DNA Encoding-Based Nucleotide Pattern and Deep Features for Instance and Class-Based Image Retrieval.

Recently, DNA encoding has shown its potential to store the vital information of the image in the form of nucleotides, namely A, C, T , and G , with the entire sequence following run-length and GC-constraint. As a result, the encoded DNA planes contain unique nucleotide strings, giving more salient image information using less storage. In this paper, the advantages of DNA encoding have been inherited to uplift the retrieval accuracy of the content-based image retrieval (CBIR) system. Initially, the most significant bit-plane-based DNA encoding scheme has been suggested to generate DNA planes from a given image. The generated DNA planes of the image efficiently capture the salient visual information in a compact form. Subsequently, the encoded DNA planes have been utilized for nucleotide patterns-based feature extraction and image retrieval. Simultaneously, the translated and amplified encoded DNA planes have also been deployed on different deep learning architectures like ResNet-50, VGG-16, VGG-19, and Inception V3 to perform classification-based image retrieval. The performance of the proposed system has been evaluated using two corals, an object, and a medical image dataset. All these datasets contain 28,200 images belonging to 134 different classes. The experimental results confirm that the proposed scheme achieves perceptible improvements compared with other state-of-the-art methods.

Deep learning techniques for observing the impact of the global warming from satellite images of water-bodies.

Global warming is a threat to modern human civilization. There are different reasons for speed up the global average temperature. The consequences are catastrophic for human existence. Seafloor rise, drought, flood, wildfire, dry riverbed are some of the consequences. This paper analyzes the changes in boundaries of different water bodies such as fresh-water lakes and glacial lakes. Over time, the area covered by a water body has been varied due to human interventions or natural causes. Here, variants of Detectron2 instance segmentation architectures have been employed to detect a water-body and compute the changes in its area from the time-lapsed images captured over 32 years, that is, 1984 to 2016. The models are validated using water-bodies images taken by the Sentinel-2 Satellite and compared based on the average precision (AP), 99.95 and 94.51 at A P 50 and A P 75 metrics, respectively. In addition, an ensemble approach has also been introduced for the efficient identification of shrinkage or expansion of water bodies.

An improved three-factor authentication scheme for patient monitoring using WSN in remote health-care system.

BACKGROUND AND OBJECTIVE:  Wireless sensor network-based remote health-care systems are becoming popular day by day with the rapid growth of Internet technologies and the proliferation of Internet-based application. A remote health-care system always demands a flexible and secure mechanism since any misuse of health-care related data leads to the risk of a patient's life. To make patient-related information more secure, we further consider that the patient related all the communication must be anonymous and untraceable to prevent traffic analysis. This particular approach makes the healthcare system more secure and suitable for real-time scenario. METHODS:  Recently, a three-factor mutual authentication scheme in wireless sensor networks (WSNs) is suggested by Challa et al. to deal with the security of the remote health-care system. They believe that their scheme is suitable and ensure the security of the remote health-care system. However, the authors of this article have found that their scheme suffers from sensor node capture attack; user identity reveals attack, session key leak attack, and message modification attack. Further, their scheme designs improper user revocation phase and re-registration phase, which produces the risk of illegal use of smartcard by a legitimate user. So, in this paper, the authors have given an enhanced mechanism for developing a three-factor secure mutual authentication scheme to attain effectively the security of the remote health-care system for patient monitoring. Further, the proper revocation and re-registration of users have been incorporated to support some additional securities in a case when the user lost his/her smartcard or smartcard is stolen. RESULTS AND CONCLUSIONS:  Testing with the BAN logic model affirms the accuracy of mutual authentication of the scheme designed in this paper. Also, the output of the AVISPA simulation depicts that the enhanced scheme efficiently tackle the active and passive attacks. Further, the comparative studies of our scheme with state-of-the-art schemes are also acceptable in terms of different security aspects.

Anonymity Preserving and Lightweight Multimedical Server Authentication Protocol for Telecare Medical Information System.

Electronic health systems, such as telecare medical information system (TMIS), allow patients to exchange their health information with a medical center/doctor for diagnosis in real time, and across borders. Given the sensitive nature of health information/medical data, ensuring the security of such systems is crucial. In this paper, we revisit Das et al.'s authentication protocol, which is designed to ensure patient anonymity and untraceability. Then, we demonstrate that the security claims are invalid, by showing how both security features (i.e., patient anonymity and untraceability) can be compromised. We also demonstrate that the protocol suffers from smartcard launch attacks. To mitigate such design flaws, we propose a new lightweight authentication protocol using the cryptographic hash function for TMIS. We then analyze the security of the proposed protocol using AVISPA and Scyther, two widely used formal specification tools. The performance analysis demonstrates that our protocol is more efficient than other competing protocols.