SNR Improvement and Bandwidth Optimization Technique Using PCM-DSSS Encryption Scheme

Cryptography, the scheme of information stashing and verification, entirely deals with protocols, algorithms and strategies to ensure the precise security facility of the signal consistently by hindering unauthorized access to the confidential information. Albeit in most of the encryption schemes, certain impediments are faced by the service providers such as the expansion of required bandwidth, the fragile encryption technique, the consumption of maximum bandwidth in security purpose, less priority to improvement of SNR of the system, the complexity in decryption and so forth.  This paper illustrates the SNR enhancement & bandwidth optimization technique in security purpose using PCM- DSSS sample by sample encryption scheme. For this purpose, after sampling of a signal, simple mathematical operation is performed in each sample with a time varying arbitrary weights. This arbitrary weight can be obtained from D/A conversion of pseudo noise sequence. Since the bandwidth consumption in security purpose can be minimized in this scheme, a significant portion of unused bandwidth can be used to improve the SNR of the system by reducing quantization noise of encrypted samples. By the same token, the possibility of SNR improvement is demonstrated by reckoning the quantization noise while introducing additional quantization step

Infrequent pattern detection for reliable network traffic analysis using robust evolutionary computation

While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have applied our previously proposed CC-based FS with random feature grouping (CCFSRFG) to a benchmark cybersecurity dataset as the preprocessing step. The dataset with original features and the dataset with a reduced number of features were used for infrequent pattern detection. Experimental analysis was performed and evaluated using 10 unsupervised anomaly detection techniques. Therefore, the proposed infrequent pattern detection is termed Unsupervised Infrequent Pattern Detection (UIPD). Then, we compared the experimental results with and without FS in terms of true positive rate (TPR). Experimental analysis indicates that the highest rate of TPR improvement was by cluster-based local outlier factor (CBLOF) of the backdoor infrequent pattern detection, and it was 385.91% when using FS. Furthermore, the highest overall infrequent pattern detection TPR was improved by 61.47% for all infrequent patterns using clustering-based multivariate Gaussian outlier score (CMGOS) with FS

Microsatellite marker assisted molecular and morpho-physiological genetic diversity assessment in 38 genotypes of sesame (Sesamum indicum L.)

Identification of genetic diversity and their relationships among breeding materials is crucial in crop improvement strategies. In this study, 38 sesame genotypes were characterized for their genetic diversity. The results revealed significant variations among various traits such as plant height, maturity, capsule plant-1 and seeds capsule-1. The number of capsule plant-1 showed significant positive correlation with seeds capsule-1. The highest heritability was found for the numbers of capsules plant-1 (98.67%). The 38 genotypes were separated into six distinct clusters. Comparison within the populations of the cluster IV and those of cluster VI had the highest capsules plant-1, seeds capsule-1 with enormous genetic diversity. For molecular characterization, 7 microsatellite markers and 5 SSR primers with polymorphism were finally chosen for genetic diversity analysis. Altogether, 19 alleles were identified among the 38 genotypes, and the average number of alleles per locus was 3.80. The lowest and the highest numbers of alleles were 3 and 5, respectively. The polymorphism information content (PIC) ranged from 0.3201 to 0.5934 and SI-ssr30 showed to be highest at 0.5934. The UPGMA based clustering depicted a significant variation at molecular level among the sesame genotypes, having a coefficient of similarity between 0.29 and 1.00. The present study confirmed that extensive genetic diversity existed among the sesame genotypes and valuable agronomic traits may result in the development of high yielding genotypes

Psychometric validation of the Bangla fear of COVID-19 Scale: confirmatory factor analysis and Rasch analysis

The recently developed Fear of COVID-19 Scale (FCV-19S) is a seven-item uni-dimensional scale that assesses the severity of fears of COVID-19. Given the rapid increase of COVID-19 cases in Bangladesh, we aimed to translate and validate the FCV-19S in Bangla. The forward-backward translation method was used to translate the English version of the questionnaire into Bangla. The reliability and validity properties of the Bangla FCV-19S were rigorously psychometrically evaluated (utilizing both confirmatory factor analysis and Rasch analysis) in relation to socio-demographic variables, national lockdown variables, and response to the Bangla Health Patient Questionnaire. The sample comprised 8550 Bangladeshi participants. The Cronbach α value for the Bangla FCV-19S was 0.871 indicating very good internal reliability. The results of the confirmatory factor analysis showed that the uni-dimensional factor structure of the FCV-19S fitted well with the data. The FCV-19S was significantly correlated with the nine-item Bangla Patient Health Questionnaire (PHQ-90) (r = 0.406,

A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound

Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator's skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05% with precision, recall, f1 score, and specificity of 96.06%, 96.05%, 96.06%, and 96.09%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time.This work was made possible by the High Impact grant of Qatar University # QUHI-CENG-22_23-548 and student grant: QUST-1-CENG-2023-796. The statements made herein are solely the responsibility of the authors.Scopu

Fear of COVID‐19 Scale (FCV‐19S) across countries: measurement invariance issues

Aim: The threats of novel coronavirus disease 2019 (COVID-19) have caused fears worldwide. The Fear of COVID-19 Scale (FCV-19S) was recently developed to assess the fear of COVID-19. Although many studies found that the FCV-19S is psychometrically sound, it is unclear whether the FCV-19S is invariant across countries. The present study aimed to examine the measurement invariance of the FCV-19S across eleven countries. Design: Cross-sectional study. Methods: Using data collected from prior research on Bangladesh (N = 8,550), United Kingdom (N = 344), Brazil (N = 1,843), Taiwan (N = 539), Italy (N = 249), New Zealand (N = 317), Iran (N = 717), Cuba (N = 772), Pakistan (N = 937), Japan (N = 1,079) and France (N = 316), comprising a total 15,663 participants, the present study used the multigroup confirmatory factor analysis (CFA) and Rasch differential item functioning (DIF) to examine the measurement invariance of the FCV-19S across country, gender and age (children aged below 18 years, young to middle-aged adults aged between 18 and 60 years, and older people aged above 60 years). Results: The unidimensional structure of the FCV-19S was confirmed. Multigroup CFA showed that FCV-19S was partially invariant across country and fully invariant across gender and age. DIF findings were consistent with the findings from multigroup CFA. Many DIF items were displayed for country, few DIF items were displayed for age, and no DIF items were displayed for gender. Conclusion: Based on the results of the present study, the FCV-19S is a good psychometric instrument to assess fear of COVID-19 during the pandemic period. Moreover, the use of FCV-19S is supported in at least ten countries with satisfactory psychometric properties

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Retracted: Prediction of success of movies using data analytics techniques

This article was withdrawn and retracted by the Journal of Fundamental and Applied Sciences and has been removed from AJOL at the request of the journal Editor in Chief and the organisers of the conference at which the articles were presented (www.iccmit.net). Please address any queries to [email protected]