Suresh Sundaram

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360,000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set.

Motivating the sales force to adopt and use sales force automation (SFA) technology remains an issue. If salespeople are not committed to the selling organization's technology strategies, customer alliances are hindered.Survey data were collected from a national sales force before and after the introduction of an SFA application. The results of this study indicate that salesperson attitudes (Perceived Usefulness, Attitude Toward the New System, and Compatibility) have an impact on intention to use new SFA systems prior to implementation. However, Personal Innovativeness, Attitude Toward the New System, and Facilitating Conditions have more of an effect on infusion of new SFA systems.

This paper presents an Oxyrrhis Marina-inspired search and dynamic formation control (OMS-DFC) framework for multi-unmanned aerial vehicle (UAV) systems to efficiently search and neutralize a dynamic target (forest fire) in an unknown/uncertain environment. The OMS-DFC framework consists of two stages, viz., the target identification stage without communication between UAVs and the mitigation stage with restricted communication. In the first stage, each UAV adapts proposed OMS with three levels to select between Levy flight, Brownian search, and directionally driven Brownian (DDB) search for accurate target identification (“fire location”). The selection of each level is based on the available sensor information about the possible fire location. In the second stage, the UAVs that identified a fire location fly in a dynamic formation to quench the fire using water. The proposed formation is achieved through decentralized control, where a UAV computes the control action based on the fire profile and also the angular position and angular separation with its succeeding neighbor. The proposed formation control law guarantees asymptotic convergence to the desired time-varying angular position profile of UAVs based on the nature of fire spread (circular/elliptical). To evaluate the performance of the proposed OMS-DFC for the multi-UAV system, a search and fire quenching mission in a typical pine forest is simulated. A Monte Carlo simulation study is conducted to evaluate the average performance of the proposed OMS-DFC-based multi-UAV mission, and the results clearly highlight the advantages of the proposed OMS-DFC in forest firefighting.

In this paper, we present a metacognitive sequential learning algorithm for a neuro-fuzzy inference system for classification tasks, which is referred to as a “metacognitive neuro-fuzzy inference system (McFIS).” The McFIS learning algorithm is developed based on the principles of the best human learning strategy, viz., a self-regulatory learning strategy in a metacognitive framework. McFIS has two components: a cognitive component and a metacognitive component. A neuro-fuzzy inference system forms the cognitive component of the McFIS, and a self-regulatory learning mechanism forms its metacognitive component. The learning ability of the cognitive component is monitored and controlled by the self-regulatory learning mechanism. For each sample in the training dataset, the metacognitive component uses its self-adaptive thresholds to choose one of the following learning strategies based on the criteria that depends on class-specific knowledge: 1) sample deletion; 2) sample learning; and 3) sample reserve. Thus, the metacognitive component decides what-to-learn, when-to-learn, and how-to-learn the training samples. When a new rule is added, the parameters of the new rule are assigned such that the rule has minimum overlapping with the adjacent rules as well as the localization property of the Gaussian rules is efficiently exploited. Performance of the McFIS is evaluated using several well-known benchmark multicategory/binary classification datasets from the University of California, Irvine machine learning repository and on a practical human action recognition problem. The results clearly indicate that the proposed metacognitive learning helps the McFIS achieve better performance than other existing classifiers.

A combination of Integer-Coded Genetic Algorithm (ICGA) and Particle Swarm Optimization (PSO), coupled with the neural-network-based Extreme Learning Machine (ELM), is used for gene selection and cancer classification. ICGA is used with PSO-ELM to select an optimal set of genes, which is then used to build a classifier to develop an algorithm (ICGA_PSO_ELM) that can handle sparse data and sample imbalance. We evaluate the performance of ICGA-PSO-ELM and compare our results with existing methods in the literature. An investigation into the functions of the selected genes, using a systems biology approach, revealed that many of the identified genes are involved in cell signaling and proliferation. An analysis of these gene sets shows a larger representation of genes that encode secreted proteins than found in randomly selected gene sets. Secreted proteins constitute a major means by which cells interact with their surroundings. Mounting biological evidence has identified the tumor microenvironment as a critical factor that determines tumor survival and growth. Thus, the genes identified by this study that encode secreted proteins might provide important insights to the nature of the critical biological features in the microenvironment of each tumor type that allow these cells to thrive and proliferate.