1. H. Pandey, Y. P. Singh, Y. Singh, Prabhishek Singh, M. Diwakar and N. K. Pandey, "Comparative analysis of different mining approaches for Blockchain in infant care application on IoT," 2021 5th International Conference on Information Systems and Computer Networks (ISCON), 2021, pp. 1-5, doi: 10.1109/ISCON52037.2021.9702357.
Abstract: Ambient means environmental elements, helped living means the kind of houses inserted with innovations that assistance in regular daily existences, in an entire it implies the utilization of environmental elements advancements to improve living and autonomous without hindering other individual life. Ambient Assisted Living (AAL) is a subclass of Ambient understanding, which concerns the use of incorporating savvy strategies, systems and advances to enable people to live uninhibitedly for a long as could be anticipated in light of the current situation, without intrusive practices. In this exploration work has been made to do relative investigation between two distinct agreement calculation verification of work and confirmation of stake in Blockchain utilizing python for information gathered and put away of elements temperature, second, moistness, pressing factor, sound and light. The time taken to mine a square in the verification of stake is less contrasted with the time taken to mine a square in confirmation of work which is putting away information of elements influencing the independence of an individual. This review assists with recognizing the most reasonable agreement calculation to store information in Blockchain.
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9702357&isnumber=9702296
2. M. Diwakar, R. Pandey, R. Sharma, S. Saun, Prabhishek Singh and N. K. Pandey, "Internet of Medical Things: A CT Image Denoising in Tetrolet Domain," 2021 5th International Conference on Information Systems and Computer Networks (ISCON), 2021, pp. 1-6, doi: 10.1109/ISCON52037.2021.9702462.
Abstract: The Internet of Medical Things (IoMT) is a huge community of linked medical equipment and technologies that communicate with a large number of Servers over the internet to provide a variety of services such as medical image denoising. Projection-based reconstructed images in computed tomography (CT) are noisy due to thermal noise and electrical noise, both of which are approximately AWGN in magnitude. Because of the variety of clinical features and textures included in CT reconstructed images, denoising is a difficult process. It is possible to estimate the true noise of a single image, and then to minimize both the real noise and the additional noise by reconstructing CT images. A unique modified approach is proposed in this work, in which the noise will be calculated actual as well as added, followed by a proposed method for denoising, which is based on thresholding in tetrolet domain. Different denoising schemes are used in the experiments, and the results are compared to one another. Experimentation has revealed that both in terms of PSNR and visual quality, the suggested technique yields promising results that are favorable to both.
3. Prabhishek Singh, M. Koranga, R. Prasad, A. Dubey, M. Diwakar and N. K. Pandey, "E-MFIF-NSST: Entropy-based Multi-Focus Image Fusion technique in NSST domain," 2021 5th International Conference on Information Systems and Computer Networks (ISCON), 2021, pp. 1-5, doi: 10.1109/ISCON52037.2021.9702327.
Abstract: Multi-focus-image-fusion (MFIF) is an approach of fusing two or more multi-focused images into a single image that contains much more information than the previous input images. An efficient MFIF technique plays a vital role in the field of real-time surveillance applications specifically in virtual sensor network architecture (VSN). This paper presents a new entropy-based MFIF technique using a non-subsampled shearlet transform (NSST) domain (E-MFIF-NSST). The high pass sub-bands of input images are fused using the entropy-based fusion law, while the low pass sub-bands are fused using average operation. The outcomes of E-MFIF-NSST are tested using visual quality analysis and also using various performance metrics. The overall outcome analysis of E-MFIF-NSST proves the robustness and effectiveness over other compared methods.
4. A. Kothari, P. Vashishtha, Prabhishek Singh, M. Diwakar and N. K. Pandey, "Ensemble Methods on NSL-KDD," 2021 5th International Conference on Information Systems and Computer Networks (ISCON), 2021, pp. 1-7, doi: 10.1109/ISCON52037.2021.9702439.
Abstract: The application of machine learning and also deep learning strategies in the field of cyber safety and security is a lot more popular than in the past. From IP (Internet Protocol) web traffic classification, a filtering system for harmful web traffic for breach discovery, Machine learning is among the appealing solution that can be reliable against zero-day dangers. This paper is a concentrated study of machine learning and also its application to the cyber division for breach discovery, web traffic classification as well and also applications such as e-mail filtering systems. Ensemble learning is being implemented in this paper which has two parts namely bagging and boosting. It is basically focused on combining multiple models in order to solve a particular problem whether it be regression or classification, which in this problem statement is classification. Applicability of these methods in different cybersecurity tasks such as breach identification, recognition of viruses, phishing, forecasting cyberattacks, e.g., denial of service, fraud recognition or cyber anomalies, and many more are going to be reviewed in this paper additionally. On the whole, the objective of this paper is to have an introduction of exactly how ensemble learning can be shown reliable and also a brilliant aspect for the academic community as well as experts in the cyber industries for identifying attacks.