Measuring Relationship between Digital Skills and Employability

In the growing economies like Pakistan and other countries, digital skils such as computer, communication, internet and advance digital skills are predictable to offer possible employees a major rim in securing their jobs and also protecting relatively high-paying jobs. Digital skills are not dual it has ranges and stages. There are number of International organizations that are making efforts on improving the people’s employability. I need more data in order to know the relationship between digital skills and employability in terms of which level of digital skills are enough for civilizing emplyability.  In this study I observe the digital skills assosiation with the employabilty using the Paf-Kiet University and National Foods Limited as a comparative study. Findings highlighted that digital skills can be a interpreter of employnment, the level of digital skills necessary to achieve these jobs is very high as one might imagine. In the rising economies perspective, digital skills are linked with high status jobs mainly combine with other reasons such as higher education. Implementation strategy  on the basis of the finiding of this study in order to improve employabilty, advising that revise the education policy and training efforts should focus on digital skills. Key words: Digital Skills, employabilty, Students’s, Employers, Relationship

A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays

Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in `Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out `lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future

Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection

Annotating medical images for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for learning effective deep neural models. Hence, it is common to 'transfer' neural networks trained on natural images to the medical image domain. However, this paradigm lacks in performance due to the large domain gap between the natural and medical image data. To address that, we propose a novel concept of Pre-text Representation Transfer (PRT). In contrast to the conventional transfer learning, which fine-tunes a source model after replacing its classification layers, PRT retains the original classification layers and updates the representation layers through an unsupervised pre-text task. The task is performed with (original, not synthetic) medical images, without utilizing any annotations. This enables representation transfer with a large amount of training data. This high-fidelity representation transfer allows us to use the resulting model as a more effective feature extractor. Moreover, we can also subsequently perform the traditional transfer learning with this model. We devise a collaborative representation based classification layer for the case when we leverage the model as a feature extractor. We fuse the output of this layer with the predictions of a model induced with the traditional transfer learning performed over our pre-text transferred model. The utility of our technique for limited and imbalanced data classification problem is demonstrated with an extensive five-fold evaluation for three large-scale models, tested for five different class-imbalance ratios for CT based COVID-19 detection. Our results show a consistent gain over the conventional transfer learning with the proposed method.Comment: Best paper at IVCN

Pediatric Coccidioidomycosis Patients: Perceptions, Quality of Life and Psychosocial Factors

Research investigating the effects of coccidioidomycosis (valley fever) on children and the psychosocial implications of this disease in general is lacking. This study reviews what is known about pediatric coccidioidomycosis patients. It documents the psychological functioning, quality of life, and illness perceptions of a sample of coccidioidomycosis patient families. Primary caregivers of pediatric patients and patients from a major hospital in the San Joaquin Valley of California were interviewed regarding their perceptions of disease detection, access to care and the patient/family experience

Going deep in medical image analysis: Concepts, methods, challenges, and future directions

Medical image analysis is currently experiencing a paradigm shift due to deep learning. This technology has recently attracted so much interest of the Medical Imaging Community that it led to a specialized conference in “Medical Imaging with Deep Learning” in the year 2018. This paper surveys the recent developments in this direction and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying pattern recognition tasks and further sub-categorize it following a taxonomy based on human anatomy. This paper does not assume prior knowledge of deep learning and makes a significant contribution in explaining the core deep learning concepts to the non-experts in the Medical Community. This paper provides a unique computer vision/machine learning perspective taken on the advances of deep learning in medical imaging. This enables us to single out “lack of appropriately annotated large-scale data sets” as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of computer vision, pattern recognition, and machine learning, where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging Community to fully harness deep learning in the future

Optimized Classification of Cardiovascular Disease Using Machine Learning Paradigms

Nearly 19 million people die each year from cardiovascular and chronic respiratory diseases, which are a global threat. It is necessary to address the causes of these diseases because of the high death rate. The investigation uncovered a number of causes, but the inability to forecast these diseases symptoms is by far the most significant. In this work, we developed a method for anticipating these diseases crucial symptoms, which will aid in early disease diagnosis and allow patients to begin treatment. This research will introduce a new computational medicine research using machine learning (ML) paradigms to forecast cardiovascular disease (CVD). Data were processed by methods in sequence with various parameters. different models created that predicts CVD risk based on individual age, gender, ethnicity, body mass etc., and lifestyle factors. The research will also focus on performing complete comparison of ML models. We will apply Five ML based algorithems such as Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), XGBOOST and Random Forest and evaluate these models on the basis of Training and Testing and also calculated the Presicion Recall and F1-Score for each model. Naïve Bayes and XGBOOST Classifier perform better with accuracy of 92.31 and 92.34 percent as compared to other models