Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm

In this study, a new swarm intelligence-based algorithm called Social Spider Algorithm (SSA), which is based on a simulation of the collaborative behaviours of spiders, was adapted for the first time for sentiment analysis (SA) within data obtained from Twitter. The SA problem was modelled as a search problem, with datasets considered as the search space and SSA modelled as a search strategy by determining an appropriate encoding scheme and objective function. The success of the SSA was compared with different Machine Learning (ML) algorithms within the same real datasets based on different metrics. Although this study is the first usage of SSA for the SA problem and there is no optimization for it, the attained results were promising and could provide new direction to related research about the use of optimized different artificial intelligence search algorithms for these types of online social network analysis problems. This study also introduced a new application domain for the optimization algorithms

Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition

Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find optimal feature set to maximize classification performance and mimics the cooperative behaviour and mechanism of social spiders in nature. This proposed feature selection method has been tested on DEAP EEG dataset with six subjects and compared with the most popular heuristic algorithms such as GA, PSO and ABC. The results show that the SSO-FS provides a remarkable and comparable performance compared to other existing methods. Whereby, the max accuracy obtained is 66.66% and 70.83%, the mean accuracy obtained is 55.51±7.17 and 60.97±8.38 for 3-level of valence emotions and 3-level of arousal emotions classification respectively

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

The pill and the will : pharmacological and psychological modulation of cognitive and affective processes

Background: Impairments in cognition are components of practically all psychiatric disorders and in that sense transdiagnostic factors. In both clinical and non-clinical populations, ‘hot’ and ‘cold’ cognitive control, i.e., in emotional context and non-emotional context, is strongly associated with daily functioning and physical and mental well-being. The paradigm shift that the National Institute of Mental Health (NIMH) Research Domain Criteria initiative (RDoC) has introduced, signifies that targeting the underlying biological and behavioural endophenotypes that determine mental health and illness might be more fruitful than simply focusing on symptom based diagnostic categories. Yet, little is known on how pharmacological interventions such as selective serotonin reuptake inhibitors (SSRI) and psychostimulants (CS), that are routinely used in everyday clinical praxis, affect cognitive and emotional processes beyond the symptoms they are supposed to treat. Aim: The aim of this thesis was to compare induction and regulation of fear and disgust in healthy subjects, and to investigate how SSRI affect these processes. This basic design was expanded to also include the effect of stimulant medication on the induction and regulation of negative emotions in healthy controls and patients with ADHD. A parallel aim was to compare pharmacological emotion regulation (SSRI and CS) with psychological emotion regulation (reappraisal) and emotion regulation with skills training/ exposure (task repetition). Methods: A multimodal approach was used to explore (i) subjective rating of emotion intensity and objective measures of performance at the behavioural level, (ii) neural underpinnings in the CNS with functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM) and (iii) physiological components of the sympathetic nervous system (electrodermal activity), which were all evaluated in the absence and presence of pharmacological and psychological interventions, during emotion induction, emotion regulation, cognitive Stroop and emotional Stroop paradigms. Results: Study I and IV demonstrated that emotion regulation with reappraisal is an effective strategy with robust effects on subjective emotional experience and electrodermal activity. Study II and III showed that task repetition improved performance during both cognitive and emotional Stroop tasks, and reduced electrodermal activity during cognitive Stroop, without significantly modifying emotion induction or emotion regulation. Study II and III showed significant effects of single dose escitalopram in reducing subjective emotional experience, improving task performance during affective interference of an ongoing cognitive process, altering prefrontal activity in a task-specific manner, and blurring the differences in the electrodermal activity between fear and disgust seen at baseline. Study IV showed that single dose CS reduced emotion induction, and that emotion regulation with reappraisal was significantly more effective in reducing subjective emotional experience compared to pharmacological emotion regulation with CS. Lastly, Study IV revealed aberrant emotion processing in patients with ADHD both at the behavioural and CNS levels, with patients reporting lower emotion induction and regulation scores, accompanied by less activation of dorsolateral prefrontal cortex, less deactivation of the default mode network and instead greater deactivation of the dorsal attention network, during emotion regulation compared to healthy controls. Structurally (VBM), less gray matter volume was found in limbic and paralimbic areas in patients with ADHD compared to healthy controls. Conclusions and implications: Dimensional approach using behavioural endophenotypes is a fruitful framework for studying normal physiology and diagnostic and treatment aspects of psychiatric disorders. In this thesis, it is demonstrated that emotional and non-emotional cognitive processes, although part of a continuum, likely respond differentially to psychological and pharmacological interventions and skills training with task repetition. Ultimately, improved knowledge in this field will help formulate hypothesisdriven and science-informed frameworks that will guide diagnosis and treatment plans, and usher a shift in psychiatric praxis

2011 IMSAloquium, Student Investigation Showcase

Inquiry Without Boundaries reflects our students’ infinite possibilities to explore their unique passions, develop new interests, and collaborate with experts around the globe.https://digitalcommons.imsa.edu/archives_sir/1003/thumbnail.jp

Washington University Record, May 11, 1995

https://digitalcommons.wustl.edu/record/1689/thumbnail.jp

DESIGN, SYNTHESIS, AND PHARMACOLOGICAL EVALUATION OF A SERIES OF NOVEL, GUANIDINE AND AMIDINE-CONTAINING NEONICOTINOID-LIKE ANALOGS OF NICOTINE: SUBTYPE-SELECTIVE INTERACTIONS AT NEURONAL NICOTINIC-ACETYLCHOLINE RECEPTOR.

The current project examined the ability of a novel series of guandine and amidine-containing nicotine analogs to interact with several native and recombinantlyexpressed mammalian neuronal nicotinic-acetylcholine receptor (nAChR) subtypes. Rational drug design methods and parallel organic synthesis was used to generate a library of guanidine-containing nicotine (NIC) analogs (AH compounds). A smaller series of amidine-containing nicotine analogs (JC compounds) were also synthesized. In total, \u3e150 compounds were examined. Compounds were first assayed for affinity in a high-throughput [3H]epibatidine radioligand-binding screen. Lead compounds were evaluated in subtype-selective binding experiments to probe for affinity at the α4β2* and α7* neuronal nAChRs. Several compounds were identified which possess affinity and selectivity for the α4β2* subtype [AH-132 (Ki=27nm) and JC-3-9 (Ki=11nM)]. Schild analysis of binding suggests a complex one-site binding interaction at the desensitized high-affinity nAChR. Whole-cell functional fluorescence (FLIPR) assays revealed mixed subtype pharmacology. AH-compounds were identified which act as activators and inhibitors at nAChR subtypes, while lead JC-compounds were found which possess full agonist activity at α4β2* and α3β4* subtypes. Compounds were identified as partial agonists, full agonists and inhibitors of multiple nAChR subtypes. Several SAR-based, ligand-receptor pharmacophore models were developed to guide future ligand design. Second-generation lead compounds were identified

Digital anthropology

The textbook supplements the lecture material with topical issues of the philosophy of neural technologies. The material belongs to the section "Philosophy of natural science and technology" of the lecture course on the philosophy and methodology of science. The natural-science aspects of human conscious-ness and technological trends in the evolution of convergent structures of digital ecosystems are described. The evolution of system computer engineering is analyzed