An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.
Members of the genus Aglaia have been reported to contain bioactive phytochemicals. The genus, belonging to the Meliaceae family, is represented by at least 120 known species of woody trees or shrubs in the tropical rain forest. As some of these species are very similar in their morphology, taxonomic identification can be difficult. A reliable and definitive molecular method which can identify Aglaia to the level of the species will hence be useful in comparing the content of specific bioactive compounds between the species of this genus. Here, we report the analysis of DNA sequences in the internal transcribed spacer (ITS) of the nuclear ribosomal DNA and the observation of a unique nucleotide signature in the ITS that can be used for the identification of Aglaia stellatopilosa. The nucleotide signature consists of nine bases over the length of the ITS sequence (654 bp). This uniqueness was validated in 37 samples identified as Aglaia stellatopilosa by an expert taxonomist, whereas the nucleotide signature was lacking in a selection of other Aglaia species and non-Aglaia genera. This finding suggests that molecular typing could be utilized in the identification of Aglaia stellatopilosa.
This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms.
Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful "emotional" interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.
This paper presents a study of the application of autonomously learning multiple neural network systems to medical pattern classification tasks. In our earlier work, a hybrid neural network architecture has been developed for on-line learning and probability estimation tasks. The network has been shown to be capable of asymptotically achieving the Bayes optimal classification rates, on-line, in a number of benchmark classification experiments. In the context of pattern classification, however, the concept of multiple classifier systems has been proposed to improve the performance of a single classifier. Thus, three decision combination algorithms have been implemented to produce a multiple neural network classifier system. Here the applicability of the system is assessed using patient records in two medical domains. The first task is the prognosis of patients admitted to coronary care units; whereas the second is the prediction of survival in trauma patients. The results are compared with those from logistic regression models, and implications of the system as a useful clinical diagnostic tool are discussed.
New Guinea is the world's largest tropical island and has fascinated naturalists for centuries1,2. Home to some of the best-preserved ecosystems on the planet3 and to intact ecological gradients-from mangroves to tropical alpine grasslands-that are unmatched in the Asia-Pacific region4,5, it is a globally recognized centre of biological and cultural diversity6,7. So far, however, there has been no attempt to critically catalogue the entire vascular plant diversity of New Guinea. Here we present the first, to our knowledge, expert-verified checklist of the vascular plants of mainland New Guinea and surrounding islands. Our publicly available checklist includes 13,634 species (68% endemic), 1,742 genera and 264 families-suggesting that New Guinea is the most floristically diverse island in the world. Expert knowledge is essential for building checklists in the digital era: reliance on online taxonomic resources alone would have inflated species counts by 22%. Species discovery shows no sign of levelling off, and we discuss steps to accelerate botanical research in the 'Last Unknown'8.
One method of grading responses of the descriptive type is by using Structure of Observed Learning Outcomes (SOLO) taxonomy. The basis of this study was the expectation that if students were oriented to SOLO taxonomy, it would provide them an opportunity to understand some of the factors that teachers consider while grading descriptive responses and possibly develop strategies to improve scores. We first sampled the perceptions of 68 second-year undergraduate medical students doing the Respiratory System course regarding the usefulness of explicit discussion of SOLO taxonomy. Subsequently, in a distinct cohort of 20 second-year medical students doing the Central Nervous System course, we sought to determine whether explicit illustration of SOLO taxonomy combined with some advice on better answering descriptive test questions (to an experimental group) resulted in better student scores in a continuous assessment test compared with providing advice for better answering test questions but without any reference to SOLO taxonomy (the control group). Student ratings of the clarity of the presentation on SOLO taxonomy appeared satisfactory to the authors, as was student understanding of our presentation. The majority of participants indicated that knowledge of SOLO taxonomy would help them study and prepare better answers for questions of the descriptive type. Although scores in the experimental and control group were comparable, this experience nonetheless provided us with the motivation to orient students to SOLO taxonomy early on in the medical program and further research factors that affect students' development of strategies based on knowledge of SOLO taxonomy.