Combat New Normal Life and Remote Emergency Learning During Pandemic Crisis: A Perspective from Public Universities Students

The Bangladeshi education system has resorted to a virtual emergency mode of learning in order to sustain teaching and learning practices in educational institutions, providing a quick fix to the challenges. From the perspective of public university students, the researcher employed a qualitative study approach to investigate the impact of the COVID-19 outbreak on tertiary education in Bangladesh. The primary data was collected via a well-designed online questionnaire, which was completed by 150 persons. Public universities in Bangladesh use virtual platforms to deliver online classes. University facilities and infrastructure, a robust national data infrastructure, appropriate computer devices, and excellent and affordable data services for students are all required for online instruction. The goal of the study is to determine the practicality and applicability of online education, as well as how students deal with the risk of Covid-19. This research identified a number of unanticipated disruptions in students' learning, as well as a drop in excitement and study hours, difficulty with student-teacher interactions, and a variety of physical, emotional, and financial issues associated to academic studies. According to the research, the most typically mentioned issues by students include network and facility-related hurdles, as well as personal and socio-psychological challenges. A lack of technological infrastructure, a high cost of internet, a slow internet connection, a family's financial difficulty, and student mental strain were also recognized as important impediments to online education by the majority of students. The study presented several recommendations to policymakers based on the findings to help them overcome the challenges of online classrooms in the future. Keywords: Emergency virtual learning, online class, network, psychological stress, academic fear, Covid-19, Tertiary level. DOI: 10.7176/JEP/12-24-10 Publication date:August 31st 202

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person’s behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users’ privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5–2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human–robot interaction for various practical applications, such as mobile robots for eldercare.publishedVersio

Affective social anthropomorphic intelligent system

Human conversational styles are measured by the sense of humor, personality, and tone of voice. These characteristics have become essential for conversational intelligent virtual assistants. However, most of the state-of-the-art intelligent virtual assistants (IVAs) are failed to interpret the affective semantics of human voices. This research proposes an anthropomorphic intelligent system that can hold a proper human-like conversation with emotion and personality. A voice style transfer method is also proposed to map the attributes of a specific emotion. Initially, the frequency domain data (Mel-Spectrogram) is created by converting the temporal audio wave data, which comprises discrete patterns for audio features such as notes, pitch, rhythm, and melody. A collateral CNN-Transformer-Encoder is used to predict seven different affective states from voice. The voice is also fed parallelly to the deep-speech, an RNN model that generates the text transcription from the spectrogram. Then the transcripted text is transferred to the multi-domain conversation agent using blended skill talk, transformer-based retrieve-and-generate generation strategy, and beam-search decoding, and an appropriate textual response is generated. The system learns an invertible mapping of data to a latent space that can be manipulated and generates a Mel-spectrogram frame based on previous Mel-spectrogram frames to voice synthesize and style transfer. Finally, the waveform is generated using WaveGlow from the spectrogram. The outcomes of the studies we conducted on individual models were auspicious. Furthermore, users who interacted with the system provided positive feedback, demonstrating the system's effectiveness.Comment: Multimedia Tools and Applications (2023

On Converting Crisp Failure Possibility into Probability for Reliability of Complex Systems

The reliability of complex systems is analyzed based on several systematic steps using many safety engineering methods. The most common technique for safety system analysis and reliability, vulnerability and criticality estimation is the fault tree analysis method. There exist numerous conventional and fuzzy extended approaches to construct such a tree. One of the steps of the fuzzy fault tree analysis method (FFTA) is the conversion of crisp failure possibility (CFP) into failure probability (FP). This paper points out the drawbacks of one of the formulas for conversion of CFP into FP, and discusses ways to improve the formula for the FFTA. The proposed approach opens a corridor for the researchers to re-think the previous studies, and is susceptible to improve the future applications for safety and reliability engineering.acceptedVersio

Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography

Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community’s research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all of them in terms of accuracy, with an accuracy of 99.30 percent. The F1 score and precision and recall comparison reveal that the Swin transformer outperforms all other models and that it is the quickest to train. The study also revealed the blackbox of the VGG16, Resnet50, and Inception models, demonstrating that VGG16 is superior than Resnet50 and Inceptionv3 in terms of monitoring the necessary anatomy abnormalities. We believe that the superior accuracy of our Swin transformer-based model and the VGG16-based model can both be useful in diagnosing kidney tumors, cysts, and stones.publishedVersio

CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis

Recognizing emotional state of human using brain signal is an active research domain with several open challenges. In this research, we propose a signal spectrogram image based CNN-XGBoost fusion method for recognising three dimensions of emotion, namely arousal (calm or excitement), valence (positive or negative feeling) and dominance (without control or empowered). We used a benchmark dataset called DREAMER where the EEG signals were collected from multiple stimulus along with self-evaluation ratings. In our proposed method, we first calculate the Short-Time Fourier Transform (STFT) of the EEG signals and convert them into RGB images to obtain the spectrograms. Then we use a two dimensional Convolutional Neural Network (CNN) in order to train the model on the spectrogram images and retrieve the features from the trained layer of the CNN using a dense layer of the neural network. We apply Extreme Gradient Boosting (XGBoost) classifier on extracted CNN features to classify the signals into arousal, valence and dominance of human emotion. We compare our results with the feature fusion-based state-of-the-art approaches of emotion recognition. To do this, we applied various feature extraction techniques on the signals which include Fast Fourier Transformation, Discrete Cosine Transformation, Poincare, Power Spectral Density, Hjorth parameters and some statistical features. Additionally, we use Chi-square and Recursive Feature Elimination techniques to select the discriminative features. We form the feature vectors by applying feature level fusion, and apply Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) classifiers on the fused features to classify different emotion levels. The performance study shows that the proposed spectrogram image based CNN-XGBoost fusion method outperforms the feature fusion-based SVM and XGBoost methods. The proposed method obtained the accuracy of 99.712% for arousal, 99.770% for valence and 99.770% for dominance in human emotion detection.publishedVersio

Arsenic Speciation Techniques in Soil Water and Plant: An Overview

There are more than 100 different arsenic with different characteristics in the soil-water-plant ecosystem. The identification and quantification of individual arsenic species is essential for understanding the distribution, environmental fate and behavior, metabolism and toxicity of arsenic. Due to the hazardous nature of arsenic, people have a high interest in the measurement of arsenic species. The reaction of the formation of arsenic speciation in the soil-water-plant environment is briefly studied. There is little information on methods used to quantify arsenic forms and species in contaminated soil, water and plant. The purpose of this article is to understand the available sample pretreatment, extraction, separation, detection and method validation techniques for arsenic speciation analysis of arsenic species in soil, water and plant. The performances of various sample preparation and extraction processes, as well as effective separation techniques, that contribute greatly to excellent sensitivity and selectivity in arsenic speciation when coupling with suitable detection mode, and method validity are discussed. The outlines of arsenic speciation techniques are discussed in view of the importance to the completeness and accuracy of analytical data in the soil-water-plant samples. To develop cheap, fast, sensitive, and reproducible techniques with low detection limits, still needed to confine research on arsenic speciation present in environmental matrices

Deep learning for prediction of depressive symptoms in a large textual dataset

Depression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.publishedVersio

An Analysis of Data Production Based on the Consistency of Decision Matrices

Multi-criteria decision making methods are used to solve numerous problems related to several disciplines such as engineering, management and business. Consistency of a decision making application is of crucial importance because of its dependability, reliability and sustainability. In this study, data production phenomenon is discussed based on consistency of a decision matrix. We complete different tests including (i) 16 tests (different triangular scales) for different 64, 125 and 216 decision matrix sets for four-criteria problems, (ii) 16 tests for different 256, 625 and 1296 decision matrix sets for five-criteria problems. Based on our observations from the results, we find that first level derivation is always consistent. However, the second and higher level derivations exhibit inconsistencies. The results are also valid if the expert evaluations for the same pairwise comparisons are considered as equal (1, 1, 1). This study is expected to improve the reliability results for the future decision making studies.acceptedVersio