A forecasting of indices and corresponding investment decision making application

Student Number : 9702018F - MSc(Eng) Dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built EnvironmentDue to the volatile nature of the world economies, investing is crucial in ensuring an individual is prepared for future financial necessities. This research proposes an application, which employs computational intelligent methods that could assist investors in making financial decisions. This system consists of 2 components. The Forecasting Component (FC) is employed to predict the closing index price performance. Based on these predictions, the Stock Quantity Selection Component (SQSC) recommends the investor to purchase stocks, hold the current investment position or sell stocks in possession. The development of the FC module involved the creation of Multi-Layer Perceptron (MLP) as well as Radial Basis Function (RBF) neural network classifiers. TCategorizes that these networks classify are based on a profitable trading strategy that outperforms the long-term “Buy and hold” trading strategy. The Dow Jones Industrial Average, Johannesburg Stock Exchange (JSE) All Share, Nasdaq 100 and the Nikkei 225 Stock Average indices are considered. TIt has been determined that the MLP neural network architecture is particularly suited in the prediction of closing index price performance. Accuracies of 72%, 68%, 69% and 64% were obtained for the prediction of closing price performance of the Dow Jones Industrial Average, JSE All Share, Nasdaq 100 and Nikkei 225 Stock Average indices, respectively. TThree designs of the Stock Quantity Selection Component were implemented and compared in terms of their complexity as well as scalability. TComplexity is defined as the number of classifiers employed by the design. Scalability is defined as the ability of the design to accommodate the classification of additional investment recommendations. TDesigns that utilized 1, 4 and 16 classifiers, respectively, were developed. These designs were implemented using MLP neural networks, RBF neural networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4 classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of concern. It has also been determined that the neural network architecture as well as the Fuzzy Inference System implementation of this design performed equally well

An IVR call performance classification system using computational intelligent techniques

Speech recognition adoption rate within Interactive Voice Response (IVR) systems is on the increase. If implemented correctly, businesses experience an increase of IVR utilization by customers, thus benefiting from reduced operational costs. However, it is essential for businesses to evaluate the productivity, quality and call resolution performance of these self-service applications. This research is concerned with the development of a business analytics for IVR application that could assist contact centers in evaluating these self-service IVR applications. A call classification system for a pay beneficiary IVR application has been developed. The system comprises of field and call performance classification components. ‘Say account’, ‘Say amount’, ‘Select beneficiary’ and ‘Say confirmation’ field classifiers were developed using Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), Radial Basis Function (RBF) ANN, Fuzzy Inference System (FIS) as well as Support Vector Machine (SVM). Call performance classifiers were also developed using these computational intelligent techniques. Binary and real coded Genetic Algorithm (GA) solutions were used to determine optimal MLP and RBF ANN classifiers. These GA solutions produced accurate MLP and RBF ANN classifiers. In order to increase the accuracy of the call performance RBF ANN classifier, the classification threshold has been optimized. This process increased the classifier accuracy by approximately eight percent. However, the field and call performance MLP ANN classifiers were the most accurate ANN solutions. Polynomial and RBF SVM kernel functions were most suited for field classifications. However, the linear SVM kernel function is most accurate for call performance classification. When compared to the ANN and SVM field classifiers, the FIS field classifiers did not perform well. The FIS call performance classifier did outperform the RBF ANN call performance network. Ensembles of MLP ANN, RBF ANN and SVM field classifiers were developed. Ensembles of FIS, MLP ANN and SVM call performance classifiers were also implemented. All the computational intelligent methods considered were compared in relation to accuracy, sensitivity and specificity performance metrics. MLP classifier solution is most appropriate for ‘Say account’ field classification. Ensemble of field classifiers and MLP classifier solutions performed the best in ‘Say amount’ field classification. Ensemble of field classifiers and SVM classifier solutions are most suited in ‘Select beneficiary’ and ‘Say confirmation’ field classifications. However, the ensemble of call performance classifiers is the preferred classification solution for call performance

A call for the aggressive treatment of oligometastatic and oligo-recurrent non-small cell lung cancer.

Metastatic non-small cell lung cancer (NSCLC) carries a dismal prognosis. Clinical evidence suggests the existence of an intermediate, or oligometastatic, state when metastases are limited in number and/or location. In addition, following initial curative therapy, many patients present with limited metastatic disease, or oligo-recurrence. Metastasis-directed, anti-cancer therapies may benefit these patients. A growing evidence-base supports the use of hypofractionated, image-guided radiotherapy (HIGRT) for a variety of malignant conditions including inoperable stage I NSCLC and many metastatic sites. When surgical resection is not possible, HIGRT offers an effective alternative for local treatment of limited metastatic disease. Early studies have produced promising results when HIGRT was delivered to all known sites of disease in patients with oligometastatic/oligo-recurrent NSCLC. In a population of patients formerly considered rapidly terminal, these studies report five year overall survival rates of 13-22%. HIGRT for metastatic NSCLC warrants further study. We call for large, intergroup, and even international randomized trials incorporating HIGRT and other metastasis-directed therapies into the treatment of patients with oligometastatic/oligo-recurrent NSCLC

Soluble fibrin inhibits monocyte adherence and cytotoxicity against tumor cells: implications for cancer metastasis

BACKGROUND: Soluble fibrin (sFn) is a marker for disseminated intravascular coagulation and may have prognostic significance, especially in metastasis. However, a role for sFn in the etiology of metastatic cancer growth has not been extensively studied. We have reported that sFn cross-linked platelet binding to tumor cells via the major platelet fibrin receptor αIIbβ3, and tumor cell CD54 (ICAM-1), which is the receptor for two of the leukocyte β2 integrins (α(L)β2 and a(M)β2). We hypothesized that sFn may also affect leukocyte adherence, recognition, and killing of tumor cells. Furthermore, in a rat experimental metastasis model sFn pre-treatment of tumor cells enhanced metastasis by over 60% compared to untreated cells. Other studies have shown that fibrin(ogen) binds to the monocyte integrin α(M)β2. This study therefore sought to investigate the effect of sFn on β2 integrin mediated monocyte adherence and killing of tumor cells. METHODS: The role of sFn in monocyte adherence and cytotoxicity against tumor cells was initially studied using static microplate adherence and cytotoxicity assays, and under physiologically relevant flow conditions in a microscope perfusion incubator system. Blocking studies were performed using monoclonal antibodies specific for β2 integrins and CD54, and specific peptides which inhibit sFn binding to these receptors. RESULTS: Enhancement of monocyte/tumor cell adherence was observed when only one cell type was bound to sFn, but profound inhibition was observed when sFn was bound to both monocytes and tumor cells. This effect was also reflected in the pattern of monocyte cytotoxicity. Studies using monoclonal blocking antibodies and specific blocking peptides (which did not affect normal coagulation) showed that the predominant mechanism of fibrin inhibition is via its binding to α(M)β2 on monocytes, and to CD54 on both leukocytes and tumor cells. CONCLUSION: sFn inhibits monocyte adherence and cytotoxicity of tumor cells by blocking α(L)β2 and α(M)β2 binding to tumor cell CD54. These results demonstrate that sFn is immunosuppressive and may be directly involved in the etiology of metastasis. Use of specific peptides also inhibited this effect without affecting coagulation, suggesting their possible use as novel therapeutic agents in cancer metastasis

Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory

This study proposed a deep learning-based tracking method for ultrasound (US) image-guided radiation therapy. The proposed cascade deep learning model is composed of an attention network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network learns a mapping from a US image to a suspected area of landmark motion in order to reduce the search region. The mask R-CNN then produces multiple region-of-interest (ROI) proposals in the reduced region and identifies the proposed landmark via three network heads: bounding box regression, proposal classification, and landmark segmentation. The LSTM network models the temporal relationship among the successive image frames for bounding box regression and proposal classification. To consolidate the final proposal, a selection method is designed according to the similarities between sequential frames. The proposed method was tested on the liver US tracking datasets used in the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 challenges, where the landmarks were annotated by three experienced observers to obtain their mean positions. Five-fold cross-validation on the 24 given US sequences with ground truths shows that the mean tracking error for all landmarks is 0.65+/-0.56 mm, and the errors of all landmarks are within 2 mm. We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0.94+/-0.83 mm. Our experimental results have demonstrated the feasibility and accuracy of our proposed method in tracking liver anatomic landmarks using US images, providing a potential solution for real-time liver tracking for active motion management during radiation therapy

Activating the knowledge-to-action cycle for geriatric care in India

Despite a rapidly aging population, geriatrics - the branch of medicine that focuses on healthcare of the elderly - is relatively new in India, with many practicing physicians having little knowledge of the clinical and functional implications of aging. Negative attitudes and limited awareness, knowledge or acceptance of geriatrics as a legitimate discipline contribute to inaccessible and poor quality care for India's old. The aim of this paper is to argue that knowledge translation is a potentially effective tool for engaging Indian healthcare providers in the delivery of high quality geriatric care. The paper describes India's context, including demographics, challenges and current policies, summarizes evidence on provider behaviour change, and integrates the two in order to propose an action plan for promoting improvements in geriatric care

Differences in failure-free survival after salvage radiotherapy guided by conventional imaging versus 18F-fluciclovine PET/CT in postprostatectomy patients : a post hoc substratification analysis of the EMPIRE-1 trial

Please read abstract in the article.The National Institutes of Health and Blue Earth Diagnostics.http://jnm.snmjournals.orghj2023Nuclear Medicin