A Reference Architecture for Quantum Computing as a Service

Quantum computers (QCs) aim to disrupt the status-quo of computing – replacing traditional systems and platforms that are driven by digital circuits and modular software – with hardware and software that operate on the principle of quantum mechanics. QCs that rely on quantum mechanics can exploit quantum circuits (i.e., quantum bits for manipulating quantum gates) to achieve ‘quantum computational supremacy’ over traditional, i.e., digital computing systems. Currently, the issues that impede mass-scale adoption of quantum systems are rooted in the fact that building, maintaining, and/or programming QCs is a complex and radically distinct engineering paradigm when compared to the challenges of classical computing and software engineering. Quantum service orientation is seen as a solution that synergises the research on service computing and quantum software engineering (QSE) to allow developers and users to build and utilise quantum software services based on pay-per-shot utility computing model. The pay-per-shot model represents a single execution of instruction on quantum processing unit and it allows vendors (e.g., Amazon Braket) to offer their QC platforms, simulators, and software services to end-users. This research contributes by (i) developing a reference architecture for enabling Quantum Computing as a Service (QCaaS), (ii) implementing microservices with the quantum-classic split pattern as an architectural use-case, and (iii) evaluating the architecture based on practitioners’ feedback. The proposed reference architecture follows a layered software pattern to support the three phases of service lifecycle namely development, deployment, and split of quantum software services. In the QSE context, the research focuses on unifying architectural methods and service-orientation patterns to promote reuse knowledge and best practices to tackle emerging and futuristic challenges of architecting QCaaS

The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia

Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images

The purpose of this research is to demonstrate the ability of machine-learning (ML) methods for liver cancer classification using a fused dataset of two-dimensional (2D) computed tomography (CT) scans and magnetic resonance imaging (MRI). Datasets of benign (hepatocellular adenoma, hemangioma, cyst) and malignant (hepatocellular carcinoma, hepatoblastoma, metastasis) liver cancer were acquired at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. The final dataset was generated by fusion of 1200 (100 × 6 × 2) MR and CT-scan images, 200 (100 MRI and 100 CT-scan) images size 512 × 512 for each class of cancer. The acquired dataset was preprocessed by employing the Gabor filters to reduce the noise and taking an automated region of interest (ROIs) using an Otsu thresholding-based segmentation approach. The preprocessed dataset was used to acquire 254 hybrid-feature data for each ROI, which is the combination of the histogram, wavelet, co-occurrence, and run-length features, while 10 optimized hybrid features were selected by employing (probability of error plus average correlation) feature selection technique. For classification, we deployed this optimized hybrid-feature dataset to four ML classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48, using a ten fold cross-validation method. MLP showed an overall accuracy of (95.78% on MRI and 97.44% on CT). Unfortunately, the obtained results were not promising, and there were some limitations due to the different modalities of the dataset. Thereafter, a fusion of MRI and CT-scan datasets generated the fused optimized hybrid-feature dataset. The MLP has shown a promising accuracy of 99% among all the deployed classifiers

Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image

The object of this study was to demonstrate the ability of machine learning (ML) methods for the segmentation and classification of diabetic retinopathy (DR). Two-dimensional (2D) retinal fundus (RF) images were used. The datasets of DR—that is, the mild, moderate, non-proliferative, proliferative, and normal human eye ones—were acquired from 500 patients at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. Five hundred RF datasets (sized 256 × 256) for each DR stage and a total of 2500 (500 × 5) datasets of the five DR stages were acquired. This research introduces the novel clustering-based automated region growing framework. For texture analysis, four types of features—histogram (H), wavelet (W), co-occurrence matrix (COM) and run-length matrix (RLM)—were extracted, and various ML classifiers were employed, achieving 77.67%, 80%, 89.87%, and 96.33% classification accuracies, respectively. To improve classification accuracy, a fused hybrid-feature dataset was generated by applying the data fusion approach. From each image, 245 pieces of hybrid feature data (H, W, COM, and RLM) were observed, while 13 optimized features were selected after applying four different feature selection techniques, namely Fisher, correlation-based feature selection, mutual information, and probability of error plus average correlation. Five ML classifiers named sequential minimal optimization (SMO), logistic (Lg), multi-layer perceptron (MLP), logistic model tree (LMT), and simple logistic (SLg) were deployed on selected optimized features (using 10-fold cross-validation), and they showed considerably high classification accuracies of 98.53%, 99%, 99.66%, 99.73%, and 99.73%, respectively

Synergy in penicillin, cephalosporin, amphenicols, and aminoglycoside against MDR S. aureus isolated from Camel milk

This study investigated the synergy testing of penicillin, cephalosporin, amphenicols, and aminoglycoside in the camel milk (n=768 samples), subsequently used for isolation of MDR S. aureus targeting mecA gene. Antibiotic susceptibility of S. aureus showed >90% isolates were sensitive to ciprofloxacin and trimethoprim and resistant against oxacillin, ampicillin, and cefoxitin. Further, 50-85% of the S. aureus were sensitive to gentamicin, oxytetracycline, and chloramphenicol and resistant against cefotaxime, vancomycin, and cefixime. Minimum inhibitory concentration (MIC) of cefotaxime, (C) and ampicillin (A) in combination with gentamicin (G) was reduced by 99.34% and 70.46%, respectively, while with chloramphenicol (Ch), reduction was 57.49% and 60%, respectively. In addition, the Fractional Inhibitory Concentration Index (FICI) of G+A, Ch+C and Ch+G combinations showed synergy against 80%, 60%, and 30% of MDR S. aureus, respectively. Similarly, C+A and Ch+G displayed indifferent interaction against 70 % and 30% of isolates, respectively, while the later showed additive interaction against 10% of MDR S. aureus. Altogether, our results described effective combination of gentamicin and chloramphenicol with ampicillin and cefotaxime to combat MDR S. aureus