Blockchain technology is frequently referred to as the fourth industrial revolution, which will change the world. Blockchain technology sometimes referred to as distributed ledger technology creates an ecosystem that is decentralized, dispersed, and devoid of central authority. Since Bitcoin's introduction, research into non-financial use cases has continued to expand the technology's usefulness. Through categorization and integrity verification in both the industry and processing terminals, the blockchain paradigm regulates data collection and dissemination instances. From the Bitcoin digital currency system to more contemporary uses, the origin of this technology is traced. This paper presents essential ideas about Blockchain and offers our perspective on the challenges, the future changes, and the predictable effect of Blockchain. Blockchain has created an immense pool of opportunities for every field of life, e-commerce, supply chain, sustainable smart cities, or adoption of e-governance. The impact on various fields by use of Blockchain is discussed, also raising the research investigation questions.
Depression refers to a condition in which mental function continues to deteriorate and adversely affects daily life. Therefore, it is very important to diagnose depression accurately in the early stages. This study extracted depression-related features such as head pose, gaze, and facial expression changes from facial images and proposed a model for diagnosing depression using a decision tree. The proposed model learned the data using two databases. The performance of the algorithm was evaluated through the confusion matrix, receiver operating characteristic (ROC) curve, and achieved value 0.99 for area under the curve (AUC).
Cardiac tumors are uncontrolled bursts of tissues and cells in the heart. The heart is composed of several different tissues, cells, or valves as well as neurons and these neurons, tissues, cells, or valves are prone to heart actions. Usually, the heart, or Cardiac, only makes new tissues, cells or valves when old or damaged ones need to be replaced. If the growth is out of control for any reason, the cells or valves will continue to separate, causing an inflammation called a tumor. Cardiac tumors can happen at any age, but most of them happen between the ages of 55 and 65 in adults and between 3 and 12 in kids. Cancer treatment has been a major research area of medical researchers for several decades; however, the development of new treatments takes time and money. The utilization of computer technology in therapeutic resolution support is currently extensive and invasive across a large range of medicinal region for instance, cancer research, Cardiac tumors, gastroenterology etc. MRI is the feasible alternative nowadays for the study of tumor in soft tissues. Parallel computing is a computation form in which numerous computations are carried out in parallelism manner, running on the concept that huge issues can repeatedly be divided into slighter ones that are resolved at the similar point.
Optimizing resource allocation in Network Functions Virtualization (NFV) deployment remains a challenging problem due to the complex interactions between network functions and the limited resources available at the network edge. Deep reinforcement learning (DRL) has achieved impressive results in a variety of domains. This paper presents EdgeGym, a reinforcement learning environment to simulate the edge network contexts and constraints for NFV resource allocation. EdgeGym allows researchers and practitioners to evaluate and compare different reinforcement learning algorithms for optimizing the allocation of resources in NFV environments, taking into account various constraints such as affinity policies and maximum latency. We demonstrate the effectiveness of EdgeGym through extensive experiments on training and action masking efficiency. EdgeGym provides a reliable framework for advancing the DRL agent performance in NFV resource allocation and paves the way for further research in this area.
ThDeveloping a goal-oriented model for digital forensic evidence is critical because the reconstructed scenarios help forensic analysts not only understand steps taken by threat actors, but also present digital evidence in ways that are understandable in the court of law. In this paper, we propose a goal-oriented approach to reconstruct attack scenarios based on a forensic evidence acquisition model. We first build the model, from which digital forensic examiners can trace and collect forensic evidence, then formalize the graph and evaluate the semantics based on the evidence found on digital devices and their supporting environments. Finally, we apply the model to a typical scenario based on the semantics of the model. Our preliminary results show that our model can give any practitioner or investigator formal instructions to gather and rebuild the evidence in the simulation or real-world environment.
