Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to ...chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect on computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discuss various technical contributions, strengths and similarities of the research work in this domain. We also elaborate upon the recent progress of different quantum machine learning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing.
This study examines Pakistan's security relations with Kenya, South Africa and Nigeria, specifically focusing on the influence of geopolitical, economic and cultural factors. Three main research ...questions guide the research: a) what are the similarities and differences in Pakistan's security relations with these three countries? b) How do geopolitical factors shape Pakistan's security relations with each country? c) What roles do cultural and economic ties play in shaping these security relationships? The study identifies and compares similarities and differences in security relations among these countries, highlighting the unique historical, geopolitical, and cultural factors through a multidimensional approach, drawing insights from Realism, Constructivism and Neoliberal Institutionalism. The findings underscore the significance of considering power dynamics and cultural influences in understanding security relations. Furthermore, the study sheds light on the potential of international institutions in promoting cooperation and building trust between Pakistan and its African counterparts.
Bibliography Entry
Khan, Tariq. 2023. "Pakistan's Security Relations with Kenya, South Africa and Nigeria: A Comparative Analysis." Margalla Papers 27 (1): 123-137.
ABSTRACT Purpose: Exploring the role of business companies in facilitating trade between Pakistan and Africa, this paper focuses in particular on Kenya and South Africa . It examines the impact of ...these companies on bilateral trade relations, lessons learned from the trade facilitation experiences of Pakistan's business community in Kenya and South Africa and their implications for future trade partnerships.
In this reported work a single feed, miniaturized, dual layer, and low profile antenna is presented for 1.575GHz frequency band. The proposed antenna offers high gain, lower noise bandwidth, with ...better sensitivity and range. The ground choke technique is used for back lobe suppression. The prototype is fabricated on FR 4 substrate using manual fabrication technique. This offers an inexpensive and readily available fabrication. Therefore, fabricated antenna is small size, low cost, easily fabricated and tested for satellite communication. The antenna comprises of two layers, containing a patch radiator and a Metasurface layer with 3x3 rectangular ring resonators. The layers are separated using foam with a 12mm width. The proposed prototype is radiating at 1.575GHz and 2.33GHz with an overall dimension of 85.6 x 68.4 x 15.204 mm. The developed antenna provides a gain of 5.9 dBi. The simulated results are verified using VNA and anechoic chamber testing. Moreover, the developed antenna has been successfully tested for L-Band Satellite communication in real time scenario without any LNA. Higher Gain due to Metasurface increase the efficiency of the system. The promising results indicate the aptness of the developed antenna for real-world applications of L-Band and S-Band.
•Preprocessing is proposed to compensate inherent noise and low contrast.•A minimal set of highly representative regional features are employed.•The problem of class imbalance at a regional level is ...addressed for the first time.•Random Forest performance is much better than Support Vector Machine.
This paper presents a fully automated brain tissue classification method for normal and abnormal tissues and its associated region from Fluid Attenuated Inversion Recovery modality of Magnetic Resonance (MR) images. The proposed regional classification method is able to simultaneously detect and segment tumours to pixel-level accuracy. The region-based features considered in this study are statistical, texton histograms, and fractal features. This is the first study to address the class imbalance problem at the regional level using Random Majority Down-sampling-Synthetic Minority Over-sampling Technique (RMD-SMOTE). A comparison of benchmark supervised techniques including Support Vector Machine, AdaBoost and Random Forest (RF) classifiers is presented, where the RF-based regional classifier is selected in the proposed approach due to its better generalization performance. The robustness of the proposed method is evaluated on the standard publicly available BRATS 2012 dataset using five standard benchmark measures. We demonstrate that the proposed method consistently outperforms three benchmark tumour classification methods in terms of Dice score and obtains significantly better results as compared to its SVM and AdaBoost counterparts in terms of precision and specificity at the 5% confidence interval. The promising results of the proposed method support its application for early detection and diagnosis of brain tumours in clinical settings.
Automatic optic disc (OD) localization and segmentation is not a simple process as the OD appearance and size may significantly vary from person to person. This paper presents a novel approach for OD ...localization and segmentation which is fast as well as robust. In the proposed method, the image is first enhanced by de-hazing and then cropped around the OD region. The cropped image is converted to HSV domain and then V channel is used for OD detection. The vessels are extracted from the Green channel in the cropped region by multi-scale line detector and then removed by the Laplace Transform. Local adaptive thresholding and region growing are applied for binarization. Furthermore, two region properties, eccentricity, and area are then used to detect the true OD region. Finally, ellipse fitting is used to fill the region. Several datasets are used for testing the proposed method. Test results show that the accuracy and sensitivity of the proposed method are much higher than the existing state-of-the-art methods.
The rising burden of cancer worldwide calls for an alternative treatment solution. Herbal medicine provides a very feasible alternative to western medicine against cancer. This article reviews the ...selected plant species with active phytochemicals, the animal models used for these studies, and their regulatory aspects. This study is based on a meticulous literature review conducted through the search of relevant keywords in databases, Web of Science, Scopus, PubMed, and Google Scholar. Twenty plants were selected based on defined selection criteria for their potent anticancer compounds. The detailed analysis of the research studies revealed that plants play an indispensable role in fighting different cancers such as breast, stomach, oral, colon, lung, hepatic, cervical, and blood cancer cell lines. The in vitro studies showed cancer cell inhibition through DNA damage and activation of apoptosis-inducing enzymes by the secondary metabolites in the plant extracts. Studies that reported in vivo activities of these plants showed remarkable results in the inhibition of cancer in animal models. Further studies should be performed on exploring more plants, their active compounds, and the mechanism of anticancer actions for use as standard herbal medicine.
ZW3 is a newly discovered exopolysaccharide (EPS) produced by Lactobacillus kefiranofaciens ZW3, isolated from Tibet kefir. Some of its properties have been characterized in our previous paper. ...Present research demonstrates some other important aspects of this EPS. The molecular weight obtained by gel permeation HPLC was 5.5 × 104 Da. Solubility, water holding and oil binding capacity of ZW3 EPS were 14.2%, 496.0%, and 884.74% respectively. Scanning electron microscopy (SEM) of ZW3 EPS demonstrated a smooth surface with compact structures. A topographical examination of EPS by atomic force microscopy (AFM) revealed that ZW3 EPS is composed of almost uniform net of molecules. Rheological study indicated that common salt did not affect the viscous behavior of ZW3 EPS and acidic pH may enhance its viscosity. Exopolymer showed a melting point of 93.38 °C. A degradation temperature (Td) of 299.62 °C was observed from the TGA curve for the polysaccharide ZW3.
ZW3 is a polymer produced by Lactobacillus kefiranofaciens ZW3 isolated from Tibet kefir. In this paper we studied some important properties of ZW3 exopolysaccharide such as Molecular weight, its solubility in water and oil and water holding capacity. EPS was further characterized by atomic force microscopy (AFM), Scanning electron microscopy (SEM) and thermogram analysis. Rheological study indicated that common salt did not affect the viscous behavior of ZW3 EPS and acidic pH may enhance its viscosity. Display omitted
Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), ...atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.
•An extensive review of the state-of-the-art methods for retinal image analysis.•Existing techniques are categorized into essential groups and compared in depth.•A summary of quantifiable performance measures is presented.•Widely used are described and their significance is emphasized.•A model system for ophthalmic healthcare and monitoring system is proposed.
The COVID-19 pandemic triggered the unprecedented 'long COVID' crisis, with persistent symptoms beyond two months post-infection. This study explores the nexus between long COVID symptoms, patient ...demographics such as age, gender, and smoking, and clinical factors like vaccination, disease severity, and comorbidities.
A retrospective analysis of records was conducted between September 2021 and December 2022. The analysis covered adults with confirmed COVID-19 diagnoses. Data encompassed demographics, medical history, vaccination, disease severity, hospitalization, treatments, and post-COVID symptoms, analyzed using logistic regression.
Among 289 participants, the average age was 51.51 years. Around 62.6% were females, and 93% received the COVID-19 vaccination, i.e., primarily the mRNA vaccine (48.4%) and the adenovirus vector-based vaccine (34.8%). Reinfections occurred in 11.76% of cases. Disease severity varied, with 75% having mild, 15% having moderate, and 10% having severe infections. Hospitalization rates were significant (25.6%), including 10.7% requiring intensive care. Thirteen distinct post-COVID symptoms were reported. Fatigue, shortness of breath upon exertion, and brain fog emerged as the most prevalent symptoms. Notably, females exhibited higher symptom prevalence. Significant correlations were established between higher BMI and smoking with augmented symptomatology. Conversely, a link between booster doses and symptom reduction was discerned. Using multinomial regression analysis, gender and smoking were identified as predictors of post-COVID-19 symptoms.
The study underscores obesity, smoking, and the female gender's impact on long COVID symptoms; boosters show promise in alleviation. Respiratory pathology might underlie persistent symptoms in cases with radiological abnormalities and abnormal spirometry. Findings contribute to risk stratification, intervention strategies, and further research.
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