Hybrid meta-heuristic algorithm based parameter optimization for extreme learning machines classification

Most classification algorithms suffer from manual parameter tuning and it affects the training computational time and accuracy performance. Extreme Learning Machines (ELM) emerged as a fast training machine learning algorithm that eliminates parameter tuning by randomly assigning the input weights and biases, and analytically determining the output weights using Moore Penrose generalized inverse method. However, the randomness assignment, does not guarantee an optimal set of input weights and biases of the hidden neurons. This will lead to ELM instability and local minimum solution. ELM performance also is affected by the network structure especially the number of hidden nodes. Too many hidden neurons will increase the network structure complexity and computational time. While too few hidden neuron numbers will affect the ELM generalization ability and reduce the accuracy. In this study, a heuristic-based ELM (HELM) scheme was designed to secure an optimal ELM structure. The results of HELM were validated with five rule-based hidden neuron selection schemes. Then HELM performance was compared with Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Classification and Regression Tree (CART) to investigate its relative competitiveness. Secondly, to improve the stability of ELM, the Moth-Flame Optimization algorithm is hybridized with ELM as MFO-ELM. MFO generates moths and optimizes their positions in the search space with a logarithm spiral model to obtain the optimal values of input weights and biases. The optimal weights and biases from the search space were passed into the ELM input space. However, it did not completely solve the problem of been stuck in the local extremum since MFO could not ensure a good balance between the exploration and exploitation of the search space. Thirdly, a co-evolutionary hybrid algorithm of the Cross-Entropy Moth-Flame Optimization Extreme Learning Machines (CEMFO-ELM) scheme was proposed. The hybrid of CE and MFO metaheuristic algorithms ensured a balance of exploration and exploitation in the search space and reduced the possibility of been trapped in the local minima. The performances of these schemes were evaluated on some selected medical datasets from the University of California, Irvine (UCI) machine learning repository, and compared with standard ELM, PSO-ELM, and CSO-ELM. The hybrid MFO-ELM algorithm enhanced the selection of optimal weights and biases for ELM, therefore improved its classification accuracy in a range of 0.4914 - 6.0762%, and up to 8.9390% with the other comparative ELM optimized meta-heuristic algorithms. The convergence curves plot show that the proposed hybrid CEMFO meta-heuristic algorithm ensured a balance between the exploration and exploitation in the search space, thereby improved the stability up to 53.75%. The overall findings showed that the proposed CEMFO-ELM provided better generalization performance on the classification of medical datasets. Thus, CEMFO-ELM is a suitable tool to be used not only in solving medical classification problems but potentially be used in other real-world problems

Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation

Queißer J. Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation. Bielefeld: Universität Bielefeld; 2018.Modern robotic applications pose complex requirements with respect to the adaptation of actions regarding the variability in a given task. Reinforcement learning can optimize for changing conditions, but relearning from scratch is hardly feasible due to the high number of required rollouts. This work proposes a parameterized skill that generalizes to new actions for changing task parameters. The actions are encoded by a meta-learner that provides parameters for task-specific dynamic motion primitives. Experimental evaluation shows that the utilization of parameterized skills for initialization of the optimization process leads to a more effective incremental task learning. A proposed hybrid optimization method combines a fast coarse optimization on a manifold of policy parameters with a fine-grained parameter search in the unrestricted space of actions. It is shown that the developed algorithm reduces the number of required rollouts for adaptation to new task conditions. Further, this work presents a transfer learning approach for adaptation of learned skills to new situations. Application in illustrative toy scenarios, for a 10-DOF planar arm, a humanoid robot point reaching task and parameterized drumming on a pneumatic robot validate the approach. But parameterized skills that are applied on complex robotic systems pose further challenges: the dynamics of the robot and the interaction with the environment introduce model inaccuracies. In particular, high-level skill acquisition on highly compliant robotic systems such as pneumatically driven or soft actuators is hardly feasible. Since learning of the complete dynamics model is not feasible due to the high complexity, this thesis examines two alternative approaches: First, an improvement of the low-level control based on an equilibrium model of the robot. Utilization of an equilibrium model reduces the learning complexity and this thesis evaluates its applicability for control of pneumatic and industrial light-weight robots. Second, an extension of parameterized skills to generalize for forward signals of action primitives that result in an enhanced control quality of complex robotic systems. This thesis argues for a shift in the complexity of learning the full dynamics of the robot to a lower dimensional task-related learning problem. Due to the generalization in relation to the task variability, online learning for complex robots as well as complex scenarios becomes feasible. An experimental evaluation investigates the generalization capabilities of the proposed online learning system for robot motion generation. Evaluation is performed through simulation of a compliant 2-DOF arm and scalability to a complex robotic system is demonstrated for a pneumatically driven humanoid robot with 8-DOF

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI

Diagnostic Significance of Exosomal miRNAs in the Plasma of Breast Cancer Patients

Poster Session AbstractsBackground and Aims: Emerging evidence that microRNAs (miRNAs) play an important role in cancer development has opened up new opportunities for cancer diagnosis. Recent studies demonstrated that released exosomes which contain a subset of both cellular mRNA and miRNA could be a useful source of biomarkers for cancer detection. Here, we aim to develop a novel biomarker for breast cancer diagnosis using exosomal miRNAs in plasma. Methods: We have developed a rapid and novel isolation protocol to enrich tumor-associated exosomes from plasma samples by capturing tumor specific surface markers containing exosomes. After enrichment, we performed miRNA profiling on four sample sets; (1) Ep-CAM marker enriched plasma exosomes of breast cancer patients; (2) breast tumors of the same patients; (3) adjacent non-cancerous tissues of the same patients; (4) Ep-CAM marker enriched plasma exosomes of normal control subjects. Profiling is performed using PCR-based array with human microRNA panels that contain more than 700 miRNAs. Results: Our profiling data showed that 15 miRNAs are concordantly up-regulated and 13 miRNAs are concordantly down-regulated in both plasma exosomes and corresponding tumors. These account for 25% (up-regulation) and 15% (down-regulation) of all miRNAs detectable in plasma exosomes. Our findings demonstrate that miRNA profile in EpCAM-enriched plasma exosomes from breast cancer patients exhibit certain similar pattern to that in the corresponding tumors. Based on our profiling results, plasma signatures that differentiated breast cancer from control are generated and some of the well-known breast cancer related miRNAs such as miR-10b, miR-21, miR-155 and miR-145 are included in our panel list. The putative miRNA biomarkers are validated on plasma samples from an independent cohort from more than 100 cancer patients. Further validation of the selected markers is likely to offer an accurate, noninvasive and specific diagnostic assay for breast cancer. Conclusions: These results suggest that exosomal miRNAs in plasma may be a novel biomarker for breast cancer diagnosis.link_to_OA_fulltex

HERITAGE 2022. International Conference on Vernacular Heritage: Culture, People and Sustainability

Vernacular architecture, tangible and intangible heritage of great importance to European and global culture, represents the response of a society culturally linked to its territory, in terms of climate and landscape. Its construction features are born from the practical experience of the inhabitants, making use of local materials, taking into consideration geographical conditions and cultural, social and constructive traditions, based on the conditions of the surrounding nature and habitat. Above all, it plays an essential role in contemporary society as it is able to teach us important principles and lessons for a respectful sustainable architecture. Vernacular Heritage: Culture, People and Sustainability will be a valuable source of information for academics and professionals in the fields of Environmental Science, Civil Engineering, Construction and Building Engineering and ArchitectureMileto, C.; Vegas López-Manzanares, F.; Cristini, V.; García Soriano, L. (2022). HERITAGE 2022. International Conference on Vernacular Heritage: Culture, People and Sustainability. Editorial Universitat Politècnica de València. https://doi.org/10.4995/HERITAGE2022.2022.15942EDITORIA