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With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.

In wireless sensor networks (WSNs), sensor devices must be equipped with the capabilities of sensing, computation and communication. These devices work continuously through non-rechargeable batteries under harsh conditions, the batter span of nodes determines the whole network lifetime. Network clustering adopts an energy neutral approach to extend the network life. The clustering methods can be divided into even and uneven clustering. If even clustering is adopted, it will cause the cluster head nodes (CHs) in vicinity of the base station to relay more data and cause energy hole phenomenon. Therefore, we adopt a non-uniform clustering method to alleviate the problem of energy hole. Furthermore, to further balance and remit resource overhead of the entire network, we combined the PEGASIS algorithm and the Hamilton loop algorithm, through a mixture of single-hop and multiple hops mechanisms, inserting a mobile agent node (MA) and designing an optimal empower Hamilton loop is obtained by the local optimization algorithm. MA is responsible for receiving and fusing packet from the CHs on the path. Network performance results show that the proposed routing algorithm can effectively prolong network lifetime, equalize resource expenditure and decrease the propagation delay.

Security issues in cloud computing have become a hot topic in academia and industry, and CP-ABE is an effective solution for managing and protecting data. When data is shared in cloud computing, they usually have multiple access structures that have hierarchical relationships. However, existing CP-ABE algorithms do not consider such relationships and just require data owners to generate multiple ciphertexts to meet the hierarchical access requirement, which would incur substantial computation overheads. To achieve fine-grained access control of multiple hierarchical files effectively, first we propose an efficient hierarchical CP-ABE algorithm whose access structure is linear secret sharing scheme. Moreover, we construct an attribute-based hierarchical access control scheme, namely AHAC. In our scheme, when a data visitor’s attributes match a part of the access control structure, he can decrypt the data that associate with this part. The experiments show that AHAC has good security and high performance. Furthermore, when the quantity of encrypted data files increases, the superiority of AHAC will be more significant.

Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system.

Various technologies such as WiFi, Bluetooth, and RFID are being used to provide indoor location-based services (LBS). In particular, a WiFi base using a WiFi AP already installed in an indoor space is widely applied, and the importance of indoor location recognition using deep running has emerged. In this study, we propose a WiFi-based indoor location recognition system using a smart watch, which is extended from an existing smartphone. Unlike the existing system, we use both the Received Signal Strength Indication (RSSI) and Basic Service Set Identifier (BSSID) to solve the problem of position recognition owing to the similar signal strength. By performing two times of filtering, we want to improve the execution time and accuracy through the learning of random forest based location awareness. In an unopened indoor space with five or more WiFi APs installed. Experiments were conducted by comparing the results according to the number of data for supposed system and a system based on existing WiFi fingerprint based random forest. The proposed system was confirmed to exhibit high performance in terms of execution time and accuracy. It has significance in that the system shows a consistent performance regardless of the number of data for location information.

The basic idea behind the vehicular ad-hoc network (VANET) is the exchange of traffic information between vehicles and the surrounding environment to offer a better driving experience. Privacy and security are the main concerns for meeting the safety aims of the VANET system. In this paper, we analyse recent VANET schemes that utilise a group authentication technique and found important vulnerabilities in terms of driving safety. These systems also suffer from vulnerabilities in terms of management efficiency and computational complexity. To defeat these problems, we propose a lightweight scheme, SD2PA, based on a general hash function for VANET. The proposed scheme overcomes the non-safe driving problem that resulted from the critical driving area. Moreover, the vehicle authentication is only done once by the VANET system administrator during the vehicle’s moving, so the authentication redundancy for the entire system is reduced and system management efficiency is enhanced. The SD2PA scheme also provides anonymity to protect the vehicle’s privacy, unless an important action needs to be taken against a malicious vehicle. A deep computational cost and communicational overhead analysis indicates that SD2PA is better than related schemes, as well as efficiently meeting VANET’s security and privacy needs.

Location-based mobile marketing recommendation has become one of the hot spots in e-commerce. The current mobile marketing recommendation system only treats location information as a recommended attribute, which weakens the role of users and shopping location information in the recommendation. This paper focuses on location feedback data of user and proposes a location-based mobile marketing recommendation model by convolutional neural network (LBCNN). First, the users’ location-based behaviors are divided into different time windows. For each window, the extractor achieves users’ timing preference characteristics from different dimensions. Next, we use the convolutional model in the convolutional neural network model to train a classifier. The experimental results show that the model proposed in this paper is better than the traditional recommendation models in the terms of accuracy rate and recall rate, both of which increase nearly 10%.

The performance of a CNN based medical-image classification network depends on the intensities of the trained images. Therefore, it is necessary to generalize medical images of various intensities against degradation of performance. For lesion classification, features of generalized images should be carefully maintained. To maintain the performance of the medical image classification network and minimize the loss of features, we propose a method using a generative adversarial network (GAN) as a generator to adapt the arbitrary intensity distribution to the specific intensity distribution of the training set. We also select CycleGAN and UNIT to train unpaired medical image data sets. The following was done to evaluate each method’s performance: the similarities between the generalized image and the original were measured via the structural similarity index (SSIM) and histogram, and the original domain data set was passed to a classifier that trained only the original domain images for accuracy comparisons. The results show that the performance evaluation of the generalized images is better than that of the originals, confirming that our proposed method is a simple but powerful solution to the performance degradation of a classification network.

Based on the stimulus-organism-reaction model, we study the direct effects of the three interpersonal attraction factors (perceived similarity, perceived familiarity, and perceived expertise) on purchase intention in the social commerce era, as well as the mediating roles of the normative and informational influence of reference groups in the above relationship. We apply structural equation model to the study samples consisting of 490 WeChat users. The results of empirical research indicate that the three interpersonal attraction factors have positive effects on purchase intention. Both the normative and informational influence fully mediate the effect of perceived familiarity on purchase intention, but only partially mediate the effects of perceived similarity and perceived expertise on purchase intention. The findings provided practitioners with insights into enhancing users’ intention to purchase in social commerce.

The amount of movie has increased to become more congested; therefore, to find a movie what users are looking for through the existing technologies are very hard. For this reason, the users want a system that can suggest the movie requirement to them and the best technology about these is the recommendation system. However, the most recommendation system is using collaborative filtering methods to predict the needs of the user due to this method gives the most accurate prediction. Today, many researchers are paid attention to develop several methods to improve accuracy rather than using collaborative filtering methods. Hence, to further improve accuracy in the recommendation system, we present the k -clique methodology used to analyze social networks to be the guidance of this system. In this paper, we propose an efficient movie recommendation algorithm based on improved k -clique methods which are the best accuracy of the recommendation system. However, to evaluate the performance; collaborative filtering methods are monitored using the k nearest neighbors, the maximal clique methods, the k -clique methods, and the proposed methods are used to evaluate the MovieLens data. The performance results show that the proposed methods improve more accuracy of the movie recommendation system than any other methods used in this experiment.