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RISS 인기검색어
- 검색키워드
- 저자 : Abid Sarwar (검색결과 5 건)
- 무료
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A Comparison of Deep Reinforcement Learning and Deep learning for Complex Image Analysis
Rishi Khajuria,Abdul Quyoom,Abid Sarwar 한국멀티미디어학회 2020 The journal of multimedia information system Vol. No.
The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.
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A Comparison of Deep Reinforcement Learning and Deep learning for Complex Image Analysis
Khajuria, Rishi,Quyoom, Abdul,Sarwar, Abid Korea Multimedia Society 2020 The journal of multimedia information system Vol.7 No.1
The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.
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Security Attacks and Challenges of VANETs : A Literature Survey
Abdul Quyoom,Aftab Ahmad Mir,Dr. Abid Sarwar 한국멀티미디어학회 2020 The journal of multimedia information system Vol. No.
This paper presented a brief introduction along with various wireless standards which provide an interactive way of interacti on among the vehicles and provides effective communication in VANET. Security issues such as confidentiality, authenticity, integri ty, availability and non repudiation, which aims to secure communication between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I). A detailed discussion and analysis of various possible attacks based on security services are also presented t hat address security and privacy concern in VANETs. Finally a general analysis of possible challenges is mentioned. This paper can serve as a source and reference in b uilding the new technique for VANETs.
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Security Attacks and Challenges of VANETs : A Literature Survey
Quyoom, Abdul,Mir, Aftab Ahmad,Sarwar, Abid Korea Multimedia Society 2020 The journal of multimedia information system Vol.7 No.1
This paper presented a brief introduction along with various wireless standards which provide an interactive way of interaction among the vehicles and provides effective communication in VANET. Security issues such as confidentiality, authenticity, integrity, availability and non-repudiation, which aims to secure communication between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). A detailed discussion and analysis of various possible attacks based on security services are also presented that address security and privacy concern in VANETs. Finally a general analysis of possible challenges is mentioned. This paper can serve as a source and reference in building the new technique for VANETs.
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( Xiao Zhao ),( Ya-juan Cui ),( Sha-sha Bai ),( Zhi-jie Yang ),( Miao-cai ),( Sarah Megrous ),( Tariq Aziz ),( Abid Sarwar ),( Dong Li ),( Zhen-nai Yang ) 한국미생물 · 생명공학회 2021 Journal of microbiology and biotechnology Vol.31 No.8
Casein-derived antioxidant peptides by using microbial proteases have gained increasing attention. Combination of two microbial proteases, Protin SD-NY10 and Protease A “Amano” 2SD, was employed to hydrolyze casein to obtain potential antioxidant peptides that were identified by LC-MS/MS, chemically synthesized and characterized in a oxidatively damaged HepG2 cell model. Four peptides, YQLD, FSDIPNPIGSEN, FSDIPNPIGSE, YFYP were found to possess high 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging ability. Evaluation with HepG2 cells showed that the 4 peptides at low concentrations (< 1.0 mg/ml) protected the cells against oxidative damage. The 4 peptides exhibited different levels of antioxidant activity by stimulating mRNA and protein expression of the antioxidant enzymes such as superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GSH-Px), as well as nuclear factor erythroid-2-related factor 2 (Nrf2), but decreasing the mRNA expression of Kelch-like ECH-associated protein 1 (Keap1). Furthermore, these peptides decreased production of reactive oxygen species (ROS) and malondialdehyde (MDA), but increased glutathione (GSH) production in HepG2 cells. Therefore, the 4 casein-derived peptides obtained by using microbial proteases exhibited different antioxidant activity by activating the Keap1-Nrf2 signaling pathway, and they could serve as potential antioxidant agents in functional foods or pharmaceutic preparation.
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