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In the past decade, substantial global efforts have been devoted to the development of reliable and efficient solutions for early flood warning and monitoring. One of the most common strategies for tackling this challenge involves the application of computer vision techniques to images obtained from the numerous surveillance cameras present in urban settings today. While there are various datasets available for training and testing these techniques, none of them specifically addresses the issue of out-of-distribution (OoD) behavior. This issue becomes particularly critical when evaluating the reliability of these methods under challenging environmental conditions. Our work stands as the first attempt to bridge this gap by introducing a new, highly controlled synthetic dataset that encompasses the essential attributes required for analyzing OoD behavior. The very high correlation between the accuracy of artificial intelligence (AI) models trained on our synthetic dataset and models trained on real-world data proves our dataset's ability to predict real-world OoD behavior reliably. [ABSTRACT FROM AUTHOR]

Objectives. This study aimed to compare postural assessment and postural awareness using the artificial intelligence posture evaluation and correction system (APECS) in individuals receiving posture training. Methods. Participants’ physical characteristics were recorded. The participants’ posture was evaluated with APECS and the New York posture rating chart test (NYPR), and their body and postural awareness was evaluated with the body awareness questionnaire (BAQ), postural awareness form (PAF) and postural habits and awareness scale (PHAS). Results. In the study, results of the PAF (p = 0.000), BAQ (p = 0.013) and PHAS (p = 0.033) were found to be different between the groups. While the groups were similar in the PHAS sub-dimension of postural habit (p = 0.331), there was a significant difference between the groups in postural awareness (p = 0.04). NYPR results of the participants in the group receiving posture training were similar to those in the group not receiving posture training (p = 0.45). Conclusion. Postural deviations measured by digital posture assessment in individuals receiving posture training were significantly different in the group receiving posture training. Postural awareness and body awareness were better in individuals who received posture training than in individuals who did not receive posture training. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence technology with medicine has rapidly evolved, with increasing demands for quality of life. However, falls remain a significant risk leading to severe injuries and fatalities, especially among the elderly. Therefore, the development and application of computer vision-based fall detection technologies have become increasingly important. In this paper, firstly, the keypoint detection algorithm ViTPose++ is used to obtain the coordinates of human body keypoints from the camera images. Human skeletal feature maps are generated from this keypoint coordinate information. Meanwhile, human dense feature maps are produced based on the DensePose algorithm. Then, these two types of feature maps are confused as dual-channel inputs for the model. The convolutional gated recurrent unit is introduced to extract the frame-to-frame relevance in the process of falling. To further integrate features across three dimensions (spatio-temporal-channel), a dual-channel fall detection algorithm based on video streams is proposed by combining the Convolutional Block Attention Module (CBAM) with the ConvGRU. Finally, experiments on the public UR Fall Detection Dataset demonstrate that the improved ConvGRU-CBAM achieves an F1 score of 92.86% and an AUC of 95.34%. [ABSTRACT FROM AUTHOR]

Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites. Using laparoscopic cholecystectomy and semantic segmentation models for predicting safe/dangerous ("Go"/"No-Go") zones of dissection as an example use case, this study aimed to develop and test the performance of a novel data pipeline linked to a web-platform that enables real-time deployment from any edge device. To test this infrastructure and demonstrate its scalability and generalizability, lightweight U-Net and SegFormer models were trained on annotated frames from a large and diverse multicenter dataset from 136 institutions, and then tested on a separate prospectively collected dataset. A web-platform was created to enable real-time inference on any surgical video stream, and performance was tested on and optimized for a range of network speeds. The U-Net and SegFormer models respectively achieved mean Dice scores of 57% and 60%, precision 45% and 53%, and recall 82% and 75% for predicting the Go zone, and mean Dice scores of 76% and 76%, precision 68% and 68%, and recall 92% and 92% for predicting the No-Go zone. After optimization of the client-server interaction over the network, we deliver a prediction stream of at least 60 fps and with a maximum round-trip delay of 70 ms for speeds above 8 Mbps. Clinical deployment of machine learning models for surgical guidance is feasible and cost-effective using a generalizable, scalable and equipment-agnostic framework that lacks dependency on hardware with high computing performance or ultra-fast internet connection speed. [ABSTRACT FROM AUTHOR]

Human-centered robotic applications are becoming pervasive in the context of robotics and smart manufacturing, and such a pervasiveness is even more expected with the shift to Industry 5.0. The always increasing level of autonomy of modern robotic platforms requires the integration of software applications from different domains to implement artificial intelligence, cognition, and human-robot/robot-robot interaction. Developing and (re)configuring such a multi-domain software to meet functional constraints is a challenging task. Even more challenging is customizing the software to satisfy non-functional requirements such as real-time, reliability, and energy efficiency. In this context, the concept of Edge-Cloud continuum is gaining consensus as a solution to address functional and non-functional constraints in a seamless way. Containerization and orchestration are becoming a standard practice, as they allow for better information flow among different network levels as well as increased modularity in the use of multi-domain software components. Nevertheless, the adoption of such a practice along the design flow, from simulation to the deployment of complex robotic applications by addressing the de facto development standards (e.g., ROS - Robotic Operating System) is still an open problem. We present a design methodology based on Docker and Kubernetes that enables containerization and orchestration of ROS-based robotic SW applications for heterogeneous and hierarchical HW architectures. The methodology aims at (i) integrating and verifying multi-domain components since early in the design flow, (ii) mapping software tasks to containers to minimize the performance and memory footprint overhead, (iii) clustering containers to efficiently distribute load across the edge-cloud architecture by minimizing resource utilization, and (iv) enabling multi-domain verification of functional and non-functional constraints before deployment. The article presents the results obtained with a real case of study, in which the design methodology has been applied to program the mission of a Robotnik RB-Kairos mobile robot in an industrial agile production chain. We have obtained reduced load on the robot's HW with minimal performance and network overhead, thanks to the optimized distributed system. [ABSTRACT FROM AUTHOR]

While the massive adoption of Artificial Intelligence (AI) is threatening the environment, new research efforts begin to be employed to measure and mitigate the carbon footprint of both training and inference phases. In this domain, two carbon-aware training strategies have been proposed in the literature: Flexible Start and Pause & Resume. Such strategies—natively Cloud-based—use the time resource to postpone or pause the training algorithm when the carbon intensity reaches a threshold. While such strategies have proved to achieve interesting results on a benchmark of modern models covering Natural Language Processing (NLP) and computer vision applications and a wide range of model sizes (up to 6.1B parameters), it is still unclear whether such results may hold also with different algorithms and in different geographical regions. In this confirmation study, we use the same methodology as the state-of-the-art strategies to recompute the saving in carbon emissions of Flexible Start and Pause & Resume in the Anomaly Detection (AD) domain. Results confirm their effectiveness in two specific conditions, but the percentage reduction behaves differently compared with what is stated in the existing literature. [ABSTRACT FROM AUTHOR]

This study presents an applied study with a pure experimental design to evaluate the effectiveness of the application, with the objective of reducing the levels of loneliness and depression through the use of a mobile application based on neural networks in older adults in the city of Trujillo. In addition, software development technologies such as Flutter, Flask, and ExpressJs were used. The results indicate a significant decrease in the levels of emotional and social loneliness and depression in the experimental group (EG) compared to the control group (CG). Reductions of 15.03% in the level of emotional loneliness (LEL), 24.74% in the level of social loneliness, and a remarkable 44.12% in the level of depression were observed in the EG. In addition, a significant increase of 6% in the mood index was evidenced in the EG. This led to the conclusion that after mobile implementation based on neural networks, the alternative hypothesis is supported, statistically demonstrating the effectiveness of the application in reducing loneliness and depression in older adults. These findings suggest a positive impact on the emotional well-being of this population, underscoring the utility of technology in addressing mental health issues in this demographic. [ABSTRACT FROM AUTHOR]

Background: Patient-specific risk profiles of clinical failure after anterior cruciate ligament reconstruction (ACLR) are meaningful for preoperative surgical planning and postoperative rehabilitation guidance. Purpose: To create an ensemble algorithm machine learning (ML) model and ML-based web-based tool that can predict the patient-specific risk of clinical failure after ACLR. Study Design: Cohort study; Level of evidence, 3. Methods: Included were 432 patients (mean age, 26.8 ± 8.4 years; 74.1% male) who underwent anatomic double-bundle ACLR with hamstring tendon autograft between January 2010 and February 2019. The primary outcome was the probability of clinical failure at a minimum 2-year follow-up. The authors included 24 independent variables for feature selection and model development. The data set was split randomly into training sets (75%) and test sets (25%). Models were built using 4 ML algorithms: extreme gradient boosting, random forest, light gradient boosting machine, and adaptive boosting. In addition, a weighted-average voting (WAV) ensemble model was constructed using the ensemble-voting technique to predict clinical failure after ACLR. Concordance (area under the receiver operating characteristic curve [AUC]), calibration, and decision curve analysis were used to evaluate predictive performances of the 5 models. Results: Clinical failure occurred in 73 of the 432 patients (16.9%). The 8 most important predictors for clinical failure were follow-up period, high-grade preoperative knee laxity, time from injury to ACLR, participation in competitive sports, posterior tibial slope, graft diameter, age at surgery, and medial meniscus resection. The WAV ensemble algorithm achieved the best predictive performance based on concordance (AUC, 0.9139), calibration (calibration intercept, –0.1806; calibration slope, 1.2794; Brier score, 0.0888), and decision curve analysis (greatest net benefits) and was used to develop an web-based application to predict a patient's clinical failure risk of ACLR. Conclusion: The WAV ensemble algorithm was able to accurately predict patient-specific risk of clinical failure after ACLR. Clinicians and patients can use the web-based application during preoperative consultation to understand individual prediction outcomes. [ABSTRACT FROM AUTHOR]

Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model's behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners. [ABSTRACT FROM AUTHOR]

Software vulnerabilities (SVs) have become a common, serious, and crucial concern due to the ubiquity of computer software. Many AI-based approaches have been proposed to solve the software vulnerability detection (SVD) problem to ensure the security and integrity of software applications (in both the development and testing phases). However, there are still two open and significant issues for SVD in terms of (i) learning automatic representations to improve the predictive performance of SVD, and (ii) tackling the scarcity of labeled vulnerability datasets that conventionally need laborious labeling effort by experts. In this paper, we propose a novel approach to tackle these two crucial issues. We first exploit the automatic representation learning with deep domain adaptation for SVD. We then propose a novel cross-domain kernel classifier leveraging the max-margin principle to significantly improve the transfer learning process of SVs from imbalanced labeled into imbalanced unlabeled projects. Our approach is the first work that leverages solid body theories of the max-margin principle, kernel methods, and bridging the gap between source and target domains for imbalanced domain adaptation (DA) applied in cross-project SVD. The experimental results on real-world software datasets show the superiority of our proposed method over state-of-the-art baselines. In short, our method obtains a higher performance on F1-measure, one of the most important measures in SVD, from 1.83% to 6.25% compared to the second highest method in the used datasets. [ABSTRACT FROM AUTHOR]

This study includes the development of a farm advisory framework known as the "AquaProFAN framework," which is concerned with the study of various requirements by the aquafarmers and stakeholder community in fisheries. AquaProFAN (Aqua Professional Farm Advisory Network) advisory committee provided careful consideration to the aquafarmers' inquiries and proper clarification with many innovative ideas, and the feasibility study on chatbot implementation was also performed. The end product, called AquaGent, is an AI‐based chatbot designed for shrimp aquafarmers and support services. It was developed and deployed in social media platforms like Facebook Messenger and Telegram for research and dissemination purposes. The effectiveness of the chatbot was analysed and the findings indicated that: (i) informational requests are more satisfactory than emotional requests, (ii) the performance of chatbot is comparatively better than other software applications concerning informational requests, (iii) participants perceive 'AquaGent' chatbot to be more user friendly and time saving. It can be concluded that the implementation of this state‐of‐art technology in aquaculture sector will improve stakeholders' understanding for future efficient and profitable production. [ABSTRACT FROM AUTHOR]

Brain organoids represent a useful tool for modeling of neurodevelopmental disorders and can recapitulate brain volume alterations such as microcephaly. To monitor organoid growth, brightfield microscopy images are frequently used and evaluated manually which is time-consuming and prone to observer-bias. Recent software applications for organoid evaluation address this issue using classical or AI-based methods. These pipelines have distinct strengths and weaknesses that are not evident to external observers. We provide a dataset of more than 1,400 images of 64 trackable brain organoids from four clones differentiated from healthy and diseased patients. This dataset is especially powerful to test and compare organoid analysis pipelines because of (1) trackable organoids (2) frequent imaging during development (3) clone diversity (4) distinct clone development (5) cross sample imaging by two different labs (6) common imaging distractors, and (6) pixel-level ground truth organoid annotations. Therefore, this dataset allows to perform differentiated analyses to delineate strengths, weaknesses, and generalizability of automated organoid analysis pipelines as well as analysis of clone diversity and similarity. [ABSTRACT FROM AUTHOR]

Objective: This paper explores machine learning methods for exercise-induced laryngeal obstruction (EILO) diagnostics. Traditional diagnostic approaches like CLE scoring face subjectivity, limiting precise objective assessments. Machine learning is introduced as a theoretical solution to potentially overcome these limitations and improve diagnostic precision. Methods: A narrative review was conducted to explore the integration of machine learning techniques in the diagnostics of EILO. Result: Three machine learning methods for the segmentation of laryngeal images were discovered: fully convolutional network, Mask R-CNN, and 3D VOSNet. Our findings reveal that the integration of machine learning with EILO diagnostics remains a largely untapped research domain, providing significant room for further exploration. Conclusions: The integration of ML techniques for EILO diagnostics has the potential to be a helpful tool for clinicians. The application of computer vision ML methods, such as image segmentation, to delineate laryngeal structures paves the way for a more objective assessment. While challenges persist, especially in differences in patients' laryngeal anatomy, the synergy of ML and medical expertise is an important field to explore in the years to come. [ABSTRACT FROM AUTHOR]

The catastrophic fire at Bucharest's Colectiv Club marks a profoundly tragic event in Romania's history, being one of the most significant human-made disasters the country has faced in recent decades. This study seeks to meticulously reconstruct the events of that fateful night through the use of advanced simulation software applications. By doing so, it aims to provide a detailed analysis of the factors leading to the tragedy, which claimed the lives of 64 individuals and caused injuries to 185 others. The objective is to understand better the dynamics and consequences of the fire, potentially leading to improvements in safety protocols and emergency response strategies to prevent such disasters in the future. [ABSTRACT FROM AUTHOR]

New generation digital technologies are being applied in new areas every day. Augmented Reality (AR) technology is already being actively used in many different sectors from healthcare to art and education. Recently, studies about AR technologies supported by Artificial Intelligence (AI) were detected. In this study, AR technologies used in sports were studied and research was carried out on the steps of implementation and application with Unity software and briefly demonstrated. In addition, the first AR applications in sports were analysed and information about their development processes was given. Then, AR development software development kits are explained. In addition, some researches in the literature about athletes and AR applications are also mentioned in this study. Information was also given about Unity Mars, a special application development software for AR, which will be published in the following years. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) in education has gained interest due to its increasing application in various fields. This study explores the potential of AI-based software robots in higher education and their ability to revolutionize educational methodologies. The research purpose is to examine the positive impact of the use of software robots in educational settings. The study focuses on evaluating the prospects of expanding the use of AI-based software robots in higher education. The research uses a combination of observational techniques and practical case studies. It includes an experimental investigation of the basic principles of developing an AI-based robot teacher, with the aim of eventually implementing it in educational processes. The research findings indicate that integrating AI-driven software robots into university education can provide substantial benefits and significant improvements over traditional teaching models. These robots can enhance the educational process and address various developmental challenges. The study highlights the transformative impact of AI-based software robots in modernizing university education. The findings demonstrate the potential of these technologies to reshape the current higher education system. [ABSTRACT FROM AUTHOR]

Rigid solid body dynamics is a key element of the undergraduate mechanical engineering curriculum. In a context of reverse engineering and/or sustainable development, being able to analyze the mechanical and material properties of a system without damaging it is a required skill. In this work, two systems based on wheels rolling over horizontal paths without sliding are studied: an unbalanced hollow cylinder and a wheel with displaced mass assembly. Four generations of last year's bachelor's students in mechanical engineering, representing a hundred people a year, followed a total of 12 h of practical sessions working on such systems. Without focusing on the programming part, this paper aims at showing how computer tools can help and improve a rigid solid body dynamics course. The objectives were to develop, apply, and analyze this teaching sequence, allowing students to (i) perform and characterize experimental measurements, (ii) establish systems motion equations, and (iii) think about dynamics results through cross‐talks between different techniques such as geometrical and analytical methods as well as computer‐assisted design tools. Different analysis methods of these systems could be discussed in the context of the classes, successfully bringing pertinent reflections to the students. The possibility of transferring such discussions to other contexts proved to be wide, thanks to computer science, dynamics software, other analysis methods and applications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods. [ABSTRACT FROM AUTHOR]

As new computing paradigms such as mobile grids and clouds become more commonplace, mobile devices are becoming increasingly attractive to scientists and HPC users who need high-performance computing capabilities. There are still many challenges to design software that uses mobile devices, such as their restricted capabilities compared to traditional devices such as PCs and servers. It's even more critical to remember that batteries are the primary power source for mobile devices. Thus people delete programmes that severely reduce their battery life. The upshot is that it is common for developers to have no idea how much power specific hardware and software components use. So even if they have been trained in software development, they still require specific standards and skills to design energy-efficient apps. This Paper investigates ways to minimize mobile device energy usage by restructuring source code. However, we did not overlook the prospect of applying our findings to other domains, such as recurring demanding computational cores in scientific applications. Specifically, this research contributes to the field of Green Computing and mobile software development. [ABSTRACT FROM AUTHOR]

This study offers continued research on the Reddit Digital Mandala phenomenon r/place. The research provides a background understanding of sand art/mandala as an ancient communication, art form and technology. A historical precis of the r/place mandala events provides details on the process and construction of the mandalas of 2017, and 2022 in which defined themes emerged giving insight into user preferences, motivations, and participation. Employing Digital Ethnographic collection and analysis for this study, the 2023 mandala experience is examined with consideration of variables affecting users: ownership of Reddit and data, pricing for participation, and technology enhancements with Artificial Intelligence and software application use. Considering these variables, major themes again emerge as direct response, disclosing characteristics and behaviors exhibited by the r/place mandala community, Reddit and Redditors and AI investors as a whole. [ABSTRACT FROM AUTHOR]

GLAM organizations have been digitizing their collections and making them available for the public for several decades. Recent methods for publishing digital collections such as "GLAM Labs" and "Collections as Data" provide guidelines for the application of computational methods to reuse the contents of cultural heritage institutions in innovative and creative ways. Jupyter Notebooks have become a powerful tool to foster use of these collections by digital humanities researchers. Based on previous approaches for quality assessment, which have been adapted for cultural heritage collections, this paper proposes a methodology for assessing the quality of projects based on Jupyter Notebooks published by relevant GLAM institutions. A list of projects based on Jupyter Notebooks using cultural heritage data has been evaluated. Common features and best practices have been identified. A detailed analysis, that can be useful for organizations interested in creating their own Jupyter Notebooks projects, has been provided. Open issues requiring further work and additional avenues for exploration are outlined. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (GenAI) systems are disrupting how research is conducted across a wide range of disciplines. Many journals have decided not to allow these tools to be co-authors for the purposes of publication, but rather they must be acknowledged by authors as having been utilised in the writing process. Furthermore, due to the hallucinations that these models sometimes produce, authors are to review what is generated and recognise that they hold it to be true and accurate. To date, there has been varying research conducted on the accuracy of GenAI systems and their production of written text. However, new functions that allow GenAI systems to produce coding for constructing tools in computer programming languages highlights a new area that warrants investigation. Therefore, this article puts forth an account of using ChatGPT 3.5 to construct coding to be utilised for a Latent Dirichlet Allocation Topic Model (LDA-TM) for use in a Systematic Literature Review. This is hoped to address three elements of using ChatGPT 3.5 for coding: code review, error resolution, and scripting new code. The code will be aimed at designating an appropriate Hyper-parameter for the Random State for use in the LDA-TM. Within this context, this article will discuss the advantages and drawbacks of utilising this new tool and what it means for researchers who wish to augment their work with computer programming-based applications. To the authors knowledge, this is the first time this has been discussed within the context of the research being conducted. [ABSTRACT FROM AUTHOR]

This document discusses the integration of artificial intelligence (AI) and the Internet of Medical Things (IoMT) in the analysis and processing of medical data. AI involves the creation of intelligent programs and algorithms, while IoMT combines interconnected biomedical equipment with software applications to improve healthcare. The document highlights recent advancements in AI and IoMT, including topics such as COVID-19 prediction, endoscopic image classification, and the use of AI in perinatology. It also addresses the limitations and challenges of AI and IoMT in the healthcare industry, such as ethical, legal, social, and technical issues. The document concludes by emphasizing the potential benefits of AI and IoMT in improving healthcare and the need for further research in this field. [Extracted from the article]

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The car manufacturing industry faces pressing issues of vehicle theft and driver conscious related accidents. This study introduces AI-powered computer applications to tackle these challenges, aiming to enhance security and safety in the automotive sector. The study developing two distinct models--one for driver identification via facial recognition prior to ignition, and another for continuous driver state monitoring during travel--this research aims to bolster vehicle security and enhance driver safety. Two carefully curated data sets consisting of images of four individuals were used to train and validate the models, one for facial recognition and the other for conscious and unconscious driver detection. The models achieved accuracy rates exceeding 99%, and cross-validation confirmed their reliability, with consistent performance showing accuracy ranging from 95% to 100%. The study underscores the potential of AI to revolutionize vehicle security and driver safety mechanisms. The implementation of these models promises to significantly curtail the incidence of car theft and the risk of accidents cause by driver un conscious, heralding a new era of ethical and advanced automotive technologies. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is not a robot character created to take over the world or destroy humans. It is a software application written by humans to mimic human intelligence. While there may be concerns over the misuse of AI, these are addressed through robust regulations and oversight. Let's explore some ways AI can help solve some of the challenges that hamper humans at home and at work, especially in finance. [ABSTRACT FROM AUTHOR]

In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented. [ABSTRACT FROM AUTHOR]

Artificial intelligence and telemedicine promise to reshape patient care to an unprecedented extent, leading to a safer and more sustainable work environment and improved patient care. In this article, we summarize how these emerging technologies can be used in the care of cardiovascular patients in such ways as fall detection and prevention, virtual nursing, remote case support, automation of instrument counts in the operating room, and efficiency optimization in the cardiovascular suite. [ABSTRACT FROM AUTHOR]

In order to solve the problem of too narrow information in the classroom due to the single content and lack of variability in the form of traditional teaching, an interactive network intelligent education system based on RBF algorithm has been proposed. Firstly, the research status of the interactive teaching intelligent system is analyzed, and then the use of artificial intelligence technology, combined with the interactive teaching method, the establishment of neuron model and RBF algorithm to determine the way of learning, and the design of an autonomous learning platform are discussed. The test results indicate that as students' learning time changes, the teaching efficiency of the system is constantly improving, and it has certain feasibility. The model proposed in this article not only changes traditional teaching methods, but also has been proven to improve students' quality, improve learning efficiency, and encourage children to enjoy learning in the environment. [ABSTRACT FROM AUTHOR]

Agent is an autonomous entity and has the ability to act on behalf of others, can understand the changes in the environment and react according to environment with the help of features like mobility, learning ability etc. An agent can be a person, a machine, a piece of software. Agents are autonomous, flexible and active according to the environment. We can say that Agents are acute and suitable for pro-active and social behavior. In this paper discuss about agents, starts from definitions, types of agent, architecture of agent and mobile agent with distributed environment. For developing latest pattern software application we can use software agents. So agent-based computing is best for software development and new revolution in software. [ABSTRACT FROM AUTHOR]

With the rise of smart water cities, water resource management has become increasingly important. The increase in the use of intelligent leak detection technologies in the water, gas, oil, and chemical industries has led to a significant improvement in safety, customer, and environmental results, and management costs. The aim of this review article is to provide a comprehensive overview of the application of software and hardware-based technologies in leak detection and bursts in water pipeline networks. This review aims to investigate the existing literature on the subject and to analyse the key leak detection systems in the water industry. The novelty of this review is the comprehensive analysis of the literature on software and hardware-based technologies for leak and burst detection in water pipe networks. Overall, this review article contributes to understanding the latest developments and challenges in the application of software- and hardware-based technologies for leak and burst detection in water pipe networks, and serves as a valuable resource for researchers, engineers, and practitioners working in the field of water distribution systems. [ABSTRACT FROM AUTHOR]

This study presents an automated software crash-diagnosis technique using a state transition graph (STG) based on GUI-component detection. An STG is a graph representation of the state changes in an application that are caused by actions that are executed in the GUI, which avoids redundant test cases and generates bug-reproduction scenarios. The proposed technique configures the software application STG using computer vision and artificial intelligence technologies and performs automated GUI testing without human intervention. Four experiments were conducted to evaluate the performance of the proposed technique: a detection-performance analysis of the GUI-component detection model, code-coverage measurement, crash-detection-performance analysis, and crash-detection-performance analysis in a self-configured multi-crash environment. The GUI-component detection model obtained a macro F1-score of 0.843, even with a small training dataset for the deep-learning model in the detection-performance analysis. Furthermore, the proposed technique achieved better performance results than the baseline Monkey in terms of code coverage, crash detection, and multi-crash detection. [ABSTRACT FROM AUTHOR]

One of JST and JICA's technical cooperation projects, "Smart Transportation Strategy to Realize Thailand 4.0," aims to reduce traffic congestion to simultaneously achieve low carbon emissions and improve citizens' quality of Life (QoL). To achieve this goal, it is necessary to develop an AI-based system that allows users to select a combination of transportation modes that meets the various needs of people of all ages and genders, and to realize this goal, evaluation of QoL from images is required. The project suggests how to improve QoL when people moves from some point to another point and some application software to recommend the time scheduling about how to optimize the QoL in a day. It recommends the highest QoL route when we have several path candidate of roads and further some application is necessary to show how QoL dynamically changes in the route. That is scenario of the QoL project. In the previous research, a method for QoL estimation from images was proposed based on semantic segmentation and MLP (Multi Layer Perceptron) by constructing a unique questionnaire form and collecting data in a car view scene. However, the previous method estimates QoL without considering differences in the attributes of the questionnaire data, so the constructed model does not satisfy the attributes of all people. In the proposed method, multiple subjects are asked to subjectively evaluate QoL in 5 levels for the same driving image data set, and the subjects are grouped by clustering based on the evaluation results. The effectiveness of the proposed method was demonstrated by comparing the MAE (mean absolute error) of the model constructed by the proposed method with that constructed by other subjects in a 5-step cross-validation. [ABSTRACT FROM AUTHOR]

Plant diseases have turned into a dilemma as it can cause significant reduction in both quality and quantity of agricultural products. To overcome that loss, we implemented a computer vision based real time system that can identify the type of plant diseases. Computer vision-based applications are computationally intensive and time consuming, so FPGA-based implementation is proposed to have a real time identification of plant diseases. In this paper an image processing algorithm is proposed for identifying two types of disease in Potato leaves. The proposed algorithm works well on images taken under different luminance conditions. The hardware/software-based implementation of the proposed algorithm is done on Xilinx ZYNQ SoC FPGA. Results show that our proposed algorithm achieves an accuracy of up to 90%, whereas the hardware implementation takes 0.095 seconds achieving a performance gain of 76.8 times as compared to the software implementation. [ABSTRACT FROM AUTHOR]

Gender recognition is an essential component of computer vision applications, with numerous real-world applications. This research paper explores the development and implementation of a dynamic gender recognition system using YOLOv7 (You Only Look Once version 7) with a focus on achieving minimal frames per second (FPS). We investigate the methodology, data preprocessing, model architecture, and real-world applications of this system, emphasizing its potential for efficient gender recognition in dynamic settings. [ABSTRACT FROM AUTHOR]