Journal of Cybersecurity and Information Management

The influence of COVID-19 has been felt in many facets of personal and professional life. As a result of the international economic crisis and the pandemic's consequences, major supply chains (SCs) have been disrupted. Our study intends to examine the effect of COVID-19 on SCs and help organizations choose options depending on their relative relevance. Phase one and phase two of the investigation are the most important. As a first step in strengthening SCs' ability to withstand the pandemic, Phase 2 examines the difficulties, concerns, actions, and solutions that have been encountered so far. As part of this phase, a MARCOS method is proposed to select solutions that address the complex interrelationships that are involved in decision-making. Positive and negative solutions are considered and it is at the start of the creation of a preliminary matrix, utility degree is determined closer to both solutions, a new method of determining utility functions and their aggregates is proposed, and the method is stable enough to take into account a huge list of conditions and alternatives. Using this approach, decision-makers will be able to more properly weigh the relevance and influence of many options before making a final choice. The findings suggest that SCs should continue to rely on innovation to endure potential competition and disasters.

In this paper, we analysis the Viola-Jones algorithm, the most real-time face detection system has been used. It is consisting from three main concepts to enable a robust detection: the integral image for Haar feature computation, Adaboost for selecting feature and cascade to make resource allocation more efficient. Here we propose each stage starting from Integral image to the end with Cascading and some of algorithmic description for stages. The Viola-Jones algorithm gives multiple detections, a post-processing step which reduce detection redundancy using Adaboost and cascading.

Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.

The main goal of HIPAA (Health Insurance Portability and Accountability Act) is to protect health information of individuals against access without consent or authorization. Security and privacy are the main issues in HIPAA. A compliant key management solution is used to reduce harm and risk while providing cryptographic mechanisms. Using ECC (Elliptic Curve Cryptography) we ensure more security for access of patient’s health records. This provides same level of security for access of patient’s health records. Patient’s health Information is stored in RFID cards. Finally, the proposed method ensures higher level of security than other existing cryptographic techniques. ECC provides more security even with small key sizes. Proposed scheme describes the various counter measures for improving security and a key recovery mechanism for the protection of keys.