Long range (LoRa) has emerged as one of the preferred communication solutions for connecting internet of things (IoT) devices due to its low power consumption and long-distance transmission capabilities. The LoRa network supports communication between multiple nodes and gateways over several kilometres. To reduce energy consumption and enhance the communication quality, the configuration of LoRa network is important. In this paper, the signal communication in a LoRa network is first analysed, and a nonlinear integer programming problem is developed to configure the spreading factor (SF) of nodes. The problem is formulated by maximising the volume of successfully transmitted data and reducing the probability of data collisions to minimise system energy consumption. Additionally, the performance of the proposed scheme is evaluated through extensive simulations. The results demonstrate that it achieves a higher number of delivered packets with lower energy consumption, outperforming both the equal-interval-based (EIB) and equal-area-based (EAB) SF allocation mechanisms. Finally, the network scalability is analysed by considering factors such as network configuration time, node density, and network radius.

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Artistic images serve as an important medium for emotional expression, and the precise identification of their emotional semantics is of great significance for art appreciation. To address the current issues of poor image quality and incomplete extraction of emotional features in artistic images, this paper first designs a multi-node wireless visual sensor network architecture to enable rapid acquisition of artistic images. Then, an improved Retina-Cortex algorithm is designed to enhance artistic images. Based on this, a feature extractor is constructed using a residual neural network to extract multidimensional emotional features, calculate the emotional weights of the three features, and perform feature fusion. Finally, the emotional features are used to identify the emotions in artistic images. The experimental results show that the average recognition accuracy and F1 score of the proposed model are 94.86% and 96.47%, enabling effective recognition of emotions in art images.

In recent years, blended learning has been widely applied in universities, introducing complex and diverse learning data. This study aims to use machine learning algorithms to extract useful information from this data for early student performance prediction. There are still some problems in current related research, including the neglect of short text data for online learning, data imbalance, and insufficient utilisation of multimodal data. To address the mentioned issues, this study proposes an innovative solution. Firstly, adjusting the generative adversarial network generator's objective function solves data imbalance in student performance prediction, and the prediction ability for minority-category students is improved. Secondly, using short text data from online learning to map the emotions of student learning states and enhance the model's accuracy and generalisation ability. Finally, this study introduces a multimodal generative adversarial network performance prediction model, which achieves the fusion of multimodal data, improves the accuracy and comprehensibility of prediction.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.