With increasing adoption and presence of Web services, designing novel approaches for efficient Web services recommendation has become steadily more important. Existing Web services discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant Web service search engines, which possess many limitations such as insufficient recommendation performance and heavy dependence on the input from users such as preparing complicated queries. In this paper, we propose a novel approach that dynamically recommends Web services that fit users' interests. Our approach is a hybrid one in the sense that it combines collaborative filtering and content-based recommendation. In particular, our approach considers simultaneously both rating data and content data of Web services using a three-way aspect model. Unobservable user preferences are represented by introducing a set of latent variables, which is statistically estimated. To verify the proposed approach, we conduct experiments using 3, 693 real-world Web services. The experimental results show that our approach outperforms the two conventional methods on recommendation performance.

Several techniques for Human Activity Recognition (HAR) in a smart indoor environment have been developed and improved along with the rapid advancement of sensor technologies. However, recognizing multiple people’s activities is still challenging due to the complexity of their activities, such as parallel and collaborative activities. To address these challenges, we propose a Transformer with a Bi-directional GRU deep learning method, called TRANS-BiGRU, to efficiently learn and recognize different types of activities performed by multiple residents. We compare the proposed model with the state-of-the-art models and various deep learning (DL) models, such as Ensemble2LSTM (Ens2-LSTM), Bidirectional Gated Recurrent Units (Bi-GRU), and traditional machine learning (ML) models, such as Support Vector Machine (SVM). Our experimental results based on the CASAS and ARAS public datasets show that our model significantly outperforms the existing models for complex activity recognition of multiple residents.

Indoor localization is a crucial component of IoT applications in many areas, such as healthcare, energy management, and security control. Passive infrared (PIR) sensor has been employed for a location estimation due to its cost effectiveness, low power consumption, and low electromagnetic interference. Compared with its binary output, PIR analog output which is an output voltage generated by a PIR sensor when its sensing elements detect changes in temperature in an environment can provide more information regarding a person’s location. However, only a few works focus on using analog signals for location estimation. During the past several years, deep learning approaches have emerged and achieved outstanding results in many applications. In this article, we harness the power of deep learning and propose a deep CNN-LSTM architecture for PIR-based indoor location estimation. In our architecture, an upper CNN network can extract features from PIR analog output automatically while a lower LSTM network can learn temporal dependencies between the extracted features. To evaluate the feasibility and performance of our proposed method, we conduct four different sets of experiments. Our results show that the proposed method can efficiently handle complex cases and can achieve the mean distance error of 0.23 m, and 80% of distance errors are within 0.4 m.