Indonesian Journal of Electrical Engineering and Computer Science
In recent years, metaheuristic methods have shown major advantages in the field of feature select... more In recent years, metaheuristic methods have shown major advantages in the field of feature selection due to its comprehensibility and possible extensive search competence. However, the majority of evolutionary computation-based feature selection algorithms in use today are wrapper approaches, which are expensive to compute, particularly for extensive biomedical data. Developing an effective evaluation strategy is crucial for significant reduction of computational cost. The proposed framework extracts deep feature from ResNet-50 and VGG-16 based convolutional neural models with initial segmentation process based on marker-controlled watershed method. Next the feature reduction is a two-fold approach with principal component analysis applied to reduce the dimensionality of large feature space from convolutional neural network (CNN) models as first step. The second step is optimal feature subset selection using a swarm intelligence method referred as modified grey wolf optimization. Fi...
Indian journal of computer science and engineering, Oct 20, 2022
Lung cancer is identified by the appearance of pulmonary nodules. The use of computer-assisted di... more Lung cancer is identified by the appearance of pulmonary nodules. The use of computer-assisted diagnosis (CAD) and the categorization of such nodules in Computed Tomography(CT) images has improved lung cancer screening. The objective of the proposed model is to segment the nodules using marker controlled watershed algorithm. A novel hybridized feature extraction approach for lung nodule classification based on transfer learning technique. The deep features are extracted by identifying the optimal layers which improved the performance of the classifiers. After using deep learning to extract image features, the principal component analysis algorithm is used to achieve dimensionality reduction. The performance of various machine learning based classifiers were analyzed based on deep features. The statistical findings show that using a deep fused model and supervised classifier algorithm to evaluate lung CT images can be very beneficial. The proposed model achieves higher classification accuracy, precision, recall, and AUC values outperforming other state of art models with an overall accuracy of 94.21%.
Anonymous communications are important for many applications of the mobile ad hoc networks (MANET... more Anonymous communications are important for many applications of the mobile ad hoc networks (MANETs) deployed in adversary environments. A major requirement on the network is to provide unidentifiability and unlink ability for mobile nodes and their traffics. Although a number of anonymous secure routing protocols have been proposed, the requirement is not fully satisfied. The existing protocols are vulnerable to the attacks of fake routing packets or denial-of-service (DoS) broadcasting , even the node identities are protected by pseudonyms. In this paper, we propose a new routing protocol, i.e., Efficient anonymous secure routing (EASR), to satisfy the requirement and defend the attacks. More specifically, the route request packets are authenticated by a group signature, to defend the potential active attacks without unveiling the node identities. The key-encrypted onion routing with a route secret verification message, is designed to prevent intermediate nodes from inferring a rea...
International Journal of Smart Sensor and Adhoc Network., 2013
Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research communi... more Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications in our lives ranging from military applications to civilian ones.. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. In this current paper, we fundamentally focus on the security issue of WSNs and propose a protocol based on public key cryptography for external agent authe...
2018 International Conference on Current Trends towards Converging Technologies (ICCTCT)
Machine Learning, a form of Artificial Intelligence (AI) is infiltrating medical field and it is ... more Machine Learning, a form of Artificial Intelligence (AI) is infiltrating medical field and it is an era where machines can play an important role in health improvement. The idea behind AI in medical field is to enhance doctor's medical expertise. Medical Imaging is experiencing a drastic acceleration with rapid advancement in Machine Learning (ML) techniques. ML plays an important role in the medical imaging field including diagnosis, registration, segmentation and image database retrieval. ML can be applied to medical data repositories that are too large for the human brain to parse. ML promises to cut cost dramatically and deliver more accurate diagnosis than that of a trained physician. The patterns built from large clinical data warehouse can help researchers to draw conclusions and predict an event. This paper presents a comprehensive survey of the state-of-art work on Machine Learning in medical domain, which identifies the contributions of different methods, applications. In addition current issues and challenges are also discussed to identify promising areas of future research.
Indonesian Journal of Electrical Engineering and Computer Science
In recent years, metaheuristic methods have shown major advantages in the field of feature select... more In recent years, metaheuristic methods have shown major advantages in the field of feature selection due to its comprehensibility and possible extensive search competence. However, the majority of evolutionary computation-based feature selection algorithms in use today are wrapper approaches, which are expensive to compute, particularly for extensive biomedical data. Developing an effective evaluation strategy is crucial for significant reduction of computational cost. The proposed framework extracts deep feature from ResNet-50 and VGG-16 based convolutional neural models with initial segmentation process based on marker-controlled watershed method. Next the feature reduction is a two-fold approach with principal component analysis applied to reduce the dimensionality of large feature space from convolutional neural network (CNN) models as first step. The second step is optimal feature subset selection using a swarm intelligence method referred as modified grey wolf optimization. Fi...
Indian journal of computer science and engineering, Oct 20, 2022
Lung cancer is identified by the appearance of pulmonary nodules. The use of computer-assisted di... more Lung cancer is identified by the appearance of pulmonary nodules. The use of computer-assisted diagnosis (CAD) and the categorization of such nodules in Computed Tomography(CT) images has improved lung cancer screening. The objective of the proposed model is to segment the nodules using marker controlled watershed algorithm. A novel hybridized feature extraction approach for lung nodule classification based on transfer learning technique. The deep features are extracted by identifying the optimal layers which improved the performance of the classifiers. After using deep learning to extract image features, the principal component analysis algorithm is used to achieve dimensionality reduction. The performance of various machine learning based classifiers were analyzed based on deep features. The statistical findings show that using a deep fused model and supervised classifier algorithm to evaluate lung CT images can be very beneficial. The proposed model achieves higher classification accuracy, precision, recall, and AUC values outperforming other state of art models with an overall accuracy of 94.21%.
Anonymous communications are important for many applications of the mobile ad hoc networks (MANET... more Anonymous communications are important for many applications of the mobile ad hoc networks (MANETs) deployed in adversary environments. A major requirement on the network is to provide unidentifiability and unlink ability for mobile nodes and their traffics. Although a number of anonymous secure routing protocols have been proposed, the requirement is not fully satisfied. The existing protocols are vulnerable to the attacks of fake routing packets or denial-of-service (DoS) broadcasting , even the node identities are protected by pseudonyms. In this paper, we propose a new routing protocol, i.e., Efficient anonymous secure routing (EASR), to satisfy the requirement and defend the attacks. More specifically, the route request packets are authenticated by a group signature, to defend the potential active attacks without unveiling the node identities. The key-encrypted onion routing with a route secret verification message, is designed to prevent intermediate nodes from inferring a rea...
International Journal of Smart Sensor and Adhoc Network., 2013
Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research communi... more Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications in our lives ranging from military applications to civilian ones.. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. In this current paper, we fundamentally focus on the security issue of WSNs and propose a protocol based on public key cryptography for external agent authe...
2018 International Conference on Current Trends towards Converging Technologies (ICCTCT)
Machine Learning, a form of Artificial Intelligence (AI) is infiltrating medical field and it is ... more Machine Learning, a form of Artificial Intelligence (AI) is infiltrating medical field and it is an era where machines can play an important role in health improvement. The idea behind AI in medical field is to enhance doctor's medical expertise. Medical Imaging is experiencing a drastic acceleration with rapid advancement in Machine Learning (ML) techniques. ML plays an important role in the medical imaging field including diagnosis, registration, segmentation and image database retrieval. ML can be applied to medical data repositories that are too large for the human brain to parse. ML promises to cut cost dramatically and deliver more accurate diagnosis than that of a trained physician. The patterns built from large clinical data warehouse can help researchers to draw conclusions and predict an event. This paper presents a comprehensive survey of the state-of-art work on Machine Learning in medical domain, which identifies the contributions of different methods, applications. In addition current issues and challenges are also discussed to identify promising areas of future research.
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Papers by Rashmi Mothkur