The events of the past 2 years related to the pandemic have shown that it is increasingly important to find new tools to help mental health experts in diagnosing mood disorders. Leaving aside the long-covid cognitive (e.g., difficulty in concentration) and bodily (e.g., loss of smell) effects, the short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the “online” handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients. To this purpose, patients with clinical depression (n = 14), individuals with high sub-clinical (diagnosed by a test rather than a doctor) depressive traits (n = 15) and healthy individuals (n = 20) were recruited and asked to perform four online drawing/handwriting tasks using a digitizing tablet and a special writing device. From the raw collected online data, seventeen drawing/writing features (categorized into five categories) were extracted, and compared among the three groups of the involved participants, through ANOVA repeated measures analyses. The main results of this study show that Time features are more effective in discriminating between healthy and participants with sub-clinical depressive characteristics. On the other hand, Ductus and Pressure features are more effective in discriminating between clinical depressed and healthy participants.
Many firms’ success hinges on their ability to use data effectively to make quicker and more informed choices. Companies may leverage Business Intelligence (BI) and other powerful and efficient technologies as catalysts to help them automate analysis, decision making, strategy creation, and forecasting. Accordingly, the study’s primary goal is to reveal whether or not integrating Business Intelligence, Organizational Learning (OL), and Innovation into operational procedures improves businesses’ bottom lines. This study is an example of applied research since it applies theoretical frameworks to a real-world setting—the Shanghai Zizhu Science-based Industrial Park—in order to show the sort of externality it creates for the enterprises involved. The introduction of business intelligence systems within a firm is directly associated with technological advancement since it allows the organization to make full use of all accessible data. In reality, BI approaches transform unstructured data sets into information to assist decision-making processes, maximize knowledge, and speed up and improve processes via the use of a wide range of coordinated operational just as chosen help apps and databases. The last factor to think about is organizational learning, which is founded on the system of organizational characteristics, practices, and problems that make possible the organization’s capacity to learn. In conclusion, studies show that Business Intelligence and innovation have a major impact on how businesses function. However, we found no statistically significant correlation between Organizational Learning and the financial success of these same businesses.
The recent pandemic crisis combined with the explosive growth of Artificial Intellignence (AI) algorithms has highlighted the potential benefits of telemedicine for decentralised, accurate and automated clinical diagnoses. One of the most popular and essential diagnoses is the auscultation; it is non-invasive, real-time and very informative diagnoses for knowing the state of the respiratory system. To implement a possible automated auscultation analysis, the decision-making explanation of complex models (such as Deep Learning models) is crucial for trusted application in the clinical domain. In this context, we will analyse the behaviour of a Convolutional Neural Network (CNN) in classifying the largest publicly available database of respiratory sounds, originally compiled to support the scientific challenge organized at Int. Conf. on Biomedical Health Informatics (ICBHI17). It contains respiratory sounds (recorded with auscultation) of normal respiratory cycles, crackles, wheezes and both. To capture the phonetically important features of breath sounds, the Mel-Frequency Cepstrum (MFC) for short-term power spectrum representation was applied. The MFC allowed us to identify latent features without losing the temporal information so that we could easily identify the correspondence of the features to the starting sound. The MFCs were used as input to the proposed CNN who was able to classify the four above-mentioned respiratory classes with an accuracy of 72.8
Breast cancer is the most common malignancy that threatening women’s health. Although Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) for breast lesions characterization is widely used in the clinical practice, physician grading performance is still not optimal, showing a specificity of about 72
Coronavirus Disease 2019 (COVID-19) emerged towards the end of 2019, and it is still causing havoc on the lives and businesses of millions of people in 2022. As the globe recovers from the epidemic and intends to return to normalcy, there is a spike of anxiety among those who expect to resume their everyday routines in person.The biggest difficulty is that no effective therapeutics have yet been reported. According to the World Health Organization (WHO), wearing a face mask and keeping a social distance of at least 2 m can limit viral transmission from person to person. In this paper, a deep learning-based hybrid system for face mask identification and social distance monitoring is developed. In the OpenCV environment, MobileNetV2 is utilized to identify face masks, while YoLoV3 is used for social distance monitoring. The proposed system achieved an accuracy of 0.99.
