Automated Detection of Autism Spectrum Disorder Using Bio-Inspired Swarm Intelligence Based Feature Selection and Classification Techniques

Autism spectrum disorders, or ASDs, are neurological conditions that affect humans. ASDs typically come with sensory issues like sensitivity to touch or soundor odour. Though genetics are the main causes, their  early discovery and treatments are imperative. In recent years, intelligent diagnosis using MLTs (Machine Learning Techniques) have been developed to support conventional clinical methods in the domain of healthcare. Feature selections from healthcare databases consume nondeterministic polynomial timesand are hard tasks where again MLTs have been of great use. AGWOs (Adaptive Grey Wolf Optimizations) were used in this study to determine most significant features and efficient classification strategies in datasets of ASDs. Initially,  pre-processing strategies based on SMOTEs (Synthetic Minority Oversampling Techniques) removed extraneous data from ASD datasets and subsequently AGWOs  repeat this procedure to find smallest features with maximum classifications values for recall and accuracy. Finally, KVSMs (Kernel Support Vector Machines) classify instances of ASDs from the input datasets. The experimental results of suggested method are evaluated for classifying ASDs from datasets instances of Toddlers, Children, Adolescents, and Adults in terms of recalls, precisions, F-measures, and classification errors

Machine learning methods for 3D object classification and segmentation

Field of study: Computer science.Dr. Ye Duan, Thesis Supervisor.Includes vita."July 2018."Object understanding is a fundamental problem in computer vision and it has been extensively researched in recent years thanks to the availability of powerful GPUs and labelled data, especially in the context of images. However, 3D object understanding is still not on par with its 2D domain and deep learning for 3D has not been fully explored yet. In this dissertation, I work on two approaches, both of which advances the state-of-the-art results in 3D classification and segmentation. The first approach, called MVRNN, is based multi-view paradigm. In contrast to MVCNN which does not generate consistent result across different views, by treating the multi-view images as a temporal sequence, our MVRNN correlates the features and generates coherent segmentation across different views. MVRNN demonstrated state-of-the-art performance on the Princeton Segmentation Benchmark dataset. The second approach, called PointGrid, is a hybrid method which combines points and regular grid structure. 3D points can retain fine details but irregular, which is challenge for deep learning methods. Volumetric grid is simple and has regular structure, but does not scale well with data resolution. Our PointGrid, which is simple, allows the fine details to be consumed by normal convolutions under a coarser resolution grid. PointGrid achieved state-of-the-art performance on ModelNet40 and ShapeNet datasets in 3D classification and object part segmentation.Includes bibliographical references (pages 116-140)

Classification of crystallization outcomes using deep convolutional neural networks

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications

Evaluating the Clinical Utility of a Novel Electroencephalography System for Assessing Perioperative Neurocognition in Older Surgical Patients

Postoperative delirium (POD) is a public health and research priority (American Society of Anesthesiologists, 2019). POD is a risk factor for long-term neurocognitive decline, and the rate of decline is directly proportional to the severity of POD (Vasunilashorn et al., 2018). Baseline cognitive function is a strong, independent predictor for POD (Culley et al., 2017). The International Perioperative Neurotoxicity Working Group recommends baseline cognitive function be assessed for older patients prior to surgery and anesthesia (Berger, et al., 2018). Perioperative cognitive screening tools trialed in anesthesia are not routinely incorporated into clinical practice related to validity, reliability, or practicality problems (Berger, et al., 2018). The ideal perioperative cognitive screening tool would be rapid, easily-administrable, valid, reliable, automatically scored, void of language, cultural, and education bias and cost-efficient (Axley & Schenning, 2015). No such tool has been identified to date. This study, guided by Donabedian’s theoretical model, evaluated the utility of a novel point-of-care (POC) electroencephalography (EEG) system, WAVi MedicalTM (Boulder, CO), for the perioperative neurocognitive assessment of older surgical patients. This study conducted a secondary analysis of data from the “Perioperative Brain Health” – IRB HM20019839 study. The “Perioperative Brain Health” study is an ongoing study collecting both pre- and postoperative questionnaire-based neurocognitive assessments alongside WAVi-derived P300 auditory evoked potentials. Data was analyzed using regression and analysis of variance. The WAVi MedicalTM system may one day offer anesthesia providers a novel neurocognitive assessment tool for predicting, identifying, and tracking perioperative neurocognitive disorders in older surgical patients

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Multilayer vectorization to develop a deeper image feature learning model

Computer-Aided Diagnosis (CAD) approaches categorise medical images substantially. Shape, colour, and texture can be problem-specific in medical imagery. Conventional approaches rely largely on them and their relationship, resulting in systems that can\u27t illustrate high-issue domain ideas and have weak prototype generalization. Deep learning techniques deliver an end-to-end model that classifies medical photos thoroughly. Due to the improved medical picture quality and short dataset size, this approach may have high processing costs and model layer restrictions. Multilayer vectorization and the Coding Network-Multilayer Perceptron (CNMP) are merged with deep learning to handle these challenges. This study extracts a high-level characteristic using vectorization, CNN, and conventional characteristics. The model\u27s steps are below. The input picture is vectorized into a few pixels during preprocessing. These pixel images are delivered to a coding network being trained to create high-level classification feature vectors. Medical imaging fundamentals determine picture properties. Finally, neural networks combine the collected features. The recommended technique is tested on ISIC2017 and HIS2828. The model\u27s accuracy is 91% and 92%

Developmental Coordination Disorder in Uzbekistan preschool children. Effects of a motor skill training program

Coordination difficulties have a widespread negative effect on children's lives in the various contexts of their daily activity. These difficulties, especially in the developmental coordination disorder (DCD) condition, continue into adulthood if there is no intervention, through appropriate programs, to help children overcome them. As a section of composing an update of the motor skill training programs in children with DCD according to gender, a systematic review of intervention studies in this domain, emphasizing gender differences, was made. This study aimed to (i) systematically review the available literature on the effect of motor intervention programs in children with DCD with a focus on gender differences (study 1). The effect of a motor skill training program in children with DCD considering their gender was investigated (study 2). Midline crossing behavior after an intervention program for children with DCD was explored (study 3). A follow-up research was applied to determine the sustainability of a given ten-week motor skill training program for children with DCD, to analyze skill changes from pre-test to post-intervention and to a retention-test, after 18 months without intervention (study 4). Outcomes of study 1 indicated that although activity- oriented and body function-oriented interventions can effectively affect motor function and skills in children with DCD, only a few studies indicated equal outcomes of the effectiveness of programs on both girls and boys. The effectiveness of the intervention program investigated in study 2 revealed to be similar across both genders. Study 3 showed that after intervention, DCD children in the experimental group (EG) showed fewer right-hand reaches in the contralateral space, but they improved their right-hand reaches in the midline, displaying a similar behavior to typical development children. Motor competence measurement from post-intervention to the retention test (after 18 months) indicated a decline in all the three motor domains of MABC-2. However, for the EG, results were better compared to those from the pre-test. It can be concluded that a 10-week motor skill training program can favorably change motor competence of children with DCD and this change lasts even after 18 months without the intervention

PREFERENCE OF CONSUMERS TOWARD NON-DISTORTED GRAPHICS ON FULL-BODY SHRINK SLEEVE LABELS

Past research has indicated that shrink sleeves can lead to higher product trial rates, better long-term sales, and greater likelihood of brand loyalty. A pilot study and a primary experiment were conducted to investigate the significance of distortion and the ability of the Human Visual System (HVS) to recognize it in packaging design. Distortion works primarily in shrink film on which an image is printed, so these studies dealt only with reductive distortion. The pilot study aimed to identify the absolute threshold, or Just-Noticeable-Difference (JND) for a change from no stimulus, for simple polygons. The primary experiment focused on graphic distortion in full body shrink sleeves (FBSS). Treatments presented each of the stimulus levels, along with a control for comparison, using a 2-AFC (Alternative Forced-Choice) Method. This study used a mixed 2 (labels) x 3 (bottles) x 5 (distortion increments) model, effectively 30 treatments in a Randomized Complete Block Design (RCBD). Label was a between-subject variable while Bottle Shape and Distortion Percentage were within-subject variables. Data indicated that distortion has a significant effect at 100%, but that there is not a threshold at which consumers are guaranteed to perceive graphic distortion on FBSS. Men detected distortion better than women. Participants who said distortion would prevent a purchase decision had the same tolerance as those who reported that it would not. Bottle shape may only impact consumer acceptance of distortion with some percentages of distortion. Familiarity with a brand name label may increase consumer tolerance and acceptance of distortion, and may do so more on some bottle shapes