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The field of behavioural finance analyses the effects of mental factors on financial decisions including risk. If you want to know how people think about money and investments, you need to study behavioural finance. People’s attitudes about investing were uncovered through this study. Hence, their views on financial investing. Overconfidence, perception, representative, anchoring cognitive dissonance, regret aversion, limited framing, and mental accounting are only few of the behavioural finance concepts that are discussed in this article, along with their effects on stock market investor decision making. In order to poll 181 Bangalore investors, we employed a conventional questionnaire. The primary focus was on learning how behavioural financing influences investors and their investment choices. Our secondary objective was to study behavioural finance and investor psychology.

In computer application scenario, data mining task is rarely utilized in power system, as an enhanced part, this work presented data mining task in power systems, to overcome frequency deviation issues. Load frequency control (LFC) is a primary challenging problem in an interconnected multi-area power system.

This paper adopts lion algorithm (LA) for the LFC of two area multi-source interconnected power systems. The LA calculates the optimal gains of the fractional order PI (FOPI) controller and hence the proposed LA-based FOPI controller (LFOPI) is developed.

For the performance analysis, the proposed algorithm compared with various algorithm is given as, 80.6% lesser than the FOPI algorithm, 2.5% lesser than the GWO algorithm, 2.5% lesser than the HSA algorithm, 4.7% lesser than the BBO algorithm, 1.6% lesser than PSO algorithm and 80.6% lesser than the GA algorithm.

The LFOPI controller is the proposed controlling method, which is nothing but the FOPI controller that gets the optimal gain using the LA. This method produces better performance in terms of converging behavior, optimization of controller gain, transient profile and steady-state response.

The unexpected epidemic of the latest coronavirus in 2019, known as COVID-19 by the Globe, a number of governments worldwide have been put in a vulnerable situation by the World Health Organization. The effect of the COVID-19 outbreak, previously experienced by China’s citizens alone, has now become more pronounced. For practically every nation in the world, this is a matter of grave concern. The lack of assets to withstand the infection of COVID-19, mixed with the perception of overwhelmed medical mechanisms, pressured a number of places in a state of partial or absolute lockdown.

The medical photos such as computed tomography (CT) and X-ray playa key role in the worldwide battle against COVID-19, while artificial intelligence (AI) has recently appeared. The power of imaging is further increased by technology tools and support for medical specialists. In comparison to the related direct health effects because of the COVID-19 disaster, this research identifies its impacts on the overall society.

This paper hereby examines the rapid answers in the medical imaging community toward COVID-19 (empowered by AI). For example, the acquisition of AI-empowered images will significantly assist automate the scanning process and reshape the procedure as well. AI, too, may improve the quality of the job by correctly delineating X-ray and CT image infections, promoting subsequent infections, quantification. In addition, computer-aided platforms support radiologists make medical choices, i.e. for illness tracking, diagnosis and prognosis.

This research encompasses the whole medical imaging pipeline and methods for research related to COVID-19, include a collection of images, segmentation, diagnosis and monitoring. In drawing stuff to minimize the effects of the COVID-19 epidemic, this paper is investigating the use of technologies such as the internet of things, unmanned aerial vehicles, blockchain, AI, big data and 5G.

In this work, an efficient architecture for memory built in self-test (MBIST) that incorporates a modified March Y algorithm using concurrent technique and a modified linear feedback shift register (LFSR)–based address generator is proposed.

Built in self-test (BIST) is emerging as the essential ingredient of the system on chip. In the ongoing high speed, high tech sophistication technology of the very large-scale integrated circuits, testing of these memories is a very tedious and challenging job, since the area overhead, the testing time and the cost of the test play an important role.

With the efficient service of the adapted architecture, switching activity is considerably cut down. As the switching activity is in direct proportion to the power consumed scaling down, the switching process of the address generator inevitably leads to the reduction in power consumption of the MBIST.

To improve the yield and fault tolerance of on-chip memories without degradation on its performance self-repair mechanisms can be implemented on chip.

Over the past decade, the cost of product development has increased drastically, and this is due to the inability of most enterprises to locate suitable and optimal collaborators for knowledge sharing. Nevertheless, knowledge sharing is a mechanism that helps people find the best collaborators with relevant knowledge. Hence, a new approach for locating optimal collaborators with relevant knowledge is needed, which could help enterprise in reducing cost and time in a knowledge-sharing environment. The paper aims to discuss these issues.

One unique challenge in the domain of knowledge sharing is that collaborators do not possess the same number of events resident in the knowledge available for sharing. In this paper, the authors present a new approach for locating optimal collaborators in knowledge-sharing environment using the combinatorial algorithm (CA-KSE).

The proposed pattern-matching approach implemented in Java is considered efficient for solving the issue peculiar to collaboration in knowledge-sharing domain. The authors benchmarked the proposed approach with its semi-global pairwise alignment and global alignment counterparts through scores comparison and the receiver operating characteristic curve. The results obtained from the comparisons showed that CA-KSE is a perfect test having an area under curve of 0.9659, compared to the other approaches.

The paper has proposed an efficient algorithm, which is considered better than related methods, for matching several collaborators (more than two) in KS environment. The method could be deployed in medical field for gene analysis, software organizations for distributed development and academics for knowledge sharing.

One sign of strength of this approach, compared to most sequence alignment approaches that can only match two collaborators at a time, is that it can match several collaborators at a faster rate.

This study aims to propose a novel deep learning (DL)-based framework, iRelevancy, for identifying the disaster relevancy of a social media (SM) message.

It is worth mentioning that a fusion-based DL model is introduced to objectively identify the relevancy of a SM message to the disaster. The proposed system is evaluated with cyclone Fani data and compared with state-of-the-art DL models and the recent relevant studies. The performance of the experiments is assessed by the accuracy, precision, recall, f1-score, area under receiver operating curve and precision–recall curve score.

The iRelevancy leads to a better performance in accuracy, precision, recall, F-score, the area under receiver operating characteristic and area under precision-recall curve, compared to other state-of-the-art methods in the literature.

The predictive performance of the proposed model is illustrated with experimental results on cyclone Fani data, along with misclassifications. Further, to analyze the performance of the iRelevancy, the results on other cyclonic disasters, i.e. cyclone Titli, cyclone Amphan and cyclone Nisarga are presented. In addition, the framework is implemented on catastrophic events of different natures, i.e. COVID-19. The research study can assist disaster managers in effectively maneuvering disasters during distress.

A mobile ad hoc network (MANET) enables providers and customers to communicate without a fixed infrastructure. Databases are extended on MANETs to have easy data access and update. As the energy and mobility limitations of both servers and clients affect the availability of data in MANETs, these data are replicated. The purpose of this paper is to provide a literature review of data replication issues and classify the available strategies based on the issues they addressed.

The selected articles are reviewed based on the defined criteria. Also, the differences, the advantages and disadvantages of these techniques are described. The methods in the literature can be categorized into three groups, including cluster-based, location-based and group-based mechanisms.

High flexibility and data consistency are the features of cluster-based mechanisms. The location-based mechanisms are also appropriate for replica allocation, and they mostly have low network traffic and delay. Also, the group-based mechanism has high data accessibility compared to other mechanisms. Data accessibility and time have got more attention to data replication techniques. Scalability as an important parameter must be considered more in the future. The reduction of storage cost in MANETs is the main goal of data replication. Researchers have to consider the cost parameter when another parameter will be influenced.

Data replication in MANETs has been covered in different available sources such as Web pages, technical reports, academic publications and editorial notes. The articles published in national journals and conferences are ignored in this study. This study includes articles from academic main international journals to get the best capability.

The paper reviews the past and the state-of-the-art mechanisms in data replication in MANET. Exclusively, data replication’s main goal, existing challenges, research terminologies and mechanisms in MANET are summarized using the answers to the research questions. This method will help researchers in the future to develop more effective data replication method in MANET.