Depression is one of the most common mental health disorders which affects thousands of lives worldwide. The variation of depressive symptoms among individuals makes it difficult to detect and diagnose early. Moreover, the diagnosing procedure relies heavily on human intervention, making it prone to mistakes. Previous research shows that smartphone sensor data correlates to the users’ mental conditions. By applying machine learning algorithms to sensor data, the mental health status of a person can be predicted. However, traditional machine learning faces privacy challenges as it involves gathering patient data for training. Newly, federated learning has emerged as an effective solution for addressing the privacy issues of classical machine learning. In this study, we apply federated learning to predict depression severity using smartphone sensing capabilities. We develop a deep neural network model and measure its performance in centralized and federated learning settings. The results are quite promising, which validates the potential of federated learning as an alternative to traditional machine learning, with the added benefit of data privacy.
Humans are naturally capable of solving mathematical expressions, but machines lack the abilityto comprehend an issue through a visual context. Computers are gradually becoming moreadvanced and catching up with the subtlety and inaccuracy of real life. The need for an automatedsystem to check answer scripts of mathematical equations has become unparallel, especiallyfor Bengali handwritten scripts. This study checks each line of the solution of a mathematicalequation to evaluate its correctness using a deep learning approach. In contrast to earliermethods, this paper introduces a CNN architecture to verify the accuracy of a handwritten mathematicalequation in addition to solving the problem. The model reads a handwritten equationand validates its mathematical symbols and operations. A dataset has been created to evaluatethe models performance which is named ”BHQED”. The experimental result shows that theaccuracy of the proposed CNN architecture is 92.25% and the recall is 90.65% on our solelycreated dataset. To further boost the performance, this study applies the pretrained ResNet18model and substantially outperforms the CNN with an accuracy of 94.57% and recall of 93.69%.
Aspergillus coinfection with Scopulariopsis brumptii in an immunosuppressed adult male is rare. Patients with Aspergillosis have been described to be coinfected with other organisms, including fungi but the combination with fungi from the Scopulariopsis group has not been so far reported to the best of our knowledge. We report the case of an adult male with monoclonal gammopathy of unknown significance and concurrent COPD, Rheumatoid arthritis on monoclonal antibody therapy and Methotrexate, presenting with recurrent chest infections. Initially, the patient was diagnosed with allergic bronchopulmonary Aspergillosis and treated with antifungal and corticosteroid therapy. During follow up, repeated chest infection was noted despite a range of broad-spectrum antibiotics. Scopulariopsis brumptii was detected on the sputum sample, and specific antifungal treatment was commenced until the full recovery. The patient was on follow up for several years. He is now asymptomatic with no further growth of Aspergillus or Scopulariopsis in his sputum.
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