Wi-Fi Navigation Using Machine Learning With Consideration Of Cyclic Dynamic Behaviour

Wi-Fi based localization using machine learning has been proven to be an attractive approach for finding the location prediction with avoidance of accumulation of errors as other sensors such as odometry and inertial sensing. Researchers have developed various models to predict locations based on trained machine learning. A site survey is typically performed to collect fingerprints and a neural network is trained on those fingerprints. The trained model is then placed into operation. However, dynamic changes in the location and navigation behavior of users make the prediction process more challenging in terms of accurate prediction of location. One common mobility behavior of navigation runs is the cyclic dynamics or re-visiting the same place more than one time. Most machine learning models, developed for location prediction, lack sufficient handling of dynamic changes or leveraging them for better predictions. To fill this gap, this study builds a new simulator with two components: one for incorporating dynamic information of navigation in given Wi-Fi dataset and using them to generate the corresponding time series of any navigation run, it is named as Wi-Fi Simulator for Cyclic Dynamic (Wi-Fi-SCD) while the other is useful for converting any dataset to time series with cyclic dynamics, it is named as Cyclic Dynamic Generator (CDG). Furthermore, in this study, two novel location prediction machine learning models were developed. The first is Knowledge Preservation Online Sequential Extreme Learning Machine (KP-OSELM) and the second is Infinite Term Memory-based Online Sequential Extreme Learning Machine (ITM-OSELM). The KP-OSELM model is distinctive from other models cited in the literature, because it preserves knowledge gained in certain areas to restore again when the person re-visits the area again. In KP-OSELM, knowledge is preserved within the neural network structure and is enabled based on feature encoding. The ITM-OSELM model is distinctive from other models cited in the literature, because it carries external memory and transfers learning to preserve old knowledge and restoration. ITM-OSELM is more efficient than KP-OSELM when the percentage of active features is low. Meanwhile, KP-OSELM does not require any external blocks to be added to the neural network (unlike ITM-OSELM), which makes it much simpler. In area based scenarios, KP-OSELM and ITM-OSELM both achieved accuracies of 68%. Moreover, when evaluating KP-OSELM and ITM-OSELM on Wi-Fi-SCD, for three navigation cycles, the highest accuracies achieved were 92.74% and 92.76%, respectively. However, the execution time of KP-OSELM was 1176 second while much less time was needed for ITM-OSELM to be executed with a value of 649 second. Furthermore, when evaluating KP-OSELM and ITM-OSELM on CDG, for three cycles, 100% accuracy was achieved for both models. As a conclusion, this study has provided the literature of machine learning in general and WiFi navigation in particular with various models to support the localization without any restriction on the type of Wi-Fi that is used and with consideration of the practical and dynamic behaviors that can be leveraged to improve the localization performance

Comparative Analysis of Machine Learning Techniques for Predicting Air Pollution

The modern and motorized way of life has cultured air pollution.  Air pollution has become the biggest rival of robust living. This situation is becoming more lethal in developing countries and so in Pakistan.  Hence, this inquiry was carried out to propose an architecture design that could make real-time prediction of air pollution with another purpose of scanning the frequently adopted algorithm in past investigations. In addition, it was also intended to narrate the toxic effects of air pollution on human health. So, this research was carried out on a large dataset of Seoul as an adequate dataset of Pakistan was not attainable. The dataset consisted of three years (2017-2019) including 647,512 instances and 11 attributes. The four distinctive algorithms termed Random Forest, Linear Regression, Decision Tree and XGBoosting were employed. It was inferred that XGB is more promising and feasible in predicting concentration level of NO2, O3, SO2, PM10, PM2.5 and CO with the lowest RMSE and MAE values of 0.0111, 0.0262, 0.0168, 49.64, 41.68 and 0.1856 and 0.0067, 0.0096, 0.0017, 12.28, 7.63 and 0.0982 respectively. Furthermore, it was found out as well that the Random Forest was preferred mostly in the previous studies related to air pollution prophecy while many probes supported that air pollution is very detrimental to human health especially long-lasting exposure causes lung cancer, respiratory and cardiovascular diseases

Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression

This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. The research focuses on one of the advanced features of neural networks, i.e., the incremental learning ability. This ability relates to continuous learning of new knowledge without disturbing the existing knowledge base and without re-iterating through the training samples. The Adaptive Resonance Theory (ART) and Generalized Regression Neural Network (GRNN) models are employed as the backbone in this research

Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines

In the past decade, deep learning techniques have powered many aspects of our daily life, and drawn ever-increasing research interests. However, conventional deep learning approaches, such as deep belief network (DBN), restricted Boltzmann machine (RBM), and convolutional neural network (CNN), suffer from time-consuming training process due to fine-tuning of a large number of parameters and the complicated hierarchical structure. Furthermore, the above complication makes it difficult to theoretically analyze and prove the universal approximation of those conventional deep learning approaches. In order to tackle the issues, multilayer extreme learning machines (ML-ELM) were proposed, which accelerate the development of deep learning. Compared with conventional deep learning, ML-ELMs are non-iterative and fast due to the random feature mapping mechanism. In this paper, we perform a thorough review on the development of ML-ELMs, including stacked ELM autoencoder (ELM-AE), residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications. In addition, we also discuss the connection between random neural networks and conventional deep learning

Towards a more efficient and cost-sensitive extreme learning machine: A state-of-the-art review of recent trend

In spite of the prominence of extreme learning machine model, as well as its excellent features such as insignificant intervention for learning and model tuning, the simplicity of implementation, and high learning speed, which makes it a fascinating alternative method for Artificial Intelligence, including Big Data Analytics, it is still limited in certain aspects. These aspects must be treated to achieve an effective and cost-sensitive model. This review discussed the major drawbacks of ELM, which include difficulty in determination of hidden layer structure, prediction instability and Imbalanced data distributions, the poor capability of sample structure preserving (SSP), and difficulty in accommodating lateral inhibition by direct random feature mapping. Other drawbacks include multi-graph complexity, global memory size, one-by-one or chuck-by-chuck (a block of data), global memory size limitation, and challenges with big data. The recent trend proposed by experts for each drawback is discussed in detail towards achieving an effective and cost-sensitive mode

Local feature extraction based facial emotion recognition: a survey

Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively

Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto

Schulz A, Queißer J, Ishihara H, Asada M. Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto. Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo (In Press)