How does Norwegian technology start-ups use open innovation Strategies for to get access of new business ideas?

Master of Science in Business / Siviløkonom - Nord universitet 201

Prevalence of depression and anxiety among university students during COVID-19 in Bangladesh: A cross sectional study

Introduction: The COVID-19 outbreak has become a challenging crisis for public health. During the COVID-19 pandemic, the indefinite closure of educational institutions in Bangladesh has a severe impact on the mental health of students. Purpose: The purpose of this study is to investigate factors that might have considerable influence on the mental health of students during quarantine in Bangladesh though they did not explore in previous studies on mental health status during the pandemic. Methodology: A standardized questionnaire was generated using PH9 and GAD7 to measure depression and anxiety levels. A total of 203 responses were collected from university students of Bangladesh through social media. Results: Descriptive statistics found that 37% of the students experienced moderate to severe anxiety while 54% faced moderate to severe depression. Ordinal Logistic Regression analysis found that anxiety is significantly related to gender, students’ current affiliation status in university (e.g., sophomore, masters), and time spent on watching TV while depression was related to family member’s contact with Covid-19, performing multiple activities as hobbies, and spending time in reading and writing. Conclusions: This study adds valuable findings in the existing literature, and it will help Students, university authorities, and government can take productive steps to tackle mental health issue

MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning

Mobile phones and other electronic gadgets or devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data. Mobile health data use mobile devices to gather clinical health data and track patient vitals in real-time. Our study is aimed to give decisions for small or big sports teams on whether one athlete good fit or not for a particular game with the compare several machine learning algorithms to predict human behavior and health using the data collected from mobile devices and sensors placed on patients. In this study, we have obtained the dataset from a similar study done on mhealth. The dataset contains vital signs recordings of ten volunteers from different backgrounds. They had to perform several physical activities with a sensor placed on their bodies. Our study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression) to analyze and predict human health behavior. XGBoost performed better compared to the other machine learning algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in specificity, and 99.66% in F1 score. Our research indicated a promising future in mhealth being used to predict human behavior and further research and exploration need to be done for it to be available for commercial use specifically in the sports industry.Comment: 6, Accepted by 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE

How does Norwegian technology start-ups use open innovation Strategies for to get access of new business ideas?

Master of Science in Business / Siviløkonom - Nord universitet 201

Synthesis of polyaniline-graphene oxide based ternary nanocomposite for supercapacitor application

This study focuses on the synthesis of Polyaniline (PANI), graphene oxide (GO), and their nanocomposites with metal oxides (MO) and hexagonal boron nitride (h-BN) for their application in the supercapacitor. In this study, PANI/GO, PANI/GO/TiO2, PANI/GO/MoS2 and PANI/GO/h-BN ternary nanocomposites were synthesized following a chemical oxidative polymerization method. The prepared nanocomposites were characterized by FTIR, XRD, TGA, SEM and electrochemical analysis. The electrochemical properties of the nanocomposites were evaluated by cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), electrochemical polarization and impedance analysis. CV profile of the nanocomposite showed that incorporation of GO, MO and h-BN in the polyaniline backbone improved capacitance, energy density and power density significantly. Among the synthesized ternary nanocomposites, PANI/GO/h-BN showed highest capacitance of 946 F/g and high capacitance retention after 100 cycles of charging and discharging. Finally, a set of hybrid asymmetric supercapacitors were constructed using the synthesized nanocomposites as positive electrode and activated carbon as negative electrode. The device was assessed following the GCD method. The supercapacitor device with PANI/GO/h-BN nanocomposite as positive electrode exhibited higher specific capacitance of 351 F/g with high power density of 4500 W/kg, and it lit up a 5 mm LED for 243 min, indicating its superiority over other electrodes

Deep Learning-Based COVID-19 Detection from Chest X-ray Images: A Comparative Study

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has rapidly spread across the globe, leading to a significant number of illnesses and fatalities. Effective containment of the virus relies on the timely and accurate identification of infected individuals. While methods like RT-PCR assays are considered the gold standard for COVID-19 diagnosis due to their accuracy, they can be limited in their use due to cost and availability issues, particularly in resource-constrained regions. To address this challenge, our study presents a set of deep learning techniques for predicting COVID-19 detection using chest X-ray images. Chest X-ray imaging has emerged as a valuable and cost-effective diagnostic tool for managing COVID-19 because it is non-invasive and widely accessible. However, interpreting chest X-rays for COVID-19 detection can be complex, as the radiographic features of COVID-19 pneumonia can be subtle and may overlap with those of other respiratory illnesses. In this research, we evaluated the performance of various deep learning models, including VGG16, VGG19, DenseNet121, and Resnet50, to determine their ability to differentiate between cases of coronavirus pneumonia and non-COVID-19 pneumonia. Our dataset comprised 4,649 chest X-ray images, with 1,123 of them depicting COVID-19 cases and 3,526 representing pneumonia cases. We used performance metrics and confusion matrices to assess the models' performance. Our study's results showed that DenseNet121 outperformed the other models, achieving an impressive accuracy rate of 99.44%