Current Issue : April-June Volume : 2023 Issue Number : 2 Articles : 5 Articles
Artificial Intelligence (AI) appears to be making important advances in the prediction and diagnosis of mental disorders. Researchers have used visual, acoustic, verbal, and physiological features to train models to predict or aid in the diagnosis, with some success. However, such systems are rarely applied in clinical practice, mainly because of the many challenges that currently exist. First, mental disorders such as depression are highly subjective, with complex symptoms, individual differences, and strong socio‐cultural ties, meaning that their diagnosis requires comprehensive consideration. Second, there are many problems with the current samples, such as artificiality, poor ecological validity, small sample size, and mandatory category simplification. In addition, annotations may be too subjective to meet the requirements of professional clinicians. Moreover, multimodal information does not solve the current challenges, and within‐group variations are greater than between‐group characteristics, also posing significant challenges for recognition. In conclusion, current AI is still far from effectively recognizing mental disorders and cannot replace clinicians’ diagnoses in the near future. The real challenge for AI‐based mental disorder diagnosis is not a technical one, nor is it wholly about data, but rather our overall understanding of mental disorders in general....
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....
Arti-cial intelligence (AI) is a potentially transformative force that is likely to change the role of management and organizational practices. AI is revolutionizing corporate decision-making and changing management structures. (e visible e4ects of AI can be observed in key competencies and corporate processes such as knowledge management, as well as consumer outcomes including service quality perceptions and satisfaction. (is study aims to optimize the human resource management (HRM) process, reduce the workload of human resource managers, and improve work e7ciency. Based on AI digitization technology, a salary prediction model (SPM) is designed using a backpropagation neural network (BPNN), and the Nesterov and Adaptive Moment Estimation (Nadam) algorithms are integrated to optimize the model. Next, the content information of the resumes are used to predict the hiring salary of the candidates and validate the model. Results show that compared with other optimization algorithms, the -nal predicted result score of the Nadam optimization algorithm is 0.75%, and the training period is 186 s, providing the best optimization e4ect and the fastest convergence speed. Moreover, the BPNN-based SPM optimized by Nadam has good performance in the learning process and the accuracy rate can reach 79.4%, which veri-es the validity of the SPM. (e outcomes of this study can provide a reference for HRM systems based on data mining technology....
In this paper, we analyze the recognition error of the general AI recognition model and propose the structurally modified and object‐augmented AI recognition model. This model has object detection features that distinguish specific target objects in target areas where players with similar shapes and characteristics overlapped in real‐time football images. We implemented the AI recognition model by reinforcing the training dataset and augmenting the object class. In addition, it is shown that the recognition rate increased by modifying the model structure based on the recognition errors analysis of general AI recognition models. Recognition errors were decreased by applying the modules of HSV processing and differentiated classes (overlapped player groups) learning to the general AI recognition model. We experimented in order to compare the recognition error reducing performance with the general AI model and the proposed AI model by the same real‐time football images. Through this study, we have confirmed that the proposed model can reduce errors. It showed that the proposed AI model structure to recognize similar objects in real‐time and in various environments could be used to analyze football games....
The suppression of natural spaces due to urban sprawl and increases in built and agricultural environments has affected water resource quality, especially in areas with high population densities. Considering the advances in the Brazilian environmental legal framework, the present study aimed to verify whether land use has still affected water quality through a case study of a peri-urban watershed in a Brazilian metropolitan region. Analyses of physical–chemical indicators, collected at several sample points with various land-use parameters at different seasons of the year, were carried out based on an approach combining variance analysis and genetic programming. As a result, some statistically significant spatiotemporal effects on water quality associated with the land use, such as urban areas and thermotolerant coliform (R = − 0.82, p < 0.01), mixed vegetation and dissolved oxygen (R = 0.80, p < 0.001), agriculture/pasture and biochemical oxygen demand (R = 0.40, p < 0.001), and sugarcane and turbidity (R = 0.65, p < 0.001), were verified. In turn, gene expression programming allowed for the computing of the importance of land-use typologies based on their capability to explain the variances of the water quality parameter. In conclusion, in spite of the advances in the Brazilian law, land use has still significantly affected water quality. Public policies and decisions are required to ensure effective compliance with legal guidelines....
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