A Dynamic Self-Structuring Neural Network

Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, the trial and error structuring method have several difficulties such as time and availability of experts. In this article, an algorithm that simplifies structuring neural network classification models has been proposed. The algorithm aims at creating a large enough structure to learn models from the training dataset that can be generalised well on the testing dataset. Our algorithm dynamically tunes the structure parameters during the training phase aiming to derive accurate non-overfitting classifiers. The proposed algorithm has been applied to phishing websites classification problem and it shows competitive results with respect to various evaluation measures such as Harmonic Mean (F1-score), precision, accuracy, etc

Development of a Hybrid Algorithm for efficient Task Scheduling in Cloud Computing environment using Artificial Intelligence

Cloud computing is developing as a platform for next generation systems where users can pay as they use facilities of cloud computing like any other utilities. Cloud environment involves a set of virtual machines, which share the same computation facility and storage. Due to rapid rise in demand for cloud computing services several algorithms are being developed and experimented by the researchers in order to enhance the task scheduling process of the machines thereby offering optimal solution to the users by which the users can process the maximum number of tasks through minimal utilization of the resources. Task scheduling denotes a set of policies to regulate the task processed by a system. Virtual machine scheduling is essential for effective operations in distributed environment. The aim of this paper is to achieve efficient task scheduling of virtual machines, this study proposes a hybrid algorithm through integrating two prominent heuristic algorithms namely the BAT Algorithm and the Ant Colony Optimization (ACO) algorithm in order to optimize the virtual machine scheduling process. The performance evaluation of the three algorithms (BAT, ACO and Hybrid) reveal that the hybrid algorithm performs better when compared with that of the other two algorithms

Detecting Autistic Traits using Computational Intelligence & Machine Learning Techniques

Autistic Spectrum Disorder (ASD) is a developmental disorder that describes certain challenges associated with communication (verbal and non-verbal), social skills and repetitive behaviours. Self-administered ASD assessment tools, also known as screening tools, are typically conducted by a caregiver, medical staff and require responses to a large number of items. The validity and accuracy of assessments based on these tools relies upon classification methods which have antiquated technologies and this should be of concern for users in the healthcare community. A possible way to improve the classification accuracy and efficiency of the current screening tools is to adopt intelligent methods based on machine learning (ML) and computational intelligence. The latter can be utilised to identify a concise set of items by using new technologies such as mobile platforms, thus improving screening, or be able to steer those in seek of help toward a more accurate diagnosis. To automate the classification process and enhance the predictive accuracy of the test, the processing of data, based on the outcome of the computational intelligence, can be conducted using the former method. This thesis proposes a new ML architecture for ASD screening that consists of a rule-based classification method called Rules Machine Learning (RML) which generates high predictive rules that can be easily understood by different users. Moreover, a new feature selection method known as Variable Analysis (Va) is proposed; this significantly reduces the number of features needed for ASD screening methods while maintaining performance. The last proposal in this thesis is an easy and accessible mobile screening application called ASDTests, which enables vital autism features to be collected from three primary datasets: adults, children, and adolescents from which thorough descriptive and predictive analyses are performed. To measure the performance of the RML and Va methods, large numbers of experiments have been conducted using various feature selection and ML techniques on the considered datasets. The bases of the comparisons are: evaluation metrics including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NVP), and harmonic mean. The results clearly demonstrated that the new ML method was able to choose fewer items from the three datasets than the other methods considered while maintaining acceptable levels of specificity, sensitivity, and predictive accuracy. The concise sets of items and classifiers generated are of high interest to the different individuals interested in ASD screening. These results can also assist in early detection of ASD traits, thus facilitating access to necessary support systems for the physical, social, and educational well-being of the patient and their family in addition to increasing the likelihood of improved outcomes for the patient

Modulatory Action by the Serotonergic System: Behavior and Neurophysiology in \u3cem\u3eDrosophila melanogaster\u3c/em\u3e

Serotonin modulates various physiological processes and behaviors. This study investigates the role of 5-HT in locomotion and feeding behaviors as well as in modulation of sensory-motor circuits. The 5-HT biosynthesis was dysregulated by feeding Drosophila larvae 5-HT, a 5-HT precursor, or an inhibitor of tryptophan hydroxylase during early stages of development. The effects of feeding fluoxetine, a selective serotonin reuptake inhibitor, during early second instars were also examined. 5-HT receptor subtypes were manipulated using RNA interference mediated knockdown and 5-HT receptor insertional mutations. Moreover, synaptic transmission at 5-HT neurons was blocked or enhanced in both larvae and adult flies. The results demonstrate that disruption of components within the 5-HT system significantly impairs locomotion and feeding behaviors in larvae. Acute activation of 5-HT neurons disrupts normal locomotion activity in adult flies. To determine which 5-HT receptor subtype modulates the evoked sensory-motor activity, pharmacological agents were used. In addition, the activity of 5-HT neurons was enhanced by expressing and activating TrpA1 channels or channelrhodopsin-2 while recording the evoked excitatory postsynaptic potentials (EPSPs) in muscle fibers. 5-HT2 receptor activation mediates a modulatory role in a sensory-motor circuit, and the activation of 5-HT neurons can suppress the neural circuit activity, while fluoxetine can significantly decrease the sensory-motor activity

Zero-Shot Motor Health Monitoring by Blind Domain Transition

Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.Comment: 13 pages, 9 figures, Journa

New challenges in studying nutrition-disease interactions in the developing world.

Latest estimates indicate that nutritional deficiencies account for 3 million child deaths each year in less-developed countries. Targeted nutritional interventions could therefore save millions of lives. However, such interventions require careful optimization to maximize benefit and avoid harm. Progress toward designing effective life-saving interventions is currently hampered by some serious gaps in our understanding of nutrient metabolism in humans. In this Personal Perspective, we highlight some of these gaps and make some proposals as to how improved research methods and technologies can be brought to bear on the problems of undernourished children in the developing world

Propensity Score Matching in Randomized Clinical Trials

Cluster randomization trials with relatively few clusters have been widely used in recent years for evaluation of health-care strategies. On average, randomized treatment assignment achieves balance in both known and unknown confounding factors between treatment groups, however, in practice investigators can only introduce a small amount of stratification and cannot balance on all the important variables simultaneously. The limitation arises especially when there are many confounding variables in small studies. Such is the case in the  INSTINCT  trial designed to investigate the effectiveness of an education program in enhancing the tPA use in stroke patients. In this article, we introduce a new randomization design, the balance match weighted (BMW) design, which applies the optimal matching with constraints technique to a prospective randomized design and aims to minimize the mean squared error (MSE) of the treatment effect estimator. A simulation study shows that, under various confounding scenarios, the BMW design can yield substantial reductions in the MSE for the treatment effect estimator compared to a completely randomized or matched-pair design. The BMW design is also compared with a model-based approach adjusting for the estimated propensity score and Robins-Mark-Newey E-estimation procedure in terms of efficiency and robustness of the treatment effect estimator. These investigations suggest that the BMW design is more robust and usually, although not always, more efficient than either of the approaches. The design is also seen to be robust against heterogeneous error. We illustrate these methods in proposing a design for the  INSTINCT  trial.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78638/1/j.1541-0420.2009.01364.x.pd

Pseudomonas Diversity Within Urban Freshwaters

Freshwater lakes are home to bacterial communities with 1000s of interdependent species. Numerous high-throughput 16S rRNA gene sequence surveys have provided insight into the microbial taxa found within these waters. Prior surveys of Lake Michigan waters have identified bacterial species common to freshwater lakes as well as species likely introduced from the urban environment. We cultured bacterial isolates from samples taken from the Chicago nearshore waters of Lake Michigan in an effort to look more closely at the genetic diversity of species found there within. The most abundant genus detected was Pseudomonas, whose presence in freshwaters is often attributed to storm water or runoff. Whole genome sequencing was conducted for 15 Lake Michigan Pseudomonas strains, representative of eight species and three isolates that could not be resolved with named species. These genomes were examined specifically for genes encoding functionality which may be advantageous in their urban environment. Antibiotic resistance, amidst other known virulence factors and defense mechanisms, were identified in the genome annotations and verified in the lab. We also tested the Lake Michigan Pseudomonas strains for siderophore production and resistance to the heavy metals mercury and copper. As the study presented here shows, a variety of pseudomonads have inhabited the urban coastal waters of Lake Michigan