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Semantic Communication (SC) has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks, beyond the theoretical capacity limit. Despite the extensive research on SC, there is a lack of comprehensive survey on technologies, solutions, applications, and challenges for SC. In this article, the development of SC is first reviewed and its characteristics, architecture, and advantages are summarized. Next, key technologies such as semantic extraction, semantic encoding, and semantic segmentation are discussed and their corresponding solutions in terms of efficiency, robustness, adaptability, and reliability are summarized. Applications of SC to UAV communication, remote image sensing and fusion, intelligent transportation, and healthcare are also presented and their strategies are summarized. Finally, some challenges and future research directions are presented to provide guidance for further research of SC.

A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initialization method specifically designed to improve the performance of DNM in classifying high-dimensional data, notable for its simplicity, speed, and straightforward implementation. Extensive experiments on benchmark datasets show that the proposed method outperforms traditional and recent initialization methods, particularly in datasets consisting of high-dimensional data. In addition, valuable insights into the behavior of DNM during training and the impact of initialization on its learning performance are provided. This research contributes to the understanding of the initialization problem in deep learning and provides insights into the development of more effective initialization methods for other types of neural network models. The proposed initialization method can serve as a reference for future research on initialization techniques in deep learning.

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm. It focuses on solving multiple optimization tasks concurrently while improving optimization performance by utilizing similarities among tasks and historical optimization knowledge. To ensure its high performance, it is important to choose proper individuals for each task. Most MTO algorithms limit each individual to one task, which weakens the effects of information exchange. To improve the efficiency of knowledge transfer and choose more suitable individuals to learn from other tasks, this work proposes a general MTO framework named individually guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones, and each individual is fully explored to learn experience from the execution of other tasks. By using the concept of skill membership, individuals with higher solving ability are selected. Besides, to further improve the effect of knowledge transfer, only inferior individuals are selected to learn from other tasks at each generation. The significant advantage of IMTO over the multifactorial evolutionary framework and baseline solvers is verified via a series of benchmark studies.

At present, most popular route navigation systems only use a few sensed or measured attributes to recommend a route. Yet the optimal route considered by drivers needs be based on multiple objectives and multiple attributes. As a result, these existing systems based on a single or few attributes may fail to meet such drivers’ needs. This work proposes a driver preference-based route planning (DPRP) model. It can recommend an optimal route by considering driver preference. We collect drivers’ preferences, and then provide a set of routes for their choice when they need. Next, we present an integrated algorithm to solve DPRP, which speeds up the search process for recommending the best routes. Its computation cost can be reduced by simplifying a road network and removing invalid sub-routes. Experimental results demonstrate its effectiveness.

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.