Due to its rapid spread and associated symptoms, conventional methods of prevention and treatment have often proven inadequate to manage SARS-CoV-2, as conventional approaches show limited effectiveness in many scenarios. The ability of the virus to transmit rapidly, even to asymptomatic individuals, and the severity of symptoms in some patients have put significant pressure on healthcare systems. Against this backdrop, the work described aimed to develop an expert system for personalised recognition and treatment of SARS-CoV-2. A clear methodology was defined, supported by conceptual diagrams to outline the logical flow of the problem and proposed solutions. This methodology is based on key components: a Bayesian network to calculate the probability of infection by analysing the patient's symptoms, contacts, and geographic context; and a semantic component to determine the most appropriate treatment using the patient's clinical and personal information, such as allergies or individual risk factors. Once the solution was defined logically, we moved on to formalising the components and designing the workflows, which were implemented using appropriate technologies and open data. Testing of the system carried out through real data simulations, confirmed the system's ability to provide customised patient responses.

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User behaviour data plays a vital role in digital decision-making, especially in education, finance, and healthcare. However, traditional methods often fail to capture the complex characteristics of user behaviour, perform poorly on multi-dimensional data, and struggle with class imbalance, which limits model performance. To overcome these challenges, this study constructs a dynamic user behaviour dataset from the Chaoxing system and adopts the synthetic minority oversampling technique (SMOTE) to address data imbalance problem. The artificial bee colony (ABC) algorithm is combined with the support vector machine (SVM) to optimise model parameters and improve performance. Experimental results show that the proposed ABC-SVM model performs well in complex classification tasks with an accuracy of 97.9%, outperforming baseline and other optimisation methods. This study highlights the potential of intelligent optimisation algorithms in multi-dimensional data analysis and provides a reference for intelligent systems in other fields.

A smaller backoff exponent (BE) can lead to higher collision probability in high-density wireless sensor networks. Consequently, this gives rise to reduced throughput and increased delay. This article improves the unslotted CSMA/CA algorithm, including: firstly, by reducing the basic unit value of backoff period (BP) and increasing the contention window (CW), the collision probability can be significantly reduced when multiple nodes simultaneously backoff, and the delay can also be reduced; Secondly, in response to the lack of a dynamic mechanism to reduce BE in the original unslotted CSMA/CA algorithm, this article proposes an adaptive adjustment algorithm for BE. It can reasonably reduce BE to further reduce delay when network status allows. This article establishes two mathematical models for the improved algorithm for analysis. Compared with the original algorithm, the simulation results show that the collision probability and network delay are improved by at least 43.45% and 52.72%, respectively.

The underwater network consists of a huge number of sensor nodes deployed sparsely and interconnected with each node to gather information about the ocean. The method by which the location of deployed sensor node is determined is called node localisation. But it is difficult to achieve the exact location coordinates of underwater sensor nodes. There are several localisation algorithms for terrestrial networks but they are not feasible for underwater wireless sensor networks (UWSN) because of the harsh environment of the ocean. In this paper, various UWSN localisation schemes are classified on the basis of range. Also, a hybrid model of node localisation is also suggested for better output and real-time detection of node positions. Here, various underwater localisation schemes are reviewed and compared to the existing schemes. This comparison is based on NS2 simulator parameters to showcase better performance out of existing UWSN localisation techniques. Further, this paper examined similar survey papers to identify subtopics that have not been reviewed till now. On behalf of reported research gaps from the literature study, an improved node localisation scheme for mobile UWSN to explore the ocean is proposed that will perform better in terms of delay, error, cost, and energy consumption for localisation.

Traffic analysis is required for successful packet transmission from source to destination after receiving network acknowledgement. Sensor nodes must send position information to nearby nodes, reducing accuracy and performance. However, precise node location determination improves network performance. This paper proposes a fuzzy clustering-based genetic evolutionary (FCGE) algorithm for effective re-localisation in heavy traffic. It chooses the best node for re-localisation, reducing network traffic, communication costs, and localisation inaccuracy. A proper intelligent re-localisation method is used within a predetermined time frame to avoid localisation errors. To determine the optimal retrigger time, the genetic algorithm uses time-bound re-localisation (TBR). It is more accurate even with many node migrations, so localisation errors are reduced. Fuzzy k-means improves network data collection by creating disjoint clusters and global time synchronisation for mobile agents. The proposed FCGE algorithm reduced RMSPE by 13.26%, 9.65%, and 6.78% for 5, 10, and 20 anchor nodes, respectively.

In order to solve the problem of low signal transmission efficiency and susceptibility to noise interference in online monitoring systems in fibre optic communication construction, this paper conducts effective research on the analysis and design of changes in fibre optic cable online monitoring systems using digital CT. This paper conducted tests from three aspects: real-time analysis of changes, accuracy, and signal transmission efficiency. The test results show that at the accuracy level of change analysis, the average relative error level of digital CT used for fibre optic cable change analysis is about 6.381%, while the average relative error result of fibre optic change analysis in traditional online monitoring systems is about 7.595%. From the comparison of accuracy results, digital CT can enhance the stability and reliability of online monitoring systems, improve the accuracy of fibre optic change analysis, and promote the healthy development of fibre optic communication.

In the field of mechanical design of motion production lines, artificial intelligence technology is mainly expressed in two ways: one is to use simulation methods to process data information, emphasise the practicality of information, and establish complementary mapping relationships; the other is to use traditional programming technology to visualise the internet. By comparing the production rhythm and production data of two traditional intelligent production lines of multiple companies, this article concludes that by improving the artificial intelligence of automated production line machinery, companies can increase output by an average of 49.6%. This article is based on the mechanical design and key technologies of automated production lines, aiming to improve the balance of complex automated production lines, which can fundamentally improve the productivity of enterprises in the field of dynamic production line mechanical design, and upgrade and modernise the entire production system in terms of conveyors, workshops and supply chains.