Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716699
Puja Banerjee, Susmita Banerjee, R. P. Barnwal
In deep learning solutions there has been a lot of ambiguity about how to make explainability inclusive of a machine learning pipeline. Recently, several deep learning techniques have been introduced to solve increasingly complicated problems with higher predictive capacity. However, this predictive power comes at the cost of high computational complexity and difficult to interpret. While these models often produce very accurate predictions, we need to be able to explain the path followed by such models for decision making. Deep learning models, in general, predict with no or very less interpretable explanations. This lack of explainability makes such models blackbox. Explainable Artificial Intelligence (XAI) aims at transforming this black box approach into a more interpretable one. In this paper, we apply the well known Grad-CAM technique for the explainability of tea-leaf classification problem. The proposed method classifies tea-leaf-bud combinations using pre-trained deep learning models. We add classification explainability in our tea-leaf dataset using the pre-trained model as an input to the Grad-CAM technique to produce class-specific heatmap. We analyzed the results and working of the classification models for their reliability and effectiveness.
{"title":"Explaining deep-learning models using gradient-based localization for reliable tea-leaves classifications","authors":"Puja Banerjee, Susmita Banerjee, R. P. Barnwal","doi":"10.1109/ICAECC54045.2022.9716699","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716699","url":null,"abstract":"In deep learning solutions there has been a lot of ambiguity about how to make explainability inclusive of a machine learning pipeline. Recently, several deep learning techniques have been introduced to solve increasingly complicated problems with higher predictive capacity. However, this predictive power comes at the cost of high computational complexity and difficult to interpret. While these models often produce very accurate predictions, we need to be able to explain the path followed by such models for decision making. Deep learning models, in general, predict with no or very less interpretable explanations. This lack of explainability makes such models blackbox. Explainable Artificial Intelligence (XAI) aims at transforming this black box approach into a more interpretable one. In this paper, we apply the well known Grad-CAM technique for the explainability of tea-leaf classification problem. The proposed method classifies tea-leaf-bud combinations using pre-trained deep learning models. We add classification explainability in our tea-leaf dataset using the pre-trained model as an input to the Grad-CAM technique to produce class-specific heatmap. We analyzed the results and working of the classification models for their reliability and effectiveness.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124590838","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}
Intrusion refers to any malicious activity done in order to access confidential data. An intrusion detection system (IDS) detects these attacks and, on detection, it reports them to the administrator. It does so either by comparing the new activity with the past activities or by analyzing the network performance. This system forms a part of the vast security module and works with several other such sub-modules in order to make sure that these unwanted intrusions do not go unreported. The system that has been implemented in this paper is an anomaly-based Intrusion Detection System (IDS). The primary purpose of this implementation is to develop an efficient system in order to detect any external or internal unauthenticated activity. Several models have been experimented with in order to find one that suits the system the best and gives a good enough accuracy. The models that have been experimented with include Logistic Regressor, Random Forest Classifier, K Nearest Neighbor classifier, XGBoost Classifier, Gaussian Naive Bayes Classifier and a Multi-Layer Perceptron Classifier (MLP). Further, the accuracy of each of these models was calculated, and a comparative analysis was done between the performance of these models. The model that performed the best in this particular use case was the Random Forest Classifier giving an accuracy of 99.8% and a macro average F1-Score of 0.98.
{"title":"Machine Learning Based Intrusion Detection","authors":"Shivam Kejriwal, Devika Patadia, Saloni Dagli, Prachi Tawde","doi":"10.1109/ICAECC54045.2022.9716648","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716648","url":null,"abstract":"Intrusion refers to any malicious activity done in order to access confidential data. An intrusion detection system (IDS) detects these attacks and, on detection, it reports them to the administrator. It does so either by comparing the new activity with the past activities or by analyzing the network performance. This system forms a part of the vast security module and works with several other such sub-modules in order to make sure that these unwanted intrusions do not go unreported. The system that has been implemented in this paper is an anomaly-based Intrusion Detection System (IDS). The primary purpose of this implementation is to develop an efficient system in order to detect any external or internal unauthenticated activity. Several models have been experimented with in order to find one that suits the system the best and gives a good enough accuracy. The models that have been experimented with include Logistic Regressor, Random Forest Classifier, K Nearest Neighbor classifier, XGBoost Classifier, Gaussian Naive Bayes Classifier and a Multi-Layer Perceptron Classifier (MLP). Further, the accuracy of each of these models was calculated, and a comparative analysis was done between the performance of these models. The model that performed the best in this particular use case was the Random Forest Classifier giving an accuracy of 99.8% and a macro average F1-Score of 0.98.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125998530","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 : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716668
J. J. Imaculate, T. Bobby
The most frequent type of cancer in humans is the skin cancer and it can be lethal. It affects in copious forms such as basal, melanoma, and squamous cell carcinoma. Among these, melanoma case is severe, most dangerous and unpredictable. When it is diagnosed in the early stages, it can be controlled and cured considerably. Thus, a novel computational approach using texture feature fusion and machine learning techniques is proposed to diagnose and classify the skin lesions as benign or malignant. The workflow of this approach is preprocessing for noise and hair strands removal, segmentation of the cancer affected region, validation of the segmentation methods, statistical feature extraction, principle feature selection, classification as benign or malignant and performance estimation of the classifier algorithm. The Otsu thresholding, enhanced Otsu thresholding and watershed segmentation methods are implemented and the segmented images are validated using the Jaccard index and Dice index. Further, several features derived from texture, colour, and shape of the segmented images are fused and fed to the variants of the Support Vector Machine (SVM) classifier after the significant features selection process and the performance of the classifiers are evaluated. The results show that cubic SVM classifier (98%, 100%, and 99%) and Fine Gaussian SVM classifier (100%, 100% and 100%) performs well in terms of sensitivity, specificity and accuracy for the considered image dataset. Hence, the proposed method can be used for early detection classification of melanoma.
{"title":"Detection of Skin Cancer Using Bi-Directional Emperical Mode Decomposition and GLCM","authors":"J. J. Imaculate, T. Bobby","doi":"10.1109/ICAECC54045.2022.9716668","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716668","url":null,"abstract":"The most frequent type of cancer in humans is the skin cancer and it can be lethal. It affects in copious forms such as basal, melanoma, and squamous cell carcinoma. Among these, melanoma case is severe, most dangerous and unpredictable. When it is diagnosed in the early stages, it can be controlled and cured considerably. Thus, a novel computational approach using texture feature fusion and machine learning techniques is proposed to diagnose and classify the skin lesions as benign or malignant. The workflow of this approach is preprocessing for noise and hair strands removal, segmentation of the cancer affected region, validation of the segmentation methods, statistical feature extraction, principle feature selection, classification as benign or malignant and performance estimation of the classifier algorithm. The Otsu thresholding, enhanced Otsu thresholding and watershed segmentation methods are implemented and the segmented images are validated using the Jaccard index and Dice index. Further, several features derived from texture, colour, and shape of the segmented images are fused and fed to the variants of the Support Vector Machine (SVM) classifier after the significant features selection process and the performance of the classifiers are evaluated. The results show that cubic SVM classifier (98%, 100%, and 99%) and Fine Gaussian SVM classifier (100%, 100% and 100%) performs well in terms of sensitivity, specificity and accuracy for the considered image dataset. Hence, the proposed method can be used for early detection classification of melanoma.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126784886","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 : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716637
Yagan Arun, G. S. Viknesh
Automatic plant species classification has always been a great challenge. Classical machine learning methods have been used to classify leaves using handcrafted features from the morphology of plant leaves which has given promising results. However, we focus on using non-handcrafted features of plant leaves for classification. So, to achieve it, we utilize a deep learning approach for feature extraction and classification of features. Recently Deep Convolution Neural Networks have shown remarkable results in image classification and object detection-based problems. With the help of the transfer learning approach, we explore and compare a set of pre-trained networks and define the best classifier. That set consists of eleven different pre-trained networks loaded with ImageNet weights: AlexNet, EfficientNet BO to B7, ResNet50, and Xception. These models are trained on the plant leaf image data set, consisting of leaf images from eleven different unique plant species. It was found that EfficientNet-B5 performed better in classifying leaf images compared to other pre-trained models. Automatic plant species classification could be helpful for food engineers, people related to agriculture, researchers, and ordinary people.
{"title":"Leaf Classification for Plant Recognition Using EfficientNet Architecture","authors":"Yagan Arun, G. S. Viknesh","doi":"10.1109/ICAECC54045.2022.9716637","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716637","url":null,"abstract":"Automatic plant species classification has always been a great challenge. Classical machine learning methods have been used to classify leaves using handcrafted features from the morphology of plant leaves which has given promising results. However, we focus on using non-handcrafted features of plant leaves for classification. So, to achieve it, we utilize a deep learning approach for feature extraction and classification of features. Recently Deep Convolution Neural Networks have shown remarkable results in image classification and object detection-based problems. With the help of the transfer learning approach, we explore and compare a set of pre-trained networks and define the best classifier. That set consists of eleven different pre-trained networks loaded with ImageNet weights: AlexNet, EfficientNet BO to B7, ResNet50, and Xception. These models are trained on the plant leaf image data set, consisting of leaf images from eleven different unique plant species. It was found that EfficientNet-B5 performed better in classifying leaf images compared to other pre-trained models. Automatic plant species classification could be helpful for food engineers, people related to agriculture, researchers, and ordinary people.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124818088","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 : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716641
S. Ramasamy, V. Chandrasekar, A. M. Viswa Bharathy
The symptoms associated with deficiencies in plants tends to appear often on the leaves. The color and shape of a leaf often used for diagnosing the nutritional deficiencies in plants and classification of these properties often pose serious problem. Since same color and shape may have many root cause problems. It is hence necessary to carefully analyze the texture of leaf with proper training of a classifier. In this paper, we design an acquisition-based classification model that utilizes Internet of Things (IoTs) for data acquisition and recurrent neural networks (RNN) for the task of classification. Prior classification, the model is trained over several iteration based on careful observation of features and its related symptoms. The simulation is conducted with fine-tuning of classification after several iterations. The results of simulation show that the proposed method obtains improved classification accuracy in terms of accuracy and F-measure than other deep learning models.
{"title":"Classification of Nutrient Deficiencies in Plants Using Recurrent Neural Network","authors":"S. Ramasamy, V. Chandrasekar, A. M. Viswa Bharathy","doi":"10.1109/ICAECC54045.2022.9716641","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716641","url":null,"abstract":"The symptoms associated with deficiencies in plants tends to appear often on the leaves. The color and shape of a leaf often used for diagnosing the nutritional deficiencies in plants and classification of these properties often pose serious problem. Since same color and shape may have many root cause problems. It is hence necessary to carefully analyze the texture of leaf with proper training of a classifier. In this paper, we design an acquisition-based classification model that utilizes Internet of Things (IoTs) for data acquisition and recurrent neural networks (RNN) for the task of classification. Prior classification, the model is trained over several iteration based on careful observation of features and its related symptoms. The simulation is conducted with fine-tuning of classification after several iterations. The results of simulation show that the proposed method obtains improved classification accuracy in terms of accuracy and F-measure than other deep learning models.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116325042","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 : 2022-01-10DOI: 10.1109/icaecc54045.2022.9716701
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icaecc54045.2022.9716701","DOIUrl":"https://doi.org/10.1109/icaecc54045.2022.9716701","url":null,"abstract":"","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116183680","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 : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716640
Ashima Yadav, Debajyoti Mukhopadhyay
The recent outbreak of coronavirus has impacted the whole world. The infectious respiratory disease has killed millions of people all over the world. The process of detecting the disease through RT-PCR and other tests is very time-consuming, and testing kits are not widely available. Chest x-rays and chest CT scans are also very effective techniques for diagnosing respiratory diseases. This paper proposes a DeepAttentiveNet, a deep-based architecture that applies the pre-trained CNN-based architecture DenseNet to extract the spatial features from the images. This is followed by the attention mechanism, which focuses on the information-rich region on the images, thus enhancing the overall classification process. The performance of our model is analyzed on the COVID 19 Radiography dataset, which contains 21,000 x-ray images corresponding to different respiratory infections like COVID 19, lung opacity, and viral pneumonia. Hence our model can categorize the x-rays with a 97.1% F1 score and 97.5% accuracy. We have also compared our architecture with other popular CNN-based models and baseline methods to demonstrate the superior performance of the model.
{"title":"DeepAttentiveNet: An automated deep based method for COVID-19 diagnosis based on chest x-rays","authors":"Ashima Yadav, Debajyoti Mukhopadhyay","doi":"10.1109/ICAECC54045.2022.9716640","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716640","url":null,"abstract":"The recent outbreak of coronavirus has impacted the whole world. The infectious respiratory disease has killed millions of people all over the world. The process of detecting the disease through RT-PCR and other tests is very time-consuming, and testing kits are not widely available. Chest x-rays and chest CT scans are also very effective techniques for diagnosing respiratory diseases. This paper proposes a DeepAttentiveNet, a deep-based architecture that applies the pre-trained CNN-based architecture DenseNet to extract the spatial features from the images. This is followed by the attention mechanism, which focuses on the information-rich region on the images, thus enhancing the overall classification process. The performance of our model is analyzed on the COVID 19 Radiography dataset, which contains 21,000 x-ray images corresponding to different respiratory infections like COVID 19, lung opacity, and viral pneumonia. Hence our model can categorize the x-rays with a 97.1% F1 score and 97.5% accuracy. We have also compared our architecture with other popular CNN-based models and baseline methods to demonstrate the superior performance of the model.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116783344","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 : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716714
Hanumant Mule, Namrata Kadam, D. Naik
Nowadays, Storing information from handwritten documents for future use is becoming necessary. An easy way to store information is to capture handwritten documents and save them in image format. Recognizing the text or characters present in the image is called Optical Character Recognition. Text extraction from the image in the recent research is challenging due to stroke variation, inconsistent writing style, Cursive handwriting, etc. We have proposed CNN and BiLSTM models for text recognition in this work. This model is evaluated on the IAM dataset and achieved 92% character recognition accuracy. This model is deployed to the Firebase as a custom model to increase usability. We have developed an android application that will allow the user to capture or browse the image and extract the text from the picture by calling the firebase model and saving text in the file. To store the text file user can browse for the appropriate location. The proposed model works on both printed and handwritten text.
{"title":"Handwritten Text Recognition from an Image with Android Application","authors":"Hanumant Mule, Namrata Kadam, D. Naik","doi":"10.1109/ICAECC54045.2022.9716714","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716714","url":null,"abstract":"Nowadays, Storing information from handwritten documents for future use is becoming necessary. An easy way to store information is to capture handwritten documents and save them in image format. Recognizing the text or characters present in the image is called Optical Character Recognition. Text extraction from the image in the recent research is challenging due to stroke variation, inconsistent writing style, Cursive handwriting, etc. We have proposed CNN and BiLSTM models for text recognition in this work. This model is evaluated on the IAM dataset and achieved 92% character recognition accuracy. This model is deployed to the Firebase as a custom model to increase usability. We have developed an android application that will allow the user to capture or browse the image and extract the text from the picture by calling the firebase model and saving text in the file. To store the text file user can browse for the appropriate location. The proposed model works on both printed and handwritten text.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123772946","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 : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716680
Venu Yarlagadda, G. Lakshminarayana, M. Nagajyothi, I. Neelima
Modern Power systems are designed for the fine tuning of frequency and less tolerance for system frequency deviation from nominal value. The Power System is dynamically subjected to the small perturbations of load leading to non-oscillatory Instability due to insufficient damping. The article entente the single area and two area load frequency control and small signal stability analysis. It dispenses the simulation of single area and two area systems with small perturbations of load, with three cases for both kinds of power systems. Case1 without any controller, case2 with PI controller and case3 with Fuzzy Controllers. The simulation is carried out for both single area and two area power systems with all three cases. In the first part of the case study, the simulation results of single area power system for all three cases have been presented. In the second part, the simulation results of two area power system for all three cases have been presented. The simulation results demonstrate the effectiveness of Fuzzy Control perpetuates the frequency with in the endurable range of frequency and subsequently it ensures the small signal stability of both the Power systems against load disturbances.
{"title":"Frequency Control and small signal stability Improvement with Fuzzy control based Single and two area power systems","authors":"Venu Yarlagadda, G. Lakshminarayana, M. Nagajyothi, I. Neelima","doi":"10.1109/ICAECC54045.2022.9716680","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716680","url":null,"abstract":"Modern Power systems are designed for the fine tuning of frequency and less tolerance for system frequency deviation from nominal value. The Power System is dynamically subjected to the small perturbations of load leading to non-oscillatory Instability due to insufficient damping. The article entente the single area and two area load frequency control and small signal stability analysis. It dispenses the simulation of single area and two area systems with small perturbations of load, with three cases for both kinds of power systems. Case1 without any controller, case2 with PI controller and case3 with Fuzzy Controllers. The simulation is carried out for both single area and two area power systems with all three cases. In the first part of the case study, the simulation results of single area power system for all three cases have been presented. In the second part, the simulation results of two area power system for all three cases have been presented. The simulation results demonstrate the effectiveness of Fuzzy Control perpetuates the frequency with in the endurable range of frequency and subsequently it ensures the small signal stability of both the Power systems against load disturbances.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127427682","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 : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716619
U. S. Babu, A. Raganna, K.N. Vidyasagar, S. Bharati, Gautam Kumar
In this work, we propose a deep convolutional neural network (DCNN) based model for static hand gestures recognition. Static hand gesture images corresponding to five different classes are presented to DCNN model without any preprocessing. The model has achieved a train and test accuracy of 97.9% and 99.6% respectively which is one of the best ever reported accuracy in static hand gesture recognition applications. It is also found that the performance of the model is good even with complex backgrounds and poor lighting conditions. Due to its accuracy and robustness, this model can be implemented in applications such as human machine interaction and autonomous cars.
{"title":"Highly Accurate Static Hand Gesture Recognition Model Using Deep Convolutional Neural Network for Human Machine Interaction","authors":"U. S. Babu, A. Raganna, K.N. Vidyasagar, S. Bharati, Gautam Kumar","doi":"10.1109/ICAECC54045.2022.9716619","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716619","url":null,"abstract":"In this work, we propose a deep convolutional neural network (DCNN) based model for static hand gestures recognition. Static hand gesture images corresponding to five different classes are presented to DCNN model without any preprocessing. The model has achieved a train and test accuracy of 97.9% and 99.6% respectively which is one of the best ever reported accuracy in static hand gesture recognition applications. It is also found that the performance of the model is good even with complex backgrounds and poor lighting conditions. Due to its accuracy and robustness, this model can be implemented in applications such as human machine interaction and autonomous cars.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114392576","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：