A Comprehensive Study on Critical Security Issues and Challenges of the IoT World

Automatic Surgery and Anesthesia Emergence Duration Prediction Using Artificial Neural Networks

Cost control is becoming increasingly important in hospital management. Hospital operating rooms have high resource consumption because they are a major part of a hospital. Thus, the optimal use of operating rooms can lead to high resource savings. However, because of the uncertainty of the operation procedures, it is difficult to arrange for the use of operating rooms in advance. In general, the durations of both surgery and anesthesia emergence determine the time requirements of operating rooms, and these durations are difficult to predict. In this study, we used an artificial neural network to construct a surgery and anesthesia emergence duration-prediction system. We propose an intelligent data preprocessing algorithm to balance and enhance the training dataset automatically. The experimental results indicate that the prediction accuracies of the proposed serial prediction systems are acceptable in comparison to separate systems.

Biometric system for protecting information and improving service delivery: The case of a developing country's social security and pension organisation

The conception of biometric systems as a means of securing sensitive information and enhancing service delivery remains under-researched. To address this knowledge gap, we explore the case of a public-sector social security and pension organisation in Ghana using a qualitative interpretative study approach and the information security model of confidentiality-integrity-availability as an analytical lens. The study's findings indicate that integrating and using biometric identification and authentication as part of delivering social security and pension services can protect availability, confidentiality, and integrity of information. The findings further show that the use of a biometric system for social security and pension information security can contribute to reducing service turnaround time and vulnerability to fraudulent manipulation of benefits payments. The study provides implications for research, practice, and policy. For research, the paper opens up biometric systems’ study from the perspective of information security and service improvement. For practice and policy, the study demonstrates the importance of aligning biometric systems’ deployment and use with domain application requirements.

Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow

Standing at the cusp of an augmented age facilitates a glance into the future of a cybernetic world aligned with planetary wellbeing. The era of exponential technological revolutions has brought with it a plethora of opportunities expanding in a multi-faceted dimension with an added emphasis towards nurturing a mutual synergy of nature with a daily dose of digitalization. The paper is written with an intent to lay out an accumulated comprehensive review of different literary works which lay the grounds for how different Smart Earth Technologies aid in monitoring and tackling the degradation of air and water resources. If an intertwined state-of-the-art centralized research source could be created, it would become a boon for seasoned researchers and neophytes succeeding portion of the article expands itself to a wide variety of research literature complimented with real-time models, case, and empirical studies which help heighten the previous limit to the research done on these Technologies tinkering the present monitoring systems. The primary aim of this work is to fuel the need of theoretical, practical, and empirical evolution in the ways the intelligent technologies help blossom a pollution-free environment. The secondary intention was to ensure that in-depth study of Smart Environmental Pollution the Monitoring Systems provisioned a multitude of prospects for upgrading one's knowledge on environmental management through current world technologies. By looking at these trends of the past, the enthusiast of technology could collaborate with the researchers of Environmental Pollution to assist in proliferation of diverse 'smart' solutions creating a Smarter, Greener, and Brighter future for research and developments in Sustainable Technologies devising a pollution-free environment.

Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap

Industrial IoT (IIoT) is a novel concept of a fully connected, transparent, automated, and intelligent factory setup improving manufacturing processes and efficiency. To achieve this, existing hierarchical models must transition to a fully connected vertical model. Since IIoT is a novel approach, the environment is susceptible to cyber threat vectors, standardization, and interoperability issues, bridging the gaps at the IT/OT ICS (industrial control systems) level. IIoT M2M communication relies on new communication models (5G, TSN ethernet, self-driving networks, etc.) and technologies which require challenging approaches to achieve the desired levels of data security. Currently there are no methods to assess the vulnerabilities/risk impact which may be exploited by malicious actors through system gaps left due to improper implementation of security standards. The authors are currently working on an Industry 4.0 cybersecurity project and the insights provided in this paper are derived from the project. This research enables an understanding of converged/hybrid cybersecurity standards, reviews the best practices, and provides a roadmap for identifying, aligning, mapping, converging, and implementing the right cybersecurity standards and strategies for securing M2M communications in the IIoT.

Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention

A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a "machine" that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques. In this paper, we describe the results of certain cases where such approaches were used, and which motivated us to pursue further research in this field. The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques. The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate. In summary, ML and computer vision techniques can bring about an evolution in law agencies.

Systematic review and meta-analysis of augmented reality in medicine, retail, and games

This paper presents a detailed review of the applications of augmented reality (AR) in three important fields where AR use is currently increasing. The objective of this study is to highlight how AR improves and enhances the user experience in entertainment, medicine, and retail. The authors briefly introduce the topic of AR and discuss its differences from virtual reality. They also explain the software and hardware technologies required for implementing an AR system and the different types of displays required for enhancing the user experience. The growth of AR in markets is also briefly discussed. In the three sections of the paper, the applications of AR are discussed. The use of AR in multiplayer gaming, computer games, broadcasting, and multimedia videos, as an aspect of entertainment and gaming is highlighted. AR in medicine involves the use of AR in medical healing, medical training, medical teaching, surgery, and post-medical treatment. AR in retail was discussed in terms of its uses in advertisement, marketing, fashion retail, and online shopping. The authors concluded the paper by detailing the future use of AR and its advantages and disadvantages in the current scenario.

Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease

Alzheimer's disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.