The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study

A neural network-based chart pattern represents adaptive parametric features, including non-linear transformations, and a template that can be applied in the feature space. The search of neural network-based chart patterns has been unexplored despite its potential expressiveness. In this paper, we formulate a general chart pattern search problem to enable cross-representational quantitative comparison of various search schemes. We suggest a HyperNEAT framework applying state-of-the-art deep neural network techniques to find attractive neural network-based chart patterns; These techniques enable a fast evaluation and search of robust patterns, as well as bringing a performance gain. The proposed framework successfully found attractive patterns on the Korean stock market. We compared newly found patterns with those found by different search schemes, showing the proposed approach has potential.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, German

Data Analytics in advancing Accounting Profession and Business Information for Decision Making

The nature of the risks and opportunities facing business has changed over time. Much of the global value today is more of technology service and knowledge based than it was 40 years ago. The study examines data analytics in advancing accounting profession and business information for decision making. Two specific objectives guided the study, the study used a survey research design approach, and the population consists of 300 respondents made up of 50 each from academics, financial analysts, accountants, business owners, investors and big data analysts. Descriptive statistics was used to analyses the data while Z test was used to test the hypotheses. The findings from the study shows that the two hypotheses tested has a high acceptance degrees level of an average percentage of (92.4%) and (86.98%) respectively, this goes to shows that the issue of big data analytics in advancing accounting profession and business information for decision making is very much germen. This also was observed in the results of Z-Test of the Standard Deviation of (0.412) and (0.303) respectively, which leads to the acceptance of the two alternatives hypotheses and rejecting of the null hypotheses. The study concluded that big data analytics improves and help business organizations take informed decisions to enhance their operational efficiency, also, that the world accepted the slogan that data is the new oil. Those who are able to gain out of that will remain in the business ie, survival of the fittest. This is the implication to the new millennium environment where the professional accountant finds itself. Therefore, should be able to deal with the complex procedures, so that the accountant will be a big data analytics professional too. The study recommended among others that all the stakeholders (academics, financial analysts, professional association bodies, accountants, business owners, investors and government) should be involves in the necessity of teaching big data and business analyses in management sciences in our higher institutions to promote students' knowledge, the continues enlightenment, holding workshops, training and retraining  courses for researchers and academics of the importance of analyzing big data and how to process, store, manage and use the analyzed data in the financial and accounting field, since using big data can lead to better disclosure which in turn  enhance investor trust

Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)

In this work, a denoising Cycle-GAN (Cycle Consistent Generative Adversarial Network) is implemented to yield high-field, high resolution, high signal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images from simulated low-field, low resolution, low SNR MRI images. Resampling and additive Rician noise were used to simulate low-field MRI. Images were utilized to train a Denoising Autoencoder (DAE) and a Cycle-GAN, with paired and unpaired cases. Both networks were evaluated using SSIM and PSNR image quality metrics. This work demonstrates the use of a generative deep learning model that can outperform classical DAEs to improve low-field MRI images and does not require image pairs.Comment: International Society of Magnetic Resonance in Medicine (ISMRM) 2023, Abstract Number 176

Amyloid-Beta Axial Plane PET Synthesis from Structural MRI: An Image Translation Approach for Screening Alzheimer's Disease

In this work, an image translation model is implemented to produce synthetic amyloid-beta PET images from structural MRI that are quantitatively accurate. Image pairs of amyloid-beta PET and structural MRI were used to train the model. We found that the synthetic PET images could be produced with a high degree of similarity to truth in terms of shape, contrast and overall high SSIM and PSNR. This work demonstrates that performing structural to quantitative image translation is feasible to enable the access amyloid-beta information from only MRI.Comment: Abstract submitted and presented to the International Society of Magnetic Resonance in Medicine (ISMRM 2023), Toronto, Canad

Three-Dimensional Amyloid-Beta PET Synthesis from Structural MRI with Conditional Generative Adversarial Networks

Motivation: Alzheimer's Disease hallmarks include amyloid-beta deposits and brain atrophy, detectable via PET and MRI scans, respectively. PET is expensive, invasive and exposes patients to ionizing radiation. MRI is cheaper, non-invasive, and free from ionizing radiation but limited to measuring brain atrophy. Goal: To develop an 3D image translation model that synthesizes amyloid-beta PET images from T1-weighted MRI, exploiting the known relationship between amyloid-beta and brain atrophy. Approach: The model was trained on 616 PET/MRI pairs and validated with 264 pairs. Results: The model synthesized amyloid-beta PET images from T1-weighted MRI with high-degree of similarity showing high SSIM and PSNR metrics (SSIM>0.95&PSNR=28). Impact: Our model proves the feasibility of synthesizing amyloid-beta PET images from structural MRI ones, significantly enhancing accessibility for large-cohort studies and early dementia detection, while also reducing cost, invasiveness, and radiation exposure.Comment: Abstract Submitted and Presented at the 2024 International Society of Magnetic Resonance in Medicine. Singapore, Singapore, May 4-9. Abstract Number 223

Environmental Issues of Livestock Production in Developing Countries: Need for Government Intervention Using the Truck Based Approach

Globally, the natural environment faces a range of unprecedented challenges which are require a well-structured strategic approach in solving it. One of these challenges is the ever-increasing greenhouse gas emission. Currently majority of our daily activities directly or indirectly contributes to greenhouse gas emission. An effort was taken to understand better the principal function of livestock production in the pollution of the natural environment and to ascertain mitigation policies to curb the effects on human life.Theories such as the Enforcement Strategic Theory, Utilitarian Commitment Theory, Deterrence Theory, and the Social factors Commitment Based theory were used in this study. Already processed statistics, policy strategies, laws in economics as well as authors intuitive proposals and ideas were used in this study. It was ascertained that population growth, fluctuating economies, food preferences, and urbanization had imposed pressure on livestock production and the agricultural sector, thereby leading to the release of odor, ammonia, pathogens, excess phosphorus and nitrogen harming the natural environment and also contribute to greenhouse gas emission. A more significant proportion of the growth in crop production is anticipated because of a rise in the demand for livestock feed. It was found that most livestock farmers do not have a well-regulated operation in most developing countries. To reduce or eliminate these effects, the “truck-based approach” was therefore propounded and proposed to enhance the smooth movement of the livestock droppings to either the crop farm or to the processing house or to the storage room to reduce or prevent unnecessary dumping. Keywords: Livestock Production, Environmental Issues, Green House Gas, Truck Based Approach, Government Regulation, and Developing Countries. DOI: 10.7176/JBAH/10-22-04 Publication date: November 30th 202

Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters

Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for peripheral recording devices. Goal(s): Investigate the hypothesis that head motion parameters contain valuable information regarding respiratory patter, which can help machine learning algorithms estimate the RV waveform. Approach: This study proposes a CNN model for reconstruction of RV waveforms using head motion parameters and BOLD signals. Results: This study showed that combining head motion parameters with BOLD signals enhances RV waveform estimation. Impact: It is expected that application of the proposed method will lower the cost of fMRI studies, reduce complexity, and decrease the burden on participants as they will not be required to wear a respiratory bellows.Comment: 6 pages, 5 figure, conference abstrac

Employee Motivation and its Effects on Employee Productivity/ Performance

One of the most important functions of management is to ensure that employee work is more satisfying and to reconcile employee motivation with organizational goals. With the diversity of current jobs, this is a dynamic challenge. What people value and enjoy is influenced by many factors, including the influence of different cultural backgrounds. This research report examines employee motivation and its impact on employee performance. The study examines some common theories of motivation that can be used in an organization to improve employee performance. The study showed that employees have their differences in terms of the concept of motivation. Various forms of theories of motivation in literature have been debated along with their applications and implications. Three questions were examined: What is motivation? What kind of motivation can best be used to increase employee performance? The results of the study show that motivation can increase or decrease employee performance. If the chosen form of motivation meets the needs of the employee, their performance increases. If, on the other hand, the chosen form of motivation does not satisfy the needs of the employee, the benefit decreases. It therefore encourages organizations to understand the motivating need of each employee to improve performance. Keywords: Motivations, Employee, Performance, Productivity DOI: 10.7176/JESD/12-16-11 Publication date:August 31st 202

Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the Human Connectome Development Cohort

In many fMRI studies, respiratory signals are unavailable or do not have acceptable quality. Consequently, the direct removal of low-frequency respiratory variations from BOLD signals is not possible. This study proposes a one-dimensional CNN model for reconstruction of two respiratory measures, RV and RVT. Results show that a CNN can capture informative features from resting BOLD signals and reconstruct realistic RV and RVT timeseries. It is expected that application of the proposed method will lower the cost of fMRI studies, reduce complexity, and decrease the burden on participants as they will not be required to wear a respiratory bellows.Comment: 6 pages, 5 figure

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013