Search

Photocatalysts have made great contributions to the degradation of pollutants to achieve environmental purification. The traditional method of developing new photocatalysts is to design and perform a large number of experiments to continuously try to obtain efficient photocatalysts that can degrade pollutants, which is time-consuming, costly, and does not necessarily achieve the best performance of the photocatalyst. The rapid development of photocatalysis has been accelerated by the rapid development of artificial intelligence. Intelligent algorithms can be utilized to design photocatalysts and predict photocatalytic performance, resulting in a reduction in development time and the cost of new catalysts. In this paper, the intelligent algorithms for photocatalyst design and photocatalytic performance prediction are reviewed, especially the artificial neural network model and the model optimized by an intelligent algorithm. A detailed discussion is given on the advantages and disadvantages of the neural network model, as well as its application in photocatalysis optimized by intelligent algorithms. The use of intelligent algorithms in photocatalysis is challenging and long term due to the lack of suitable neural network models for predicting the photocatalytic performance of photocatalysts. The prediction of photocatalytic performance of photocatalysts can be aided by the combination of various intelligent optimization algorithms and neural network models, but it is only useful in the early stages. Intelligent algorithms can be used to design photocatalysts and predict their photocatalytic performance, which is a promising technology. [ABSTRACT FROM AUTHOR]

In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Machine Learning (ML) have rapidly gained prominence as transformative technologies with immense potential to revolutionize various industries and domains. This research paper presents a comprehensive review of AI and ML, encompassing their fundamental concepts, techniques, and applications. Additionally, it explores recent advancements in the field and offers valuable insights into the future prospects of AI and ML. The paper discusses the historical evolution of AI, the different approaches to AI development, and the components that constitute AI systems. Furthermore, it delves into the core concepts and algorithms of ML, including supervised, unsupervised, and reinforcement learning, as well as the advent of deep learning and neural networks. The applications of AI and ML across diverse domains such as natural language processing, computer vision, healthcare, and finance are also discussed. Recent advancements, such as transfer learning, generative adversarial networks, explainable AI, and federated learning, are highlighted, along with the challenges and limitations faced by these technologies, such as ethical concerns, data quality issues, and interpretability challenges. The paper concludes by presenting future perspectives, including the integration of AI with other technologies, advancements in human-computer interaction, and the impact of quantum computing on ML. This research emphasizes the importance of ongoing research and development in AI and ML and the need to address ethical, security, and interpretability considerations for responsible and beneficial implementation in society. [ABSTRACT FROM AUTHOR]

Deep neural network models have become powerful tools of machine learning and artificial intelligence. They can approximate functions and dynamics by learning from examples. This paper reviews the state-of-art of deep learning-based object detection frameworks that are used for fruit detection in general and for olive fruit in particular. A dataset of olive fruit on the tree is built to train and evaluate deep models. The ultimate goal of this work is the capability of on-edge real-time olive fruit detection on the tree from digital videos. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed You Only Look Once version five (YOLOv5). This paper builds a dataset of 1.2 K source images of olive fruit on the tree and evaluates the latest object detection algorithms focusing on variants of YOLOv5 and YOLOR. The results of the YOLOv5 models show that the YOLOv5 new network models are able to extract rich olive features from images and detect the olive fruit with a high precision of higher than 0.75 mAP_0.5. YOLOv5s performs better for real-time olive fruit detection on the tree over other YOLOv5 variants and YOLOR. [ABSTRACT FROM AUTHOR]

Simple Summary: Melanoma is the most dangerous type of skin cancer worldwide. Early detection of melanoma is crucial for better outcomes, but this often can be challenging. This research explores the use of artificial intelligence (AI) techniques combined with non-invasive imaging methods to improve melanoma detection. The authors aim to evaluate the current state of AI-based techniques using tools including dermoscopy, optical coherence tomography (OCT), and reflectance confocal microscopy (RCM). The findings demonstrate that several AI algorithms perform as well as or better than dermatologists in detecting melanoma, particularly in the analysis of dermoscopy images. This research highlights the potential of AI to enhance diagnostic accuracy, leading to improved patient outcomes. Further studies are needed to address limitations and ensure the reliability and effectiveness of AI-based techniques. Background: Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. Objective: The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. Methods: A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. Results: We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. Conclusions: Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability. [ABSTRACT FROM AUTHOR]

Chat generative pre-trained transformer (ChatGPT) is a chatbot developed by OpenAI that answers questions in a human-like manner. ChatGPT is a GPT language model that understands and responds to natural language created using a transformer, which is a new artificial neural network algorithm first introduced by Google in 2017. ChatGPT can be used to identify research topics and proofread English writing and R scripts to improve work efficiency and optimize time. Attempts to actively utilize generative artificial intelligence (AI) are expected to continue in clinical settings. However, ChatGPT still has many limitations for widespread use in clinical research, owing to AI hallucination symptoms and its training data constraints. Researchers recommend avoiding scientific writing using ChatGPT in many traditional journals because of the current lack of originality guidelines and plagiarism of content generated by ChatGPT. Further regulations and discussions on these topics are expected in the future. [ABSTRACT FROM AUTHOR]

The incidence of oral cancer is still increasing. It has become very common in patients with malignant tumors, which has forced medical personnel to continuously explore its treatment methods. What kind of method can effectively and correctly diagnose the disease in the early stage and improve the survival rate has become one of the research topics that have attracted much attention. Aiming at this problem, it has great research significance for the field of oral precancerous lesions diagnosis. With the in-depth research on oral precancerous diagnosis, the research on artificial neural network (ANN) in medical diagnosis is gradually carried out. Its performance advantage is of great significance to solve the problem of early and correct disease diagnosis. This paper aimed to investigate the application of ANN-assisted cancer risk prediction method in risk prediction of oral precancerous lesions. Through the analysis and research of ANN and oral cancer, the construction of oral cancer risk prediction model was applied to solve the problem of improving the survival rate of oral cancer patients. In this paper, ANN and oral precancerous lesions were analyzed, the performance of the algorithm was experimentally analyzed, and the relevant theoretical formulas were used to explain. The results showed that the method had higher accuracy than traditional forecasting methods. When N = 2 , the output accuracy was above 90%. It can be seen that the algorithm can meet the needs of the diagnosis of high-risk groups of oral cancer lesions, and the diagnosis efficiency and patient survival rate has been greatly improved. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurately grading gliomas, which are the most common and aggressive malignant brain tumors in adults, poses a significant challenge for radiologists. This study explores the application of Deep Learning techniques in assisting tumor grading using Magnetic Resonance Images (MRIs). By analyzing a glioma database sourced from multiple public datasets and comparing different settings, the aim of this study is to develop a robust and reliable grading system. The study demonstrates that by focusing on the tumor region of interest and augmenting the available data, there is a significant improvement in both the accuracy and confidence of tumor grade classifications. While successful in differentiating low-grade gliomas from high-grade gliomas, the accurate classification of grades 2, 3, and 4 remains challenging. The research findings have significant implications for advancing the development of a non-invasive, robust, and trustworthy data-driven system to support clinicians in the diagnosis and therapy planning of glioma patients. Glioma is the most common type of tumor in humans originating in the brain. According to the World Health Organization, gliomas can be graded on a four-stage scale, ranging from the most benign to the most malignant. The grading of these tumors from image information is a far from trivial task for radiologists and one in which they could be assisted by machine-learning-based decision support. However, the machine learning analytical pipeline is also fraught with perils stemming from different sources, such as inadvertent data leakage, adequacy of 2D image sampling, or classifier assessment biases. In this paper, we analyze a glioma database sourced from multiple datasets using a simple classifier, aiming to obtain a reliable tumor grading and, on the way, we provide a few guidelines to ensure such reliability. Our results reveal that by focusing on the tumor region of interest and using data augmentation techniques we significantly enhanced the accuracy and confidence in tumor classifications. Evaluation on an independent test set resulted in an AUC-ROC of 0.932 in the discrimination of low-grade gliomas from high-grade gliomas, and an AUC-ROC of 0.893 in the classification of grades 2, 3, and 4. The study also highlights the importance of providing, beyond generic classification performance, measures of how reliable and trustworthy the model's output is, thus assessing the model's certainty and robustness. [ABSTRACT FROM AUTHOR]

Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. We use Latent Neural Ordinary Differential Equations (LNODEs) to learn the pressure-volume dynamics of a heart failure patient. Our surrogate model is trained from 400 simulations while accounting for 43 parameters describing cell-to-organ cardiac electromechanics and cardiovascular hemodynamics. LNODEs provide a compact representation of the 3D-0D model in a latent space by means of an Artificial Neural Network that retains only 3 hidden layers with 13 neurons per layer and allows for numerical simulations of cardiac function on a single processor. We employ LNODEs to perform global sensitivity analysis and parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor. [ABSTRACT FROM AUTHOR]

An increasing number of artificial intelligence (AI) tools are moving towards the clinical realm in histopathology and across medicine. The introduction of such tools will bring several benefits to diagnostic specialities, namely increased diagnostic accuracy and efficiency, however, as no AI tool is infallible, their use will inevitably introduce novel errors. These errors made by AI tools are, most fundamentally, misclassifications made by a computational algorithm. Understanding of how these translate into clinical impact on patients is often lacking, meaning true reporting of AI tool safety is incomplete. In this Perspective we consider AI diagnostic tools in histopathology, which are predominantly assessed in terms of technical performance metrics such as sensitivity, specificity and area under the receiver operating characteristic curve. Although these metrics are essential and allow tool comparison, they alone give an incomplete picture of how an AI tool's errors could impact a patient's diagnosis, management and prognosis. We instead suggest assessing and reporting AI tool errors from a pathological and clinical stance, demonstrating how this is done in studies on human pathologist errors, and giving examples where available from pathology and radiology. Although this seems a significant task, we discuss ways to move towards this approach in terms of study design, guidelines and regulation. This Perspective seeks to initiate broader consideration of the assessment of AI tool errors in histopathology and across diagnostic specialities, in an attempt to keep patient safety at the forefront of AI tool development and facilitate safe clinical deployment. [ABSTRACT FROM AUTHOR]

Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%. [ABSTRACT FROM AUTHOR]

Despite limitations in payload and range, cargo drones have promising applications in emergency logistics and remote delivery. In this study, we tackle these challenges by developing a high-capacity 3.84 kW battery specifically designed for a 50-kg-payload cargo drone operating in demanding terrains. Focusing on the transport of emergency goods, we investigate key drone design aspects and details of the battery pack development, including cell selection, internal configuration, and critical circuits for cell balancing, charging/discharging, and advanced battery management. A key innovation is the integration of a backpropagation artificial neural network (BPANN) algorithm to predict the depth of discharge (DoD) and the state of charge (SoC). Research results show that BPANN offers highly accurate predictions, with error percentages as low as 0.12% for DoD and 0.02% for SoC, ensuring optimized and safe battery operation. Comprehensive field testing is carried out to evaluate the effectiveness of the proposed cell balancing strategy, robust battery management system (BMS), and BPANN implementation. We investigate the drone's performance in terms of DoD, SoC, and overall field operation with the designed battery pack and demonstrate its feasibility and potential for realworld applications. [ABSTRACT FROM AUTHOR]

Wearable inertial measurement units (IMUs) are being used to quantify gait characteristics that are associated with increased fall risk, but the current limitation is the lack of contextual information that would clarify IMU data. Use of wearable video-based cameras would provide a comprehensive understanding of an individual's habitual fall risk, adding context to clarify abnormal IMU data. Generally, there is taboo when suggesting the use of wearable cameras to capture real-world video, clinical and patient apprehension due to ethical and privacy concerns. This perspective proposes that routine use of wearable cameras could be realized within digital medicine through AI-based computer vision models to obfuscate/blur/shade sensitive information while preserving helpful contextual information for a comprehensive patient assessment. Specifically, no person sees the raw video data to understand context, rather AI interprets the raw video data first to blur sensitive objects and uphold privacy. That may be more routinely achieved than one imagines as contemporary resources exist. Here, to showcase/display the potential an exemplar model is suggested via off-the-shelf methods to detect and blur sensitive objects (e.g., people) with an accuracy of 88%. Here, the benefit of the proposed approach includes a more comprehensive understanding of an individual's free-living fall risk (from free-living IMU-based gait) without compromising privacy. More generally, the video and AI approach could be used beyond fall risk to better inform habitual experiences and challenges across a range of clinical cohorts. Medicine is becoming more receptive to wearables as a helpful toolbox, camera-based devices should be plausible instruments. [ABSTRACT FROM AUTHOR]

Simple Summary: In radiotherapy treatment, organs at risk and target volumes are contoured by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not often standardised to universal names across the patients plans, which is required to enable data mining and analysis. In this paper, a new method was proposed and evaluated to automatically standardise radiotherapy structures names using machine learning algorithms. The proposed approach was deployed over a dataset with 1613 patients collected from Liverpool & Macarthur Cancer Therapy Centres, New South Wales, Australia. It was concluded that machine learning techniques can standardise the dosimetry plan structures, taking into consideration the integration of multiple modalities representing each structure during the training process. In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume. [ABSTRACT FROM AUTHOR]

In machine learning (ML), association patterns in the data, paths in decision trees, and weights between layers of the neural network are often entangled due to multiple underlying causes, thus masking the pattern-to-source relation, weakening prediction, and defying explanation. This paper presents a revolutionary ML paradigm: pattern discovery and disentanglement (PDD) that disentangles associations and provides an all-in-one knowledge system capable of (a) disentangling patterns to associate with distinct primary sources; (b) discovering rare/imbalanced groups, detecting anomalies and rectifying discrepancies to improve class association, pattern and entity clustering; and (c) organizing knowledge for statistically supported interpretability for causal exploration. Results from case studies have validated such capabilities. The explainable knowledge reveals pattern-source relations on entities, and underlying factors for causal inference, and clinical study and practice; thus, addressing the major concern of interpretability, trust, and reliability when applying ML to healthcare, which is a step towards closing the AI chasm. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs) are intricate mathematical models inspired by the human neural system, holding great promise in the development of artificial intelligence. In the context of biotechnological applications within logistics, ANNs offer robust adaptability and fault tolerance. However, to unlock their full potential, the integration of swarm intelligence algorithms for parameter optimization has gained attention. This paper addresses the intersection of artificial intelligence, biotechnology, and logistics. Specifically, we focus on the critical task of efficiently constructing neural network models to enhance their performance in biotechnological logistics applications. Two key research areas are explored: Innovative Optimization Strategies: We propose novel strategies for optimizing ANNs using swarm intelligence algorithms. These strategies incorporate inverse learning techniques and neighborhood perturbation operations with dynamic probabilities. They expedite the escape from local optima and enhance the accurate exploration of adjacent regions. Such optimization is vital for effectively adapting ANNs to the complex challenges presented by biotechnological logistics. Freight Volume Forecasting in Biotechnology Logistics: Our research investigates the multifaceted factors influencing freight volume in the context of biotechnological logistics. We consider both the demands of the biotechnology sector and the logistical supply chain, identifying key indicators with strong correlations to freight volume. This analysis aids in refining predictions and optimizing resource allocation in biotechnological logistics. This paper underscores the convergence of artificial neural networks and swarm intelligence algorithms, emphasizing their application within the biotechnological logistics domain. The proposed optimization strategies aim to elevate the performance of ANNs in addressing the unique challenges posed by biotechnological logistics, ultimately fostering efficiency and precision. We outline future research directions to further harness the potential of these techniques for biotechnological applications in logistics. [ABSTRACT FROM AUTHOR]

The identification and characterization of lung diseases is one of the most interesting research topics in recent years. They require accurate and rapid diagnosis. Although lung imaging techniques have many advantages for disease diagnosis, the interpretation of medial lung images has always been a major problem for physicians and radiologists due to diagnostic errors. This has encouraged the use of modern artificial intelligence techniques such as deep learning. In this paper, a deep learning architecture based on EfficientNetB7, known as the most advanced architecture among convolutional networks, has been constructed for classification of medical X-ray and CT images of lungs into three classes namely: common pneumonia, coronavirus pneumonia and normal cases. In terms of accuracy, the proposed model is compared with recent pneumonia detection techniques. The results provided robust and consistent features to this system for pneumonia detection with predictive accuracy according to the three classes mentioned above for both imaging modalities: radiography at 99.81% and CT at 99.88%. This work implements an accurate computer-aided system for the analysis of radiographic and CT medical images. The results of the classification are promising and will certainly improve the diagnosis and decision making of lung diseases that keep appearing over time. [ABSTRACT FROM AUTHOR]

In recent years, people's living standards are getting higher and higher, and life pressure is also increasing, and there are also many problems in eating habits. This is also the direct cause of colon cancer. The aim of this paper was to investigate whether anesthetic drugs could positively affect the proliferation and apoptosis of colon cancer cells. In this paper, the significance of anesthetic drugs is proposed, and an artificial neural network algorithm based on artificial intelligence is proposed. It is well known that artificial neural networks play an important role in medicine. The experimental results of this paper show that the incidence of colon cancer in 2020 will be in the range of 5%-35%, and the incidence of colon cancer in 2021 will be in the range of 7%-30%. While colon cancer rates in 2021 do not appear to be as high as colon cancer rates in 2020, they are generally much higher than colon cancer rates in 2020. It can be seen that as the population ages, the number of colon cancer patients is increasing due to the lack of emphasis on health. This also means that the incidence of colon cancer is getting higher and higher, and traditional drug chemotherapy has been unable to play a good role in inhibiting the proliferation of colon cancer cells. Therefore, this paper investigated the effects of anesthetic drugs on the proliferation and apoptosis of human colon cancer cells. [ABSTRACT FROM AUTHOR]

Game data collection system is a tool used to collect the behavior data of users about the game. It can be used for data analysis of user behavior so that game manufacturers can keep abreast of market dynamics and popular trends, and they also can have a deeper understanding of the behavioral habits and psychology of player user groups. The defects of the current data acquisition system include that the data are not encrypted. The network transmission efficiency is relatively low. The acquisition speed is slow, and the settings cannot be dynamically changed. This paper proposes to study how to enhance the acquisition ability and improve the analysis efficiency in the design of data acquisition system for solving these problems. Therefore, on the basis of artificial intelligence algorithm, this paper designs a game data collection system by using artificial neural network algorithm, support vector algorithm, and cluster analysis algorithm, which solves the basic problem of slow data collection in current data collection and plays a role in improving the efficiency of network transmission. The experimental results in this paper show that when the number of data is more than 300, the time-consuming time reaches more than 68 ms. When the number of written data is more than 300, it takes more than 181 ms. When the number of deleted data is more than 300, it takes more than 236 ms. From the above data, it shows that the designed game data collection system is rapid and efficient. [ABSTRACT FROM AUTHOR]

In this paper, in-depth research and analysis of juvenile delinquency prevention and occupational therapy education guidance using artificial intelligence are conducted, and its response mechanism is designed in this way. Two crime type prediction algorithms based on time-crime type count vectorization and dense neural network and crime type prediction based on the fusion of dense neural network and long- and short-term memory neural network are proposed. The outputs of both are fed into a new neural network for training to achieve the fusion of the two neural networks. Among them, the use of the dense neural network can effectively fit the relationship between the constructed features and crime types. The behavioral manifestations and causes of the formation of deviant behavior in adolescents are discussed. They can only read numerical data, but there is a lot of information in the textual data that is closely related to the training effect. When experimenting, it is necessary to extract knowledge and build applications. The practical work with adolescents with deviant behaviors is again carried out from group work and casework, respectively, with problem diagnosis, needs assessment, and service plan development for specific clients, to carry out relevant practical service work. The causes of juvenile delinquency in the Internet culture are discussed in terms of the Internet environment, juvenile use of the Internet, Internet supervision, and crime prevention education, respectively. The fourth chapter focuses on the analysis of the prevention and control measures for juvenile delinquency in cyberculture. In response to the above-mentioned causes of juvenile delinquency in cyberculture, the prevention and control measures are discussed in four aspects, namely, strengthening the construction of cyberculture and building a healthy cyber environment, strengthening the capacity building of guiding juveniles to use cyber correctly, building a prevention and supervision system to promote the improvement of the legal system, and improving and innovating the crime prevention education in the cyber era. [ABSTRACT FROM AUTHOR]

In this study, we demonstrate the viability of applying artificial intelligence (AI) techniques to conduct inspections at the OLED cell level using simulated data. The implementation of AI technologies necessitates training data, which we addressed by generating an OLED dataset via our proprietary A2G algorithm, integrating the finite element method among concerns over data security. Our A2G algorithm is designed to produce time-dependent datasets and establish threshold conditions for the expansion of dark spots based on OLED parameters and predicted lifespan. We explored the potential integration of AI in the inspection phase, performing cell-based evaluations using three distinct convolutional neural network models. The test results yielded a promising 95% recognition rate when classifying OLED data into pass and fail categories, demonstrating the practical effectiveness of this approach. Through this research, we not only confirmed the feasibility of using simulated OLED data in place of actual data but also highlighted the potential for the automation of manual inspection processes. Furthermore, by introducing OLED defect detection models at the cell level, as opposed to the traditional panel level during inspections, we anticipate higher classification rates and improved yield. This forward-thinking approach underscores significant advancement in OLED inspection processes, indicating a potential shift in industry standards. [ABSTRACT FROM AUTHOR]

Virtual Rehabilitation (VRehab) is a promising approach to improving the physical and mental functioning of patients living in the community. The use of VRehab technology results in the generation of multi-modal datasets collected through various devices. This presents opportunities for the development of Artificial Intelligence (AI) techniques in VRehab, namely the measurement, detection, and prediction of various patients' health outcomes. The objective of this scoping review was to explore the applications and effectiveness of incorporating AI into home-based VRehab programs. PubMed/MEDLINE, Embase, IEEE Xplore, Web of Science databases, and Google Scholar were searched from inception until June 2023 for studies that applied AI for the delivery of VRehab programs to the homes of adult patients. After screening 2172 unique titles and abstracts and 51 full-text studies, 13 studies were included in the review. A variety of AI algorithms were applied to analyze data collected from various sensors and make inferences about patients' health outcomes, most involving evaluating patients' exercise quality and providing feedback to patients. The AI algorithms used in the studies were mostly fuzzy rule-based methods, template matching, and deep neural networks. Despite the growing body of literature on the use of AI in VRehab, very few studies have examined its use in patients' homes. Current research suggests that integrating AI with home-based VRehab can lead to improved rehabilitation outcomes for patients. However, further research is required to fully assess the effectiveness of various forms of AI-driven home-based VRehab, taking into account its unique challenges and using standardized metrics. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance. [ABSTRACT FROM AUTHOR]

Simple Summary: This study proposes an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images, which is crucial for fully automated immunohistochemistry quantification. The proposed method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional staining slides. The model demonstrated an impressive intersection over union score on the test set, and a fully automated Ki-67 scoring system based on the model's predictions exhibited high consistency with the scores given by experienced pathologists. The proposed method brings the breast cancer fully immunohistochemistry quantitative scoring system one step closer to clinical application. Aims: The automation of quantitative evaluation for breast immunohistochemistry (IHC) plays a crucial role in reducing the workload of pathologists and enhancing the objectivity of diagnoses. However, current methods face challenges in achieving fully automated immunohistochemistry quantification due to the complexity of segmenting the tumor area into distinct ductal carcinoma in situ (DCIS) and invasive carcinoma (IC) regions. Moreover, the quantitative analysis of immunohistochemistry requires a specific focus on invasive carcinoma regions. Methods and Results: In this study, we propose an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images (WSIs). Our method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional H&E and P63 staining slides. In addition, we introduced an advanced semi-supervised learning algorithm, allowing efficient training of the model using unlabeled data. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 618 IHC-stained WSIs from 170 cases, including four types of staining (ER, PR, HER2, and Ki-67). Notably, the model demonstrated an impressive intersection over union (IoU) score exceeding 80% on the test set. Furthermore, to ascertain the practical utility of our model in IHC quantitative evaluation, we constructed a fully automated Ki-67 scoring system based on the model's predictions. Comparative experiments convincingly demonstrated that our system exhibited high consistency with the scores given by experienced pathologists. Conclusions: Our developed model excels in accurately distinguishing between DCIS and invasive carcinoma regions in breast cancer immunohistochemistry WSIs. This method paves the way for a clinically available, fully automated immunohistochemistry quantitative scoring system. [ABSTRACT FROM AUTHOR]

Simple Summary: The exponential growth in artificial intelligence development, particularly its application in capsule endoscopy, serves as a compelling model for gastroenterologists. This review focusses on the latest advancements in capsule endoscopy, analyzing the possible benefits and ethical challenges that artificial intelligence may bring to the field of minimally invasive capsule panendoscopy, while also offering insights into future directions. Specifically in the context of oncological gastrointestinal screening, there is still a need to explore alternative strategies for enhancing this process. Artificial intelligence-enhanced capsule panendoscopy has the potential to positively impact the future by addressing time constraints and improve accessibility through the use of highly efficient diagnostic models. In the early 2000s, the introduction of single-camera wireless capsule endoscopy (CE) redefined small bowel study. Progress continued with the development of double-camera devices, first for the colon and rectum, and then, for panenteric assessment. Advancements continued with magnetic capsule endoscopy (MCE), particularly when assisted by a robotic arm, designed to enhance gastric evaluation. Indeed, as CE provides full visualization of the entire gastrointestinal (GI) tract, a minimally invasive capsule panendoscopy (CPE) could be a feasible alternative, despite its time-consuming nature and learning curve, assuming appropriate bowel cleansing has been carried out. Recent progress in artificial intelligence (AI), particularly in the development of convolutional neural networks (CNN) for CE auxiliary reading (detecting and diagnosing), may provide the missing link in fulfilling the goal of establishing the use of panendoscopy, although prospective studies are still needed to validate these models in actual clinical scenarios. Recent CE advancements will be discussed, focusing on the current evidence on CNN developments, and their real-life implementation potential and associated ethical challenges. [ABSTRACT FROM AUTHOR]

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of attention lately due to its promise of reducing the computational energy, latency, as well as learning complexity in artificial neural networks. Taking inspiration from neuroscience, this interdisciplinary field performs a multi-stack optimization across devices, circuits, and algorithms by providing an end-to-end approach to achieving brain-like efficiency in machine intelligence. On one side, neuromorphic computing introduces a new algorithmic paradigm, known as Spiking Neural Networks (SNNs), which is a significant shift from standard deep learning and transmits information as spikes (“1” or “0”) rather than analog values. This has opened up novel algorithmic research directions to formulate methods to represent data in spike-trains, develop neuron models that can process information over time, design learning algorithms for event-driven dynamical systems, and engineer network architectures amenable to sparse, asynchronous, event-driven computing to achieve lower power consumption. On the other side, a parallel research thrust focuses on development of efficient computing platforms for new algorithms. Standard accelerators that are amenable to deep learning workloads are not particularly suitable to handle processing across multiple timesteps efficiently. To that effect, researchers have designed neuromorphic hardware that rely on event-driven sparse computations as well as efficient matrix operations. While most large-scale neuromorphic systems have been explored based on CMOS technology, recently, Non-Volatile Memory (NVM) technologies show promise toward implementing bio-mimetic functionalities on single devices. In this article, we outline several strides that neuromorphic computing based on spiking neural networks (SNNs) has taken over the recent past, and we present our outlook on the challenges that this field needs to overcome to make the bio-plausibility route a successful one. [ABSTRACT FROM AUTHOR]

This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The sudden arrival of the new crown epidemic has had a significant and long-lasting impact on the division's economic environment as well as the production and operation activities of businesses. As far as the financial management is concerned, opportunities and difficulties are faced by enterprises of all types. With reference to the available research data, enterprises have an important contribution to GDP and jobs, but they still face a series of difficulties and challenges in their development in the context of the normalization of the epidemic. By analyzing the impact of the new crown pneumonia epidemic on the financial management work of enterprises, this paper proposes an artificial neural network-based enterprise financial forecasting and early warning method to provide an effective method for enterprise financial management. For the time-series characteristics of enterprise finances, a prediction model based on long- and short-term memory networks is developed which acknowledges the necessity of combining the temporal dimension with the spatial dimension for forecasting. This model incorporates time qualities into the data to the existing forecasting model. It also considers both working and nonworking day data and thoroughly considers the factors influencing corporate finance. Then, using BP neural network for financial risk prediction, nonfinancial index factors should be added to the financial early warning model thus eliminating the limitations of the financial early warning model. At the same time, the accuracy of the prediction can be improved which is more suitable for enterprises to apply in practice. The experimental results demonstrate that the financial prediction model built by multilayer feed forward neural networks and recurrent neural networks based on error back propagation training is inferior to the prediction model built by long- and short-term memory network. Regardless of the degree of fitting or prediction accuracy, the BP neural network model outperforms the conventional model for enterprise financial warning. Under the normalization of the pandemic, the combined use of both can offer an efficient technique for enterprise management. [ABSTRACT FROM AUTHOR]

This paper constructs an algorithm for youth school violence recognition and an occupational therapy education model for victims through the extraction of action speech features. For the characteristics of violent actions and daily actions, action features in time and frequency domains are extracted and action categories are recognized by BP neural network; for complex actions, it is proposed to decompose complex actions into basic actions to improve the recognition rate; then, LDA dimensionality reduction algorithm is introduced for the problem of the high complexity of algorithm due to high dimensionality of features, and the feature dimensionality is reduced to 8 dimensions by LDA dimensionality reduction algorithm, which reduces the system running time by about 51% and improves the accuracy of violent action recognition by 3.3% while ensuring the overall performance of the system. The LDA dimensionality reduction algorithm reduces the number of features to 8 dimensions, which reduces the running time of the system by 51%, increases the accuracy rate of violent action recognition by 3.3%, and increases the recall rate of violent action recognition by 8.86% while ensuring the overall performance of the system. Based on the classical D-S theory, we proposed an improved D-S evidence fusion algorithm by modifying the original evidence model with a new probability distribution function and constructing new fusion rules, which can solve the fusion conflict problem well. The recall rate for violent actions is increased to 90.0%, thus reducing the missed alarm rate of the system. [ABSTRACT FROM AUTHOR]

This paper analyzes the simulation of special effects animation through fluid equations and data-driven methods. This paper also considers the needs of computer fluid animation simulation in terms of computational accuracy and simulation efficiency, takes high real-time, high interactivity, and high physical accuracy of simulation algorithm as the research focus and target, and proposes a solution algorithm and acceleration scheme based on deep neural network framework for the key problems of simulation of natural phenomena including smoke and liquid. With the deep development of artificial intelligence technology, deep neural network models are widely used in research fields such as computer image classification, speech recognition, and fluid detail synthesis with their powerful data learning capability. Its stable and efficient computational model provides a new problem-solving approach for computerized fluid animation simulation. In terms of time series reconstruction, this paper adopts a tracking-based reconstruction method, including target tracking, 2D trajectory fitting and repair, and 3D trajectory reconstruction. For continuous image sequences, a linear dynamic model algorithm based on pyramidal optical flow is used to track the feature centers of the objects, and the spatial coordinates and motion parameters of the feature points are obtained by reconstructing the motion trajectories. The experimental results show that in terms of spatial reconstruction, the matching method proposed in this paper is more accurate compared with the traditional stereo matching algorithm; in terms of time series reconstruction, the error of target tracking reduced. Finally, the 3D motion trajectory of the point feature object and the motion pattern at a certain moment are shown, and the method in this paper obtains more ideal results, which proves the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Portal hypertension is a complex medical condition characterized by elevated blood pressure in the portal venous system. The conventional diagnosis of such disease often involves invasive procedures such as liver biopsy, endoscopy, or imaging techniques with contrast agents, which can be uncomfortable for patients and carry inherent risks. This study presents a deep neural network method in support of the non-invasive diagnosis of portal hypertension in patients with chronic liver diseases. The proposed method utilizes readily available clinical data, thus eliminating the need for invasive procedures. A dataset composed of standard laboratory parameters is used to train and validate the deep neural network regressor. The experimental results exhibit reasonable performance in distinguishing patients with portal hypertension from healthy individuals. Such performances may be improved by using larger datasets of high quality. These findings suggest that deep neural networks can serve as useful auxiliary diagnostic tools, aiding healthcare professionals in making timely and accurate decisions for patients suspected of having portal hypertension. [ABSTRACT FROM AUTHOR]

The nanoindentation technique is employed to characterize the behaviors of biomaterials. Nevertheless, there is a lack of development of a miniaturized precise positioner for in situ nanoindentation. Besides, modeling behaviors of the positioner are restricted due to its complex kinematic characteristics. Therefore, this paper proposes a novel compliant two degrees of freedom (dof) stage for positioning a biomaterial sample in in situ nanoindentation. In addition, a new modeling and dimensional optimization synthesis method of the stage is developed. The proposed effective methodology is developed based on a kinetostatic analysis-based calculation method, the Lagrange approach, and a neural network algorithm. With an increased advance in artificial intelligence, a neural network algorithm is proposed to extend the applicability of artificial neural networks in optimizing the parameters of the stage. First, the 2-dof stage is built via a combination of an eight-lever displacement amplifier and a symmetric parallelogram mechanism. Second, a chain of mathematical equations of the 2-dof stage is constructed using a kinetostatic analysis-based method to calculate the ratio of displacement amplification and the input stiffness of the 2-dof stage. Then, the Lagrange method is utilized to formulate the dynamic equation of the 2-dof stage. Finally, a neural network algorithm is adopted to maximize the natural first frequency of the proposed stage. The optimal results determined that the frequency of the stage can achieve a high value of 112.0995 Hz. Besides, the formed mathematical models were relatively precise by comprising the simulation verifications. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence and computer technology, the deep neural network algorithm is applied to the intelligentization of various fields of production and life. However, from the current application status, the application of artificial intelligence technology has many shortcomings. Based on this, this paper starts with the deep neural network algorithm, takes face recognition as the research tool, and deeply studies how to use the deep neural network algorithm to demonstrate the application of intelligent face recognition in complex environments. A face recognition neural network algorithm is proposed, and the accuracy of the algorithm is checked by testing. The results show that the average accuracy of a single sample in the LFW dataset is 99.17%, and the efficiency of using a single sample is close to that of many smelting models, which can be applied to various intelligent recognition scenarios. [ABSTRACT FROM AUTHOR]

As the competition between enterprises intensifies, employees have witnessed a decline of psychological health year by year and severe anxiety and depression. To ensure the normal work of employees, it is important to implement psychological occupational therapy intervention (POTI). The previous studies on corporate occupational values focus on the behavioral variables and attitude variables related to occupations. The research paradigm does not suit a specific group of employees. Therefore, this paper explores the individual differences in POTI guided by the realization of occupational values. For a specific group of employees, the correlations of the psychological problems with job attitude, job involvement, and psychological health level were reasoned logically, a theoretical model was established, and the full-model map was plotted to illustrate the influence of the realization of occupational values over POTI. Then, the evaluation data on the psychological intervention effect of 2,800 employees were explored based on multikernel learning, and an individual difference prediction model was constructed through multikernel learning. Finally, the predicted results of different groups of employees were compared, revealing the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The digital transformation of the energy sector toward the Smart Grid paradigm, intelligent energy management, and distributed energy integration poses new requirements for computer science. Issues related to the automation of power grid management, multidimensional analysis of data generated in Smart Grids, and optimization of decision-making processes require urgent solutions. The article aims to analyze the use of selected artificial intelligence (AI) algorithms to support the abovementioned issues. In particular, machine learning methods, metaheuristic algorithms, and intelligent fuzzy inference systems were analyzed. Examples of the analyzed algorithms were tested in crucial domains of the energy sector. The study analyzed cybersecurity, Smart Grid management, energy saving, power loss minimization, fault diagnosis, and renewable energy sources. For each domain of the energy sector, specific engineering problems were defined, for which the use of artificial intelligence algorithms was analyzed. Research results indicate that AI algorithms can improve the processes of energy generation, distribution, storage, consumption, and trading. Based on conducted analyses, we defined open research challenges for the practical application of AI algorithms in critical domains of the energy sector. [ABSTRACT FROM AUTHOR]

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset. [ABSTRACT FROM AUTHOR]

The new generation of programmable networks allow mechanisms to be deployed for the efficient control of dynamic bandwidth allocation and ensure Quality of Service (QoS) in terms of Key Performance Indicators (KPIs) for delay or loss sensitive Internet of Things (IoT) services. To achieve flexible, dynamic and automated network resource management in Software-Defined Networking (SDN), Artificial Intelligence (AI) algorithms can provide an effective solution. In the paper, we propose the solution for network resources allocation, where the AI algorithm is responsible for controlling intent-based routing in SDN. The paper focuses on the problem of optimal switching of intents between two designated paths using the Deep-Q-Learning approach based on an artificial neural network. The proposed algorithm is the main novelty of this paper. The Developed Networked Application Emulation System (NAPES) allows the AI solution to be tested with different patterns to evaluate the performance of the proposed solution. The AI algorithm was trained to maximize the total throughput in the network and effective network utilization. The results presented confirm the validity of applied AI approach to the problem of improving network performance in next-generation networks and the usefulness of the NAPES traffic generator for efficient economical and technical deployment in IoT networking systems evaluation. [ABSTRACT FROM AUTHOR]

This paper discusses the factors affecting quality of school educational process. It describes the application of artificial intelligence approaches (neural networks) into the educational process. The analysis of methods for tracking changes in the position of the head while studying at school or at home is presented. The results obtained make it possible to evaluate the psycho‐physiological, psycho‐emotional state of primary school students in the process of interacting with a computer when performing educational tasks. The goal is to create computer tools that monitors changes in head position using images from a laptop or tablet digital camera. This article presents the stages of developing a neural network for assessing head turns when performing a school assignment and the results of applying the program. A new tool is proposed for assessing the state of a student in the learning process, to determine the ability to perceive different types of educational information. [ABSTRACT FROM AUTHOR]

The complexity of forward kinematic modelling increases with the increase in the degrees of freedom for a manipulator. To reduce the computational weight and time lag for desired output transformation, this paper proposes a forward kinematic model mapped with the help of the Radial Basis Function Neural Network (RBFNN) architecture tuned by a novel meta-heuristic algorithm, namely, the Cooperative Search Optimisation Algorithm (CSOA). The architecture presented is able to automatically learn the kinematic properties of the manipulator. Learning is accomplished iteratively based only on the observation of the input–output relationship. Related simulations are carried out on a 3-Degrees of Freedom (DOF) manipulator on the Robot Operating System (ROS). The dataset created from the simulation is divided 65–35 for training–testing of the proposed model. The metrics used for model validation include spread value, cost and runtime for the training dataset, and Mean Relative Error, Normal Mean Square Error, and Mean Absolute Error for the testing dataset. A comparative analysis of the CSOA-RBFNN model is performed with an artificial neural network, support vector regression model, and with with other meta-heuristic RBFNN models, i.e., PSO-RBFNN and GWO-RBFNN, that show the effectiveness and superiority of the proposed technique. [ABSTRACT FROM AUTHOR]

In e-commerce and on social media, identifying fake opinions has become a tremendous challenge. Such opinions are widely generated on the internet by fake viewers, also called fraudsters. They write deceptive reviews that purport to reflect actual user experience either to promote some products or to defame others. They also target the reputations of e-businesses. Their aim is to mislead customers to make a wrong purchase decision by selecting undesired products. Such reviewers are often paid by rival e-business companies to compose positive reviews of their products and/or negative reviews of other companies’ products. The main objective of this paper is to detect, analyze and calculate the difference between fake and truthful product reviews. To do this, the methodology has planned to have seven phases: reviewing online products, analyzing features through linguistic enquiry and word count (LIWC), preprocessing the data to clean and normalize them, embedding words (Word2Vec) and analyzing performance using artificial deep-learning algorithms for classifying fake and truthful reviews. Two deep-learning neural network models have been evaluated based on standard Yelp product reviews. These models are bidirectional long-short term memory (BiLSTM) and convolutional neural network (CNN). The results from comparing the performance of the two models showed that the BiLSTM model provided higher accuracy for detecting fake reviews than the CNN model. [ABSTRACT FROM AUTHOR]

The feedback alignment provides a biologically plausible learning mechanism, which can directly transmit error signals with a random weight matrix to multiple layers of a neural network. This paper proposes an online supervised learning algorithm based on the feedback alignment mechanism for multilayer spiking neural networks, named Multi-OSLFA, which can support real-time learning for the spatio-temporal pattern of spike trains. The online learning rule is represented by the kernel function of spike trains and adjusts the synaptic weights when the output neuron fires a spike during the running process of spiking neural networks. The Multi-OSLFA algorithm is successfully applied to spike train learning tasks and nonlinear pattern classification problems on two UCI datasets. Simulation results indicate that the proposed algorithm can improve learning accuracy in comparison with other supervised learning algorithms. It shows that the proposed learning algorithm is effective for solving spatio-temporal pattern learning problems. [ABSTRACT FROM AUTHOR]

The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep-convolutional neural network (D-CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D-CNN AI model based on the U-Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University. A U-Net implemented with PyTorch model (version 1.4.0) was used for the segmentation of apical lesions. In the test data set, the AI model segmented 63 periapical lesions on 47 panoramic radiographs. The sensitivity, precision, and F1-score for segmentation of periapical lesions at 70% IoU values were 0.92, 0.84, and 0.88, respectively. AI systems have the potential to overcome clinical problems. AI may facilitate the assessment of periapical pathology based on panoramic radiographs. [ABSTRACT FROM AUTHOR]

Natural Language Understanding and Speech Understanding systems are now a global trend, and with the advancement of artificial intelligence and machine learning techniques, have drawn attention from both the academic and business communities. Domain prediction, intent detection and entity extraction or slot fillings are the most important parts for such intelligent systems. Various traditional machine learning algorithms such as Bayesian algorithm, Support Vector Machine, and Artificial Neural Network, along with recent Deep Neural Network techniques, are used to predict domain, intent, and entity. Most language understanding systems process user input in a sequential order: domain is first predicted, then intent and slots are filled according to the semantic frames of the predicted domain. This pipeline approach, however, has many disadvantages including downstream error; i.e., if the system fails to predict the domain, then the system also fails to predict intent and slot. The main purpose of this paper is to mitigate the risk of downstream error propagation in traditional pipelined models and improve the predictive performance of domain, intent, and slot- all of which are critical steps for speech understanding and dialog systems- with a deep learning-based single joint model trained with an adversarial approach and long shortterm memory (LSTM) algorithm. The systematic experimental analysis shows significant improvements in predictive performance for domain, intent, and entity with the proposed adversarial joint model, compared to the base joint model. [ABSTRACT FROM AUTHOR]

Unconventional reservoirs are the productive zones in other words the rock quality and the mechanical properties of the rocks this process is devastating if humans or people try to search for the best reservoirs. So we can use machine learning (ML) algorithms to help us find and search easily and fast for the best reservoirs with less human interaction as possible. The objectives of this paper is to use machine learning (ML) techniques to predict and classify the reservoirs based on the properties of each reservoirs and choose the best reservoir. In this paper we have made a comparison between the different types of machine learning algorithm and described how we get the best and worst result for each one, the comparison we made gave us that the AdaBoost algorithm gave the worst performance measured in the accuracy while the random forest (RF) algorithm gave the best performance, this paper aim to make improvement of the process of searching for productive zones using ML algorithms. [ABSTRACT FROM AUTHOR]

In large datasets, irrelevant, redundant, and noisy attributes are often present. These attributes can have a negative impact on the classification model accuracy. Therefore, feature selection is an effective pre-processing step intended to enhance the classification performance by choosing a small number of relevant or significant features. It is important to note that due to the NP-hard characteristics of feature selection, the search agent can become trapped in the local optima, which is extremely costly in terms of time and complexity. To solve these problems, an efficient and effective global search method is needed. Sand cat swarm optimization (SCSO) is a newly introduced metaheuristic algorithm that solves global optimization algorithms. Nevertheless, the SCSO algorithm is recommended for continuous problems. bSCSO is a binary version of the SCSO algorithm proposed here for the analysis and solution of discrete problems such as wrapper feature selection in biological data. It was evaluated on ten well-known biological datasets to determine the effectiveness of the bSCSO algorithm. Moreover, the proposed algorithm was compared to four recent binary optimization algorithms to determine which algorithm had better efficiency. A number of findings demonstrated the superiority of the proposed approach both in terms of high prediction accuracy and small feature sizes. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore how machine learning technologies can improve healthcare operations management. A machine learning-based model to solve a specific medical problem is developed to achieve this research purpose. Specifically, this study presents an AI solution for malaria infection diagnosis by applying the CNN (convolutional neural network) algorithm. Based on malaria microscopy image data from the NIH National Library of Medicine, a total of 24,958 images were used for deep learning training, and 2600 images were selected for final testing of the proposed diagnostic architecture. The empirical results indicate that the CNN diagnostic model correctly classified most malaria-infected and non-infected cases with minimal misclassification, with performance metrics of precision (0.97), recall (0.99), and f1-score (0.98) for uninfected cells, and precision (0.99), recall (0.97), and f1-score (0.98) for parasite cells. The CNN diagnostic solution rapidly processed a large number of cases with a high reliable accuracy of 97.81%. The performance of this CNN model was further validated through the k-fold cross-validation test. These results suggest the advantage of machine learning-based diagnostic methods over conventional manual diagnostic methods in improving healthcare operational capabilities in terms of diagnostic quality, processing costs, lead time, and productivity. In addition, a machine learning diagnosis system is more likely to enhance the financial profitability of healthcare operations by reducing the risk of unnecessary medical disputes related to diagnostic errors. As an extension for future research, propositions with a research framework are presented to examine the impacts of machine learning on healthcare operations management for safety and quality of life in global communities. [ABSTRACT FROM AUTHOR]

The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor's approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data. [ABSTRACT FROM AUTHOR]