Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v

Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions

Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials-by-design, namely representation learning of microstructure morphology (i.e., shape descriptors), structure-property-performance (S-P-P) linkage estimation, and optimization/design exploration. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials-by-design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge the gap between machine learning research and EM research

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Computationally efficient algorithms and implementations of adaptive deep brain stimulation systems for Parkinson's disease

Clinical deep brain stimulation (DBS) is a tool used to mitigate pharmacologically intractable neurodegenerative diseases such as Parkinson's disease (PD), tremor and dystonia. Present implementations of DBS use continuous, high frequency voltage or current pulses so as to mitigate PD. This results in some limitations, among which there is stimulation induced side effects and shortening of pacemaker battery life. Adaptive DBS (aDBS) can be used to overcome a number of these limitations. Adaptive DBS is intended to deliver stimulation precisely only when needed. This thesis presents work undertaken to investigate, propose and develop novel algorithms and implementations of systems for adapting DBS. This thesis proposes four system implementations that could facilitate DBS adaptation either in the form of closed-loop DBS or spatial adaptation. The first method involved the use of dynamic detection to track changes in local field potentials (LFP) which can be indicative of PD symptoms. The work on dynamic detection included the synthesis of validation dataset using mainly autoregressive moving average (ARMA) models to enable the evaluation of a subset of PD detection algorithms for accuracy and complexity trade-offs. The subset of algorithms consisted of feature extraction (FE), dimensionality reduction (DR) and dynamic pattern classification stages. The combination with the best trade-off in terms of accuracy and complexity consisted of discrete wavelet transform (DWT) for FE, maximum ratio method (MRM) for DR and k-nearest neighbours (k-NN) for classification. The MRM is a novel DR method inspired by Fisher's separability criterion. The best combination achieved accuracy measures: F1-score of 97.9%, choice probability of 99.86% and classification accuracy of 99.29%. Regarding complexity, it had an estimated microchip area of 0.84 mm² for estimates in 90 nm CMOS process. The second implementation developed the first known PD detection and monitoring processor. This was achieved using complementary detection, which presents a hardware-efficient method of implementing a PD detection processor for monitoring PD progression in Parkinsonian patients. Complementary detection is achieved by using a combination of weak classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers in the configuration. The PD detection processor using the same processing stages as the first implementation was validated on an FPGA platform. By mapping the implemented design on a 45 nm CMOS process, the most optimal implementation achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.2384 mm². It also achieved mean accuracy measures: Mathews correlation coefficient (MCC) of 0.6162, an F1-score of 91.38%, and mean classification accuracy of 91.91%. The third implementation proposed a framework for adapting DBS based on a critic-actor control approach. This models the relationship between a trained clinician (critic) and a neuro-modulation system (actor) for modulating DBS. The critic was implemented and validated using machine learning models, and the actor was implemented using a fuzzy controller. Therapy is modulated based on state estimates obtained through the machine learning models. PD suppression was achieved in seven out of nine test cases. The final implementation introduces spatial adaptation for aDBS. Spatial adaptation adjusts to variation in lead position and/or stimulation focus, as poor stimulation focus has been reported to affect therapeutic benefits of DBS. The implementation proposes dynamic current steering systems as a power-efficient implementation for multi-polar multisite current steering, with a particular focus on the output stage of the dynamic current steering system. The output stage uses dynamic current sources in implementing push-pull current sources that are interfaced to 16 electrodes so as to enable current steering. The performance of the output stage was demonstrated using a supply of 3.3 V to drive biphasic current pulses of up to 0.5 mA through its electrodes. The preliminary design of the circuit was implemented in 0.18 μm CMOS technology

Stock Market Prediction via Deep Learning Techniques: A Survey

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction

On the development of evolutionary artificial artists

The creation and the evaluation of aesthetic artifacts are tasks related to design, music and art, which are highly interesting from the computational point of view. Nowadays, Artificial Intelligence systems face the challenge of performing tasks that are typically human, highly subjective, and eventually social. The present paper introduces an architecture which is capable of evaluating aesthetic characteristics of artifacts and of creating artifacts that obey certain aesthetic properties. The development methodology and motivation, as well as the results achieved by the various components of the architecture, are described. The potential contributions of this type of systems in the context of digital art are also considered.http://www.sciencedirect.com/science/article/B6TYG-4PTMXVB-1/1/265a0f6c8e478822e6de32b87bc2fb1