Enhancing Recommendations in Specialist Search Through Semantic-based Techniques and Multiple Resources

Information resources abound on the Internet, but mining these resources is a non-trivial task. Such abundance has raised the need to enhance services provided to users, such as recommendations. The purpose of this work is to explore how better recommendations can be provided to specialists in specific domains such as bioinformatics by introducing semantic techniques that reason through different resources and using specialist search techniques. Such techniques exploit semantic relations and hidden associations that occur as a result of the information overlapping among various concepts in multiple bioinformatics resources such as ontologies, websites and corpora. Thus, this work introduces a new method that reasons over different bioinformatics resources and then discovers and exploits different relations and information that may not exist in the original resources. Such relations may be discovered as a consequence of the information overlapping, such as the sibling and semantic similarity relations, to enhance the accuracy of the recommendations provided on bioinformatics content (e.g. articles). In addition, this research introduces a set of semantic rules that are able to extract different semantic information and relations inferred among various bioinformatics resources. This project introduces these semantic-based methods as part of a recommendation service within a content-based system. Moreover, it uses specialists' interests to enhance the provided recommendations by employing a method that is collecting user data implicitly. Then, it represents the data as adaptive ontological user profiles for each user based on his/her preferences, which contributes to more accurate recommendations provided to each specialist in the field of bioinformatics

PhD Program in TEFL Curriculum & Instruction: A course proposal based on international educational standards and programs

Higher Education is becoming an additional governing social issue in a new information-based world economy. Social change in Saudi Arabia in the recent years has gravitated young people towards continuing higher education and efforts to rebalance the labor market. With the Saudi Vision 2030 national developmental plan as the basic guideline for the education sector, the target is to make institutions of higher education capable to compete with best international standards, and equip the young Saudi population with attributes that prepare them for a global socio-economic role. The ability of the Saudi nationals to communicate successfully and effectively with global communities is the logical and stated goal set for the education sector by the administration. In keeping with these aims, this study proposes a PhD Program in TEFL Curriculum and Instruction to ensure execution of the best research in English education in the interests of the Saudi people. The highlight of the proposed program is its comprehensive and exhaustive analysis of international educational standards and programs from which the most feasible, essential, and salient features are culled to prepare a detailed outlined for Saudi Arabia. The study will prove to be of special interests to the national policy advisors, academic planners, and the entire language research community

Electronic Assessment of English Language Learning for Secondary School Students in KSA during Covid-19 pandemic: A multidimensional status check

The study aims to find out the status of the electronic assessment of English language learning for secondary school students in Saudi Arabia during the Covid-19 pandemic. The study also aims to present the challenges and obstacles that faced the assessment of learning English online, and then to develop a vision for the electronic assessment of learning English for secondary school students during the Covid-19 pandemic. The study sample consists of (67) secondary school English language teachers in the second semester of the academic year 1442/1443 AH. They are distributed according to the following variables: gender,  academic qualification,  teaching experience period, level of experience in using technology. To achieve the objectives of the study, the study uses the descriptive analytical method. The study tool is a 59 item questionnaire  to determine the status of electronic assessment of English language learning for secondary school students in KSA during the Covid-19 pandemic. The results of the study show the most commonly used tools in electronic assessment of English language learning for secondary school students in KSA during the pandemic are electronic assignments, followed by presentations, while research projects came with a low degree of use. The results also show that there are no statistically significant differences in the methods and practices of the assessment of English language learning for secondary school students during the Covid-19 pandemic, according to the variables (gender, academic qualification, number of years of teaching experience, level of experience in using technology). The study also found that the degree of difficulties/challenges faced by English language teachers in the assessment of English language learning for secondary school students during the Covid-19 pandemic are perceived as being high. On the other hand, there are no statistically significant differences in the degree of difficulties due to (gender, academic qualification, number of years of teaching experience, level of experience using technology). The study proposes a plan for evaluating students' English language learning by integrating assessment in a virtual environment as well as in a real environment

Exploring and exploiting knowledge in multiple resources

With the huge growth of the Internet and the WWW, several scientific fields have witnessed a proliferation of information available online. Techniques that are able to extract relations and associations that may exist in various resources including structured (ontologies and taxonomies) and unstructured (corpora) to inform search and can offer enhanced recommendations are highly desirable. The aim of this work is to pull together multiple bioinformatics resources with different structures such as ontologies and corpora and develop reasoning methods to extract semantic relations and hidden associations, which do not exist in the original resources. We have designed methods which reason through these resources and contain semantic rules (for instance, identifying sibling relations) that allow us to infer more hidden information between resources and aim to enhance recommendations in situations as described above. Moreover, we have designed a recommender approach for the bioinformatics field that provides recommendations on content (i.e. articles) as a personalised service for users based on information extracted from multiple resources as well as their individual profiles

A semantic method for multiple resources exploitation

Being able to extract and exploit information that is included in multiple resources (repositories, corpora, etc.) is essential to benefiting from the increasing availability and complementary nature of such data scattered across the World Wide Web. However, such an endeavour raises a number of challenges including dealing with the diverse structures of such resources, different relationships among such data, and the overlapping and complementary nature of the in- formation. Thus, developing a semantic method that can extract semantic information and hidden associations would help overcome such difficulties that occur when dealing with multiple resources. This paper presents a new semantic method that exploits the overlap between various resources with different structures (i.e. ontologies as forms of structured data and corpora as examples of unstructured data) and employs semantic relations, specifically sibling relations, to infer new information that may not exist in the original resources. Then, this method employs the new information in a content-based recommender system to enhance the quality of the provided recommendations (i.e. articles) in complex fields that are inherently characterised by varying relations and structures, such as bioinformatics. In addition, this method is accompanied by an automatic tool that is responsible for tailoring individual recommendations to each user based on his/her profile

Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach

Leukemia is a form of blood cancer that develops when the human body’s bone marrow contains too many white blood cells. This medical condition affects adults and is considered a prevalent form of cancer in children. Treatment for leukaemia is determined by the type and the extent to which cancer has developed across the body. It is crucial to diagnose leukaemia early in order to provide adequate care and to cure patients. Researchers have been working on advanced diagnostics systems based on Machine Learning (ML) approaches to diagnose leukaemia early. In this research, we employ deep learning (DL) based convolutional neural network (CNN) and hybridized two individual blocks of CNN named CNN-1 and CNN-2 to detect acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), and multiple myeloma (MM). The proposed model detects malignant leukaemia cells using microscopic blood smear images. We construct a dataset of about 4150 images from a public directory. The main challenges were background removal, ripping out un-essential blood components of blood supplies, reduce the noise and blurriness and minimal method for image segmentation. To accomplish the pre-processing and segmentation, we transform RGB color-space into the greyscale 8-bit mode, enhancing the contrast of images using the image intensity adjustment method and adaptive histogram equalisation (AHE) method. We increase the structure and sharpness of images by multiplication of binary image with the output of enhanced images. In the next step, complement is done to get the background in black colour and nucleus of blood in white colour. Thereafter, we applied area operation and closing operation to remove background noise. Finally, we multiply the final output to source image to regenerate the images dataset in RGB colour space, and we resize dataset images to [400, 400]. After applying all methods and techniques, we have managed to get noiseless, non-blurred, sharped and segmented images of the lesion. In next step, enhanced segmented images are given as input to CNNs. Two parallel CCN models are trained, which extract deep features. The extracted features are further combined using the Canonical Correlation Analysis (CCA) fusion method to get more prominent features. We used five classification algorithms, namely, SVM, Bagging ensemble, total boosts, RUSBoost, and fine KNN, to evaluate the performance of feature extraction algorithms. Among the classification algorithms, Bagging ensemble outperformed the other algorithms by achieving the highest accuracy of 97.04%

Forex Investment Optimization Using Instantaneous Stochastic Gradient Ascent—Formulation of an Adaptive Machine Learning Approach

In the current complex financial world, paper currencies are vulnerable and unsustainable due to many factors such as current account deficit, gold reserves, dollar reserves, political stability, security, the presence of war in the region, etc. The vulnerabilities not limited to the above, result in fluctuation and instability in the currency values. Considering the devaluation of some Asian countries such as Pakistan, Sri Lanka, Türkiye, and Ukraine, there is a current tendency of some countries to look beyond the SWIFT system. It is not feasible to have reserves in only one currency, and thus, forex markets are likely to have significant growth in their volumes. In this research, we consider this challenge to work on having sustainable forex reserves in multiple world currencies. This research is aimed to overcome their vulnerabilities and, instead, exploit their volatile nature to attain sustainability in forex reserves. In this regard, we work to formulate this problem and propose a forex investment strategy inspired by gradient ascent optimization, a robust iterative optimization algorithm. The dynamic nature of the forex market led us to the formulation and development of the instantaneous stochastic gradient ascent method. Contrary to the conventional gradient ascent optimization, which considers the whole population or its sample, the proposed instantaneous stochastic gradient ascent (ISGA) optimization considers only the next time instance to update the investment strategy. We employed the proposed forex investment strategy on forex data containing one-year multiple currencies’ values, and the results are quite profitable as compared to the conventional investment strategies

Fuzzy Win-Win: A Novel Approach to Quantify Win-Win Using Fuzzy Logic

The classic notion of a win–win situation has a key flaw in that it cannot always offer the parties equal amounts of winningsbecause each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We present a novel way to measure the win–win situation in this paper. The proposed method employs fuzzy logic to create a mathematical model that aids negotiators in quantifying their winning percentages. The model is put to the test on real-life negotiation scenarios such as the Iraqi–Jordanian oil deal and iron ore negotiation (2005–2009), in addition to scenarios from the game of chess. The presented model has proven to be a useful tool in practice and can be easily generalized to be utilized in other domains as well

Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach

Leukemia is a form of blood cancer that develops when the human body’s bone marrow contains too many white blood cells. This medical condition affects adults and is considered a prevalent form of cancer in children. Treatment for leukaemia is determined by the type and the extent to which cancer has developed across the body. It is crucial to diagnose leukaemia early in order to provide adequate care and to cure patients. Researchers have been working on advanced diagnostics systems based on Machine Learning (ML) approaches to diagnose leukaemia early. In this research, we employ deep learning (DL) based convolutional neural network (CNN) and hybridized two individual blocks of CNN named CNN-1 and CNN-2 to detect acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), and multiple myeloma (MM). The proposed model detects malignant leukaemia cells using microscopic blood smear images. We construct a dataset of about 4150 images from a public directory. The main challenges were background removal, ripping out un-essential blood components of blood supplies, reduce the noise and blurriness and minimal method for image segmentation. To accomplish the pre-processing and segmentation, we transform RGB color-space into the greyscale 8-bit mode, enhancing the contrast of images using the image intensity adjustment method and adaptive histogram equalisation (AHE) method. We increase the structure and sharpness of images by multiplication of binary image with the output of enhanced images. In the next step, complement is done to get the background in black colour and nucleus of blood in white colour. Thereafter, we applied area operation and closing operation to remove background noise. Finally, we multiply the final output to source image to regenerate the images dataset in RGB colour space, and we resize dataset images to [400, 400]. After applying all methods and techniques, we have managed to get noiseless, non-blurred, sharped and segmented images of the lesion. In next step, enhanced segmented images are given as input to CNNs. Two parallel CCN models are trained, which extract deep features. The extracted features are further combined using the Canonical Correlation Analysis (CCA) fusion method to get more prominent features. We used five classification algorithms, namely, SVM, Bagging ensemble, total boosts, RUSBoost, and fine KNN, to evaluate the performance of feature extraction algorithms. Among the classification algorithms, Bagging ensemble outperformed the other algorithms by achieving the highest accuracy of 97.04%

Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach

Brain tumors affect the normal functioning of the brain and if not treated in time these cancerous cells may affect the other tissues, blood vessels, and nerves surrounding these cells. Today, a large population worldwide is affected by the precarious disease of the brain tumor. Healthy tissues of the brain are suspected to be damaged because of tumors that become the most significant reason for a large number of deaths nowadays. Therefore, their early detection is necessary to prevent patients from unfortunate mishaps resulting in loss of lives. The manual detection of brain tumors is a challenging task due to discrepancies in appearance in terms of shape, size, nucleus, etc. As a result, an automatic system is required for the early detection of brain tumors. In this paper, the detection of tumors in brain cells is carried out using a deep convolutional neural network with stochastic gradient descent (SGD) optimization algorithm. The multi-classification of brain tumors is performed using the ResNet-50 model and evaluated on the public Kaggle brain-tumor dataset. The method achieved 99.82% and 99.5% training and testing accuracy, respectively. The experimental result indicates that the proposed model outperformed baseline methods, and provides a compelling reason to be applied to other diseases