Search

Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH). We present the segmentation results for both the whole abnormal volume and for each abnormal component inside the examinations of the validation set. In the first case, a dice score coefficient (DSC), sensitivity, and precision of 0.76, 0.78, and 0.82, respectively, were found, while in the second case the model detected and segmented correct (True positives) the 48.8% (DSC ≥ 0.5) of abnormal components, partially correct the 27.1% (0.05 > DSC > 0.5), and missed (False Negatives) the 24.1%, while it produced 25.1% False Positives. Finally, we present an extended analysis between the True positives, False Negatives, and False positives versus their position inside the brain, their intensity at three MRI modalities (FLAIR, T2, and T1ce) and their volume. [ABSTRACT FROM AUTHOR]

In many industrial applications, datasets are often obtained in a sequence associated with a series of similar but different tasks. To model these datasets, a machine‐learning algorithm, which performed well on the previous task, may not have as strong a performance on the current task. When the architecture of the algorithm is trained to adapt to new tasks, often the whole architecture needs to be revised and the old knowledge of modeling can be forgotten. Efforts to make the algorithm work for all the relevant tasks can cost large computational resources and data storage. Continual learning, also called lifelong learning or continual lifelong learning, refers to the concept that these algorithms have the ability to continually learn without forgetting the information obtained from previous task. In this work, we provide a broad view of continual learning techniques and their industrial applications. Our focus will be on reviewing the current methodologies and existing applications, and identifying a gap between the current methodology and the modern industrial needs. This article is categorized under:Technologies > Artificial IntelligenceFundamental Concepts of Data and Knowledge > Knowledge RepresentationApplication Areas > Business and Industry [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the prognostic utility of CT-imaging-derived biomarkers in distinguishing acute pulmonary embolism (PE) resolution and its progression to chronic PE, as well as their association with clot burden. Materials and Methods: We utilized a cohort of 45 patients (19 male (42.2%)) and 96 corresponding CT scans with exertional dyspnea following an acute PE. These patients were referred for invasive cardiopulmonary exercise testing (CPET) at the University of Pittsburgh Medical Center from 2018 to 2022, for whom we have ground truth classification of chronic PE, as well as CT-derived features related to body composition, cardiopulmonary vasculature, and PE clot burden using artificial intelligence (AI) algorithms. We applied Lasso regularization to select parameters, followed by (1) Ordinary Least Squares (OLS) regressions to analyze the relationship between clot burden and the selected parameters and (2) logistic regressions to differentiate between chronic and resolved patients. Results: Several body composition and cardiopulmonary factors showed statistically significant association with clot burden. A multivariate model based on cardiopulmonary features demonstrated superior performance in predicting PE resolution (AUC: 0.83, 95% CI: 0.71–0.95), indicating significant associations between airway ratio (negative correlation), aorta diameter, and heart volume (positive correlation) with PE resolution. Other multivariate models integrating demographic features showed comparable performance, while models solely based on body composition and baseline clot burden demonstrated inferior performance. Conclusions: Our analysis suggests that cardiopulmonary and demographic features hold prognostic value for predicting PE resolution, whereas body composition and baseline clot burden do not. Clinical Relevance: Our identified prognostic factors may facilitate the follow-up procedures for patients diagnosed with acute PE. [ABSTRACT FROM AUTHOR]

This study focuses on slope stability analysis, a critical process for understanding the conditions, durability, mass properties, and failure mechanisms of slopes. The research specifically addresses rotational-type failure, the primary instability mechanism affecting earth slopes. Identifying and understanding key factors such as slope height, slope angle, density, cohesion, friction, water pore pressure, and tensile cracks are essential for effective stabilization strategies. The objective of this study is to develop accurate predictive models for slope stability analysis using advanced intelligent techniques, including data mining mapping and complex decision tree regression (DTR). The models were validated using performance metrics such as mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and the coefficient of determination (R²). Additionally, overall accuracy was assessed using a confusion matrix. The predictive model was tested on a dataset of 120 slope cases, achieving an accuracy of approximately 91.07% with DTR. The error rates for the training set were MAE = 0.1242, MSE = 0.1722, and RMSE = 0.1098, demonstrating the model’s capability to effectively analyze and predict slope stability in earth slopes and embankments. The study concludes that these intelligent techniques offer a reliable approach for stability analysis, contributing to safer and more efficient slope management.

Cancer screening and diagnosis with the utilization of innovative Artificial Intelligence tools improved the treatment strategies and patients' survival. With the rapid development of imaging technologies and the rise of artificial intelligence (AI), there is a significant opportunity to improve cancer diagnostics through the combination of image analysis and AI algorithms. This article provides a comprehensive review of studies that have investigated the application of AI-assisted image processing in cancer diagnosis. We searched the Web of Science and Scopus databases to identify relevant studies published between 2014 and January 2024. The search strategy utilized targeted keywords such as cancer diagnostics, image analysis, artificial intelligence, and advanced imaging techniques. We limited the review to articles written in English and using AI-assisted image processing in cancer diagnosis. The results show that by leveraging machine learning algorithms, including deep learning, computer-aided diagnosis systems have been developed that are efficient in detecting tumors, thereby facilitating early cancer detection. Additionally, various authors have explored the integration of personalized treatment approaches and precision medicine, allowing for the development of treatment plans tailored to individual patient characteristics and needs. The review emphasizes the potential of AI-assisted image processing in revolutionizing cancer diagnostics. The insights gained from this study contribute to the current understanding of the field and pave the way for future research and development aimed at advancing cancer diagnostics using image analysis and artificial intelligence. [ABSTRACT FROM AUTHOR]

Abstract Spinal deformities, including adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD), affect many patients. The measurement of the Cobb angle on coronal radiographs is essential for their diagnosis and treatment planning. To enhance the precision of Cobb angle measurements for both AIS and ASD, we developed three distinct artificial intelligence (AI) algorithms: AIS/ASD-trained AI (trained with both AIS and ASD cases); AIS-trained AI (trained solely on AIS cases); ASD-trained AI (trained solely on ASD cases). We used 1612 whole-spine radiographs, including 1029 AIS and 583 ASD cases with variable postures, as teaching data. We measured the major and two minor curves. To assess the accuracy, we used 285 radiographs (159 AIS and 126 ASD) as a test set and calculated the mean absolute error (MAE) and intraclass correlation coefficient (ICC) between each AI algorithm and the average of manual measurements by four spine experts. The AIS/ASD-trained AI showed the highest accuracy among the three AI algorithms. This result suggested that learning across multiple diseases rather than disease-specific training may be an efficient AI learning method. The presented AI algorithm has the potential to reduce errors in Cobb angle measurements and improve the quality of clinical practice.

To understand the basic concepts and principles of artificial intelligence (AI) and to solve problems using AI, it is necessary to use various platforms. Among AI machine-learning (ML) models, the prediction algorithm is a basic AI model that can be used in various fields, such as for predicting weather, grades, product prices, and population, and is likely to be used to gain a basic understanding of AI. Many educational AI platforms implement prediction algorithms to help understand these AI models. In this study, prediction algorithms were implemented using the following AI platforms: Orange3, Entry, and Python to learn the temperature data in the Seoul area of Korea using a linear regression model, predict the value of temperature change, and evaluate the performance of the prediction algorithm for each platform. Additionally, to understand machine learning classification models and develop effective teaching methods, we conducted a prototype test to compare and analyze each platform's photo classification methods and performance. As a result of the comparison, Python exhibited the best performance, followed by Orange3 and Entry, with differences in accuracy and predicted values. To understand AI, it is necessary to understand the reliability of AI models and use an appropriate platform that considers the development level of the learner. In the future, we aim to research different ways to efficiently understand AI by comparing and analyzing its performance using various AI models. [ABSTRACT FROM AUTHOR]

Spinal deformities, including adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD), affect many patients. The measurement of the Cobb angle on coronal radiographs is essential for their diagnosis and treatment planning. To enhance the precision of Cobb angle measurements for both AIS and ASD, we developed three distinct artificial intelligence (AI) algorithms: AIS/ASD-trained AI (trained with both AIS and ASD cases); AIS-trained AI (trained solely on AIS cases); ASD-trained AI (trained solely on ASD cases). We used 1612 whole-spine radiographs, including 1029 AIS and 583 ASD cases with variable postures, as teaching data. We measured the major and two minor curves. To assess the accuracy, we used 285 radiographs (159 AIS and 126 ASD) as a test set and calculated the mean absolute error (MAE) and intraclass correlation coefficient (ICC) between each AI algorithm and the average of manual measurements by four spine experts. The AIS/ASD-trained AI showed the highest accuracy among the three AI algorithms. This result suggested that learning across multiple diseases rather than disease-specific training may be an efficient AI learning method. The presented AI algorithm has the potential to reduce errors in Cobb angle measurements and improve the quality of clinical practice. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) promises to revolutionize many fields, but its clinical implementation in cardiovascular imaging is still rare despite increasing research. We sought to facilitate discussion across several fields and across the lifecycle of research, development, validation, and implementation to identify challenges and opportunities to further translation of AI in cardiovascular imaging. Furthermore, it seemed apparent that a multidisciplinary effort across institutions would be essential to overcome these challenges. This paper summarizes the proceedings of the National Heart, Lung, and Blood Institute-led workshop, creating consensus around needs and opportunities for institutions at several levels to support and advance research in this field and support future translation.

ObjectiveOral cancer is a widespread global health problem characterised by high mortality rates, wherein early detection is critical for better survival outcomes and quality of life. While visual examination is the primary method for detecting oral cancer, it may not be practical in remote areas. AI algorithms have shown some promise in detecting cancer from medical images, but their effectiveness in oral cancer detection remains Naïve. This systematic review aims to provide an extensive assessment of the existing evidence about the diagnostic accuracy of AI-driven approaches for detecting oral potentially malignant disorders (OPMDs) and oral cancer using medical diagnostic imaging.MethodsAdhering to PRISMA guidelines, the review scrutinised literature from PubMed, Scopus, and IEEE databases, with a specific focus on evaluating the performance of AI architectures across diverse imaging modalities for the detection of these conditions.ResultsThe performance of AI models, measured by sensitivity and specificity, was assessed using a hierarchical summary receiver operating characteristic (SROC) curve, with heterogeneity quantified through I2 statistic. To account for inter-study variability, a random effects model was utilized. We screened 296 articles, included 55 studies for qualitative synthesis, and selected 18 studies for meta-analysis. Studies evaluating the diagnostic efficacy of AI-based methods reveal a high sensitivity of 0.87 and specificity of 0.81. The diagnostic odds ratio (DOR) of 131.63 indicates a high likelihood of accurate diagnosis of oral cancer and OPMDs. The SROC curve (AUC) of 0.9758 indicates the exceptional diagnostic performance of such models. The research showed that deep learning (DL) architectures, especially CNNs (convolutional neural networks), were the best at finding OPMDs and oral cancer. Histopathological images exhibited the greatest sensitivity and specificity in these detections.ConclusionThese findings suggest that AI algorithms have the potential to function as reliable tools for the early diagnosis of OPMDs and oral cancer, offering significant advantages, particularly in resource-constrained settings.Systematic Review Registrationhttps://www.crd.york.ac.uk/, PROSPERO (CRD42023476706).

The F-16 aircraft, widely used by the Polish Army Air Force, requires modifications based on Artificial Intelligence (AI) algorithms to enhance its combat capabilities and performance. This study aims to develop comprehensive guidelines for this purpose by first describing F-16 systems and categorizing AI algorithms. Machine learning, deep learning, fuzzy logic, evolutionary algorithms, and swarm intelligence are reviewed for their potential applications in modern aircraft. Subsequently, specific algorithms applicable to F-16 systems are identified, with conclusions drawn on their suitability based on system features. The resultant analysis informs potential F-16 modifications and anticipates future AI applications in military aircraft, facilitating the guidance of new algorithmic developments and offering benefits to similar aircraft types. Moreover, directions for future research and development work are delineated.

Personalized medicine has become more prominent in the course of the last few years to improve treatment methods by taking into account patients' genetic makeup. Combining the genomic information into powerful new AI platforms in drug therapies opens up the way of reducing drug toxicity while enhancing the prospects for drug efficacy. This pilot study aims to determine the possibilities of using AI to analyze genomics data to help improve the approachability and effectiveness of drug therapies, which has been a major challenge given the lacunae in precision in the treatment strategies used. This pilot study is intended to enroll 50 patients with diverse chronic diseases. Targeted gene-specific sequencing was performed to obtain polymorphic loci on drug metabolism and treatment efficacy. AI tools such as machine learning models are used to help find patterns and relationships between genomic data and treatment results and risks. These were then compared to clinical outcomes in order to determine the viability of the AI-integrated method for recommending drug regimens. This study shows that the incorporation of genomic data in conjunction with AI greatly improves the accuracy of individualized pharmacotherapy. The AI-generated suggestions matched well with the enhanced patient experience to show the potential of this concept in the real world. It employs a broader clinically ascertained population and is warranted to replicate these findings, supporting the benefits of using genomic-informed AI applications for drug therapy to drive further development of personalized medicine. [ABSTRACT FROM AUTHOR]

Gastric cancer is a prevalent and deadly malignancy, and the response to immunotherapy varies among patients. This study aimed to develop a prognostic model for gastric cancer patients and investigate immune escape mechanisms using deep machine learning and single‐cell sequencing analysis. Data from public databases were analysed, and a prediction model was constructed using 101 algorithms. The high‐AIDPS group, characterized by increased AIDPS expression, exhibited worse survival, genomic variations and immune cell infiltration. These patients also showed immunotherapy tolerance. Treatment strategies targeting the high‐AIDPS group identified three potential drugs. Additionally, distinct cluster groups and upregulated AIDPS‐associated genes were observed in gastric adenocarcinoma cell lines. Inhibition of GHRL expression suppressed cancer cell activity, inhibited M2 polarization in macrophages and reduced invasiveness. Overall, AIDPS plays a critical role in gastric cancer prognosis, genomic variations, immune cell infiltration and immunotherapy response, and targeting GHRL expression holds promise for personalized treatment. These findings contribute to improved clinical management in gastric cancer. [ABSTRACT FROM AUTHOR]

Technology has played a critical role in enhancing the effectiveness and development of forensic processes. Digitalization and the use of automated biometric technologies, like the Automated Fingerprint Identification Systems (AFIS) for instance, has been a significant step forward, increasing accountability and transparency, and improving the accuracy and efficiency of evidence processing. Traditionally these systems are search systems only, and require the implementation of additional workflow strategies to support forensic processes in best practice approaches. More recently, the introduction of statistical models represents strategies to strengthen the reliability of forensic science by combining human-based methods with computer-assisted approaches. [ABSTRACT FROM AUTHOR]

The intricate ensemble of human wellbeing resounds inside both our actual developments and the unpretentious songs of our psyches. While conventional medical services frequently centers around either, a hole exists in grasping the perplexing exchange between them. This examination, named "Body as Information, Brain as Guide: A Combination of Brain and Actual Signs for Customized Medical services," overcomes this issue by investigating the capability of multimodal information combination in driving customized protection and remedial mediations. The human body is an ensemble of information, where development and thought complicatedly interweave. Customary medical services frequently compartmentalizes these signs, sitting above the rich embroidered artwork of data woven from both physical and brain action. This research plunges into the unfamiliar domain of biosignal combination, endeavoring to divulge the maximum capacity of joined brain and actual information for customized medical services. Make an exhaustive system for biosignal combination, coordinating information from different sources such that jam security, guarantees moral information dealing with, and enables people to take part effectively in their medical services venture. Objectives: This examination plans to: Foster an original structure for multimodal information combination that incorporates continuous biosignals (EEG, EMG, physiological) with actual work information (development sensors, GPS) to make an extensive image of individual wellbeing status. Distinguish and separate key biomarkers from the melded information that precisely reflect individual varieties in wellbeing, mental prosperity, and sickness risk. Plan and execute simulated intelligence fueled customized intercessions that influence the experiences acquired from biosignal combination to forestall infection, improve wellbeing ways of behaving, and oversee ongoing circumstances. Investigate the moral and cultural ramifications of multimodal information combination in customized medical care, resolving issues of information security, algorithmic predisposition, and evenhanded admittance to innovation. This exploration will utilize a blended strategies approach, consolidating state of the art man-made intelligence calculations for information combination and examination with true information assortment from different member gatherings. This multi-pronged methodology will guarantee the generalizability and clinical significance of the discoveries. This exploration is supposed to: Advance the field of customized medical care by giving a structure to far reaching biosignal combination and its ensuing interpretation into noteworthy experiences. Foster novel simulated intelligence fueled mediations custom fitted to individual requirements and wellbeing weaknesses for deterrent and restorative applications. Illuminate moral structures for mindful information assortment, examination, and use with regards to multimodal medical services. Add to a future where human wellbeing isn't only observed however effectively directed by the agreeable combination of body and brain. This examination vows to open the extraordinary capability of biosignal combination for customised medical care. By organising the ensemble of information from our bodies and brains, we can move towards a fate of individualised medical services mediations, enabling people to become dynamic guides of their own prosperity. [ABSTRACT FROM AUTHOR]

Diagnostic and interventional aspects of psychiatric care can be augmented by the use of digital health technologies. Recent studies have tried to explore the use of artificial intelligence-driven technologies in screening, diagnosing, and treating psychiatric disorders. This short communication presents a current perspective on using Artificial Intelligence in psychiatry.

Diagnostic and interventional aspects of psychiatric care can be augmented by the use of digital health technologies. Recent studies have tried to explore the use of artificial intelligence-driven technologies in screening, diagnosing, and treating psychiatric disorders. This short communication presents a current perspective on using Artificial Intelligence in psychiatry. [ABSTRACT FROM AUTHOR]

Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts’ accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH). We present the segmentation results for both the whole abnormal volume and for each abnormal component inside the examinations of the validation set. In the first case, a dice score coefficient (DSC), sensitivity, and precision of 0.76, 0.78, and 0.82, respectively, were found, while in the second case the model detected and segmented correct (True positives) the 48.8% (DSC ≥ 0.5) of abnormal components, partially correct the 27.1% (0.05 > DSC > 0.5), and missed (False Negatives) the 24.1%, while it produced 25.1% False Positives. Finally, we present an extended analysis between the True positives, False Negatives, and False positives versus their position inside the brain, their intensity at three MRI modalities (FLAIR, T2, and T1ce) and their volume.

Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy-and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware. [ABSTRACT FROM AUTHOR]

The F-16 aircraft, widely used by the Polish Army Air Force, requires modifications based on Artificial Intelligence (AI) algorithms to enhance its combat capabilities and performance. This study aims to develop comprehensive guidelines for this purpose by first describing F-16 systems and categorizing AI algorithms. Machine learning, deep learning, fuzzy logic, evolutionary algorithms, and swarm intelligence are reviewed for their potential applications in modern aircraft. Subsequently, specific algorithms applicable to F-16 systems are identified, with conclusions drawn on their suitability based on system features. The resultant analysis informs potential F-16 modifications and anticipates future AI applications in military aircraft, facilitating the guidance of new algorithmic developments and offering benefits to similar aircraft types. Moreover, directions for future research and development work are delineated. [ABSTRACT FROM AUTHOR]

The wrist is one of the most complex joints in our body, composed of eight bones. Therefore, measuring the angles of this intricate wrist movement can prove valuable in various fields such as sports analysis and rehabilitation. Textile stretch sensors can be easily produced by immersing an E-band in a SWCNT solution. The lightweight, cost-effective, and reproducible nature of textile stretch sensors makes them well suited for practical applications in clothing. In this paper, wrist angles were measured by attaching textile stretch sensors to an arm sleeve. Three sensors were utilized to measure all three axes of the wrist. Additionally, sensor precision was heightened through the utilization of the Multi-Layer Perceptron (MLP) technique, a subtype of deep learning. Rather than fixing the measurement values of each sensor to specific axes, we created an algorithm utilizing the coupling between sensors, allowing the measurement of wrist angles in three dimensions. Using this algorithm, the error angle of wrist angles measured with textile stretch sensors could be measured at less than 4.5°. This demonstrated higher accuracy compared to other soft sensors available for measuring wrist angles. [ABSTRACT FROM AUTHOR]

Copyright of European Review of Public Law is the property of European Public Law Organization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The electronics industry faces a challenge posed by cracks in multilayer ceramic capacitors (MLCC), which can undermine device reliability and longevity. In this study, we investigate the multifaceted factors underpinning crack formation, unveiling their intimate connections with corrosion, contamination, and mold. We show that hygroscopic properties, humidity exposure, and ion migration, play a role as precursors triggering both the inception and escalation of cracks. The correlation between corrosion, contamination, and cracking mechanisms in MLCCs presents a unique opportunity, as the visibility of corrosion and contamination on the component's exterior offers a distinct advantage for detection, unlike the elusive nature of cracks which are often challenging to identify. We introduce a solution—an encompassing visual inspection methodology designed to detect corrosion evidence on electronic components. This approach employs advanced AI algorithms and pick-and-place machine cameras already in-place to examine all components during assembly. The algorithm detects corrosion indicators, effectively neutralizing the detrimental effects of corrosion and mitigating its potential role in crack formation. Our work includes the presentation of the AI model, which showcases exceptional accuracy in identifying corrosion-associated concerns. This innovative tool is directly confronting a major root cause of cracks. This novel solution marks a substantial stride toward fortifying product reliability and extending the operational lifespan of electronic devices. [ABSTRACT FROM AUTHOR]

The study aims to recognize how efficiently Educational DataMining (EDM) integrates into Artificial Intelligence (AI) to develop skills for predicting students' performance. The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University. The first step's initial population placements were created using Particle Swarm Optimization (PSO). Then, using adaptive feature space search, Educational Grey Wolf Optimization (EGWO) was employed to choose the optimal attribute combination. The second stage uses the SVM classifier to forecast classification accuracy. Different classifiers were utilized to evaluate the performance of students. According to the results, it was revealed that AI could forecast the final grades of students with an accuracy rate of 97% on the test dataset. Furthermore, the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50% and could be categorized as having equal information ratio gain after the semester. While the random forest provided an accuracy of 28%. These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy (12%). The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance, reducing chances of failure, and taking appropriate steps at the right time to raise the standards of education. The study also motivates academics to assess and discover EDM at several other universities. [ABSTRACT FROM AUTHOR]

The identification of digital market segments to make value-creating propositions is a major challenge for entrepreneurs and marketing managers. New technologies and the Internet have made it possible to collect huge volumes of data that are difficult to analyse using traditional techniques. The purpose of this research is to address this challenge by proposing the use of AI algorithms to cluster customers. Specifically, the proposal is to compare the suitability of supervised algorithms, XGBoost, versus unsupervised algorithms, K-means, for segmenting the digital market. To do so, both algorithms have been applied to a sample of 5 million Spanish users captured between 2010 and 2022 by a lead generation start-up. The results show that supervised learning with this type of data is more useful for segmenting markets than unsupervised learning, as it provides solutions that are better suited to entrepreneurs' commercial objectives. [ABSTRACT FROM AUTHOR]

Artificial intelligence tools in radiology practices have surged, with modules developed to target specific findings becoming increasingly prevalent and proving valuable in the daily emergency room radiology practice. The number of US Food and Drug Administration-cleared radiology-related algorithms has soared from just 10 in early 2017 to over 200 presently. This review will concentrate on the present utilization of AI tools in clinical ER radiology setting, including a brief discussion of the limitations of the technique. As radiologists, it is essential that we embrace this technology, comprehend its constraints, and use it to improve patient care. [ABSTRACT FROM AUTHOR]

The emergence of the infectious diseases, such as the novel coronavirus, as a significant global health threat has emphasized the urgent need for effective treatments and vaccines. As infectious diseases become more common around the world, it is important to have strategies in place to prevent and monitor them. This study reviews hybrid models that incorporate emerging technologies for preventing and monitoring infectious diseases. It also presents a comprehensive review of the hybrid models employed for preventing and monitoring infectious diseases since the outbreak of COVID-19. The review encompasses models that integrate emerging and innovative technologies, such as blockchain, Internet of Things (IoT), big data, and artificial intelligence (AI). By harnessing these technologies, the hybrid system enables secure contact tracing and source isolation. Based on the review, a hybrid conceptual framework model proposes a hybrid model that incorporates emerging technologies. The proposed hybrid model enables effective contact tracing, secure source isolation using blockchain technology, IoT sensors, and big data collection. A hybrid model that incorporates emerging technologies is proposed as a comprehensive approach to preventing and monitoring infectious diseases. With continued research on and the development of the proposed model, the global efforts to effectively combat infectious diseases and safeguard public health will continue. [ABSTRACT FROM AUTHOR]

The aim of this cross-sectional study was to investigate the factors associated with trust in AI algorithms used in the e-commerce industry in Romania. The motivation for conducting this analysis arose from the observation of a research gap in the Romanian context regarding this specific topic. The researchers utilized a non-probability convenience sample of 486 college students enrolled at a public university in Romania, who participated in a web-based survey focusing on their attitudes towards AI in e-commerce. The findings obtained from an ordinal logistic model indicated that trust in AI is significantly influenced by factors such as transparency, familiarity with other AI technologies, perceived usefulness of AI recommenders, and the students' field of study. To ensure widespread acceptance and adoption by consumers, it is crucial for e-commerce companies to prioritize building trust in these new technologies. This study makes significant contributions to our understanding of how young consumers in Romania perceive and evaluate AI algorithms utilized in the e-commerce sector. The findings provide valuable guidance for e-commerce practitioners in Romania seeking to effectively leverage AI technologies while building trust among their target audience. [ABSTRACT FROM AUTHOR]

The electrical energy industry depends, among other things, on the ability of networks to deal with uncertainties from several directions. Smart-grid systems in high-voltage direct current (HVDC) networks, being an application of artificial intelligence (AI), are a reliable way to achieve this goal as they solve complex problems in power system engineering using AI algorithms. Due to their distinctive characteristics, they are usually effective approaches for optimization problems. They have been successfully applied to HVDC systems. This paper presents a number of issues in HVDC transmission systems. It reviews AI applications such as HVDC transmission system controllers and power flow control within DC grids in multi-terminal HVDC systems. Advancements in HVDC systems enable better performance under varying conditions to obtain the optimal dynamic response in practical settings. However, they also pose difficulties in mathematical modeling as they are non-linear and complex. ANN-based controllers have replaced traditional PI controllers in the rectifier of the HVDC link. Moreover, the combination of ANN and fuzzy logic has proven to be a powerful strategy for controlling excessively non-linear loads. Future research can focus on developing AI algorithms for an advanced control scheme for UPFC devices. Also, there is a need for a comprehensive analysis of power fluctuations or steady-state errors that can be eliminated by the quick response of this control scheme. This survey was informed by the need to develop adaptive AI controllers to enhance the performance of HVDC systems based on their promising results in the control of power systems. Doi: 10.28991/ESJ-2023-07-02-024 Full Text: PDF