Intelligent Approach Research Articles

Detecting GNSS Spoofing and Re-localization on UAV based on Imagery Matching

Abstract Due to rapid advancements in GNSS, computer, and microelectronics technologies, there is a growing popularity and widespread promotion of high-performance, cost-effective intelligent UAVs across various applications. Recently, GNSS spoofing attacks have emerged as a significant obstacle hindering the long-term development of UAVs. UAVs rely heavily on unprotected GNSS signals for navigation, making them highly vulnerable to spoofing. This paper presents an algorithm that employs image matching for detecting GNSS spoofing and re-localization on UAVs using a deep learning methodology. This method functions autonomously solely reliance on camera and does not require alterations to the antenna configuration. Utilizing a camera-equipped UAV, we evaluate the likeness between real-time aerial photographs and satellite imagery by leveraging the position information provided by UAV. By identifying disparities between images taken by a spoofing affected drone and authentic ones, the spoofing can be identified using deep neural network models. Upon detecting spoofing, this paper presents a vision-based re-localization method for UAVs. Experimental results demonstrate an approximately 88.4% accuracy of our model in detecting GNSS spoofing attacks within 100 milliseconds and 88% success rate of re-localization with the accuracy of less than 15 meters. Our algorithm exclusively depends on publicly accessible satellite imagery, offering an intelligent and efficient approach for detecting UAV GNSS spoofing and re-localization in GNSS denied environment.

A novel risk-stratification model that predicts long-term clinical outcomes in patients with recurrent pericarditis

Abstract Background Recurrent Pericarditis (RP) is condition associated with significant debility and morbidity. Previous studies have identified variables such as elevated C-Reactive protein, corticosteroid usage, and presence of delayed hyper-enhancement (DHE) on cardiac magnetic resonance imaging are associated with recurrence. To date however, there is no established model for risk stratifying outcomes in these patients, therefore we attempted to develop a model that could predict long-term outcomes in patients with RP and stratify them into groups based on risk. Methods A total of 365 consecutive patients with RP from 2012-2019 were retrospectively examined. Our primary outcome was clinical remission (CR) defined as symptom resolution in addition to complete cessation of all anti-inflammatory therapy. We employed 5 survival models: CoxPH, RSF, SSVM, GBSA and XGBoost with Cox loss function to determine the likelihood of achieving CR within 5 years and to stratify patients into specific risk groups. We divided the dataset into training (70%) and test (30%) sets. Model training and optimization of the training set occurred via 5-fold cross validation. Model evaluation and selection was based on C-index. Finally, we employed an explainable artificial intelligence approach, SHapley Additive exPlanations to generate both individual and global explanation of the models decision. Results Amongst the cohort, the mean age was 46 ± 15 years, 205 (56%) were female, and the main etiology was idiopathic in 223 (61%). CR was achieved in 118 (32%) patients. The final model included steroid dependency, total number of recurrences, pericardial DHE, age, etiology, gender, ejection fraction and heart rate as its most important parameters. We observed similar CR rates across three risk score ranges and accordingly stratified patients into low (n=62), intermediate (n=154), and high-risk (n=149) groups. Scores of 3 or lower were categorized as low risk, with CR rates exceeding 90%. Scores of 4 to 7 were associated with CR rates of 50%. Scores of 8 or higher were deemed high-risk, with CR rates notably below 10%. Overall, the model predicted the outcome with C-index of 0.800 on the test set and exhibited a significant ability in stratification of patients into low-, intermediate-, and high-risk groups (log-rank test; P <0.0001). Conclusion We created a novel risk prediction model that can aid in predicting rates clinical remission in patients with recurrent pericarditis as well as aid in stratification into low, medium, and high risk groups. This risk prediction model may be helpful in diagnosis and management of these complicated patients.Figure 1

An Artificial Intelligence approach to monitor infectious diseases: lessons learned from COVID-19

Abstract Background The World Health Organization declared the start (March 11th 2020) and the end (May 5th 2023) of COVID19 pandemic, while in Italy it has been present since February 2020. Indicators such as number of new positive cases, deaths and hospitalizations are used to monitor epidemiological trends, but they suffer from biases limiting their effectiveness. Methods We used data from the Emergency Medical Services Activities as an alternative and studied three types of contributions: COVID19, flu and baseline during three time frames: -period 1 (July 1st-Oct 11th 2016; Feb 10th-May 20th 2017) used to model the baseline, when flu contribution should be negligible. -Period 2: (Dec 15th-Feb 14th 2017), when flu in 2017 is very evident. -Period 3: (March 11th-31st 2020), when the first COVID19 wave is dominant. To extract the pure contribution from flu and COVID19, the baseline contribution was properly subtracted. Results From these data we developed a machine learning approach (MLA) that offers a simple and powerful tool to monitor COVID19 or future infectious diseases. To maximize the identification power of COVID19, we used the Toolkit for multivariate analysis package. An artificial neural network, multilayer perceptron, deep neural network and boosted decision tree methods have been trained, considering the events in period 3 as signal and period 1 as background. To avoid overtraining, the samples were divided in two: one half to train the algorithm, and the other to check the performance. The results are stable over time and able to efficiently discriminate COVID19. With 50% efficiency of accepting COVID19 patients, roughly 95% of baseline patients can be rejected. Conclusions MLA can be used to early assign a probability of COVID19 without specific test, only relying on standard triage and emergency call details. This tool could be very useful to early detect the presence of new pandemics and tag positive patients before the official healthcare reporting system. Key messages • AI model using data from Emergency Medical Services Activities can be a useful tool to early detect new pandemics and tag positive patients before the official healthcare reporting system. • Covid19 pandemic gave us the possibility to develop new digital tools to use in the analysis of epidemiological trends.

Integrating artificial intelligence into a real-world clinical pathway to facilitate clinician treatment optimisation in patients with HFrEF on suboptimal medical therapy

Abstract Background Despite evidence-based pharmacological recommendations, many patients with HFrEF remain undertreated. (1) These patients remain at risk of decompensation and might benefit treatment optimisation. Many patients may also have been diagnosed with HFrEF prior to the use of sacubitril/valsartan or SGLT2 inhibitors. Identification of patients with historical HF diagnosis is time-consuming. Artificial intelligence approaches using deep learning might allow rapid identification of such patients. Aim To develop an artificial intelligence deep-learning algorithm for rapid identification of HF from electronic health records (EHRs) and to determine the potential benefit of our AI-driven algorithm for the detection of undertreated HFrEF patients in the community. Methodology In this study data was collected from EHRs of 1,200 patients who underwent transthoracic echocardiography. The records were screened to identify HF diagnosis through an AI deep-learning algorithm using a Convolutional Neural Network (CNN). Medication status of patients identified as having HFrEF was assessed to determine whether they were on optimal guideline-directed medical therapy. Accuracy of the AI algorithm was assessed by a manual clinician review in a subset of 150 patients. Eligible patients not on optimal medical therapy where no further optimisation was planned (judged to be "stable" at last visit) were invited for a clinical evaluation including NT-proBNP and Kansas City Cardiomyopathy Questionnaire (KCCQ) and offered a treatment optimization that could include either uptitration or initiation of a new medication if symptomatic. NT-proBNP and KCCQ were repeated at 12 week, pre- and post-optimization results were compared to evaluate patient outcomes. Results The deep-learning algorithm accurately identified HFrEF patients (99%) and use of RAAS inhibitors (97%) and beta-blockers (92%). 73 patients attended for initial clinical assessment (mean age 70 years, 77% male) and demonstrated a moderate level of HF-symptom burden (median NT-proBNP was 679 pg/mL, interquartile range was from 220 to 1300) and mean KCCQ overall summary score was 70, indicating a moderate level of HF symptom burden. 27 patients agreed to treatment optimisation. After 12 weeks there was a significant 22% reduction in NT-proBNP (from 1991 pg/mL (SD+/- 1796) to 1630 pg/mL (SD +/- 1588),p=0.049) and improvements in KCCQ (total symptom score 79 (+/-22) to 85 (+/-20), p=0.005; clinical summary score 78 (+/-23) to 82 (+/-20); overall summary score from 75 (+/- 24) to 80 (+/-22), both p=0.04). 23 patients (85%) and 18 patients (67%) out of 27 had an improvement in NT-proBNP and KCCQ overall summary scores respectively. Conclusion In our study we demonstrated the feasibility and potential benefits incorporating AI into a clinical workflow, allowing optimising treatment of patients with HF, with improvements in biomarkers and quality of life that could translate to long-term patient benefits.Changes in NT-proBNP after treatmentChanges in KCCQ after treatment

Interaction between subclinical carotid atherosclerosis endotype and biological sex: a study from IMPROVE cohort

Abstract Background Carotid-intima media thickness (c-IMT) is a measure of subclinical carotid atherosclerosis and a predictor of future atherosclerotic cardiovascular events (ASCVD). We have recently generated, using an artificial intelligence approach, four endotypes (E) of subclinical atherosclerosis, i.e. subgroups of individuals predisposed to both c-IMT progression and ASCVD. Each E is defined by a set of demographic, clinical, and molecular data and characterize individuals with a low (E1), intermendiate (E2), high (E3) and very high (E4) cardiovascular risk profile. We observed an unbalanced distribution of males and females across the 4 endotypes, with a higher prevalence of female in the low risk E1. Purpose To estimate the interaction between biological sex and the 4 endotypes on measures of c-IMT progression and ASCVD risk. Methods We performed our study on 3121 individuals from the IMPROVE cohort, which includes participants with at least three ASCVD risk factors. We estimated the interaction between biological sex and the endotypes generated in the IMPROVE on (1) c-IMT and carotid plaque measurements after 30 months of follow-up (c-IMTfastest-progr, Area of plaquesbulb-progr) by linear regression (β, SE) and (2) with the 3-year ASCVD risk by Cox [hazard ratio (HR), 95% confidence interval (CI)] regression model. Crude estimates were adjusted by batch effect for biomarker measurement, geographical region and/or baseline ultrasonographic measures. Adjusted models further included ASCVD common risk factors. Results As shown in Figure 1, we did not observe any significant interaction between endotypes and biological sex on 30-month carotid ultrasound progression measures, and 3-year-ASCVD risk. In both the crude and adjusted models on c-IMTfastest-progr, Area of plaquesbulb-progr, and 3-year ASCVD, the p-value for interaction ranged from 0.18 to 0.98. While E4 was associated with the strongest cardiovascular risk profile in both males and females. Conclusions Our results indicate that the association between endotypes and progression of subclinical atherosclerosis and ASCVD risk mirrors a specific risk profile not entirely explained by differences in biological sex.

Forecasting cognitive functioning: an artificial intelligence approach with Dynamic Bayesian Networks

Abstract Background Several potentially modifiable risk factors are associated with subjective cognitive decline (SCD). However, developmental patterns of these risk factors have not been used before to forecast later SCD. Practical tools for the prevention of cognitive decline are needed. We examined multifactorial trajectories of risk factors, and their associations with SCD using an artificial intelligence approach to build a score that forecasts later SCD. Methods Five repeated surveys (2000-2022) of the Helsinki Health Study (n = 8960, 79% women, aged 40-60 at Phase 1) were used to build dynamic Bayesian networks (DBN) for estimating the odds of SCD. A score-based approach was implemented for learning DBN using the quotient normalized maximum likelihood criterion. The model was used to predict SCD based on the history of consumption of fruit and vegetables, smoking, alcohol consumption, leisure-time physical activity, body mass index, and insomnia symptoms, adjusting for sociodemographic covariates. Results Of the participants, 31-48% reported decline in memory, learning, and concentration in 2022. Physical activity was the strongest predictor of SCD in a 5-year interval, with an odds ratio of 0.76 (95% Bayesian credible interval 0.59-0.99) for physically active compared to inactive participants. Alcohol consumption showed a U-shaped relationship with SCD. Other risk factors had minor effects. Conclusions A new online risk score tool was developed that enables individuals to inspect their own risk profiles, as well as explore potential targets for interventions and their estimated contributions to later SCD. Dynamic decision heatmap was presented as a communication tool to be used at healthcare consultations. Key messages • Potentially modifiable health-related behaviours such as physical activity can contribute to subjective cognitive decline. • Artificial intelligence can be applied to discover the complex interactions between risk factors and subjective cognitive decline, to support preventive healthcare at individual and population levels.

Siamese neural networks can identify subjects from anonymised ECGs

Abstract Background/Introduction Many research databases contain anonymised electrocardiograms (ECGs) linked to other sensitive information. ECGs hold features unique to individuals, potentially enabling subject identification from anonymised ECGs. Purpose We assessed if artificial intelligence approaches to output ECG pair similarity can re-identify individuals from anonymised ECGs. Additionally, we aimed to explore clinical risk prediction using ECG similarity over time. Methods We used a convolutional Siamese neural network (SNN), with a triplet loss function, to train a deep learning model determining if two ECGs belong to the same individual (Figure 1). The model aims to encode ECG inputs from same subjects closer than ECG inputs from different subjects. An ECG similarity score is output, corresponding to the probability two ECGs belong to the same subject. This continuous metric can be used in a binary manner. Below a threshold, two ECGs are classed as from the same individual. Our dataset comprised 72,455 secondary care subjects from a USA cohort with 4 to 75 ECGs each, totalling 864,283 ECGs. These were split 50:10:40% at the subject level for training, validation and testing. Results In 2,689,124 same-subject pairs and 2,689,124 different-subject pairs from the test set, the model achieves an accuracy of 91.68% against the binary threshold. This improves to 93.61% and 95.97% in outpatient and normal ECG subsets, respectively. We tested the model using a subject’s ECG to identify them from a group where only one ECG also belongs to that subject. 89.4% success rates occur in groups of 100 normal ECGs (if random, success would be 1%). 91.4%, 95.1%, and 96.3% success occurs when supplemented with subject gender, age (decade-bracket), and both, respectively. We calculated a model certainty score for this task. 99% success occurs when >95% certain. This is in 65% of group size 100 trials. In groups of 1000 normal ECGs, the success rate is 72.5% with no additional information, and 75.9%, 84.2% and 87.5% when supplemented as before (if random, success would be 0.1%). >95% certainty occurs in 33% of group size 1000 trials. For a given subject, ECG pair similarity proved clinically informative. The model accounts for slight variations within a subject’s ECGs, even when far apart in time (Figure 2A). However, it incorrectly predicts two ECGs as being from different individuals for substantial ECG morphology changes (Figure 2B), which could be used to identify clinical deterioration. The temporal evolution of ECG pair similarity reflects clinical trajectory, with greater dissimilarity associating with all-cause mortality (Hazard ratio, 1.22 per 1 standard deviation change, p < 0.0001). Conclusion(s) Anonymised ECGs retain information useful for subject re-identification. Whilst this implies possible ethical and data protection issues with large datasets, such approaches offer potential for continuous monitoring and identifying clinical deterioration.Figure 1Figure 2

An Intelligent Scheduling Approach on Mobile OS for Optimizing UI Smoothness and Power

Mobile devices need to respond quickly to diverse user inputs. The existing approaches often heuristically raise the CPU/GPU frequency according to the empirical rules when facing burst inputs and various changes. Although doing so can be effective sometimes, the existing approaches still need improvements. For instance, raising processors’ frequency can lead to high power consumption when the frequency is over-provisioned or fails to meet user demands when the frequency is under-provisioned. To this end, we propose MobiRL, a reinforcement learning-based scheduler for intelligent adjusting CPU/GPU frequency to satisfy user demands accurately on mobile systems. MobiRL monitors the mobile system status and autonomously learns to optimize UI smoothness and power consumption by conducting CPU/GPU frequency-adjusting actions. The experimental results on the latest delivered smartphones show that MobiRL outperforms the widely used commercial scheduler on real devices – reducing the frame drop rate by 4.1% and reducing power consumption by 42.8%, respectively. Moreover, compared with a study using Q-Learning for CPU frequency scheduling, MobiRL achieves up to 2.5% lower frame drop rate and reduces power consumption by 32.6%, respectively. Our approach has been deployed in mobile phone products.

Enhancing Performance in Construction Project Management Teams

The seminar paper provides a pragmatic perspective of the topic of enhancing performance in construction project management teams. The paper provides a detailed collated review of several key areas pertaining to construction project management teams and their performance based on the findings of previous research. The study has focused on a range of aspects namely, leadership, competence and capabilities, effective communication, decision-making, interpersonal relationships and emotional intelligence, project management and governance approaches, trust, psychological safety, and team culture. Each sub-topic (aspect) has been structured in a manner that includes a brief introduction to the aspect, its importance for construction projects, the current state in construction projects, strategies, and approaches to eliminate the issues and contribute to increase in the team’s overall performance and finally, the novel strategies and tools pertaining to the focus area. The author anticipates that the content provided in this seminar paper shall be beneficial for effective decision making by organizational leadership and for the project leadership and the team to pay more consideration towards the strategies and approaches to enhance the elaborated aspects that are often overlooked in construction projects.

Current Trends in ICT Security: A Comprehensive Overview of Machine Learning and Soft Computing Applications

In an era marked by pervasive digitization, information and communication technology (ICT) security stands as a cornerstone of our digital infrastructure. The rapid evolution of cyber threats necessitates equally agile defence mechanisms. This research paper provides a comprehensive overview of the contemporary landscape of ICT security with a specific focus on the emergent trends in machine learning (ML) and soft computing (SC) applications. The paper commences by outlining the escalating challenges posed by cyber adversaries, underlining the inadequacy of traditional security paradigms in contending with the intricacies of modern-day attacks. This backdrop emphasizes the need for innovative, adaptable, and intelligent approaches to bolster ICT security.

Dynamic Temporal Denoise Neural Network with Multi-Head Attention for Fault Diagnosis Under Noise Background

Fault diagnosis plays a crucial role in maintaining the operational safety of mechanical systems. As intelligent data-driven approaches evolve, deep learning (DL) has emerged as a pivotal technique in fault diagnosis research. However, the collected vibrational signals from mechanical systems are usually corrupted by unrelated noises due to complicated transfer path modulations and component coupling. To solve the above problems, this paper proposed the dynamic temporal denoise neural network with multi-head attention (DTDNet). Firstly, this model transforms one-dimensional signals into two-dimensional tensors based on the periodic self-similarity of signals, employing multi-scale two-dimensional convolution kernels to extract signal features both within and across periods. Secondly, for the problem of lacking denoising structure in traditional convolutional neural networks, a temporal variable denoise (TVD) module with dynamic nonlinear processing is proposed to filter the noises. Lastly, a multi-head attention fusion (MAF) module is used to weight the denoted features of signals with different periods. Evaluation on two datasets, Case Western Reserve University bearing dataset (single sensor) and Real aircraft sensor dataset (multiple sensors), demonstrates that the DTDNet can reduce the useless noises in signals and achieve a remarkable improvement in classification performance compared with the state-of-the-art method. DTDNet provides a high-performance solution for potential noise that may occur in actual fault diagnosis tasks, which has important application value.

Generalized SmartScan: An Intelligent LPBF Scan Sequence Optimization Approach for Reduced Residual Stress and Distortion in 3D Part Geometries

Abstract Laser powder bed fusion (LPBF) is an additive manufacturing technique that is gaining popularity for producing metallic parts in various industries. However, parts produced by LPBF are prone to residual stress, deformation, cracks and other quality defects due to uneven temperature distribution during the LPBF process. To address this issue, in prior work, the authors have proposed SmartScan, a method for determining laser scan sequence in LPBF using an intelligent (i.e., model-based and optimization-driven) approach, rather than using heuristics, and applied it to simple 2D geometries. This paper presents a generalized SmartScan methodology that is applicable to arbitrary 3D geometries. This is achieved by: (1) expanding the thermal model and optimization approach used in SmartScan to multiple layers; (2) enabling SmartScan to process shapes with arbitrary contours and infill patterns within each layer; (3) providing the optimization in SmartScan with a balance of exploration and exploitation to make it less myopic; and (4) improving SmartScan’s computational efficiency via model order reduction using singular value decomposition. Sample 3D test artifacts are simulated and printed using SmartScan in comparison with common heuristic scan sequences. Reductions of up to 92% in temperature inhomogeneity, 86% in residual stress, 24% in maximum deformation and 50% in geometric inaccuracy were observed using SmartScan. An approach for using SmartScan for printing complex 3D parts in practice, by integrating it as a plug-in to a commercial slicing software, was also demonstrated experimentally, along with its benefits in significantly improving printed part quality.

Role of artificial intelligence in haematolymphoid diagnostics.

The advent of digital pathology and the deployment of high-throughput molecular techniques are generating an unprecedented mass of data. Thanks to advances in computational sciences, artificial intelligence (AI) approaches represent a promising avenue for extracting relevant information from complex data structures. From diagnostic assistance to powerful research tools, the potential fields of application of machine learning techniques in pathology are vast and constitute the subject of considerable research work. The aim of this article is to provide an overview of the potential applications of AI in the field of haematopathology and to define the role that these emerging technologies could play in our laboratories in the short to medium term.

SRS: An intelligent and robust approach for confirmation of plant transcription factor-DNA interactions.

SRS: An intelligent and robust approach for confirmation of plant transcription factor-DNA interactions.

A comparative analysis of intelligent energy modelling approaches for a grid-tied PVT system application

Renewable energy resources are a long-term answer to the current chronic energy dilemma. Solar photovoltaic (PV) systems are the most often used form of a practical solution for power supply and energy management in buildings. A PV-thermal (PVT) system is essential to lowering the demand for grid energy by capturing electrical and thermal energy from sunlight, optimising energy usage and encouraging sustainable energy practices. The most significant difficulty faced by developing countries, such as South Africa, is a lack of power supply to meet demand while limiting grid overload. An alternative source of energy is a critical component. This research compares two intelligent energy modelling methodologies: optimal control scheme-based open loop and model predictive control (MPC) in a PVT application. The primary goal of the modelling is to limit the electricity required from the grid while stressing the system’s energy efficiency and cost-effectiveness. The open loop approach uses mathematical optimisation techniques to establish the best control strategy for the PVT system. The ideal set-points for various system parameters are found by presenting the problem as an optimisation task to reduce grid power usage. The optimal control technique delivers a global optimisation solution based on the system dynamics and constraints. The MPC technique employs a predictive control technique of the PVT system to create optimal control actions in a receding horizon manner. The MPC algorithm anticipates future system behaviour and generates control actions that minimise grid power drawn by iteratively solving an optimisation problem at each time step. This robust online control scheme enables real-time modifications and responses to system disruptions that effectively and dynamically coordinate system behaviour.

Synthesis and evaluation of smart drugs with integrated functions for identifying and treating oxidative microenvironments associated with cellular ferroptosis

AbstractFerroptosis is a novel form of cell death driven by oxidative damage, and is implicated in various pathological conditions, including neurodegenerative diseases, retinal damage, and ischemia‐reperfusion injury of organs. Inhibiting ferroptosis has shown great promise as a therapeutic strategy for these diseases, underscoring the urgent need to develop effective ferroptosis inhibitors. Although Ferrostatin‐1 (Fer‐1) is a potent ferroptosis inhibitor, its susceptibility to oxidation and metabolic inactivation limits its clinical utility. In this study, the accumulation of peroxides and the resulting oxidative damage in the cellular microenvironment during ferroptosis were utilized to design Ferrostatin‐1 prodrugs with reactive oxygen species‐responsive features. This approach led to the development of a series of ferroptosis inhibitors that were capable of recognizing oxidative damage in diseased areas, allowing for targeted release and improved stability. The novel compounds demonstrated significant inhibitory effects and selectivity against RSL‐3‐induced ferroptosis in HK‐2 cells, with compound a1 exhibiting an EC50 of 15.4 ± 0.7 μM, outperforming Fer‐1. These compounds effectively identify the oxidative microenvironment associated with ferroptosis, enabling the targeted release of Fer‐1, which prevents lipid peroxide accumulation and inhibits ferroptosis. This strategy holds promise for treating diseases related to ferroptosis, offering a targeted and intelligent therapeutic approach.

A systematic review on artificial intelligence approaches for smart health devices

In the context of smart health, the use of wearable Internet of Things (IoT) devices is becoming increasingly popular to monitor and manage patients’ health conditions in a more efficient and personalized way. However, choosing the most suitable artificial intelligence (AI) methodology to analyze the data collected by these devices is crucial to ensure the reliability and effectiveness of smart healthcare applications. Additionally, protecting the privacy and security of health data is an area of growing concern, given the sensitivity and personal nature of such information. In this context, machine learning (ML) and deep learning (DL) are emerging as successful technologies because they are suitable for application to advanced analysis and prediction of healthcare scenarios. Therefore, the objective of this work is to contribute to the current state of the literature by identifying challenges, best practices, and future opportunities in the field of smart health. We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. Through this systematic review, we aim to offer practical guidelines for the design, development, and implementation of AI solutions in smart health, to improve the quality of care provided and promote patient well-being. To pursue our goal, several articles focusing on ML or DL network architectures were selected and reviewed. The final discussion highlights research gaps yet to be investigated, as well as the drawbacks and vulnerabilities of existing IoT applications in smart healthcare.

An Intelligent Approach to Automated Operating Systems Log Analysis for Enhanced Security

Self-healing systems have become essential in modern computing for ensuring continuous and secure operations while minimising downtime and maintenance costs. These systems autonomously detect, diagnose, and correct anomalies, with effective self-healing relying on accurate interpretation of system logs generated by operating systems (OSs). Manual analysis of these logs in complex environments is often cumbersome, time-consuming, and error-prone, highlighting the need for automated, reliable log analysis methods. Our research introduces an intelligent methodology for creating self-healing systems for multiple OSs, focusing on log classification using CountVectorizer and the Multinomial Naive Bayes algorithm. This approach involves preprocessing OS logs to ensure quality, converting them into a numerical format with CountVectorizer, and then classifying them using the Naive Bayes algorithm. The system classifies multiple OS logs into distinct categories, identifying errors and warnings. We tested our model on logs from four major OSs; Mac, Android, Linux, and Windows; sourced from Zenodo to simulate real-world scenarios. The model’s accuracy, precision, and reliability were evaluated, demonstrating its potential for deployment in practical self-healing systems.

A Framework to Design Engaging Interactions in Socially Assistive Robots to Mitigate Dementia-related Symptoms

People with dementia (PwD) often exhibit behavioral, physiological and social symptoms that affect their relationships and reduce their quality of life. Socially assistive robots (SARs) have proven effective in reducing some of these symptoms. However, the design of SARs for dementia care remains a challenging task. In this paper, we present a human-centered design framework to assist researchers, designers, and roboticist from the conceptualization of engaging prototypes of SARs until the assessment of their efficacy at producing dementia-relevant health outcomes. The framework incorporates several key principles of a Human-Centered Artificial Intelligence (HCAI) approach. It incorporates the needs and perspectives of the different stakeholders into the design and evaluation of the interactions. It fosters accessibility and inclusion thru personalization and encourages human-robot collaboration by facilitating natural and engaging interactions that build trust and rapport. We illustrate the use of the framework with the design of a SAR to support a Cognitive Stimulation Therapy. From this case study we derive a set of design insights for the development of SARs behaviors and interactions for dementia care.

Heat transfer enhancement using ternary hybrid nanofluid for cross-viscosity model with intelligent Levenberg-Marquardt neural networks approach incorporating entropy generation

Thermal heat transfer analysis of trihybrid nanofluids using an intelligent Levenberg-Marquardt neural network (ANN-LMA) approach, with a focus on entropy generation, has been conducted. The flow equations were modeled in Cartesian coordinates and simplified using dimensionless variables. Partial differential equations were converted into ordinary differential equations through appropriate similarity transformations. These ordinary differential equations were then solved using the finite element method applied to a data set evaluated from (ANN-LMA) approach. This dataset can be input into MATLAB to generate predicted solutions for flow patterns. The ANN-LMA technique was employed to evaluate the efficiency of heat transfer characteristics for nanofluids in various scenarios. Incorporating carbon nanotubes (both single-wall (SWCNT) and multi-wall (MWCNT)) along with iron oxide in water, the study demonstrates their effectiveness in enhancing heat transfer. These nanofluids have broad industrial applications, such as in coolant enhancement, cancer therapy, and solar radiation management, and show promising results. This study specifically examines the flow properties of water-based CNT cross-trihybrid nanofluids over a convectively heated surface, leveraging their unique characteristics. Improvements in heat transfer are achieved through the introduction of dissipative heat, thermal radiation, and external heat sources or sinks. The performance of the computational solver was assessed using error histograms, regression analyses, and Mean Squared Error (MSE) results. The physical significance of the designed factors is graphically depicted and discussed in detail. It was found that radiative heat increases surface heat energy through substantial accumulation, thereby enhancing heat transfer properties, while dissipative heat, due to Joule dissipation and other external sources, significantly raises the fluid temperature.