Pub Date : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028917
Satabdi Barun Paul, S. Mishra
Environmental Awareness is a necessity in today’s world. With the insane amount of neglect and carelessness shown by private and public bodies, there are several issues - climate change, deforestation, etc. - that needs attention. EcoKonnect is proposed with the goal to create a mindful community of environmentalists (ecotizens) and help change daily habits of the user. It is a social networking app that visualises the carbon footprint left by several parts of everyday living; mainly food consumption, housing and public transportation, and encourages users to reduce it. In today’s atmosphere of resource depletion and human responsibility in environmental destruction, this app is a lifesaver. The frontend was built with React, and the backend with Firebase. We discovered that using EcoKonnect will also assist in the creation of a market for eco-friendly brands. It will solve a major marketing problem for sustainable brands. EcoKonnect’s technology and design are user-friendly and simple to use for all types of users. To ensure cost-effectiveness, some of the entities used are recommended to be third-party integrations.
{"title":"EcoKonnect: A Social Network for Environmentalist","authors":"Satabdi Barun Paul, S. Mishra","doi":"10.1109/SILCON55242.2022.10028917","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028917","url":null,"abstract":"Environmental Awareness is a necessity in today’s world. With the insane amount of neglect and carelessness shown by private and public bodies, there are several issues - climate change, deforestation, etc. - that needs attention. EcoKonnect is proposed with the goal to create a mindful community of environmentalists (ecotizens) and help change daily habits of the user. It is a social networking app that visualises the carbon footprint left by several parts of everyday living; mainly food consumption, housing and public transportation, and encourages users to reduce it. In today’s atmosphere of resource depletion and human responsibility in environmental destruction, this app is a lifesaver. The frontend was built with React, and the backend with Firebase. We discovered that using EcoKonnect will also assist in the creation of a market for eco-friendly brands. It will solve a major marketing problem for sustainable brands. EcoKonnect’s technology and design are user-friendly and simple to use for all types of users. To ensure cost-effectiveness, some of the entities used are recommended to be third-party integrations.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129581609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028938
S. Simon, L. Dewan, M. Prasad
Robust control is one of the most evergreen control techniques which has practical significance in actual engineering applications primarily because almost all systems have uncertainties and to deal such uncertain systems there is no better control than robust techniques. In this regard PID controllers have been a long time favourite in the industry but for robustness, suitable tuning of PID controller is essential. This has always been a subject of interest and been evolving ever since its inception. In this paper a well established technique of Integral Time Absolute Error (ITAE) has been used to develop a robust PID controller for a brushed DC motor speed control. The DC motor model is first analysed using LFT technique to obtain the various system transfer functions. Later the robustness of the controller is experimented and established through simulations for parametric variations.
{"title":"Design and Analysis of ITAE Tuned Robust PID Controller for Brushed DC Motor","authors":"S. Simon, L. Dewan, M. Prasad","doi":"10.1109/SILCON55242.2022.10028938","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028938","url":null,"abstract":"Robust control is one of the most evergreen control techniques which has practical significance in actual engineering applications primarily because almost all systems have uncertainties and to deal such uncertain systems there is no better control than robust techniques. In this regard PID controllers have been a long time favourite in the industry but for robustness, suitable tuning of PID controller is essential. This has always been a subject of interest and been evolving ever since its inception. In this paper a well established technique of Integral Time Absolute Error (ITAE) has been used to develop a robust PID controller for a brushed DC motor speed control. The DC motor model is first analysed using LFT technique to obtain the various system transfer functions. Later the robustness of the controller is experimented and established through simulations for parametric variations.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129651214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028828
Sonali Sen, Manjistha Paul, Rajarshi SinhaRoy, Chayan Kumar Sengupta
For over a century, fingerprint analysis has been used to solve crimes, identify and track offenders. A common problem during the identification of a fingerprint is overlapped portion identification. This work aims to separate an overlapped fingerprint first and then identify the two separated fingerprints. In this approach, the parameters from a fingerprint are extracted and then represents with a graph based model using an adjacency matrix. Neighbour-finding algorithm has been applied to the matrix to track the ridge bifurcations. The proposed work aims to suggest the matrix representation of a sample with overlapping varies by a deviation factor from a non-overlapping segment. As a result, it can be made a concerted effort to sort out the overlapped fingerprints before relying on fingerprint matching in any significant way. The proposed graph-based approach successfully separated the overlapped portion and then match both the fingerprints.
{"title":"Overlapped Fingerprint Separation using Graph based Model","authors":"Sonali Sen, Manjistha Paul, Rajarshi SinhaRoy, Chayan Kumar Sengupta","doi":"10.1109/SILCON55242.2022.10028828","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028828","url":null,"abstract":"For over a century, fingerprint analysis has been used to solve crimes, identify and track offenders. A common problem during the identification of a fingerprint is overlapped portion identification. This work aims to separate an overlapped fingerprint first and then identify the two separated fingerprints. In this approach, the parameters from a fingerprint are extracted and then represents with a graph based model using an adjacency matrix. Neighbour-finding algorithm has been applied to the matrix to track the ridge bifurcations. The proposed work aims to suggest the matrix representation of a sample with overlapping varies by a deviation factor from a non-overlapping segment. As a result, it can be made a concerted effort to sort out the overlapped fingerprints before relying on fingerprint matching in any significant way. The proposed graph-based approach successfully separated the overlapped portion and then match both the fingerprints.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128671017","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 growing field of Artificial Intelligence research necessitates the development of non-standard bit-width number format arithmetic hardware units to improve the energy efficiency of the underlying hardware. However, building these hardware units using hardware description language is error-prone. It is difficult to catch these errors in the early design stage without having the proper tools or instruments to cross-check the results. Furthermore, floating-point hardware designs contain many stages by which the final result is calculated; therefore, it is essential to identify the erroneous stage for debugging. This paper proposes an easy-to-use Python library for IEEE-754-based floating-point numbers with arbitrary exponent and mantissa width. This library provides not only the result for cross-checking HDL results but also debugging the hardware’s intermediate stage results for easier and faster development. The support of this module in converting the numbers to and fro from decimal to binary makes it ideal to use it as a full-fledged calculator to perform the complex arithmetic in the required format and debugger in binary form for the development of hardware to perform these computations on.
{"title":"Floating-Point Hardware Design: A Test Perspective","authors":"T.K.R Arvind, Ashish Reddy Bommana, Srinivas Boppu","doi":"10.1109/SILCON55242.2022.10028826","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028826","url":null,"abstract":"The growing field of Artificial Intelligence research necessitates the development of non-standard bit-width number format arithmetic hardware units to improve the energy efficiency of the underlying hardware. However, building these hardware units using hardware description language is error-prone. It is difficult to catch these errors in the early design stage without having the proper tools or instruments to cross-check the results. Furthermore, floating-point hardware designs contain many stages by which the final result is calculated; therefore, it is essential to identify the erroneous stage for debugging. This paper proposes an easy-to-use Python library for IEEE-754-based floating-point numbers with arbitrary exponent and mantissa width. This library provides not only the result for cross-checking HDL results but also debugging the hardware’s intermediate stage results for easier and faster development. The support of this module in converting the numbers to and fro from decimal to binary makes it ideal to use it as a full-fledged calculator to perform the complex arithmetic in the required format and debugger in binary form for the development of hardware to perform these computations on.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127926347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028950
Yagnyasenee Sen Gupta, S. Mukherjee
One of the major challenges faced by the recycling industry is waste segregation. Unsegregated wastes are not favorable for the environment and manual segregation is quite harmful to the health of the engaged workforce. Therefore, this paper aims to propose an efficient waste classification model to classify and identify the different types of waste. Convolution Neural Network-based models such as VGG16, MobileNetV2, In-ceptionV3, DenseNet201, and ResNet152V2, trained on ImageNet have been considered for the weighted average-based ensembling technique to classify waste images. Five approaches based on accuracy, specificity, precision, recall, and F1-score are used to calculate the weight of each model to evaluate the performance metrics of the proposed model. The F1-based approach for weight calculation of the models outperforms the other existing CNN models by achieving an average performance of 93.881%.
{"title":"An Ensembling Approach for Efficient Waste Classification","authors":"Yagnyasenee Sen Gupta, S. Mukherjee","doi":"10.1109/SILCON55242.2022.10028950","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028950","url":null,"abstract":"One of the major challenges faced by the recycling industry is waste segregation. Unsegregated wastes are not favorable for the environment and manual segregation is quite harmful to the health of the engaged workforce. Therefore, this paper aims to propose an efficient waste classification model to classify and identify the different types of waste. Convolution Neural Network-based models such as VGG16, MobileNetV2, In-ceptionV3, DenseNet201, and ResNet152V2, trained on ImageNet have been considered for the weighted average-based ensembling technique to classify waste images. Five approaches based on accuracy, specificity, precision, recall, and F1-score are used to calculate the weight of each model to evaluate the performance metrics of the proposed model. The F1-based approach for weight calculation of the models outperforms the other existing CNN models by achieving an average performance of 93.881%.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130386677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028886
Harsha K, H. I, Sumi M
Antennas are one of the most essential components of any wireless communication system. As wireless communication has advanced, the same development has been seen in antenna designs as well. The requirement for large bandwidth, high gain, multi-band antenna, and miniaturization of antenna size is increased. Metamaterials can be used to increase the performance of antennas either by enhancing parameters such as bandwidth or power gain or by creating compact, multi-frequency band antennas. This paper presents the review of the antenna performance enhancement by incorporating metamate-rials in different ways into antenna design.
{"title":"Metamaterial for Antenna Performance Enhancement: A Review","authors":"Harsha K, H. I, Sumi M","doi":"10.1109/SILCON55242.2022.10028886","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028886","url":null,"abstract":"Antennas are one of the most essential components of any wireless communication system. As wireless communication has advanced, the same development has been seen in antenna designs as well. The requirement for large bandwidth, high gain, multi-band antenna, and miniaturization of antenna size is increased. Metamaterials can be used to increase the performance of antennas either by enhancing parameters such as bandwidth or power gain or by creating compact, multi-frequency band antennas. This paper presents the review of the antenna performance enhancement by incorporating metamate-rials in different ways into antenna design.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133487520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028821
Vikas Singh, A. Pati, V. C. Pal
The contrast of an image plays a crucial role in distinguishing the object from the other objects and the background, especially for analyzing and diagnosing essential information. Contrast enhancement improves the quality of the image for any subjective evaluation. This paper presents novel fuzzy- based techniques for contrast enhancement. In the developed method, absolute luminance difference (ALD) have utilized to decide the threshold for contrast enhancement. We have used Gaussian fuzzy membership function to find the appropriate weight corresponding to the pixels. The mean of the Gaussian membership function is determined using the means of k-middle, and the variance of the MF in a window is evaluated by taking the average deviation from the mean. The proposed approach is validated on the standard datasets and compared with various state-of-the-art methods.
{"title":"Adaptive Fuzzy Approach for Image Contrast Enhancement","authors":"Vikas Singh, A. Pati, V. C. Pal","doi":"10.1109/SILCON55242.2022.10028821","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028821","url":null,"abstract":"The contrast of an image plays a crucial role in distinguishing the object from the other objects and the background, especially for analyzing and diagnosing essential information. Contrast enhancement improves the quality of the image for any subjective evaluation. This paper presents novel fuzzy- based techniques for contrast enhancement. In the developed method, absolute luminance difference (ALD) have utilized to decide the threshold for contrast enhancement. We have used Gaussian fuzzy membership function to find the appropriate weight corresponding to the pixels. The mean of the Gaussian membership function is determined using the means of k-middle, and the variance of the MF in a window is evaluated by taking the average deviation from the mean. The proposed approach is validated on the standard datasets and compared with various state-of-the-art methods.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133506061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028912
Zakir Hussain, M. Borah
Drug resistance is increasing with a very fast rate now-a-days. There are a lot of reasons that lead to drug resistance in our body. But, the most common reasons are Genetic conditions, Mutation of the pathogen, Side effects of other medications, and Medicinal abuse. Nutritional status of our body has significant role in combating the development of drug resistance. In this study, we use a computational aspect to analyse the impact of nutritional status on drug resistance. We use all the three situations of nutritional status like under-nutrition, normal-nutrition, and over-nutrition in the formulation of equations to relate drug resistance. Our experimental results show that the situation of under-nutrition sharply boosts the development of drug resistance, normal-nutrition deliberately controls the drug resistance while over-nutrition boost the drug resistance but it is better compared to under-nutrition. These results clearly show the impact of nutritional status on drug resistance at par clinical expectations.
{"title":"A Computational Aspect to Analyse Impact of Nutritional Status on Drug Resistance","authors":"Zakir Hussain, M. Borah","doi":"10.1109/SILCON55242.2022.10028912","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028912","url":null,"abstract":"Drug resistance is increasing with a very fast rate now-a-days. There are a lot of reasons that lead to drug resistance in our body. But, the most common reasons are Genetic conditions, Mutation of the pathogen, Side effects of other medications, and Medicinal abuse. Nutritional status of our body has significant role in combating the development of drug resistance. In this study, we use a computational aspect to analyse the impact of nutritional status on drug resistance. We use all the three situations of nutritional status like under-nutrition, normal-nutrition, and over-nutrition in the formulation of equations to relate drug resistance. Our experimental results show that the situation of under-nutrition sharply boosts the development of drug resistance, normal-nutrition deliberately controls the drug resistance while over-nutrition boost the drug resistance but it is better compared to under-nutrition. These results clearly show the impact of nutritional status on drug resistance at par clinical expectations.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121072456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028934
Joyal Sunny, A. P. E, Renjith H Kumar
In a communication system, it is more challenging to receive a signal at the receiver than it is to transmit one. The receiver’s task and complexity are larger than the transmitter’s because the received signal must travel over a channel where it will be attenuated and distorted. Communication over unstable noisy channels is made possible by channel coding. Channel encoding is done at the transmitter in the baseband domain and at the receiver, it can be effectively retrieved by using a variety of techniques. This study discusses Belief Propagation (BP) and the Min sum technique for decoding the LDPC encoded codewords using Tanner graphs. We also examine a method to decode the encoded data in a communication system, where the decoding algorithm at the receiver is recast as a machine learning process. So a receiver designed using deep learning techniques can always adapt to the changes in the channel optimization techniques and thus reduce the overall computational complexity.
{"title":"Design of Machine learning based Decoding Algorithms for Codes on Graph","authors":"Joyal Sunny, A. P. E, Renjith H Kumar","doi":"10.1109/SILCON55242.2022.10028934","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028934","url":null,"abstract":"In a communication system, it is more challenging to receive a signal at the receiver than it is to transmit one. The receiver’s task and complexity are larger than the transmitter’s because the received signal must travel over a channel where it will be attenuated and distorted. Communication over unstable noisy channels is made possible by channel coding. Channel encoding is done at the transmitter in the baseband domain and at the receiver, it can be effectively retrieved by using a variety of techniques. This study discusses Belief Propagation (BP) and the Min sum technique for decoding the LDPC encoded codewords using Tanner graphs. We also examine a method to decode the encoded data in a communication system, where the decoding algorithm at the receiver is recast as a machine learning process. So a receiver designed using deep learning techniques can always adapt to the changes in the channel optimization techniques and thus reduce the overall computational complexity.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116846796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028896
S. Kulkarni, Rinku Rabidas
Breast cancer is one of the threatening diseases among women throughout the world. The early detection is the only way to cure from cancer. Architectural distortion (AD) is one of the earliest symptoms of breast cancer which is mostly malignant in nature. Computer-aided detection (CAD) and particularly deep learning (DL) gives prominent solution for the detection and diagnosis of breast cancer. This paper presents a deep convolutional neural network (DCNN) architecture designed for the automatic detection of AD in digital mammography images. The proposed deep learning based model consists of series combination of down sampler and ResNet blocks. Due to stacking of these blocks, these layers learn more complex features which help to improved in sensitivity and performance of the model. A total of 150 mammograms are considered for experimentation purpose from publicly available dataset namely, DDSM. Hence the best result obtained in the proposed approach with Leave-One-Out cross validation technique, in terms of true positive rate 86% at 0.42 false positives per image (FPs/I).
{"title":"Detection of Architectural Distortion using Deep Convolutional Neural Network","authors":"S. Kulkarni, Rinku Rabidas","doi":"10.1109/SILCON55242.2022.10028896","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028896","url":null,"abstract":"Breast cancer is one of the threatening diseases among women throughout the world. The early detection is the only way to cure from cancer. Architectural distortion (AD) is one of the earliest symptoms of breast cancer which is mostly malignant in nature. Computer-aided detection (CAD) and particularly deep learning (DL) gives prominent solution for the detection and diagnosis of breast cancer. This paper presents a deep convolutional neural network (DCNN) architecture designed for the automatic detection of AD in digital mammography images. The proposed deep learning based model consists of series combination of down sampler and ResNet blocks. Due to stacking of these blocks, these layers learn more complex features which help to improved in sensitivity and performance of the model. A total of 150 mammograms are considered for experimentation purpose from publicly available dataset namely, DDSM. Hence the best result obtained in the proposed approach with Leave-One-Out cross validation technique, in terms of true positive rate 86% at 0.42 false positives per image (FPs/I).","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121892298","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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