Author
Neera Bhansali
Bio: Neera Bhansali is an academic researcher from Florida International University. The author has contributed to research in topics: Cuckoo search & Feature selection. The author has an hindex of 3, co-authored 5 publications receiving 64 citations.
Papers
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TL;DR: A comparative analysis of various nature inspired algorithms to select optimal features/variables required for aiding in the classification of affected patients from the rest shows Binary Bat Algorithm outperformed traditional techniques like Particle Swarm Optimization (PSO), Genetic Algorithm and Modified Cuckoo Search Algorithm with a competitive recognition rate on the dataset of selected features.
70 citations
TL;DR: Researcher compliance has significantly improved as evidenced by medical device researchers having the lowest rate of the most significant noncompliance, compared to rates of biologic or pharmaceutical researchers.
Abstract: Background:The US Food and Drug Administration (FDA) ensures that clinical trials meet regulatory and ethical standards through inspections of researchers, also known as clinical investigators. Ins...
6 citations
TL;DR: In the last decade, the number of violations observed under the Bioresearch Monitoring program has decreased; however, significant improvements can still be made regarding adherence to study procedures, the consenting of human research subjects, and creation of adequate and accurate study documentation.
Abstract: Background:The US Food and Drug Administration (FDA) ensures clinical trials meet regulatory/ethical standards through inspections. If FDA Investigators observe potential violations of regulatory r...
4 citations
01 Jan 2014
TL;DR: The focus is on operational aspects of HIE to improve the process of sharing electronic health-related information among various organizations and strategy development, project management, architecture and infrastructure management.
Abstract: We discuss barriers to implementation of Health Information Exchange (HIE). The focus is on operational aspects of HIE to improve the process of sharing electronic health-related information among various organizations. Various topics include: strategy development, project management, architecture and infrastructure management.
4 citations
01 Jan 2016
TL;DR: In this chapter, authors have proposed an automated classification system based on Artificial Neural Network using Feed Forward Back-propagation Algorithm for Parkinson’s disease diagnosis by analyzing gait of a person.
Abstract: Parkinson’s disease is a degenerative disorder of the central nervous system which occurs as a result of dopamine loss, a chemical mediator that is responsible for body’s ability to control the movements. It’s a very common disease among elder population effecting approx 6.3 million people worldwide across all genders, races and cultures. In this chapter, authors have proposed an automated classification system based on Artificial Neural Network using Feed Forward Back-propagation Algorithm for Parkinson’s disease diagnosis by analyzing gait of a person. The system is trained, tested and validated by a gait dataset consisting data of Parkinson’s disease patients and healthy persons. The system is evaluated based on several measuring parameters like sensitivity, specificity, and classification accuracy. For the proposed system observed classification accuracy is 97.11% using 19 features of gait, and 95.55% using 10 prominent features of gait selected by Genetic Algorithm.
2 citations
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TL;DR: In this paper, the authors present a taxonomy of nature-inspired and bio-inspired algorithms, and provide a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper.
Abstract: In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.
109 citations
TL;DR: A multi-objective PSO based method named RFPSOFS that ranks the features based on their frequencies in the archive set that improves the performance of model, decreasing the computational cost, and adjusting the "curse of dimensionality" is proposed.
Abstract: Feature selection is an important preprocessing task in classification that eliminates the irrelevant, redundant, and noisy features Improving the performance of model, decreasing the computational cost, and adjusting the “curse of dimensionality” are the key advantages of feature selection task The evolution process of the existing multi-objective based feature selection algorithms is relied on the objective space while the problem space contains useful information This paper proposes a multi-objective PSO based method named RFPSOFS that ranks the features based on their frequencies in the archive set Then, these ranks are used to refine the archive set and guide the particles The proposed method is compared with three PSO based and one genetic based multi-objective methods on 9 Benchmark datasets Qualitative and quantitative analyses of the results are performed by visual analysis of the Pareto fronts and three performance metrics respectively Finally, remarkable performance in datasets with more than hundred features and satisfactory performance in datasets with less than hundred features are obtained
92 citations
TL;DR: The proposed wavelet transform based representation of spatiotemporal gait variables can efficiently extract relevant features from the different levels of the wavelet towards the classification of Parkinson's and healthy subjects and thus, the present work is a potential candidate for the automatic noninvasive neurodegenerative disease classification.
92 citations
TL;DR: This is the first approach developed to properly consider intra-subject variability for variable selection and classification and it can be applied in other contexts with similar replication-based experimental designs.
86 citations
TL;DR: A novel approach for estimation of temporal association pattern prevalence values and a novel temporal fuzzy similarity measure which holds monotonicity to find similarity between any two temporal patterns are proposed.
86 citations