scholarly journals Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5416 ◽  
Author(s):  
Zhengwu Yuan ◽  
Xupeng Zha ◽  
Xiaojian Zhang
Keyword(s):  
Nearest Neighbor ◽  
Prediction Models ◽  
Weighted Average ◽  
Indoor Positioning ◽  
Positioning Error ◽  
K Nearest Neighbor ◽  
Localization Method ◽  
Task Learning ◽  
Positioning Errors ◽  
Weight Coefficients

The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients K-Nearest Neighbor (WCKNN), which integrates magnetic field, Wi-Fi and Bluetooth fingerprints for positioning and localization. The MTL fuses the features of different types of fingerprints to search the potential relationship between them. It also exploits the synergy between the tasks, which can boost up positioning and localization performance. Then the WCKNN predicts another position of the fingerprints in a certain class determined by the obtained location. The final position is obtained by fusing the predicted positions using a weighted average method whose weights are the positioning errors provided by positioning error prediction models. Experimental results indicated that the proposed method achieved 98.58% accuracy in classifying locations with a mean positioning error of 1.95 m.

scholarly journals Efficient and Scalable Multi-Task Regression on Massive Number of Tasks

2019 ◽  
Vol 33 ◽  
pp. 3763-3770
Author(s):  
Xiao He ◽  
Francesco Alesiani ◽  
Ammar Shaker
Keyword(s):  
Real World ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Optimization Method ◽  
K Nearest Neighbor ◽  
Task Learning ◽  
Massive Number ◽  
Real World Datasets ◽  
Convex Clustering

Many real-world large-scale regression problems can be formulated as Multi-task Learning (MTL) problems with a massive number of tasks, as in retail and transportation domains. However, existing MTL methods still fail to offer both the generalization performance and the scalability for such problems. Scaling up MTL methods to problems with a tremendous number of tasks is a big challenge. Here, we propose a novel algorithm, named Convex Clustering Multi-Task regression Learning (CCMTL), which integrates with convex clustering on the k-nearest neighbor graph of the prediction models. Further, CCMTL efficiently solves the underlying convex problem with a newly proposed optimization method. CCMTL is accurate, efficient to train, and empirically scales linearly in the number of tasks. On both synthetic and real-world datasets, the proposed CCMTL outperforms seven state-of-the-art (SoA) multi-task learning methods in terms of prediction accuracy as well as computational efficiency. On a real-world retail dataset with 23,812 tasks, CCMTL requires only around 30 seconds to train on a single thread, while the SoA methods need up to hours or even days.

Data mining and social networks processing method based on support vector machine and k-nearest neighbor

2020 ◽  
pp. 1-13
Author(s):  
Youli Lu ◽  
Jintong Li ◽  
Zhihe Yang ◽  
Xianfeng Ou ◽  
Wenwu Xie
Keyword(s):  
Support Vector Machine ◽  
Customer Service ◽  
Nearest Neighbor ◽  
Evaluation Criteria ◽  
Weighted Average ◽  
Recall Rate ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Sina Weibo ◽  
Weight Coefficients

OBJECTIVE: With Sina Weibo data as the background, support vector machine (SVM) and k-nearest neighbor (KNN) method are used to predict and analyze the user’s micro-blog emotion and related behavior in social network, hoping to obtain rich potential business value. METHODS: First, the API interface of Sina Weibo is utilized to obtain the information of users in Sina Weibo; then, the Excel software is utilized to sort and analyze the extracted data to extract the features of micro- blogs posted by users. Second, SVM and KNN algorithms are utilized to calculate the weighted average and propose a hybrid multi-classifier-based Mixed Classifier Emotion Prediction Model (MCEPM). Finally, through the evaluation criteria, including precision (P), recall rate (R), and harmonic average (F1), the specific experimental results of SVM and KNN weight coefficients are compared with the prediction results of MCEPM. RESULTS: The prediction effect of MCEPM is associated with the weight coefficients of SVM and KNN. If the weight coefficients of SVM and KNN are 0.6 and 0.4, the prediction effect of MCEPM will be optimal. Comprehensive analysis shows that the MCEPM model can balance the prediction results of the positive and negative samples of the two classifiers. CONCLUSION: MCEPM model is superior to other algorithms in micro-blog emotion prediction, which can help enterprises analyze users’ product inclination and provide accurate customer service requirements for enterprises.

scholarly journals GEOMAGNETIC FIELD-BASED INDOOR POSITIONING USING BACK-PROPAGATION NEURAL NETWORKS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 557-563
Author(s):  
K. Y. Qiu ◽  
H. Huang ◽  
A. El-Rabbany
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Indoor Localization ◽  
Back Propagation ◽  
Weighted Average ◽  
Back Propagation Neural Network ◽  
Indoor Positioning ◽  
Experimental Results ◽  
Positioning Error ◽  
Main Research

Abstract. High-precision indoor positioning in complex environments has always been a hot research topic within the positioning and robotic communities. As one of the indoor positioning technologies, geomagnetic positioning is receiving widespread attention due to its global coverage. Additionally, geomagnetic positioning does not require special infrastructure configuration, its hardware cost is low, and its positioning errors do not accumulate over time. However, geomagnetic positioning is prone to mismatching, which causes serious problems at the positioning points. To tackle this challenge, this paper proposes an indoor localization method based on spectral clustering and weighted back-propagation neural network. The main research contribution is that in the offline phase, the spatial specificity of geomagnetism is used to define the similarity between fingerprints. In addition, a clustering-based reference point algorithm is proposed to divide the sub-fingerprint database, and a positioning prediction model based on back-propagation neural network is trained. Subsequently, in the online stage, the weights of different positioning prediction models are calculated according to the defined fingerprint similarity, weighted average prediction coordinates are obtained, and thereby the positioning accuracy is improved. Experimental results show that, in comparison with other neural network-based positioning methods, the positioning error of our proposed algorithm is reduced by approximately 26.6% and the positioning time is reduced by 24.7%. Experimental results show that the average positioning error of the algorithm is 1.81m.

scholarly journals Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction

2021 ◽  
Author(s):  
Piotr Szczuko ◽  
Adam Kurowski ◽  
Piotr Odya ◽  
Andrzej Czyżewski ◽  
Bożena Kostek ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Granular Computing ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Respiratory Pattern ◽  
Breathing Patterns ◽  
K Nearest Neighbor ◽  
Normal Breathing ◽  
Abnormal Pattern ◽  
Male Sex

AbstractThe described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis were applied to granular computing. Signal attributes and anthropomorphic parameters were explored to develop prediction models to determine the percentage contribution of periodic-like, intermediate, and normal breathing patterns in the analyzed signals. The proposed methodology was validated employing k-nearest neighbor (k-NN) and UMAP (uniform manifold approximation and projection). The presented approach applied to respiratory pattern evaluation shows that median accuracies in a considerable number of cases exceeded 0.75. Overall, parameters related to signal analysis are indicated as more important than anthropomorphic features. It was also found that obesity characterized by a high WHR (waist-to-hip ratio) and male sex were predisposing factors for the occurrence of periodic-like or intermediate patterns of respiration. It may be among the essential findings derived from this study. Based on classification measures, it may be observed that a physician may use such a methodology as a respiratory pattern evaluation-aided method.

scholarly journals Similarity-based error prediction approach for real-time inflow forecasting

2013 ◽  
Vol 45 (4-5) ◽  
pp. 589-602 ◽  
Author(s):  
Mahmood Akbari ◽  
Abbas Afshar
Keyword(s):  
Real Time ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Error Prediction ◽  
K Nearest Neighbor ◽  
Forecasting Models ◽  
Main Challenge ◽  
Inflow Forecasting ◽  
Artificial Neural Network Ann ◽  
Prediction Approach

Regardless of extensive researches on hydrologic forecasting models, the issue of updating the outputs from forecasting models has remained a main challenge. Most of the existing output updating methods are mainly based on the presence of persistence in the errors. This paper presents an alternative approach to updating the outputs from forecasting models in order to produce more accurate forecast results. The approach uses the concept of the similarity in errors for error prediction. The K nearest neighbor (KNN) algorithm is employed as a similarity-based error prediction model and improvements are made by new data, and two other forms of the KNN are developed in this study. The KNN models are applied for the error prediction of flow forecasting models in two catchments and the updated flows are compared to those of persistence-based methods such as autoregressive (AR) and artificial neural network (ANN) models. The results show that the similarity-based error prediction models can be recognized as an efficient alternative for real-time inflow forecasting, especially where the persistence in the error series of flow forecasting model is relatively low.

scholarly journals Research on Wi-Fi indoor positioning in a smart exhibition hall based on received signal strength indication

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Qing Yang ◽  
Shijue Zheng ◽  
Ming Liu ◽  
Yawen Zhang
Keyword(s):  
Nearest Neighbor ◽  
Science And Technology ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Experimental Simulation ◽  
K Nearest Neighbor ◽  
Improvement Strategy ◽  
Fingerprint Algorithm ◽  
Received Signal Strength Indication

AbstractTo improve the management of science and technology museums, this paper conducts an in-depth study on Wi-Fi (wireless fidelity) indoor positioning based on mobile terminals and applies this technology to the indoor positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments.

scholarly journals An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

2020 ◽  
Vol 10 (4) ◽  
pp. 280-292
Author(s):  
Allemar Jhone P. Delima
Keyword(s):  
Genetic Algorithm ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Faculty Evaluation ◽  
The Novel ◽  
Improved Genetic Algorithm ◽  
K Nearest Neighbor ◽  
Negative Effects

The k-nearest neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employ variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. The genetic algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem that its mating scheme is bounded on its crossover operator. Thus, the use of the novel inversed bi-segmented average crossover (IBAX) is observed. In the present work, the crossover improved genetic algorithm (CIGAL) is instrumental in the enhancement of KNN’s prediction accuracy. The use of the unmodified genetic algorithm has removed 13 variables, while the CIGAL then further removes 20 variables from the 30 total variables in the faculty evaluation dataset. Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improves the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN), the use of the lone KNN algorithmand the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique reveals 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. As the result, the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.

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