A Review on Machine Learning Techniques for Neurological Disorders Estimation by Analyzing EEG Waves

With the fast improvement of neuroimaging data acquisition strategies, there has been a significant growth in learning neurological disorders among data mining and machine learning communities. Neurological disorders are the ones that impact the central nervous system (including the human brain) and also include over 600 disorders ranging from brain aneurysm to epilepsy. Every year, based on World Health Organization (WHO), neurological disorders affect much more than one billion people worldwide and count for up to seven million deaths. Hence, useful investigation of neurological disorders is actually of great value. The vast majority of datasets useful for diagnosis of neurological disorders like electroencephalogram (EEG) are actually complicated and poses challenges that are many for data mining and machine learning algorithms due to their increased dimensionality, non stationarity, and non linearity. Hence, an better feature representation is actually key to an effective suite of data mining and machine learning algorithms in the examination of neurological disorders. With this exploration, we use a well defined EEG dataset to train as well as test out models. A preprocessing stage is actually used to extend, arrange and manipulate the framework of free data sets to the needs of ours for better training and tests results. Several techniques are used by us to enhance system accuracy. This particular paper concentrates on dealing with above pointed out difficulties and appropriately analyzes different EEG signals that would in turn help us to boost the procedure of feature extraction and enhance the accuracy in classification. Along with acknowledging above issues, this particular paper proposes a framework that would be useful in determining man stress level and also as a result, differentiate a stressed or normal person/subject

FUZZY CLUSTERING BASED BAYESIAN FRAMEWORK TO PREDICT MENTAL HEALTH PROBLEMS AMONG CHILDREN

According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD), Pervasive Developmental Disorder(PDD), etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering. The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Personality Traits and Drug Consumption. A Story Told by Data

This is a preprint version of the first book from the series: "Stories told by data". In this book a story is told about the psychological traits associated with drug consumption. The book includes: - A review of published works on the psychological profiles of drug users. - Analysis of a new original database with information on 1885 respondents and usage of 18 drugs. (Database is available online.) - An introductory description of the data mining and machine learning methods used for the analysis of this dataset. - The demonstration that the personality traits (five factor model, impulsivity, and sensation seeking), together with simple demographic data, give the possibility of predicting the risk of consumption of individual drugs with sensitivity and specificity above 70% for most drugs. - The analysis of correlations of use of different substances and the description of the groups of drugs with correlated use (correlation pleiades). - Proof of significant differences of personality profiles for users of different drugs. This is explicitly proved for benzodiazepines, ecstasy, and heroin. - Tables of personality profiles for users and non-users of 18 substances. The book is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of machine learning, advanced data mining concepts or modern psychology of personality is assumed. For more detailed introduction into statistical methods we recommend several undergraduate textbooks. Familiarity with basic statistics and some experience in the use of probabilities would be helpful as well as some basic technical understanding of psychology.Comment: A preprint version prepared by the authors before the Springer editorial work. 124 pages, 27 figures, 63 tables, bibl. 24

Computational behavioral analytics: estimating psychological traits in foreign languages.

The rise of technology proliferating into the workplace has increased the threat of loss of intellectual property, classified, and proprietary information for companies, governments, and academics. This can cause economic damage to the creators of new IP, companies, and whole economies. This technology proliferation has also assisted terror groups and lone wolf actors in pushing their message to a larger audience or finding similar tribal groups that share common, sometimes flawed, beliefs across various social media platforms. These types of challenges have created numerous studies in psycholinguistics, as well as commercial tools, that look to assist in identifying potential threats before they have an opportunity to conduct malicious acts. This has led to an area of study that this dissertation defines as ``Computational Behavioral Analytics. A common practice espoused in various Natural Language Processing studies (both commercial and academic) conducted on foreign language text is the use of Machine Translation (MT) systems before conducting NLP tasks. In this dissertation, we explore three psycholinguistic traits conducted on foreign language text. We explore the effects (and failures) of MT systems in these types of psycholinguistic tasks in order to help push the field of study into a direction that will greatly improve the efficacy of such systems. Given the results of the experimentation in this dissertation, it is highly recommended to avoid the use of translations whenever the greatest levels of accuracy are necessary, such as for National Security and Law Enforcement purposes. If translations must be used for any reason, scientist should conduct a full analysis of the impact of their chosen translation system on their estimates to determine which traits are more significantly affected. This will help ensure that analysts and scientists are better informed of the potential inaccuracies and change any resulting decisions from the data accordingly. This dissertation introduces psycholinguistics and the benefits of using Machine Learning technologies in estimating various psychological traits, and provides a brief discussion on the potential privacy and legal issues that should be addressed in order to avoid the abuse of such systems in Chapter I. Chapter II outlines the datasets that are used during the experimentation and evaluation of the algorithms. Chapter III discusses each of the various implementations of the algorithms used in the three psycholinguistic tasks - Affect Analysis, Authorship Attribution, and Personality Estimation. Chapter IV discusses the experiments that were run in order to understand the effects of MT on the psycholinguistic tasks, and to understand how these tasks can be accomplished in the face of MT limitations, including rationale on the selection of the MT system used in this study. The dissertation concludes with Chapter V, providing a discussion and speculating on the findings and future experimentation that should be done

Evolutionary multi-objective decision support systems for conceptual design

Merged with duplicate record 10026.1/2328 on 07.20.2017 by CS (TIS)In this thesis the problem of conceptual engineering design and the possible use of adaptive search techniques and other machine based methods therein are explored. For the multi-objective optimisation (MOO) within conceptual design problem, genetic algorithms (GA) adapted to MOO are used and various techniques explored: weighted sums, lexicographic order, Pareto method with and without ranking, VEGA-like approaches etc. Large number of runs are performed for findingZ Dth e optimal configuration and setting of the GA parameters. A novel method, weighted Pareto method is introduced and applied to a real-world optimisation problem. Decision support methods within conceptual engineering design framework are discussed and a new preference method developed. The preference method for translating vague qualitative categories (such as "more important 91 , 4m.9u ch less important' 'etc. ) into quantitative values (numbers) is based on fuzzy preferences and graph theory methods. Several applications of preferences are presented and discussed: * in weighted sum based optimisation methods; s in weighted Pareto method; * for ordering and manipulating constraints and scenarios; e for a co-evolutionary, distributive GA-based MOO method; The issue of complexity and sensitivity is addressed as well as potential generalisations of presented preference methods. Interactive dynamical constraints in the form of design scenarios are introduced. These are based on a propositional logic and a fairly rich mathematical language. They can be added, deleted and modified on-line during the design session without need for recompiling the code. The use of machine-based agents in conceptual design process is investigated. They are classified into several different categories (e. g. interface agents, search agents, information agents). Several different categories of agents performing various specialised task are developed (mostly dealing with preferences, but also some filtering ones). They are integrated with the conceptual engineering design system to form a closed loop system that includes both computer and designer. All thesed ifferent aspectso f conceptuale ngineeringd esigna re applied within Plymouth Engineering Design Centre / British Aerospace conceptual airframe design project.British Aerospace Systems, Warto

Computational Approaches to Drug Profiling and Drug-Protein Interactions

Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

When a good thing goes bad: using personality theory to reconceptualise overcontrolled pathways to offending

This thesis disputes the commonly accepted view that all offending is driven by undercontrolled coping, and in the following chapters compelling arguments are put forward that a substantial proportion of individuals who have committed violent, sexual and/or general offending have too much self-control. Theoretically, this challenges the accepted wisdom in forensic psychology and criminology that self-control is a unidimensional construct that is inversely related to offending, which posits that the lower one's self-control the greater likelihood of criminal behaviour, while higher self-control protects against offending. It is argued in this thesis that the form of the relationship between self-control and offending is not linear, but can be better described as quadratic, where high self-control (overcontrol) is a multi-faceted phenomenon rather than simply the opposite of low self-control. The systematic review in Chapter 4 is the first synthesis of the extant literature on overcontrol and offending, and this applies for the first time a novel theory of overcontrol (Lynch, 2018a) borrowed from clinical psychology. The mixed studies systematic review confirms that a substantial proportion of people in contact with the criminal justice system could be identified as overcontrolled, with as many as half of forensic psychiatric in-patients and a third of prisoners identified as overcontrolled. Cross-sectional studies were the most robust designs amongst the eligible studies in the systematic review, and overcontrolled individuals were consistently characterised by high levels of restraint, which included high defensive denial, low impulsivity, excessive emotional inhibition, and cognitive and interpersonal rigidity. Two potential overcontrolled clusters have also been confirmed, that is inhibited suppressors and controlled suppressors. A shared feature is high restraint, but affective and interpersonal functioning is more impaired in the inhibited suppressor than the in controlled repressor cluster. The original clinical descriptor of the "chronically overcontrolled violent offender" offered by Megargee (1966, p.2) was considered too narrow and incomplete, and its core premise that violent offending by overcontrolled individuals is driven by excessive anger regulation is unsubstantiated (Chapters 4, 5, and 6). It was therefore concluded that Megargee's theory offers limited explanatory value in understanding the concept of overcontrol, and it is contended that the evidence points to a need for an alternative guiding theory. Lynch's (2018a) newer and more comprehensive neurobiosocial theory of overcontrol, comprises three factors: biotemperamental biases (nature), socio-developmental experiences (nurture), and compulsive self-control (coping). The systematic review reveals that the biotemperamental characteristics and socio-developmental experiences of overcontrolled individuals with convictions have rarely been examined, and these are explored in Chapters 6 and 7, respectively. The coping component is more frequently studied, with some support for the five coping themes and the four markers of maladaptive overcontrol outlined by Lynch (2018a). Initial proof of concept testing in Chapters 6 and 7 confirms that overcontrol is more than an excessive anger regulation issue as proposed by Megagree (1966), rather it is a restricted way of managing emotions and relating. According to Lynch (2018a), this highly restricted and inhibited way of being results in chronic emotional loneliness and often high levels of hidden distress. Expression of these needs for connection and distress are often rare but intense, with some of these episodes of emotional leakage bringing overcontrolled individuals into contact with the criminal justice system. Finally, the findings in this thesis suggest that millions of overcontrolled individuals are in prison and forensic hospitals across the world, with many people being inaccurately assessed and treated using outdated models predicated on undercontrolled coping that emphasise the use of central cognitive-control strategies linked to inhibition to restore normative functioning. Emerging evidence tells us these treatments are at best ineffective and at worse iatrogenic (Low & Day, 2015; Redondo et al., 2019), as overcontrolled individuals do not need to learn more skills to inhibit, rather they need to learn how to relax inhibitory control and increase emotional expressiveness, receptivity, and flexibility. The findings in this thesis indicate that further work is needed to understand overcontrol in a forensic context, and the ethical, practical, and economic challenges associated with identifying this substantial untreated or mistreated forensic population needs urgent attention by policymakers, treatment providers, and researchers

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 380)

This bibliography lists 192 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Oct. 1993. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance