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Detection of Voice Pathology using Fractal Dimension in a Multiresolution Analysis of Normal and Disordered Speech Signals

Ali Z, Elamvazuthi I, Alsulaiman M, Muhammad G

J Med Syst, 2016 Jan;40(1):20.
PMID: 26531753 DOI: 10.1007/s10916-015-0392-2

Voice disorders are associated with irregular vibrations of vocal folds. Based on the source filter theory of speech production, these irregular vibrations can be detected in a non-invasive way by analyzing the speech signal. In this paper we present a multiband approach for the detection of voice disorders given that the voice source generally interacts with the vocal tract in a non-linear way. In normal phonation, and assuming sustained phonation of a vowel, the lower frequencies of speech are heavily source dependent due to the low frequency glottal formant, while the higher frequencies are less dependent on the source signal. During abnormal phonation, this is still a valid, but turbulent noise of source, because of the irregular vibration, affects also higher frequencies. Motivated by such a model, we suggest a multiband approach based on a three-level discrete wavelet transformation (DWT) and in each band the fractal dimension (FD) of the estimated power spectrum is estimated. The experiments suggest that frequency band 1-1562 Hz, lower frequencies after level 3, exhibits a significant difference in the spectrum of a normal and pathological subject. With this band, a detection rate of 91.28 % is obtained with one feature, and the obtained result is higher than all other frequency bands. Moreover, an accuracy of 92.45 % and an area under receiver operating characteristic curve (AUC) of 95.06 % is acquired when the FD of all levels is fused. Likewise, when the FD of all levels is combined with 22 Multi-Dimensional Voice Program (MDVP) parameters, an improvement of 2.26 % in accuracy and 1.45 % in AUC is observed.
Automatic Voice Pathology Detection With Running Speech by Using Estimation of Auditory Spectrum and Cepstral Coefficients Based on the All-Pole Model

Ali Z, Elamvazuthi I, Alsulaiman M, Muhammad G

J Voice, 2016 Nov;30(6):757.e7-757.e19.
PMID: 26522263 DOI: 10.1016/j.jvoice.2015.08.010

BACKGROUND AND OBJECTIVE: Automatic voice pathology detection using sustained vowels has been widely explored. Because of the stationary nature of the speech waveform, pathology detection with a sustained vowel is a comparatively easier task than that using a running speech. Some disorder detection systems with running speech have also been developed, although most of them are based on a voice activity detection (VAD), that is, itself a challenging task. Pathology detection with running speech needs more investigation, and systems with good accuracy (ACC) are required. Furthermore, pathology classification systems with running speech have not received any attention from the research community. In this article, automatic pathology detection and classification systems are developed using text-dependent running speech without adding a VAD module.
METHOD: A set of three psychophysics conditions of hearing (critical band spectral estimation, equal loudness hearing curve, and the intensity loudness power law of hearing) is used to estimate the auditory spectrum. The auditory spectrum and all-pole models of the auditory spectrums are computed and analyzed and used in a Gaussian mixture model for an automatic decision.
RESULTS: In the experiments using the Massachusetts Eye & Ear Infirmary database, an ACC of 99.56% is obtained for pathology detection, and an ACC of 93.33% is obtained for the pathology classification system. The results of the proposed systems outperform the existing running-speech-based systems.
DISCUSSION: The developed system can effectively be used in voice pathology detection and classification systems, and the proposed features can visually differentiate between normal and pathological samples.
Mimosa pudica L., a High-Value Medicinal Plant as a Source of Bioactives for Pharmaceuticals

Muhammad G, Hussain MA, Jantan I, Bukhari SNA

Compr Rev Food Sci Food Saf, 2016 Mar;15(2):303-315.
PMID: 33371596 DOI: 10.1111/1541-4337.12184

Mimosa pudica Linn. (Family: Mimosaceae) is used as an ornamental plant due to its thigmonastic and nyctinastic movements. M. pudica is also used to avoid or cure several disorders like cancer, diabetes, hepatitis, obesity, and urinary infections. M. pudica is famous for its anticancer alkaloid, mimosine, along with several valuable secondary metabolites like tannins, steroids, flavonoids, triterpenes, and glycosylflavones. A wide array of pharmacological properties like antioxidant, antibacterial, antifungal, anti-inflammatory, hepatoprotective, antinociceptive, anticonvulsant, antidepressant, antidiarrheal, hypolipidemic activities, diuretic, antiparasitic, antimalarial, and hypoglycemic have been attributed to different parts of M. pudica. Glucuronoxylan polysaccharide extruded from seeds of M. pudica is used for drug release formulations due to its high swelling index. This review covers a thorough examination of functional bioactives as well as pharmacological and phytomedicinal attributes of the plant with the purpose of exploring its pharmaceutical and nutraceutical potentials.
Fulltext Medical image-based detection of COVID-19 using Deep Convolution Neural Networks

Gaur L, Bhatia U, Jhanjhi NZ, Muhammad G, Masud M

Multimed Syst, 2023;29(3):1729-1738.
PMID: 33935377 DOI: 10.1007/s00530-021-00794-6

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.
Fulltext Cydonia oblonga M., A Medicinal Plant Rich in Phytonutrients for Pharmaceuticals

Ashraf MU, Muhammad G, Hussain MA, Bukhari SN

Front Pharmacol, 2016;7:163.
PMID: 27445806 DOI: 10.3389/fphar.2016.00163

Cydonia oblonga M. is a medicinal plant of family Rosaceae which is used to prevent or treat several ailments such as cancer, diabetes, hepatitis, ulcer, respiratory, and urinary infections, etc. Cydonia oblonga commonly known as Quince is rich in useful secondary metabolites such as phenolics, steroids, flavonoids, terpenoids, tannins, sugars, organic acids, and glycosides. A wide range of pharmacological activities like antioxidant, antibacterial, antifungal, anti-inflammatory, hepatoprotective, cardiovascular, antidepressant, antidiarrheal, hypolipidemic, diuretic, and hypoglycemic have been ascribed to various parts of C. oblonga. The polysaccharide mucilage, glucuronoxylan extruded from seeds of C. oblonga is used in dermal patches to heal wounds. This review focuses on detailed investigations of high-valued phytochemicals as well as pharmacological and phytomedicinal attributes of the plant.
An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification

Al-Nasheri A, Muhammad G, Alsulaiman M, Ali Z, Mesallam TA, Farahat M, et al.

J Voice, 2017 Jan;31(1):113.e9-113.e18.
PMID: 27105857 DOI: 10.1016/j.jvoice.2016.03.019

BACKGROUND AND OBJECTIVE: Automatic voice-pathology detection and classification systems may help clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. The main aim of this paper is to investigate Multidimensional Voice Program (MDVP) parameters to automatically detect and classify the voice pathologies in multiple databases, and then to find out which parameters performed well in these two processes.
MATERIALS AND METHODS: Samples of the sustained vowel /a/ of normal and pathological voices were extracted from three different databases, which have three voice pathologies in common. The selected databases in this study represent three distinct languages: (1) the Arabic voice pathology database; (2) the Massachusetts Eye and Ear Infirmary database (English database); and (3) the Saarbruecken Voice Database (German database). A computerized speech lab program was used to extract MDVP parameters as features, and an acoustical analysis was performed. The Fisher discrimination ratio was applied to rank the parameters. A t test was performed to highlight any significant differences in the means of the normal and pathological samples.
RESULTS: The experimental results demonstrate a clear difference in the performance of the MDVP parameters using these databases. The highly ranked parameters also differed from one database to another. The best accuracies were obtained by using the three highest ranked MDVP parameters arranged according to the Fisher discrimination ratio: these accuracies were 99.68%, 88.21%, and 72.53% for the Saarbruecken Voice Database, the Massachusetts Eye and Ear Infirmary database, and the Arabic voice pathology database, respectively.
Impact of Ar Flow Rates on Micro-Structural Properties of WS2 Thin Film by RF Magnetron Sputtering

Akhtaruzzaman M, Shahiduzzaman M, Amin N, Muhammad G, Islam MA, Rafiq KSB, et al.

Nanomaterials (Basel), 2021 Jun 22;11(7).
PMID: 34206518 DOI: 10.3390/nano11071635

Tungsten disulfide (WS2) thin films were deposited on soda-lime glass (SLG) substrates using radio frequency (RF) magnetron sputtering at different Ar flow rates (3 to 7 sccm). The effect of Ar flow rates on the structural, morphology, and electrical properties of the WS2 thin films was investigated thoroughly. Structural analysis exhibited that all the as-grown films showed the highest peak at (101) plane corresponds to rhombohedral phase. The crystalline size of the film ranged from 11.2 to 35.6 nm, while dislocation density ranged from 7.8 × 1014 to 26.29 × 1015 lines/m2. All these findings indicate that as-grown WS2 films are induced with various degrees of defects, which were visible in the FESEM images. FESEM images also identified the distorted crystallographic structure for all the films except the film deposited at 5 sccm of Ar gas flow rate. EDX analysis found that all the films were having a sulfur deficit and suggested that WS2 thin film bears edge defects in its structure. Further, electrical analysis confirms that tailoring of structural defects in WS2 thin film can be possible by the varying Ar gas flow rates. All these findings articulate that Ar gas flow rate is one of the important process parameters in RF magnetron sputtering that could affect the morphology, electrical properties, and structural properties of WS2 thin film. Finally, the simulation study validates the experimental results and encourages the use of WS2 as a buffer layer of CdTe-based solar cells.
Calixarene: A Versatile Material for Drug Design and Applications

Hussain MA, Ashraf MU, Muhammad G, Tahir MN, Bukhari SNA

Curr Pharm Des, 2017;23(16):2377-2388.
PMID: 27779081 DOI: 10.2174/1381612822666160928143328

The therapy of various diseases by the drugs entrapped in calixarene derivatives is gaining attraction of researchers nowadays. Calixarenes are macrocyclic nano-baskets which belong to cavitands class of host-guest chemistry. They are the marvelous hosts with distinct hydrophobic three dimensional cavities to entrap and encapsulate biologically active guest drugs. Calixarene and its derivatives develop inclusion complexes with various types of drugs and vitamins for their sustained/targeted release. Calixarene and its derivatives are used as carriers for anti-cancer, anti-convulsant, anti-hypertensive, anthelmentic, anti-inflammatory, antimicrobial and antipsychotic drugs. They are the important biocompatible receptors to improve solubility, chemical reactivity and decrease cytotoxicity of poorly soluble drugs in supramolecular chemistry. This review focuses on the calixarene and its derivatives as the state-of-the-art in host-guest interactions for important drugs. We have also critically evaluated calixarenes for the development of prodrugs.
Intra- and Inter-database Study for Arabic, English, and German Databases: Do Conventional Speech Features Detect Voice Pathology?

Ali Z, Alsulaiman M, Muhammad G, Elamvazuthi I, Al-Nasheri A, Mesallam TA, et al.

J Voice, 2017 May;31(3):386.e1-386.e8.
PMID: 27745756 DOI: 10.1016/j.jvoice.2016.09.009

A large population around the world has voice complications. Various approaches for subjective and objective evaluations have been suggested in the literature. The subjective approach strongly depends on the experience and area of expertise of a clinician, and human error cannot be neglected. On the other hand, the objective or automatic approach is noninvasive. Automatic developed systems can provide complementary information that may be helpful for a clinician in the early screening of a voice disorder. At the same time, automatic systems can be deployed in remote areas where a general practitioner can use them and may refer the patient to a specialist to avoid complications that may be life threatening. Many automatic systems for disorder detection have been developed by applying different types of conventional speech features such as the linear prediction coefficients, linear prediction cepstral coefficients, and Mel-frequency cepstral coefficients (MFCCs). This study aims to ascertain whether conventional speech features detect voice pathology reliably, and whether they can be correlated with voice quality. To investigate this, an automatic detection system based on MFCC was developed, and three different voice disorder databases were used in this study. The experimental results suggest that the accuracy of the MFCC-based system varies from database to database. The detection rate for the intra-database ranges from 72% to 95%, and that for the inter-database is from 47% to 82%. The results conclude that conventional speech features are not correlated with voice, and hence are not reliable in pathology detection.
Fulltext Defect Study and Modelling of SnX3-Based Perovskite Solar Cells with SCAPS-1D

Samiul Islam M, Sobayel K, Al-Kahtani A, Islam MA, Muhammad G, Amin N, et al.

Nanomaterials (Basel), 2021 May 05;11(5).
PMID: 34063020 DOI: 10.3390/nano11051218

Recent achievements, based on lead (Pb) halide perovskites, have prompted comprehensive research on low-cost photovoltaics, in order to avoid the major challenges that arise in this respect: Stability and toxicity. In this study, device modelling of lead (Pb)-free perovskite solar cells has been carried out considering methyl ammonium tin bromide (CH3NH3SnBr3) as perovskite absorber layer. The perovskite structure has been justified theoretically by Goldschmidt tolerance factor and the octahedral factor. Numerical modelling tools were used to investigate the effects of amphoteric defect and interface defect states on the photovoltaic parameters of CH3NH3SnBr3-based perovskite solar cell. The study identifies the density of defect tolerance in the absorber layer, and that both the interfaces are 1015 cm-3, and 1014 cm-3, respectively. Furthermore, the simulation evaluates the influences of metal work function, uniform donor density in the electron transport layer and the impact of series resistance on the photovoltaic parameters of proposed n-TiO2/i-CH3NH3SnBr3/p-NiO solar cell. Considering all the optimization parameters, CH3NH3SnBr3-based perovskite solar cell exhibits the highest efficiency of 21.66% with the Voc of 0.80 V, Jsc of 31.88 mA/cm2 and Fill Factor of 84.89%. These results divulge the development of environmentally friendly methyl ammonium tin bromide perovskite solar cell.
Fulltext Resorcinol-Formaldehyde (RF) as a Novel Plasticizer for Starch-Based Solid Biopolymer Electrolyte

Selvanathan V, Ruslan MH, Aminuzzaman M, Muhammad G, Amin N, Sopian K, et al.

Polymers (Basel), 2020 Sep 22;12(9).
PMID: 32972016 DOI: 10.3390/polym12092170

A starch-resorcinol-formaldehyde (RF)-lithium triflate (LiTf) based biodegradable polymer electrolyte membrane was synthesized via the solution casting technique. The formation of RF crosslinks in the starch matrix was found to repress the starch's crystallinity as indicated by the XRD data. Incorporation of the RF plasticizer improved the conductivity greatly, with the highest room-temperature conductivity recorded being 4.29 × 10-4 S cm-1 achieved by the starch:LiTf:RF (20 wt.%:20 wt.%:60 wt.%) composition. The enhancement in ionic conductivity was an implication of the increase in the polymeric amorphous region concurrent with the suppression of the starch's crystallinity. Chemical complexation between the plasticizer, starch, and lithium salt components in the electrolyte was confirmed by FTIR spectra.
Fulltext A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals

Bamatraf S, Hussain M, Aboalsamh H, Qazi EU, Malik AS, Amin HU, et al.

Comput Intell Neurosci, 2016;2016:8491046.
PMID: 26819593 DOI: 10.1155/2016/8491046

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM.
Fulltext Degradation of Perovskite Thin Films and Solar Cells with Candle Soot C/Ag Electrode Exposed in a Control Ambient

Islam MA, Mohafez H, Sobayel K, Wan Muhamad Hatta SF, Hasan AKM, Khandaker MU, et al.

Nanomaterials (Basel), 2021 Dec 20;11(12).
PMID: 34947812 DOI: 10.3390/nano11123463

Perovskite solar cells (PSCs) have already achieved efficiencies of over 25%; however, their instability and degradation in the operational environment have prevented them from becoming commercially viable. Understanding the degradation mechanism, as well as improving the fabrication technique for achieving high-quality perovskite films, is crucial to overcoming these shortcomings. In this study, we investigated details in the changes of physical properties associated with the degradation and/or decomposition of perovskite films and solar cells using XRD, FESEM, EDX, UV-Vis, Hall-effect, and current-voltage (I-V) measurement techniques. The dissociation, as well as the intensity of perovskite peaks, have been observed as an impact of film degradation by humidity. The decomposition rate of perovskite film has been estimated from the structural and optical changes. The performance degradation of novel planner structure PSCs has been investigated in detail. The PSCs were fabricated in-room ambient using candle soot carbon and screen-printed Ag electrode. It was found that until the perovskite film decomposed by 30%, the film properties and cell efficiency remained stable.
Fulltext Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer

Michailidou K, Beesley J, Lindstrom S, Canisius S, Dennis J, Lush MJ, et al.

Nat Genet, 2015 Apr;47(4):373-80.
PMID: 25751625 DOI: 10.1038/ng.3242

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748 breast cancer cases and 18,084 controls together with 46,785 cases and 42,892 controls from 41 studies genotyped on a 211,155-marker custom array (iCOGS). Analyses were restricted to women of European ancestry. We generated genotypes for more than 11 million SNPs by imputation using the 1000 Genomes Project reference panel, and we identified 15 new loci associated with breast cancer at P < 5 × 10(-8). Combining association analysis with ChIP-seq chromatin binding data in mammary cell lines and ChIA-PET chromatin interaction data from ENCODE, we identified likely target genes in two regions: SETBP1 at 18q12.3 and RNF115 and PDZK1 at 1q21.1. One association appears to be driven by an amino acid substitution encoded in EXO1.
Fulltext Identification of four novel susceptibility loci for oestrogen receptor negative breast cancer

Couch FJ, Kuchenbaecker KB, Michailidou K, Mendoza-Fandino GA, Nord S, Lilyquist J, et al.

Nat Commun, 2016 Apr 27;7:11375.
PMID: 27117709 DOI: 10.1038/ncomms11375

Common variants in 94 loci have been associated with breast cancer including 15 loci with genome-wide significant associations (P<5 × 10(-8)) with oestrogen receptor (ER)-negative breast cancer and BRCA1-associated breast cancer risk. In this study, to identify new ER-negative susceptibility loci, we performed a meta-analysis of 11 genome-wide association studies (GWAS) consisting of 4,939 ER-negative cases and 14,352 controls, combined with 7,333 ER-negative cases and 42,468 controls and 15,252 BRCA1 mutation carriers genotyped on the iCOGS array. We identify four previously unidentified loci including two loci at 13q22 near KLF5, a 2p23.2 locus near WDR43 and a 2q33 locus near PPIL3 that display genome-wide significant associations with ER-negative breast cancer. In addition, 19 known breast cancer risk loci have genome-wide significant associations and 40 had moderate associations (P<0.05) with ER-negative disease. Using functional and eQTL studies we implicate TRMT61B and WDR43 at 2p23.2 and PPIL3 at 2q33 in ER-negative breast cancer aetiology. All ER-negative loci combined account for ∼11% of familial relative risk for ER-negative disease and may contribute to improved ER-negative and BRCA1 breast cancer risk prediction.

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