Using Artificial Intelligence and Cybersecurity in Medical and Healthcare Applications

Healthcare fields have made substantial use of cybersecurity systems to provide excellent patient safety in many healthcare situations. As dangers increase and hackers work tirelessly to elude law enforcement, cybersecurity has been a rapidly expanding field in the news over the past ten years. Although the initial motivations for conducting cyberattacks have generally remained the same over time, hackers have improved their methods. It is getting harder to identify and stop evolving threats using conventional cybersecurity tools. The development of AI methodologies offers hope for equipping cybersecurity professionals to fend against the ever-evolving threat posed by attackers. Therefore, an artificial intelligence- based Convolutional Neural Network (CNN) is introduced in this paper in which the cyberattacks are detected with more excellent performance. This paper presents unique conditions using the Ant Colony Optimization based Convolutional Neural Network (ACO-CNN) mechanism. This model has been built and supplied collaboratively with a dataset containing samples of web attacks for detecting cyberattacks in the healthcare sector. The results show that the created framework performs better than the modern techniques by detecting cyberattacks more accurately

The effects of an 8-week supervised exercise program on appetite and appetite-regulating gut hormones in overweight and obese adults

Aim To determine the effects of 8-week supervised and structured exercise training programs – of different modes, intensities and volumes – on subjective appetite, total ghrelin, peptide tyrosine tyrosine (PYY) and anthropometric measurements in overweight and obese adults. Methods A longitudinal randomised controlled pilot trial was conducted at the University of Sydney over 15 months period. Twenty-eight eligible overweight and obese adults were randomly allocated to one of 4 intervention groups or a control group: High-intensity/low energy expenditure aerobic exercise training (HI:LO) (n=7), (LO:HI) (n=8), (LO:LO) (n=4), Progressive resistance training (PRT) (n=4), and control group (n=5). Subjective appetite, the plasma concentrations of total ghrelin and (PYY) gut hormones were assessed at baseline and after intervention. Anthropometric measurements were similarly recorded. Results None of the exercise interventions had any effect on BMI, but HI:LO and LO:HI resulted in a statistically significant reductions in waist circumference. There were no statistically significant differences in the appetite responses, plasma ghrelin and PYY concentrations among the studied groups, either pre- or post-intervention. Conclusions Eight weeks of different intensities and volumes of regular aerobic and (PRT) exercise did not significantly influence subjective appetite or the plasma levels of total ghrelin and PYY in overweight and obese adults

Exploring Factors Affecting User Trust Across Different Human-Robot Interaction Settings and Cultures

Trust is one of the necessary factors for building a successful human-robot interaction (HRI). This paper investigated how human trust in robots differs across HRI scenarios in two cultures. We conducted two studies in two countries: Saudi Arabia (study 1) and the United Kingdom (study 2). Each study presented three HRI scenarios: a dog robot guiding people with sight impairments, a teleoperated robot in healthcare, and a manufacturing robot. Study 1 shows that participants' trust perception score (TPS) was significantly different across the three scenarios. However, Study 2 results show a slightly significant variation in TPS across the scenarios. We also found that the relevance of trust for a given task is an indicator of a participant's trust. Furthermore, the findings showed that trust scores or factors affecting users' trust vary across cultures. The findings identified novel factors that might affect human trust, such as controllability, usability and risk. The findings direct the HRI community to consider a dynamic and evolving design for modelling human-robot trust because factors affecting humans' trust are evolving and will vary across different settings and cultures

Design Disjunction for Resilient Reconfigurable Hardware

Contemporary reconfigurable hardware devices have the capability to achieve high performance, power efficiency, and adaptability required to meet a wide range of design goals. With scaling challenges facing current complementary metal oxide semiconductor (CMOS), new concepts and methodologies supporting efficient adaptation to handle reliability issues are becoming increasingly prominent. Reconfigurable hardware and their ability to realize self-organization features are expected to play a key role in designing future dependable hardware architectures. However, the exponential increase in density and complexity of current commercial SRAM-based field-programmable gate arrays (FPGAs) has escalated the overhead associated with dynamic runtime design adaptation. Traditionally, static modular redundancy techniques are considered to surmount this limitation; however, they can incur substantial overheads in both area and power requirements. To achieve a better trade-off among performance, area, power, and reliability, this research proposes design-time approaches that enable fine selection of redundancy level based on target reliability goals and autonomous adaptation to runtime demands. To achieve this goal, three studies were conducted: First, a graph and set theoretic approach, named Hypergraph-Cover Diversity (HCD), is introduced as a preemptive design technique to shift the dominant costs of resiliency to design-time. In particular, union-free hypergraphs are exploited to partition the reconfigurable resources pool into highly separable subsets of resources, each of which can be utilized by the same synthesized application netlist. The diverse implementations provide reconfiguration-based resilience throughout the system lifetime while avoiding the significant overheads associated with runtime placement and routing phases. Evaluation on a Motion-JPEG image compression core using a Xilinx 7-series-based FPGA hardware platform has demonstrated the potential of the proposed FT method to achieve 37.5% area saving and up to 66% reduction in power consumption compared to the frequently-used TMR scheme while providing superior fault tolerance. Second, Design Disjunction based on non-adaptive group testing is developed to realize a low-overhead fault tolerant system capable of handling self-testing and self-recovery using runtime partial reconfiguration. Reconfiguration is guided by resource grouping procedures which employ non-linear measurements given by the constructive property of f-disjunctness to extend runtime resilience to a large fault space and realize a favorable range of tradeoffs. Disjunct designs are created using the mosaic convergence algorithm developed such that at least one configuration in the library evades any occurrence of up to d resource faults, where d is lower-bounded by f. Experimental results for a set of MCNC and ISCAS benchmarks have demonstrated f-diagnosability at the individual slice level with average isolation resolution of 96.4% (94.4%) for f=1 (f=2) while incurring an average critical path delay impact of only 1.49% and area cost roughly comparable to conventional 2-MR approaches. Finally, the proposed Design Disjunction method is evaluated as a design-time method to improve timing yield in the presence of large random within-die (WID) process variations for application with a moderately high production capacity

Predicting Solar Irradiance using Time Series Neural Networks

Increasing the accuracy of prediction improves the performance of photovoltaic systems and alleviates the effects of intermittence on the systems stability. A Nonlinear Autoregressive Network with Exogenous Inputs (NARX) approach was applied to the Vichy-Rolla National Airport\u27s photovoltaic station. The proposed model uses several inputs (e.g. time, day of the year, sky cover, pressure, and wind speed) to predict hourly solar irradiance. Data obtained from the National Solar Radiation Database (NSRDB) was used to conduct simulation experiments. These simulations validate the use of the proposed model for short-term predictions. Results show that the NARX neural network notably outperformed the other models and is better than the linear regression model. The use of additional meteorological variables, particularly sky cover, can further improve the prediction performance

A Case of Gastroesophageal Cancer after Laparoscopic Sleeve Gastrectomy

Gastric cancer has been reported in relatively few cases after sleeve gastrectomy, which has become a common bariatric procedure. In this paper, we present a 58-year-old woman diagnosed with gastric cancer by esophagogastroduodenoscopy (EGD) 4 years after sleeve gastrectomy. For that, she underwent distal esophagectomy and total gastrectomy with Roux-en-Y esophagojejunostomy. Preoperative endoscopy is recommended before planning surgery in patients with gastroesophageal reflux symptoms. In addition, annual EGD should be considered after sleeve gastrectomy in patients with risk factors for gastric cancer

Modeling and Simulation of Microgrid

Complex computer systems and electric power grids share many properties of how they behave and how they are structured. A microgrid is a smaller electric grid that contains several homes, energy storage units, and distributed generators. The main idea behind microgrids is the ability to work even if the main grid is not supplying power. That is, the energy storage unit and distributed generation will supply power in that case, and if there is excess in power production from renewable energy sources, it will go to the energy storage unit. Therefore, the electric grid becomes decentralized in terms of control and production. To deal with this change, one needs to interpret the electrical grid as a system of systems (SoS) and build new models that capture the dynamic behavior of the microgrid. In this paper, different models of electric components in a microgrid are presented. These models use complex system modeling techniques such as agent-based methods and system dynamics, or a combination of different methods to represent various electric elements. Examples show the simulation of the solar microgrid is presented to show the emergent properties of the interconnected system. Results and waveforms are discussed