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Harris Hawk Optimization: A Survey onVariants and Applications.

Computational intelligence and neuroscience
In this review, we intend to present a complete literature survey on the conception and variants of the recent successful optimization algorithm, Harris Hawk optimizer (HHO), along with an updated set of applications in well-established works. For th...

A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings.

Journal of chemical theory and computation
Co-crystals are a highly interesting material class as varying their components and stoichiometry in principle allows tuning supramolecular assemblies toward desired physical properties. The prediction of co-crystal structures represents a daunting ...

Model Analysis and Experimental Investigation of Soft Pneumatic Manipulator for Fruit Grasping.

Sensors (Basel, Switzerland)
With the superior ductility and flexibility brought by compliant bodies, soft manipulators provide a nondestructive manner to grasp delicate objects, which has been developing gradually as a rising focus of soft robots. However, the unexpected phenom...

Successfully and efficiently training deep multi-layer perceptrons with logistic activation function simply requires initializing the weights with an appropriate negative mean.

Neural networks : the official journal of the International Neural Network Society
The vanishing gradient problem (i.e., gradients prematurely becoming extremely small during training, thereby effectively preventing a network from learning) is a long-standing obstacle to the training of deep neural networks using sigmoid activation...

Model Predictive Control of a Novel Wheeled-Legged Planetary Rover for Trajectory Tracking.

Sensors (Basel, Switzerland)
Amid increasing demands for planetary exploration, wide-range autonomous exploration is still a great challenge for existing planetary rovers, which calls for new planetary rovers with novel locomotive mechanisms and corresponding control strategies....

Weighted Incremental-Decremental Support Vector Machines for concept drift with shifting window.

Neural networks : the official journal of the International Neural Network Society
We study the problem of learning the data samples' distribution as it changes in time. This change, known as concept drift, complicates the task of training a model, as the predictions become less and less accurate. It is known that Support Vector Ma...

Statistical methods for validation of predictive models.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Predictive models are widely used in clinical practice. Despite of the increasing number of published AI systems, recent systematic reviews have identified lack of statistical rigor in the development and validation of predictive models. This work re...

An ASIP for Neural Network Inference on Embedded Devices with 99% PE Utilization and 100% Memory Hidden under Low Silicon Cost.

Sensors (Basel, Switzerland)
The computation efficiency and flexibility of the accelerator hinder deep neural network (DNN) implementation in embedded applications. Although there are many publications on deep neural network (DNN) processors, there is still much room for deep op...

Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models.

IEEE transactions on cybernetics
Fuzzy-rough cognitive networks (FRCNs) are recurrent neural networks (RNNs) intended for structured classification purposes in which the problem is described by an explicit set of features. The advantage of this granular neural system relies on its t...

Target Convergence Analysis of Cancer-Inspired Swarms for Early Disease Diagnosis and Targeted Collective Therapy.

IEEE transactions on neural networks and learning systems
Sensing and perception is generally a challenging aspect of decision-making. In the nanoscale, however, these processes face further complications due to the physical limitations of devising the nanomachines with more limited perception, more noise, ...