A Tensor-Based Big-Data-Driven Routing Recommendation Approach for Heterogeneous Networks

This paper presents a statistical feature approach in fully convolutional time series classification (TSC), which is aimed at improving the accuracy and efficiency of TSC. This method is based on fully convolutional neural networks (FCN), and there are the following two properties: statistical features in data preprocessing and finetuning strategies in network training. The key steps are described as follows: firstly, by the window slicing principle, dividing the original time series into multiple equal-length subsequences; secondly, by extracting statistical features on each subsequence, in order to form a new sequence as the input of the neural network, and training neural network by the fine-tuning idea; thirdly, by evaluating the classification performance about test sets; and finally, by comparing the sample sequence complexity and network classification loss accuracy with the FCN using the original sequence. Our experimental results show that the proposed method improved the classification effects of FCN and the residual network (ResNet), which means that it has a generalization ability to the network structures.

Section: Discussionmentioning

confidence: 99%

Time series classification based on statistical features

Lei

2020

“…With the development of IoT and data computing technologies [16][17][18], researchers have access to achieve more data with various types and large amount. However, imbalanced data problem leads to artificial intelligence models built on these data which behave extremely poor in performance.…”

Section: Smote Methodsmentioning

confidence: 99%

SMOTE-Boost-based sparse Bayesian model for flood prediction

Ding

Feng

2020

With a significant development of big data analysis and cloud-fog-edge computing, human-centered computing (HCC) has been a hot research topic worldwide. Essentially, HCC is a cross-disciplinary research domain, in which the core idea is to build an efficient interaction among persons, cyber space, and real world. Inspired by the improvement of HCC on big data analysis, we intend to involve related core and technologies to help solve one of the most important issues in the real world, i.e., flood prediction. To minimize the negative impacts brought by floods, researchers pay special attention to improve the accuracy of flood forecasting with quantity of technologies including HCC. However, historical flood data is essentially imbalanced. Imbalanced data causes machine learning classifiers to be more biased towards patterns with majority samples, resulting in poor classification of pattern with minority samples. In this paper, we propose a novel Synthetic Minority Over-sampling Technique (SMOTE)-Boost-based sparse Bayesian model to perform flood prediction with both high accuracy and robustness. The proposed model consists of three modules, namely, SMOTE-based data enhancement, AdaBoost training strategy, and sparse Bayes model construction. In SMOTE-based data enhancement, we adopt a SMOTE algorithm to effectively cover diverse data modes and generate more samples for prediction pattern with minority samples, which greatly alleviates the problem of imbalanced data by involving experts' analysis and users' intentions. During AdaBoost training strategy, we propose a specifically designed AdaBoost training strategy for sparse Bayesian model, which not only adaptively and inclemently increases prediction ability of Bayesian model, but also prevents its over-fitting performance. Essentially, the design of AdaBoost strategy helps keep balance between prediction ability and model complexity, which offers different but effective models over diverse rivers and users. Finally, we construct a sparse Bayesian model based on AdaBoost training strategy, which could offer flood prediction results with high rationality and robustness. We demonstrate the accuracy and effectiveness of the proposed model for flood prediction by conducting experiments on a collected dataset with several comparative methods.

“…Step 3: recommend new services to target user u* through returned neighbors in Neig_Set For each user u i in Neig_Set, if he or she has invoked candidate service ws j (1 ≤ j ≤ n) in the past, i.e., r i,j = 1, then u i is put into a new set Neig_Set*; furthermore, ws j 's historical quality value by u i (denoted by q i,j ) can be used to predict the missing quality value of ws j by the target user u* (denoted by q *,j ), based on the prediction equation in (5), where | Neig_Set* | is the size of set Neig_Set*.…”

Section: (3)mentioning

confidence: 99%

“…With the advent of the big data age, the volume and variety of available data both increase quickly, which make it hard for a user to select valuable information that matches his/her preferences [1][2][3][4]. Therefore, to decrease the heavy burden on users' service selection decisions, diverse service recommendation techniques are brought forth accordingly [5,6]. Typically, through analyzing the service lists ever-executed or ever-invoked by historical users, a recommender system, such as the collaborative filtering (CF) recommender system, can infer the possible user preferences and find the users who are similar with a target user (i.e., the "friends" of the target user); afterward, appropriate new services are recommended to the target user according to the service list ever-executed by his/her similar friends.…”

Section: Introductionmentioning

confidence: 99%

Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records

Wei

Wang²,

Zhang

et al. 2019

In the age of big data, service recommendation has provided an effective manner to filter valuable information from massive data. Generally, by observing the past service invocation records (Boolean values) distributed across different cloud platforms, a recommender system can infer personalized preferences of a user and recommend him/her new services to gain more profits. However, the historical service invocation records are a kind of private information for users. Therefore, how to protect sensitive user data distributed across multiple cloud platforms is becoming a necessity for successful service recommendations. Additionally, the historical service invocation records often update with time, which call for an efficient and scalable service recommendation method. In view of these challenges, we introduce the multi-probe Simhash technique in information retrieval domain into the recommendation process and further put forward a privacy-preserving recommendation method based on historical service invocation records. At last, we design several experiments on the real-world service quality data in set WS-DREAM. Experimental results show the feasibility of the proposal in terms of producing accurate recommended results while protecting users' private information contained in historical service invocation records.