Browsing by Author "Rehan, Imran"

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    Application of Laser Spectroscopy and Machine Learning for Diagnostics of Uncontrolled Type 2 Diabetes
    (Sage, 2025-04-15) Rehman, Mujeeb Ur; Rehan, Imran; Rehan, Kamran; Sultana, Sabiha
    Diabetes, a chronic metabolic disorder affecting millions worldwide, presents a persistent need for reliable and non-invasive diagnostic techniques. Here, we suggest a highly effective approach for differentiating between fingernails from diabetic individuals and those from healthy controls using laser-induced breakdown spectroscopy (LIBS). The excitation source employed was a Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser emitting light with a wavelength of 1064  nm. The initial differentiation between individuals with and without diabetes was achieved by applying principal component analysis (PCA) to LIBS spectral data, which was then incorporated into a novel machine-learning model. The classification model designed for a non-invasive system included random forest (RF), an extreme learning machine (ELM) classifier, and a hybrid classification model incorporating cross-validation techniques to evaluate the outcomes. The algorithm analyses the complete spectrum of both healthy and diseased samples, categorizing them according to differences in LIBS spectral intensity. The classification performance of the model was assessed using a k-fold cross-validation method. Seven parameters, i.e., specificity, sensitivity, area under curve (AUC), accuracy, precision, recall, and F-score, were used to evaluate the model's overall performance. The findings affirmed that the suggested non-invasive model could predict diabetic diseases with an accuracy of 95%.
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    Fabrication of nano filler doped PVA/starch biodegradable composites with enhanced thermal conduction, water barrier and antimicrobial performance for food industry
    (Elsevier, 2024-03-25) Sultana, Sabiha; Imran, Sohail; Naveed, Amir; Hussain, Sardar; Khattak, Rozina; Ali Shah, Luqman; Rehan, Kamran; Rehan, Imran; Rehman, Mujeeb Ur; Hashmat, Uzma; Haider, Farzana
    In this work there was investigated the synergistic effect of the nanomaterials-the Montmorillonite (MMT) and the vanadium pentoxide (V2O5) on the polyvinyl alcohol (PVA)/starch composite. The composite films were prepared by the solvent casting method. The characterization of the composites showed that the addition of the MMT and the V2O5 to PVA/starch composite decreased the water solubility and water absorption capacity of the film. Both of the reinforcement materials enriched values of thermal conductivity and thermal stability of the composite. The TG/DTA and universal testing machine (UTM) analysis exhibited that MMT and V2O5 augmented the thermal robustness and tensile strength of composites and decreased the strain to break. It was also observed that greater MMT concentration accelerates mechanical strength deterioration of the film owing to agglomeration. The scanning electron microscopy (SEM) analysis reflected great change in the surface morphology of the films in the presence and absence of MMT and V2O5. This was due to the interaction amid constituents of the composite. The chemical interaction between the PVA, Starch, MMT and the V2O5 was also established via Fourier-transform infrared spectroscopy (FTIR) analysis, which revealed fluctuations in the absorbance position and intensity of the PVA/Starch. Antimicrobial activities against seven different cultures of bacteria (both-gram positive and -negative) and one fungus (Candida albicans), exposed that antimicrobial performance of the PVA amplified upon addition of the starch, MMT and V2O5, making these composites prospective candidates for the biodegradable packaging materials.
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    Fingernail Diagnostics: Advancing type II diabetes detection using machine learning algorithms and laser spectroscopy
    (Elsevier, 2024-05-14) Rehman, Mujeeb Ur; Rehan, Imran; Rehan, Kamran; Sultana, Sabiha
    Prolonged Type-II diabetes disrupts the typical function of the heart, kidneys, nerves, blood vessels, bones, and joints. Type-II diabetes gradually modifies the inherent material properties and structural integrity of tissues, while extended periods of hyperglycemia result in the chronic deterioration of tissue quality. With this intention, machine learning and Artificial Intelligence (AI) have been employed in recent years. In the current work, nanosecond pulsed laser-induced breakdown spectroscopy (LIBS) has been utilized to explore the impact of type II-controlled diabetes mellitus upon the chemical contents of fingernails. Discrimination was executed on 80 nail clippings, with 40 from individuals with diabetes and 40 from control subjects. From these fingernail samples, a total of 4800 LIBS emission spectra were acquired. The differentiation between individuals with and without diabetes was initially accomplished through the utilization of principal component analysis (PCA) on LIBS spectral data and subsequently integrated into a novel machine-learning model. The proposed categorization framework for a non-invasive scheme utilized seven distinct classifiers and employed cross-validation procedures to assess and compare the outcomes. The classification outcomes were encouraging, achieving satisfactory accuracy, precision, sensitivity, specificity levels, and F1-score of 96 %, 99.9 %, 96.7 %, 99.9 % and 96.8 respectively. The preliminary finding demonstrates that utilizing LIBS spectra of fingernails in conjunction with machine learning can be a valid method for classifying individuals as either diabetic or nondiabetic, making it a feasible approach for screening purposes.
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    Nondestructive Determination of Chromium, Nickel, and Zinc in Neem Leaves and Facial Care Products by Laser Induced Breakdown Spectroscopy (LIBS)
    (Taylor and Francis, 2021-09-28) Rehan, Imran; Gondal, Mohammed A.; Rehan, Kamran; Sultana, Sabiha; Khan, Saranjam; Rehman, Mujeeb Ur; Waheed, Abdul; Salman, Syed Muhammad
    Laser-induced breakdown spectroscopy (LIBS) was employed to determine Cr, Ni, and Zn in Neem-based beauty soaps and Neem leaves. The measurements were accomplished by attaining the optically thin plasma under the condition of local thermodynamic equilibrium (LTE) using standard calibration curve (SCC)-LIBS. The detection system was calibrated for these elements. The results showed 5.0 to 12 ppm by mass of nickel and 9.0 to 16 ppm by mass of chromium, which are above the secure allowed levels of these elements. Similarly, zinc was present from 6.2 to 14 ppm. The relative accuracy of LIBS was compared to inductively coupled plasma – optical emission spectrometry (ICP-OES) was from 0.1 to 0.3 with 2.5% error confidence.
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    Photo-Fenton oxidation of dichlorophene in aqueous solution: kinetics investigation and effects of operational parameters
    (Elsevier, 2021-05) Sultana, Sabiha; Hayat, Arshad; Sayed, Murtaza; Rehan, Imran; Rehan, Kamran; Tabassum, Safia; Amin, Noor Ul; Khan, Sanaullah; Khan, Abbas; Shah, Luqman Ali; Rehman, Mujeeb Ur
    Dichlorophene (DCP), a widely detected contaminant in the aquatic environment, was removed under the UV-254 nm irradiations based on advanced oxidation processes. The removal efficiency of DCP from water was enhanced by coupling UV with oxidant (H2O2) and/or catalyst (Fe2+), that is, UV/H2O2 and UV/H2O2/Fe2+ owing to the •OH radical generation in the system, which is chiefly responsible for degradation of DCP. Removal of DCP was maximum, that is, 96% degradation after 50 min UV irradiation by UV/H2O2/Fe2+ at pH 3, with apparent rate constant (kapp) of 0.058 min–1. The degradation rate of DCP increased with the increasing initial concentrations of DCP, represented by 0.011, 0.018 and 0.023 ppm min−1 at using 3, 4 and 5 ppm initial concentrations of DCP, respectively. The presence of NO3− and HCO3− deteriorated the degradation proficiency of DCP from 96% to 86% and 74%, respectively, attributed to scavenging of •OH radical.