AutoScor: An Automated System for Essay Questions Scoring

The automated scoring or evaluation for written student responses have been, and are still a highly interesting topic for both education and natural language processing, NLP, researchers alike. With the obvious motivation of the difficulties teachers face when marking or correcting open essay questions; the development of automatic scoring methods have recently received much attention. In this paper, we developed and compared number of NLP techniques that accomplish this task. The baseline for this study is based on a vector space model, VSM. Where after normalisation, the baseline-system represents each essay by a vector, and subsequently calculates its score using the cosine similarity between it and the vector of the model answer. This baseline is then compared with the improved model, which takes the document structure into account. To evaluate our system, we used real essays that submitted for computer science course. Each essay was independently scored by two teachers, which we used as our gold standard. The systems’ scoring was then compared to both teachers. A high emphasis was added to the evaluation when the two human assessors are in agreement. The systems’ results show a high and promising performance

An interval type-2 fuzzy logic based system for improved instruction within intelligent e-learning platforms

E-learning is becoming increasingly more popular. However, for such platforms (where the students and tutors are geographically separated), it is necessary to estimate the degree of students' engagement with the course contents. Such feedback is highly important and useful for assessing the teaching quality and adjusting the teaching delivery in large-scale online learning platforms. When the number of attendees is large, it is essential to obtain overall engagement feedback, but it is also challenging to do so because of the high levels of uncertainty associated with the environments and students. To handle such uncertainties, we present a type-2 fuzzy logic based system using visual RGB-D features including head pose direction and facial expressions captured from a low-cost but robust 3D camera (Kinect v2) to estimate the engagement degree of the students for both remote and on-site education. This system enriches another self- learning type-2 fuzzy logic system which provides the instructors with suggestions to vary their teaching means to suit the level of course students and improve the course instruction and delivery. This proposed dynamic e-learning environment involves on-site students, distance students, and a teacher who delivers the lecture to all attending onsite and remote students. The rules are learned from the students' behavior and the system is continuously updated to give the teacher the ability to adapt the lecture delivery instructional approach to varied learners' engagement levels. The efficiency of the proposed system has been evaluated through various real-world experiments in the University of Essex iClassroom on a sample of thirty students and six teachers. These experiments demonstrate the efficiency of the proposed interval type-2 fuzzy logic based system to handle the faced uncertainties and produce superior improved average learners' engagements when compared to type-1 fuzzy systems and nonadaptive systems

Exploring adjustable autonomy in online tutoring systems

Learning and teaching have been influenced greatly by the rapid development of technology. For instance, through the use of soft computing techniques, it would be possible to create an artificially intelligent autonomous tutor agent, which can ease the burden on teachers and enhance learning outcomes through its more personalised interaction with students. Providing students with automated guidance, such as directing students through the most appropriate content sequence is one aim of online tutoring systems. However, in most of the available tutoring systems, users neither have the ability to adjust the tutor agent’s autonomy level nor fully control the rules applied by the tutor agent. Thus, this thesis has sought to overcome these shortcomings by proposing a system called the ‘Adaptive Course Sequencing Approach’ (ACSA) which enables students to adjust the autonomy level of the tutor agent and gives teachers the ability to directly communicate with the tutor agent to create the sequencing rules and alter them at any time during the learning experience. This is achieved with fuzzy logic, which has the capability of producing human-readable sequencing rules as well as managing the uncertainty of measuring some students’ levels of knowledge. We hypothesise that by equipping intelligent educational environments with adjustable autonomy mechanisms, the students’ learning outcomes will be enhanced. This research was divided into seven phases and involved a large number of participants (1725 in total) to assess the need for adjustable autonomy mechanisms in online tutoring systems and to explore the way of providing these mechanisms in ACSA, thereby demonstrating the hypothesis by two empirical experiments. The results showed that applying adjustable autonomy mechanisms significantly improved the students’ learning outcomes and that the students who adjusted the autonomy level more than once performed slightly better than those who adjusted it once only. In addition, applying the collaborative-driven agent method, which relies on machine learning to generate and optimise the sequencing rules, led to improving the students’ learning outcomes and highly satisfying the teachers

Exploring adjustable autonomy in online tutoring systems

Learning and teaching have been influenced greatly by the rapid development of technology. For instance, through the use of soft computing techniques, it would be possible to create an artificially intelligent autonomous tutor agent, which can ease the burden on teachers and enhance learning outcomes through its more personalised interaction with students. Providing students with automated guidance, such as directing students through the most appropriate content sequence is one aim of online tutoring systems. However, in most of the available tutoring systems, users neither have the ability to adjust the tutor agent’s autonomy level nor fully control the rules applied by the tutor agent. Thus, this thesis has sought to overcome these shortcomings by proposing a system called the ‘Adaptive Course Sequencing Approach’ (ACSA) which enables students to adjust the autonomy level of the tutor agent and gives teachers the ability to directly communicate with the tutor agent to create the sequencing rules and alter them at any time during the learning experience. This is achieved with fuzzy logic, which has the capability of producing human-readable sequencing rules as well as managing the uncertainty of measuring some students’ levels of knowledge. We hypothesise that by equipping intelligent educational environments with adjustable autonomy mechanisms, the students’ learning outcomes will be enhanced. This research was divided into seven phases and involved a large number of participants (1725 in total) to assess the need for adjustable autonomy mechanisms in online tutoring systems and to explore the way of providing these mechanisms in ACSA, thereby demonstrating the hypothesis by two empirical experiments. The results showed that applying adjustable autonomy mechanisms significantly improved the students’ learning outcomes and that the students who adjusted the autonomy level more than once performed slightly better than those who adjusted it once only. In addition, applying the collaborative-driven agent method, which relies on machine learning to generate and optimise the sequencing rules, led to improving the students’ learning outcomes and highly satisfying the teachers

A Deep Convolutional Neural Network for the Early Detection of Heart Disease

Heart disease is one of the key contributors to human death. Each year, several people die due to this disease. According to the WHO, 17.9 million people die each year due to heart disease. With the various technologies and techniques developed for heart-disease detection, the use of image classification can further improve the results. Image classification is a significant matter of concern in modern times. It is one of the most basic jobs in pattern identification and computer vision, and refers to assigning one or more labels to images. Pattern identification from images has become easier by using machine learning, and deep learning has rendered it more precise than traditional image classification methods. This study aims to use a deep-learning approach using image classification for heart-disease detection. A deep convolutional neural network (DCNN) is currently the most popular classification technique for image recognition. The proposed model is evaluated on the public UCI heart-disease dataset comprising 1050 patients and 14 attributes. By gathering a set of directly obtainable features from the heart-disease dataset, we considered this feature vector to be input for a DCNN to discriminate whether an instance belongs to a healthy or cardiac disease class. To assess the performance of the proposed method, different performance metrics, namely, accuracy, precision, recall, and the F1 measure, were employed, and our model achieved validation accuracy of 91.7%. The experimental results indicate the effectiveness of the proposed approach in a real-world environment

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%

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