language due to age, gender, region, and religion, etc. This paper presents deep Convolution Neural Network based SER for Marathi language Our novel Marathi data set consists of 300 recordings of 15 speakers for Anger, Happy, Sad and Neutral emotions. The performance of the proposed DCNN is evaluated on the novel data set based on accuracy, precision, recall and F1-score. The suggested scheme provides overall accuracy of raw data is 0.4750, 0.4076 and 0.3927 for 5,10 and 15 speakers respectively and the overall accuracy after feature extraction is 0.6652, 0.6361 and 0.5800 for 5, 10 and 15 speakers respectively shows improvement in existing state of arts utilized for SER for Marathi Corpus.
{"title":"Analysis of Affective Computing for Marathi Corpus using Deep Learning","authors":"Nehul Gupta, Vedangi Thakur, Vaishnavi Patil, Tamanna Vishnoi, K. Bhangale","doi":"10.1109/INCET57972.2023.10170346","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170346","url":null,"abstract":"Speech Emotion Recognition (SER) offers a wide range of potential uses, including strengthening human-computer interaction in virtual reality and gaming settings, enhancing the detection and tracking of mental health disorders, and enhancing the precision of speech based assistants and chat bots. It faces the challenge of cross corpus SER, intonation variations, dialects variations and prosodic changes in language due to age, gender, region, and religion, etc. This paper presents deep Convolution Neural Network based SER for Marathi language Our novel Marathi data set consists of 300 recordings of 15 speakers for Anger, Happy, Sad and Neutral emotions. The performance of the proposed DCNN is evaluated on the novel data set based on accuracy, precision, recall and F1-score. The suggested scheme provides overall accuracy of raw data is 0.4750, 0.4076 and 0.3927 for 5,10 and 15 speakers respectively and the overall accuracy after feature extraction is 0.6652, 0.6361 and 0.5800 for 5, 10 and 15 speakers respectively shows improvement in existing state of arts utilized for SER for Marathi Corpus.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"23 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120911977","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10169957
Pengyu Cheng
The work path optimization of AUV manipulator based on PSO GA algorithm is a method to find the best work path of AUV manipulator. It is an extension of the original PSO GA algorithm, and uses the concept of pseudo Gaussian distribution to find a better solution under multiple local optimizations. The working path optimization of the underwater robot manipulator is to make the control of the underwater robot manipulator move along the working path with the minimum energy consumption. It is realized by using some mathematical techniques and algorithms. The main idea behind this technology is to find out the best point of the mobile underwater robot manipulator to minimize its total energy consumption. This technology is used for many purposes, such as motion planning, path planning and control design.. The main idea behind this algorithm is that if there are multiple local optima, the global optimal can be found by minimizing the total cost function of all local optima. This can be achieved by using Lagrange multiplication (LMM). In addition, this technology requires less computing power. In the actual working environment and experimental environment, the magnetic field interference may have an impact on the attitude parameters of AUV, which leads to the unsatisfactory control effect of AUV motion. In order to accurately measure the attitude of AUV system, this paper proposes an anti-jamming and fault-tolerant processing algorithm for MEMS inertial navigation system. This algorithm first estimates the signal residual, then dynamically adjusts the confidence level of local filter through the residual value, and finally fuses sensor signals with different working principles through the confidence level, which can significantly improve the stability and reliability of attitude feedback signals.
{"title":"Working Path Optimization of AUV Manipulator Based on PSO-GA Algorithm","authors":"Pengyu Cheng","doi":"10.1109/INCET57972.2023.10169957","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10169957","url":null,"abstract":"The work path optimization of AUV manipulator based on PSO GA algorithm is a method to find the best work path of AUV manipulator. It is an extension of the original PSO GA algorithm, and uses the concept of pseudo Gaussian distribution to find a better solution under multiple local optimizations. The working path optimization of the underwater robot manipulator is to make the control of the underwater robot manipulator move along the working path with the minimum energy consumption. It is realized by using some mathematical techniques and algorithms. The main idea behind this technology is to find out the best point of the mobile underwater robot manipulator to minimize its total energy consumption. This technology is used for many purposes, such as motion planning, path planning and control design.. The main idea behind this algorithm is that if there are multiple local optima, the global optimal can be found by minimizing the total cost function of all local optima. This can be achieved by using Lagrange multiplication (LMM). In addition, this technology requires less computing power. In the actual working environment and experimental environment, the magnetic field interference may have an impact on the attitude parameters of AUV, which leads to the unsatisfactory control effect of AUV motion. In order to accurately measure the attitude of AUV system, this paper proposes an anti-jamming and fault-tolerant processing algorithm for MEMS inertial navigation system. This algorithm first estimates the signal residual, then dynamically adjusts the confidence level of local filter through the residual value, and finally fuses sensor signals with different working principles through the confidence level, which can significantly improve the stability and reliability of attitude feedback signals.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122299950","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}
Brain tumour segmentation is a crucial task in medical imaging that involves identifying and delineating the boundaries of tumour tissues in the brain from MRI scans. Accurate segmentation plays an indispensable role in the diagnosis, treatment planning, and monitoring of patients with brain tumours. This study presents a novel approach to address the class imbalance prevalent in brain tumour segmentation using a shared-encoder multi-class segmentation framework. The proposed method involves training a single encoder class learner and multiple decoder class learners, which are designed to learn feature representation of a certain class subset, in addition to a shared encoder between them that extracts common features across all classes. The outputs of the complement-class learners are combined and propagated to a meta-learner to obtain the final segmentation map. The authors evaluate their method on a publicly available brain tumour segmentation dataset (BraTS20) and assess performance against the 2D U-Net model trained on all classes using standard evaluation metrics for multi-class semantic segmentation. The IoU and DSC scores for the proposed architecture stands at 0.644 and 0.731, respectively, as compared to 0.604 and 0.690 obtained by the base models. Furthermore, our model exhibits significant performance boosts in individual classes, as evidenced by the DSC scores of 0.588, 0.734, and 0.684 for the necrotic tumour core, peritumoral edema, and the GD-enhancing tumour classes, respectively. In contrast, the 2D-Unet model yields DSC scores of 0.554, 0.699, and 0.641 for the same classes, respectively. The approach exhibits notable performance gains in segmenting the T1-Gd class, which not only poses a formidable challenge in terms of segmentation but also holds paramount clinical significance for radiation therapy.
{"title":"SEMC-Net: A Shared-Encoder Multi-Class Learner","authors":"Rahul Jain, Satvik Dixit, Vikas Kumar, Bindu Verma","doi":"10.1109/INCET57972.2023.10170284","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170284","url":null,"abstract":"Brain tumour segmentation is a crucial task in medical imaging that involves identifying and delineating the boundaries of tumour tissues in the brain from MRI scans. Accurate segmentation plays an indispensable role in the diagnosis, treatment planning, and monitoring of patients with brain tumours. This study presents a novel approach to address the class imbalance prevalent in brain tumour segmentation using a shared-encoder multi-class segmentation framework. The proposed method involves training a single encoder class learner and multiple decoder class learners, which are designed to learn feature representation of a certain class subset, in addition to a shared encoder between them that extracts common features across all classes. The outputs of the complement-class learners are combined and propagated to a meta-learner to obtain the final segmentation map. The authors evaluate their method on a publicly available brain tumour segmentation dataset (BraTS20) and assess performance against the 2D U-Net model trained on all classes using standard evaluation metrics for multi-class semantic segmentation. The IoU and DSC scores for the proposed architecture stands at 0.644 and 0.731, respectively, as compared to 0.604 and 0.690 obtained by the base models. Furthermore, our model exhibits significant performance boosts in individual classes, as evidenced by the DSC scores of 0.588, 0.734, and 0.684 for the necrotic tumour core, peritumoral edema, and the GD-enhancing tumour classes, respectively. In contrast, the 2D-Unet model yields DSC scores of 0.554, 0.699, and 0.641 for the same classes, respectively. The approach exhibits notable performance gains in segmenting the T1-Gd class, which not only poses a formidable challenge in terms of segmentation but also holds paramount clinical significance for radiation therapy.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126624318","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}
Skin disease is a major issue of global health problem affecting a large amount of persons. The advancement of dermatological diseases categorization has grown more accurate in recent years due to the rapid growth of technology and the use of various machine learning techniques. Therefore the creation of machine learning methods that can accurately differentiate between the classifications of skin diseases is one of the great importance. This research work focuses on the classification of different kinds of skin diseases using machine learning techniques. In this research, we introduce a novel approach that makes use of four distinct data mining techniques like support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF) and, naive bayes (NB) algorithm. This research work proposed an ensemble model that is combination of SVM, KNN, RF and NB using voting scheme. The proposed model classified the skin disease into five different classes that are Acne, Skin allergy, Nail fungus, Hair loss, and Normal skin. The proposed ensemble model used on skin disease classification that gives better performance over other classifier algorithms. The proposed ensemble model achieved highest 97.33% of accuracy as compared to others.
{"title":"Skin Disease Classification using Machine Learning based Proposed Ensemble Model","authors":"Bisahu Ram Sahu, Akhilesh Kumar Shrivas, Abhinav Shukla","doi":"10.1109/INCET57972.2023.10170128","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170128","url":null,"abstract":"Skin disease is a major issue of global health problem affecting a large amount of persons. The advancement of dermatological diseases categorization has grown more accurate in recent years due to the rapid growth of technology and the use of various machine learning techniques. Therefore the creation of machine learning methods that can accurately differentiate between the classifications of skin diseases is one of the great importance. This research work focuses on the classification of different kinds of skin diseases using machine learning techniques. In this research, we introduce a novel approach that makes use of four distinct data mining techniques like support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF) and, naive bayes (NB) algorithm. This research work proposed an ensemble model that is combination of SVM, KNN, RF and NB using voting scheme. The proposed model classified the skin disease into five different classes that are Acne, Skin allergy, Nail fungus, Hair loss, and Normal skin. The proposed ensemble model used on skin disease classification that gives better performance over other classifier algorithms. The proposed ensemble model achieved highest 97.33% of accuracy as compared to others.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125476484","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}
This paper proposes the comparison between three text summarization models - BERT, BART and T5. All the three models focus on summarizing a single research paper for generating a summary which is automatic and relevant. After the analysis and implementation of the three pretrained models, it is noticed that T5 is the best suited for our problem statement. Many researchers, professionals as well as students need to be up-to-date about the new scientific documents for the project they are working on or to gain something new out of it. They frequently feel that the abstract is not informative enough in order to establish significance. The final system aims at resolving the mentioned problem.
{"title":"Synopsis Creation for Research Paper using Text Summarization Models","authors":"Sanskruti Badhe, Mubashshira Hasan, Vidhi Rughwani, Reeta Koshy","doi":"10.1109/INCET57972.2023.10170144","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170144","url":null,"abstract":"This paper proposes the comparison between three text summarization models - BERT, BART and T5. All the three models focus on summarizing a single research paper for generating a summary which is automatic and relevant. After the analysis and implementation of the three pretrained models, it is noticed that T5 is the best suited for our problem statement. Many researchers, professionals as well as students need to be up-to-date about the new scientific documents for the project they are working on or to gain something new out of it. They frequently feel that the abstract is not informative enough in order to establish significance. The final system aims at resolving the mentioned problem.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124201691","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170211
S. Joga, S. Surisetti, S. Karri, Shaik Jalaluddin, Konatala Madhu, J. Shiva
Power quality refers to the characteristics of the electrical power supply that affect the performance, reliability, and safety of electrical equipment. With the growing demand for reliable and efficient power supply, power quality has become an important area of research and development. The detection and classification of power quality disturbances through discrete wavelet transform (DWT) and machine learning is a promising approach that can improve the accuracy and efficiency of power quality analysis. DWT is a powerful signal processing technique that can decompose complex signals into different frequency bands, allowing for the identification of various types of power quality disturbances, such as voltage sags, swells, and interruptions. Supervised machine learning algorithms such as Decision Tree, SVM, KNN and Adaboost, can then be used to classify these disturbances based on their features extracted from the DWT coefficients. This paper detects and classify PQD’s using DWT and machine learning and discusses the advantages and limitations of this approach. It also provides insights into the future research directions in this area, such as the development of more sophisticated machine learning models and the integration of real-time monitoring and control systems. Overall, this paper highlights the potential of using DWT and machine learning for power quality analysis and its relevance to the development of smart grid technologies.
{"title":"Detection and Classification of Changes in Voltage Magnitude During Various Power Quality Disturbances","authors":"S. Joga, S. Surisetti, S. Karri, Shaik Jalaluddin, Konatala Madhu, J. Shiva","doi":"10.1109/INCET57972.2023.10170211","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170211","url":null,"abstract":"Power quality refers to the characteristics of the electrical power supply that affect the performance, reliability, and safety of electrical equipment. With the growing demand for reliable and efficient power supply, power quality has become an important area of research and development. The detection and classification of power quality disturbances through discrete wavelet transform (DWT) and machine learning is a promising approach that can improve the accuracy and efficiency of power quality analysis. DWT is a powerful signal processing technique that can decompose complex signals into different frequency bands, allowing for the identification of various types of power quality disturbances, such as voltage sags, swells, and interruptions. Supervised machine learning algorithms such as Decision Tree, SVM, KNN and Adaboost, can then be used to classify these disturbances based on their features extracted from the DWT coefficients. This paper detects and classify PQD’s using DWT and machine learning and discusses the advantages and limitations of this approach. It also provides insights into the future research directions in this area, such as the development of more sophisticated machine learning models and the integration of real-time monitoring and control systems. Overall, this paper highlights the potential of using DWT and machine learning for power quality analysis and its relevance to the development of smart grid technologies.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126591003","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170020
Kaki Ramprasad, A. Raganna, Nagendra B R, Prashanth A R, M. M, G. P.
Accelerometer is a sensor that converts mechanical vibrations into electrical signals. During vibration tests of spacecraft and its subsystems, accelerometer is a primary device to measure the level of vibrations. As vibration tests are potentially destructive in nature, control accelerometer plays a vital role during closed-loop vibration tests. A deviation in the control accelerometers’ output will cause over test and under test of the test specimen. In this regard, accelerometers are required to perform with predefined accuracy and range under extreme environmental conditions. For this purpose, accelerometers are required to be calibrated every year to confirm performance of the accelerometer. This paper proposes a Closed Loop Amplitude Control Automated Accelerometer Calibration System as per International Standard for Organization (ISO) 16063 PART 21. This system was designed and realized to meet the above standard. Lab View was used to incorporate the proposed system’s GUI features, computations, and automation.
{"title":"Design and Realization of Closed Loop Amplitude Control Automated Accelerometer Calibration System","authors":"Kaki Ramprasad, A. Raganna, Nagendra B R, Prashanth A R, M. M, G. P.","doi":"10.1109/INCET57972.2023.10170020","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170020","url":null,"abstract":"Accelerometer is a sensor that converts mechanical vibrations into electrical signals. During vibration tests of spacecraft and its subsystems, accelerometer is a primary device to measure the level of vibrations. As vibration tests are potentially destructive in nature, control accelerometer plays a vital role during closed-loop vibration tests. A deviation in the control accelerometers’ output will cause over test and under test of the test specimen. In this regard, accelerometers are required to perform with predefined accuracy and range under extreme environmental conditions. For this purpose, accelerometers are required to be calibrated every year to confirm performance of the accelerometer. This paper proposes a Closed Loop Amplitude Control Automated Accelerometer Calibration System as per International Standard for Organization (ISO) 16063 PART 21. This system was designed and realized to meet the above standard. Lab View was used to incorporate the proposed system’s GUI features, computations, and automation.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126360402","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}
Brain tumors account for having the lowest survival rate and being the most fatal cancer in the world. This makes detection and early diagnosis of the same to be of utmost importance. Classification of tumors depends on the shape, size, texture, and location. Magnetic Resonance Images (MRI) prove to be the most effective technique for distinguishing tumors. The main aim of the proposed work is to capture the distribution of unique features from the input MRI dataset images. These images are then synthesized using a generative model which classifies the dataset to detect the presence of a tumour in brain. Deep learning algorithms such as Convolutional Neural Network (CNN) help in classification of the different tumours. The proposed model is experimentally evaluated on three datasets. The suggested methods provide for the successful comparison and convincing performance. An accuracy of 98.02% was achieved with ResNet50 architecture and 98.32% with Xception architecture.
{"title":"Brain Tumor Detection and Classification Using Deep Learning Approaches","authors":"Ankitha G, Hafsa Tuba J, Akhilesh J, Archana Bhanu, Naveen Ig","doi":"10.1109/INCET57972.2023.10169933","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10169933","url":null,"abstract":"Brain tumors account for having the lowest survival rate and being the most fatal cancer in the world. This makes detection and early diagnosis of the same to be of utmost importance. Classification of tumors depends on the shape, size, texture, and location. Magnetic Resonance Images (MRI) prove to be the most effective technique for distinguishing tumors. The main aim of the proposed work is to capture the distribution of unique features from the input MRI dataset images. These images are then synthesized using a generative model which classifies the dataset to detect the presence of a tumour in brain. Deep learning algorithms such as Convolutional Neural Network (CNN) help in classification of the different tumours. The proposed model is experimentally evaluated on three datasets. The suggested methods provide for the successful comparison and convincing performance. An accuracy of 98.02% was achieved with ResNet50 architecture and 98.32% with Xception architecture.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127831676","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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