Depression is a common psychiatric disorder that is becoming more prevalent in developing countries like Bangladesh. Depression has been found to be prevalent among youths and influences a person's lifestyle and t...
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Depression is a common psychiatric disorder that is becoming more prevalent in developing countries like Bangladesh. Depression has been found to be prevalent among youths and influences a person's lifestyle and thought process. Unfortunately, due to the public and social stigma attached to this disease, the mental health issue of individuals are often overlooked. Early diagnosis of patients who may have depression often helps to provide effective treatment. This research aims to develop mechanisms to detect and predict depression levels and was applied to university students in Bangladesh. In this work, a questionnaire containing 106 questions has been constructed. The questions in the questionnaire are primarily of two kinds - (i) personal, and (ii) clinical. The questionnaire was distributed amongst Bangladeshi students and a total of 684 responses (aged between 19 and 35) were obtained. After appropriate consents from the participants, they were allowed to take the survey. After carefully scrutinizing the responses, 520 samples were taken into final consideration. A hybrid depression assessment scale was developed using a voting algorithm that employs eight well-known existing scales to assess the depression level of an individual. This hybrid scale was then applied to the collected samples that comprise personal information and questions from various familiar depression measuring scales. In addition, ten machine learning and two deep learning models were applied to predict the three classes of depression (normal, moderate and extreme). Five hyperparameter optimizers and nine feature selection methods were employed to improve the predictability. Accuracies of 98.08%, 94.23%, and 92.31% were obtained using Random Forest, Gradient Boosting, and CNN models, respectively. Random Forest accomplished the lowest false negatives and highest F Measure with its optimized hyperparameters. Finally, LIME, an explainable AI framework, was applied to interpret and retrace the
A gait is a walking pattern that can help identify a person. Recently, gait analysis employed a vision-based pose estimation for further feature extraction. This research aims to identify a person by analyzing their w...
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A gait is a walking pattern that can help identify a person. Recently, gait analysis employed a vision-based pose estimation for further feature extraction. This research aims to identify a person by analyzing their walking pattern. Moreover, the authors intend to expand gait analysis for other tasks, e.g., the analysis of clinical, psychological, and emotional tasks. The vision-based human pose estimation method is used in this study to extract the joint angles and rank correlation between them. We deploy the multi-view gait databases for the experiment, i.e., CASIA-B and OUMVLP-Pose. The features are separated into three parts, i.e., whole, upper, and lower body features, to study the effect of the human body part features on an analysis of the gait. For person identity matching, a minimum Dynamic Time Warping (DTW) distance is determined. Additionally, we apply a majority voting algorithm to integrate the separated matching results from multiple cameras to enhance accuracy, and it improved up to approximately 30% compared to matching without majority voting.
Distributed storage systems with replication are well known for storing large amount of data. A large number of replication is done in order to provide reliability. This makes the system expensive. Various methods hav...
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Distributed storage systems with replication are well known for storing large amount of data. A large number of replication is done in order to provide reliability. This makes the system expensive. Various methods have been proposed over time to reduce the degree of replication and yet provide same level of reliability. One recently suggested scheme is of Regenerating codes, where a file is divided into parts which are then processed by a coding mechanism and network coding to provide large number of parts. These are stored at various nodes with more than one part at each node. These codes can generate whole file and can repair a failed node by contacting some out of total existing nodes. This property ensures reliability in case of node failure and uses clever replication. This also optimizes bandwidth usage. In a practical scenario, the original file will be read and updated many times. With every update, we will have to update the data stored at many nodes. Handling multiple requests at the same time will bring a lot of complexity. Reading and writing or multiple writing on the same data at the same time should also be prevented. In this paper, we propose an algorithm that manages and executes all the requests from the users which reduces the update complexity in cloud computing.
Average (mean) voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are *** this paper, a new ...
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Average (mean) voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are *** this paper, a new generation of average voter based on parallel algorithms and parallel random access machine (PRAM) structure are *** analysis shows that this algorithm is optimal due to its improved time complexity, speed-up, and efficiency and is especially appropriate for applications where the size of input space is large.
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