Superiority of Islamic Banking in Comparison with Conventional Banking in Bangladesh - A Comparative Study

This paper investigates the financial performance of interest- based conventional commercial banks and interestfree Islamic banks in Bangladesh using descriptive statistics ttest and test of hypotheses Data has been processed through Statistical Package for Social Science SPSS software The data consist of accounting figures of 4 interests based conventional commercial banks and 4 interest free Islamic banks from 2009 to 2013 The study revealed mixed results The study found that conventional commercial banks are superior in terms of performance regarding in commitment to economy and community development productivity and efficiency where performance of Islamic banks in terms of business development profitability liquidity and solvency is superior to that of conventional bank

Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

Fuzz, Penetration, and AI Testing for SoC Security Verification: Challenges and Solutions

The ever-increasing usage and application of system-on-chips (SoCs) has resulted in the tremendous modernization of these architectures. For a modern SoC design, with the inclusion of numerous complex and heterogeneous intellectual properties (IPs), and its privacy-preserving declaration, there exists a wide variety of highly sensitive assets. These assets must be protected from any unauthorized access and against a diverse set of attacks. Attacks for obtaining such assets could be accomplished through different sources, including malicious IPs, malicious or vulnerable firmware/software, unreliable and insecure interconnection and communication protocol, and side-channel vulnerabilities through power/performance profiles. Any unauthorized access to such highly sensitive assets may result in either a breach of company secrets for original equipment manufactures (OEM) or identity theft for the end-user. Unlike the enormous advances in functional testing and verification of the SoC architecture, security verification is still on the rise, and little endeavor has been carried out by academia and industry. Unfortunately, there exists a huge gap between the modernization of the SoC architectures and their security verification approaches. With the lack of automated SoC security verification in modern electronic design automation (EDA) tools, we provide a comprehensive overview of the requirements that must be realized as the fundamentals of the SoC security verification process in this paper. By reviewing these requirements, including the creation of a unified language for SoC security verification, the definition of security policies, formulation of the security verification, etc., we put forward a realization of the utilization of self-refinement techniques, such as fuzz, penetration, and AI testing, for security verification purposes. We evaluate all the challenges and resolution possibilities, and we provide the potential approaches for the realization of SoC security verification via these self-refinement techniques

A Novel Non-Invasive Estimation of Respiration Rate from Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model

Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-Threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a real-Time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. Recent researches have proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR estimation however, the usage of ECG is limited due to the unavailability of it in wearable devices. Due to the advent of wearable smartwatches with built-in PPG sensors, it is now being considered for continuous monitoring of RR. This paper describes a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features. Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. The best ML model and the best feature selection algorithm combination were fine-Tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. Patients would be able to track RR at a lower cost and with less inconvenience if RR can be extracted efficiently and reliably from the PPG signal. 2013 IEEE.Corresponding authors: Muhammad E. H. Chowdhury ([email protected]), Mamun Bin Ibne Reaz ([email protected]), and Md. Shafayet Hossain ([email protected]) This work was supported in part by the Qatar National Research under Grant NPRP12S-0227-190164, and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021-001. The statements made herein are solely the responsibility of the authors.Scopu

Construction of copy number variation landscape and characterization of associated genes in a Bangladeshi cohort of neurodevelopmental disorders

Introduction: Copy number variations (CNVs) play a critical role in the pathogenesis of neurodevelopmental disorders (NDD) among children. In this study, we aim to identify clinically relevant CNVs, genes and their phenotypic characteristics in an ethnically underrepresented homogenous population of Bangladesh. Methods: We have conducted chromosomal microarray analysis (CMA) for 212 NDD patients with male to female ratio of 2.2:1.0 to identify rare CNVs. To identify candidate genes within the rare CNVs, gene constraint metrics [i.e., “Critical-Exon Genes (CEGs)”] were applied to the population data. Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) was followed in a subset of 95 NDD patients to assess the severity of autism and all statistical tests were performed using the R package. Results: Of all the samples assayed, 12.26% (26/212) and 57.08% (121/212) patients carried pathogenic and variant of uncertain significance (VOUS) CNVs, respectively. While 2.83% (6/212) patients’ pathogenic CNVs were found to be located in the subtelomeric regions. Further burden test identified females are significant carriers of pathogenic CNVs compared to males (OR = 4.2; p = 0.0007). We have observed an increased number of Loss of heterozygosity (LOH) within cases with 23.85% (26/109) consanguineous parents. Our analyses on imprinting genes show, 36 LOH variants disrupting 69 unique imprinted genes and classified these variants as VOUS. ADOS-2 subset shows severe social communication deficit (p = 0.014) and overall ASD symptoms severity (p = 0.026) among the patients carrying duplication CNV compared to the CNV negative group. Candidate gene analysis identified 153 unique CEGs in pathogenic CNVs and 31 in VOUS. Of the unique genes, 18 genes were found to be in smaller (<1 MB) focal CNVs in our NDD cohort and we identified PSMC3 gene as a strong candidate gene for Autism Spectrum Disorder (ASD). Moreover, we hypothesized that KMT2B gene duplication might be associated with intellectual disability. Conclusion: Our results show the utility of CMA for precise genetic diagnosis and its integration into the diagnosis, therapy and management of NDD patients

Heliotropium indicum L.: From Farm to a Source of Bioactive Compounds with Therapeutic Activity

This study aimed to summarize the available data on the ethnomedicinal and phytopharmacological activities of Heliotropium indicum L. based on database reports. For this purpose, an up-to-date literature search was carried out in the Google Scholar, Scopus, Springer Link, Web of Science, ScienceDirect, ResearchGate, PubMed, Chem Spider, Elsevier, BioMed Central, and patent offices (e.g., USPTO, CIPO, NPI, Google patents, and Espacenet) for the published materials. The findings suggest that the plant contains many important phytochemicals, including pyrrolizidine alkaloids, indicine, echinitine, supinine, heleurine, heliotrine, lasiocarpine, acetyl indicine, indicinine, indicine N-oxide, cynoglossine, europine N-oxide, heleurine N-oxide, heliotridine N-oxide, heliotrine N-oxide, heliotrine, volatile oils, triterpenes, amines, and sterols. Scientific reports revealed that the herb showed antioxidant, analgesic, antimicrobial, anticancer, antituberculosis, antiplasmodial, anticataract, antifertility, wound healing, antiinflammatory, antinociceptive, antihyperglycemic, anthelmintic, diuretic, antitussive, antiglaucoma, antiallergic, and larvicidal activity. In conclusion, in vitro studies with animal models seem to show the potential beneficial effects of H. indicum against a wide variety of disorders and as a source of phytotherapeutic compounds. However, clinical studies are necessary to confirm the effects observed in animal models, determine the toxicity of the therapeutic dose and isolate the truly bioactive components

Allium sativum L. Improves Visual Memory and Attention in Healthy Human Volunteers

Studies have shown that Allium sativum L. (AS) protects amyloid-beta peptide-induced apoptosis, prevents oxidative insults to neurons and synapses, and thus prevent Alzheimer’s disease progression in experimental animals. However, there is no experimental evidence in human regarding its putative role in memory and cognition. We have studied the effect of AS consumption by healthy human volunteers on visual memory, verbal memory, attention, and executive function in comparison to control subjects taking placebo. The study was conducted over five weeks and twenty volunteers of both genders were recruited and divided randomly into two groups: A (AS) and B (placebo). Both groups participated in the 6 computerized neuropsychological tests of the Cambridge Neuropsychological Test Automated Battery (CANTAB) twice: at the beginning and after five weeks of the study. We found statistically significant difference (p<0.05) in several parameters of visual memory and attention due to AS ingestion. We also found statistically nonsignificant (p>0.05) beneficial effects on verbal memory and executive function within a short period of time among the volunteers. Study for a longer period of time with patients suffering from neurodegenerative diseases might yield more relevant results regarding the potential therapeutic role of AS

Machine learning-based classification of healthy and impaired gaits using 3D-GRF signals

Gait analysis is helpful for rehabilitation, clinical diagnoses, and sporting activities. Among the gathered signals, ground reaction forces (GRF) may be used for assisting doctors in recognizing and categorizing gait patterns using Machine-Learning methods. In this study, GaitRec and Gutenberg databases were used, where GaitRec contains 2645 gait disorder (GD) patients and 211 Healthy Controls (HCs), and the Gutenberg database has 350 HCs. The combined database has HCs and four GD classes: hip, knee, ankle, and calcaneus. GD is an abnormality in the hip, knee, or ankle joints, whereas Calcaneus gait is calcaneus fractures or ankle fusions. We pre-processed the GRF signals, applied different feature extraction techniques, removed the highly correlated features, and ranked the features using three feature selection algorithms. K-nearest neighbour model (KNN) showed the top performance in terms of accuracy in all experiments. Four different experimental schemes were pursued: (i) 6 binary classifications; (ii) 1 three-class classification; (iii) 2 four-class classifications; (iv) one five-class classification. We also compared the performance of vertical GRF with three-dimensional GRF. We found that using three-dimensional GRF increased the overall performance. Furthermore, it is found that time-domain and Wavelet features are among the most useful in identifying gait patterns. The findings show promising performance in automated gait disorder classification. 2022 Elsevier LtdThis work was made possible by Qatar National Research Fund (QNRF) NPRP12S-0227-190164 and International Research Collaboration Co-Fund (IRCC) grant: IRCC-2021-001 and Universiti Kebangsaan Malaysia under Grant GUP-2021-019 and DPK-2021-001. The statements made herein are solely the responsibility of the authors.Scopu