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Finding strategies to profit from the stock markets is a topic of immense interest to both institutional and retail investors. Possible methods range from machine learning, time series prediction, and network science to game theory. Game theory is a useful mathematical tool to model the relationship between agents and the stocks. In this paper, we ask an important question: Why do some (non-optimal) traders end up winning despite alternating between losing stocks? Our empirical observations reveal that by using a switching strategy abstractly derived from the switching in Parrondo's paradox, we can demonstrate that losing stocks can be combined in a time-based switching scheme to achieve a winning outcome. This is akin to an automatic investment plan. Several stocks within a given time period are being analyzed. Our analysis may provide a descriptive explanation for why some traders end up winning despite switching between losing stocks. However, it is not the focus of this paper to provide any normative advice or support for switching between stocks. [ABSTRACT FROM AUTHOR]

Building Information Modeling (BIM) has been increasingly used in coordinating the different mechanical, electrical, and plumbing (MEP) services in the construction industries. As the construction industries are slowly adapting to BIM, the use of 2D software may become obsolete in the future as MEP services are technically more complicated to coordinate, due to respective services' codes of practice to follow and limit ceiling height. The 3D MEP designs are easy to visualize before installing the respective MEP services on the construction site to prevent delay in the construction process. The aid of current advanced technology has brought BIM to the next level to reduce manual work through automation. Combining both innovative technology and suitable management methods not only improves the workflow in design coordination, but also decreases conflict on the construction site and lowers labor costs. Therefore, this paper tries to explore possible advance technology in BIM and management strategies that could help MEP services to increase productivity, accuracy, and efficiency with a lower cost of finalizing the design of the building. This will assist the contractors to complete construction works before the targeted schedule and meet the client's expectations. [ABSTRACT FROM AUTHOR]

• This paper has reviewed relevant AI techniques in the detection of Alzheimer. • It focuses on the different methods that make use of structural MRI. • Suggestions for future research are also given. Alzheimer's disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. It is one of the leading types of dementia for persons aged above 65 worldwide. In order to achieve accurate and timely diagnosis, and for detection of AD in its early stages, numerous Artificial Intelligence (AI) based Computer-aided Diagnostic (CAD) approaches have been proposed using data from brain imaging. In this paper, we review the recent application of AI based CAD systems on AD and its stages, with a particular focus on the use of structural MRI due to its cost effectiveness and lack of ionizing radiation. We will review important factors of different AI techniques pertinent to AD, summarize contributions from different research groups, critically discuss challenges involved and propose directions for future research. Ultimately, it would be ideal for development of a diagnostic framework that could be applicable to not only AD, but to different types of dementia as well in the future. [ABSTRACT FROM AUTHOR]

The advent of Artificial Intelligence (AI) has resulted in development of novel applications in a multitude of fields, such as in Medicine, to aid medical professionals in clinical diagnosis. Specifically, the field of Emergency Medicine has been of immense interest to researchers, with vast untapped potential for AI solutions to improve operational efficiencies and quality of healthcare. Aside from primary healthcare facilities, the Emergency Department serves as the first line of contact to patients, who often present with varying and undifferentiated symptoms. Several challenges faced by clinicians and patients alike, such as waiting times and diagnostic dilemmas, present opportunities for application of AI solutions. In this paper, we aim to summarise the applications of AI in the field of Emergency Medicine by reviewing recent developments in Emergency Department operations and in the clinical management of patients. [ABSTRACT FROM AUTHOR]

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta–Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas. [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM) is a recurrent neural network (RNN) framework designed to solve the gradient disappearance and explosion problems of traditional RNNs. In recent years, LSTM has become a state-of-the-art model for solving various machine-learning problems. This paper propose a novel regularized LSTM based on the batch gradient method. Specifically, the L 2 regularization is appended to the objective function as a systematic external force, effectively controlling the excessive growth of weights in the network and preventing the overfitting phenomenon. In addition, a rigorous convergence analysis of the proposed method is carried out, i.e., monotonicity, weak convergence, and strong convergence results are obtained. Finally, comparative simulations are conducted on the benchmark data set for regression and classification problems, and the simulation results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]