8 research outputs found

    Optimized deep learning model for early detection and classification of lung cancer on CT images.

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    Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Recently, researchers have shown an increased interest in the early diagnosis and detection of lung cancer using the characteristics of computed tomography (CT) images. The accurate classification of lung cancer assists the physician to know the targeted treatment, reducing mortality, and as a result, supporting human survival. Several studies have been carried out on lung cancer detection using a convolutional neural network (CNN) models. However, it still remains a challenge to improve the model’s performance. Moreover, CNN models have some limitations that affect their performance, including choosing the optimal architecture, selecting suitable model parameters, and picking the best parameter values for weights and bias. To address the problem of selecting the best combination of weights and bias needed for the classification of lung cancer in CT images, this study proposes a hybrid of Ebola optimization search algorithm (EOSA) and the CNN model. We proposed a hybrid deep learning model with preprocessing features for lung cancer classification using publicly accessible Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) dataset. The proposed EOSA-CNN hybrid model was trained using 80% of the cases to obtain the optimal configuration, while the remaining 20% was applied for validation. Also, we compared the proposed model with similar five hybrid algorithms and the traditional CNN. The results indicated that EOSA-CNN scored 0.9321 classification accuracy. Furthermore, the result showed that EOSA-CNN achieved a specificity of 0.7941, 0.97951, 0.9328, and sensitivity of 0.9038, 0.13333, 0.9071 for normal, benign, and malignant cases, respectively. This confirmed that the hybrid algorithm provides a good solution for the classification of lung cancer

    Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

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    The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

    Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

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    An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    The geometry of colour

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    This thesis explores the geometric description of animal colour vision. It examines the relationship of colour spaces to behavior and to physiology. I provide a derivation of, and explore the limits of, geometric spaces derived from the notion of risk and uncertainty aversion as well as the geometric objects that enumerate the variety of achievable colours. Using these principles I go on to explore evolutionary questions concerning colourfulness, such as aposematism, mimicry and the idea of aesthetic preference
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