An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units,in large indoor spaces demands a precise knowledge of their positions.Technologies like WiFi and Bluetooth,despite their low-cost and availability,are sensitive to signal noise and fading effects.For these reasons,a hybrid approach,which uses two different signal sources,has proven to be more resilient and accurate for the positioning determination in indoor environments.Hence,this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning,using Received Signal Strength information from available Wireless Local Area Network access points,together with the Wireless Sensor Networks technology.Six signals were recorded on a regular grid of anchor points,covering the research space.An optimization was performed by relative signal weighting,to minimize the average positioning error over the research space.The optimization process was conducted using a standard Quantum Particle Swarm Optimization,while the position error estimate for all given sets of weighted signals was performed using aMultilayer Perceptron(MLP)neural network.Compared to our previous research works,the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned.These experimental results led to the 20%reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process.Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.

Deep-piRNA:Bi-Layered Prediction Model for PIWI-Interacting RNA Using Discriminative Features

Piwi-interacting Ribonucleic acids(piRNAs)molecule is a wellknown subclass of small non-codingRNAmolecules that are mainly responsible for maintaining genome integrity,regulating gene expression,and germline stem cell maintenance by suppressing transposon elements.The piRNAs molecule can be used for the diagnosis of multiple tumor types and drug development.Due to the vital roles of the piRNA in computational biology,the identification of piRNAs has become an important area of research in computational biology.This paper proposes a two-layer predictor to improve the prediction of piRNAs and their function using deep learning methods.The proposed model applies various feature extraction methods to consider both structure information and physicochemical properties of the biological sequences during the feature extraction process.The outcome of the proposed model is extensively evaluated using the k-fold cross-validation method.The evaluation result shows that the proposed predictor performed better than the existing models with accuracy improvement of 7.59%and 2.81%at layer I and layer II respectively.It is anticipated that the proposed model could be a beneficial tool for cancer diagnosis and precision medicine.