79 research outputs found
Brain Computations and Connectivity [2nd edition]
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations.
Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed.
The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes.
Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions.
This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press.
Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
A Hybrid Continual Machine Learning Model for Efficient Hierarchical Classification of Domain-Specific Text in The Presence of Class Overlap (Case Study: IT Support Tickets)
In today’s world, support ticketing systems are employed by a wide range of businesses. The ticketing system facilitates the interaction between customers and the support teams when the customer faces an issue with a product or a service. For large-scale IT companies with a large number of clients and a great volume of communications, the task of automating the classification of incoming tickets is key to guaranteeing long-term clients and ensuring business growth.
Although the problem of text classification has been widely studied in the literature, the majority of the proposed approaches revolve around state-of-the-art deep learning models. This thesis addresses the following research questions: What are the reasons behind employing black box models (i.e., deep learning models) for text classification tasks? What is the level of polysemy (i.e., the coexistence of many possible meanings for a word or phrase) in a technical (i.e., specialized) text? How do static word embeddings like Word2vec fare against traditional TFIDF vectorization? How do dynamic word embeddings (e.g., PLMs) compare against a linear classifier such as Support Vector Machine (SVM) for classifying a domain-specific text?
This integrated article thesis aims to investigate the aforementioned issues through five empirical studies that were conducted over the past four years. The observation of our studies is an emerging theory that demonstrates why traditional ML models offer a more efficient solution to domain-specific text classification compared to state-of-the-art DL language models (i.e., PLMs).
Based on extensive experiments on a real-world dataset, we propose a novel Hybrid Online Offline Model (HOOM) that can efficiently classify IT Support Tickets in a real-time (i.e., dynamic) environment. Our classification model is anticipated to build trust and confidence when deployed into production as the model is interpretable, efficient, and can detect concept drifts in the data
Artificial intelligence in predicting the bankruptcy of non-financial corporations
Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future develop-ment becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice.Purpose of the article: This study aims to predict the bankruptcy of companies in the engineer-ing and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engi-neering and automotive industries, which can be applied in countries with undeveloped capital markets.
Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regres-sion to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts.
Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bank-ruptcy using six of these indicators. Almost all sampled industries are privatised, and most com-panies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct com-parative analyses of their own model with ours to reveal areas of model improvements.KEGA [001PU-4/2022]; Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic; Slovak Academy Sciences [1/0590/22]1/0590/22; Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR, KEGA: 001PU-4/202
Improving the sustainability of coal SC in both developed and developing countries by incorporating extended exergy accounting and different carbon reduction policies
In the age of Industry 4.0 and global warming, it is inevitable for decision-makers to change the way they view the coal supply chain (SC). In nature, energy is the currency, and nature is the source of energy for humankind. Coal is one of the most important sources of energy which provides much-needed electricity, as well as steel and cement production. This manuscript-based PhD thesis examines the coal SC network as well as the four carbon reduction strategies and plans to develop a comprehensive model for sustainable design. Thus, the Extended Exergy Accounting (EEA) method is incorporated into a coal SC under economic order quantity (EOQ) and economic production quantity (EPQs) in an uncertain environment. Using a real case study in coal SC in Iran, four carbon reduction policies such as carbon tax (Chapter 5), carbon trade (Chapter 6), carbon cap (Chapter 7), and carbon offset (Chapter 8) are examined. Additionally, all carbon policies are compared for sustainable performance of coal SCs in some developed and developing countries (the USA, China, India, Germany, Canada, Australia, etc.) with the world's most significant coal consumption. The objective function of the four optimization models under each carbon policy is to minimize the total exergy (in Joules as opposed to Dollars/Euros) of the coal SC in each country. The models have been solved using three recent metaheuristic algorithms, including Ant lion optimizer (ALO), Lion optimization algorithm (LOA), and Whale optimization algorithm (WOA), as well as three popular ones, such as Genetic algorithm (GA), Ant colony optimization (ACO), and Simulated annealing (SA), are suggested to determine a near-optimal solution to an exergy fuzzy nonlinear integer-programming (EFNIP). Moreover, the proposed metaheuristic algorithms are validated by using an exact method (by GAMS software) in small-size test problems. Finally, through a sensitivity analysis, this dissertation compares the effects of applying different percentages of exergy parameters (capital, labor, and environmental remediation) to coal SC models in each country. Using this approach, we can determine the best carbon reduction policy and exergy percentage that leads to the most sustainable performance (the lowest total exergy per Joule). The findings of this study may enhance the related research of sustainability assessment of SC as well as assist coal enterprises in making logical and measurable decisions
Computational Optimizations for Machine Learning
The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity
Biomedical image analysis of brain tumours through the use of artificial intelligence
Thesis (MCom)--Stellenbosch University, 2022.ENGLISH SUMMARY: Cancer is one of the leading causes of morbidity and mortality on a global scale. More specifically, cancer of the brain, which is one of the rarest forms. One of the major challenges is that of timely diagnoses. In the ongoing fight against cancer early and accurate detection in combination with effective treatment strategy planning remains one of the best tools for improved patient outcomes and success. Emphasis has been placed on the identification and classification of brain lesions in patients - that is, either the absence or presence of brain tumours. In the case of malignant brain tumours it is critical to classify patients into either high-grade or low-grade brain lesion groups: different gradings of brain tumours have different prognoses, thus different survival rates. The growth in the availability and accessibility of big data due to digitisation has led individuals in the area of bioinformatics in both academia and industry to apply and evaluate artificial intelligence techniques. However, one of the most important challenges, not only in the field of bioinformatics but also in other realms, is transforming the raw data into valuable insights and knowledge. In this research thesis artificial intelligence techniques that can detect vital and fundamental underlying patterns in the data are reviewed. The models may provide significant predictive performance to assist with decision making. Much artificial intelligence has been applied to brain tumour classification and segmentation in the research literature. However, in this study the theoretical background of two more traditional machine learning methods, namely -nearest neighbours and support vector machines, is discussed. In recent years, deep learning (artificial neural networks) has gained prominence due to its ability to handle copious amounts of data. The specialised version of the artificial neural network that is reviewed is convolutional neural networks. The rationale behind this particular technique is that it is applied to visual imagery. In addition to making use of the convolutional neural network architecture, the study reviews the training of neural networks that involves the use of optimisation techniques, considered to be one of the most difficult parts. Utilising only one learning algorithm (optimisation technique) in the architecture of convolutional neural network models for classification tasks may be regarded as insufficient unless there is strong support in the design of the analysis for using a particular technique. Nine state-of-the-art optimisation techniques formed part of a comparative study to determine if there was any improvement in the classification and segmentation of high-grade or low-grade brain tumours. These machine learning and deep learning techniques have proved to be successful in image classification and - more relevant to this research – brain tumours. To supplement the theoretical knowledge, these artificial intelligence methodologies (models) are applied through the exploration of magnetic resonance imaging scans of brain lesions.AFRIKAANSE OPSOMMING: Kanker is wêreldwyd een van die hoofoorsake van morbiditeit en sterftes; veral breinkanker, wat een van die mees seldsame soorte is. Een van die groot uitdagings is om dit betyds te diagnoseer. In die voortgesette stryd teen kanker is vroeë en akkurate opsporing, in kombinasie met doeltreffende beplanning van die behandelingstrategie, een van die beste hulpmiddels vir verbeterde pasiëntuitkomste en sukses. Klem word geplaas op die identifikasie en klassifikasie van breinletsels in pasiënte – dit wil sê, die teenwoordigheid of afwesigheid van breingewasse. In die geval van kwaadaardige breingewasse is dit noodsaaklik om pasiënte in groepe as hetsy hoëgraad- of laegraadbreingewasse te klassifiseer: verskillende graderings van breingewasse het verskillende prognoses, en dus verskillende oorlewingskoerse. Die toename in die beskikbaarheid en toeganklikheid van groot data danksy digitalisering, het daartoe gelei dat individue op die gebied van bio-informatika in die akademie en die bedryf begin het om kunsmatige-intelligensie-tegnieke toe te pas en te evalueer. Een van die belangrikste uitdagings, nie slegs op die gebied van bio-informatika nie, maar ook op ander terreine, is egter die omskakeling van rou data na waardevolle insigte en kennis. Hierdie navorsingstesis hersien die kunsmatige-intelligensie-tegnieke wat lewensbelangrike en grondliggende onderliggende patrone in die data kan opspoor. Die modelle kan beduidende voorspellende prestasie bied om met besluitneming te help. Die navorsingsliteratuur dek heelwat toepassings van kunsmatige intelligensie op breingewasklassifikasie en -segmentasie. In hierdie studie word die teoretiese agtergrond van meer tradisionele masjienleermetodes, naamlik die -naaste-bure-algoritme (-nearest neighbour algorithm) en steunvektormasjiene, bespreek. Diep leer (kunsmatige neurale netwerke) het onlangs op die voorgrond getree weens die vermoë daarvan om groot hoeveelhede data te kan hanteer. Die gespesialiseerde weergawe van die kunsmatige neurale netwerk wat hersien word, is konvolusionele neurale netwerkargitektuur. Die rasionaal vir hierdie spesifieke tegniek is dat dit op visuele beelde toegepas word. Buiten dat dit van konvolusionele neurale netwerkargitektuur gebruik maak, hersien die studie ook die afrigting van neurale netwerke met behulp van optimaliseringstegnieke, wat as een van die moeilikste dele beskou word. Die aanwending van slegs een leeralgoritme (optimaliseringstegniek) in die argitektuur van konvolusionele neurale netwerkmodelle vir klassifikasietake, kan as onvoldoende beskou word, tensy daar sterk steun vir die gebruik van ʼn spesifieke tegniek in die ontwerp van die ontleding is. Nege van die jongste optimaliseringstegnieke was deel van ʼn vergelykende studie om vas te stel of daar enige verbetering in die klassifikasie en segmentasie van hoëgraad- en laegraadbreingewasse was. Hierdie masjienleer- en diep-leertegnieke was suksesvol met beeldklassifikasie en – meer relevant vir hierdie navorsing – breingewasklassifikasie. Ter aanvulling van die teoretiese kennis, word hierdie kunsmatige-intelligensie-metodologieë (-modelle) deur die verkenning van magnetiese resonansbeelding van breingewasse toegepas.Master
Corporate Bankruptcy Prediction
Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy
Multivariate Analysis in Management, Engineering and the Sciences
Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field
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