Background: Nowadays, intelligent medicine is gaining widespread attention, and great progress has been made in Western medicine with the help of artificial intelligence to assist in decision making. Compared with Western medicine, traditional Chinese medicine (TCM) involves selecting the specific treatment method, prescription, and medication based on the dialectical results of each patient’s symptoms. For this reason, the development of a TCM-assisted decision-making system has lagged. Treatment based on syndrome differentiation is the core of TCM treatment; TCM doctors can dialectically classify diseases according to patients’ symptoms and optimize treatment in time. Therefore, the essence of a TCM-assisted decision-making system is a TCM intelligent, dialectical algorithm. Symptoms stored in electronic medical records are mostly associated with patients’ diseases; however, symptoms of TCM are mostly subjectively identified. In general electronic medical records, there are many missing values. TCM medical records, in which symptoms tend to cause high-dimensional sparse data, reduce algorithm accuracy. Objective: This study aims to construct an algorithm model compatible for the multidimensional, highly sparse, and multiclassification task of TCM syndrome differentiation, so that it can be effectively applied to the intelligent dialectic of different diseases. Methods: The relevant terms in electronic medical records were standardized with respect to symptoms and evidence-based criteria of TCM. We structuralized case data based on the classification of different symptoms and physical signs according to the 4 diagnostic examinations in TCM diagnosis. A novel cross-feature generation by convolution neural network model performed evidence-based recommendations based on the input embedded, structured medical record data. Results: The data set included 5273 real dysmenorrhea cases from the Sichuan TCM big data management platform and the Chinese literature database, which were embedded into 60 fields after being structured and standardized. The training set and test set were randomly constructed in a ratio of 3:1. For the classification of different syndrome types, compared with 6 traditional, intelligent dialectical models and 3 click-through-rate models, the new model showed a good generalization ability and good classification effect. The comprehensive accuracy rate reached 96.21%. Conclusions: The main contribution of this study is the construction of a new intelligent dialectical model combining the characteristics of TCM by treating intelligent dialectics as a high-dimensional sparse vector classification task. Owing to the standardization of the input symptoms, all the common symptoms of TCM are covered, and the model can differentiate the symptoms with a variety of missing values. Therefore, with the continuous improvement of disease data sets, this model has the potential to be applied to the dialectical classification of different diseases in TCM.

One of the main problems in representing information in the form of nonsystematic logic is the lack of flexibility, which leads to potential overfitting. Although nonsystematic logic improves the representation of the conventional k Satisfiability, the formulations of the first, second, and third-order logical structures are very predictable. This paper proposed a novel higher-order logical structure, named G-Type Random k Satisfiability, by capitalizing the new random feature of the first, second, and third-order clauses. The proposed logic was implemented into the Discrete Hopfield Neural Network as a symbolic logical rule. The proposed logic in Discrete Hopfield Neural Networks was evaluated using different parameter settings, such as different orders of clauses, different proportions between positive and negative literals, relaxation, and differing numbers of learning trials. Each evaluation utilized various performance metrics, such as learning error, testing error, weight error, energy analysis, and similarity analysis. In addition, the flexibility of the proposed logic was compared with current state-of-the-art logic rules. Based on the simulation, the proposed logic was reported to be more flexible, and produced higher solution diversity.

The sine and cosine algorithm is a new simple and effective population optimization method proposed in recent years that has been studied in many works of literature. Based on the basic principle of the sine and cosine algorithm, this paper fully studies the main parameters affecting the performance of the sine and cosine algorithm, integrates the reverse learning algorithm, adds an elite opposition solution and forms the hybrid sine and cosine algorithm (hybrid SCA). Combined with the fuzzy k-nearest neighbor method and the hybrid SCA, this paper numerically simulates two-class datasets and multi-class datasets, obtains a large number of numerical results and analyzes the results. The hybrid SCA FKNN proposed in this paper has achieved good accuracy in classification and prediction results under 10 different types of data sets. Compared with SCA FKNN, LSCA FKNN, BA FKNN, PSO FKNN and SSA FKNN, the prediction accuracy is significantly improved. In the Wilcoxon signed rank test with SCA FKNN and LSCA FKNN, the zero hypothesis (significance level 0.05) is rejected and the two classifiers have a significantly different accuracy.

The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order to fill in this gap and improve the efficiency of the metaheuristic algorithm in systematic logic, we proposed a metaheuristic algorithm based on mutation tabu search and embedded it in probabilistic satisfiability logic in discrete Hopfield neural networks. Based on the traditional tabu search algorithm, the mutation operators of the genetic algorithm were combined to improve its global search ability during the learning phase and ensure that the cost function of the systematic logic converged to zero at different proportions of positive literals. Additionally, further optimization was carried out in the retrieval phase to enhance the diversity of solutions. Compared with nine other metaheuristic algorithms and exhaustive search algorithms, the proposed algorithm was superior to other algorithms in terms of time complexity and global convergence, and showed higher efficiency in the search solutions at the binary search space, consolidated the efficiency of systematic logic in the learning phase, and significantly improved the diversity of the global solution in the retrieval phase of systematic logic.

The Sparrow search algorithm is a new and effective swarm intelligence method proposed in recent years and studied in many publications. Based on the basic principle of sparrow search algorithm, this paper combines the inverse learning algorithm with the refined inverse solution to form an improved sparrow search (SSA) algorithm. Combining the fuzzy k-nearest neighbor method and the improved SSA, the numerical simulation of two-classes datasets and multi-classes datasets is carried out, and many numerical results are obtained, and the results are analyzed. At the same time, this paper lists the data comparison results and tables with other models. The hybrid SSA-FKNN proposed in this paper has a clear advantage in terms of accuracy (ACC).

In the realm of activity recognition, the Fuzzy K-Nearest Neighbors (FKNN) algorithm stands as a pivotal technique for classification tasks. However, the performance of FKNN is heavily reliant on the choice of its hyperparameters, notably the number of neighbors (k) and the fuzziness parameter (m). Optimizing these hyperparameters is a non-trivial challenge, significantly affecting classification accuracy. To address this challenge, an integrated approach that combines Principal Component Analysis (PCA) and an adaptive grid search algorithm for hyperparameter tuning is proposed. Utilizing a dataset captured from a Wireless Sensor Network (WSN) that measures Received Signal Strength (RSS) from sensor nodes placed on a user’s chest and ankles, the methodology initiates with preprocessing to extract time-domain features, such as mean and variance, from the raw RSS data. Strategic ranges for hyperparameters are defined to effectively explore the parameter space. PCA is employed to reduce dimensionality and enhance data structure visibility. The core of the approach lies in adaptive grid search, which iteratively refines the hyperparameter search space based on observed performance metrics, converging on optimal values that yield the highest classification accuracy. The FKNN model, trained with these optimized parameters, is then evaluated on a separate test set. Results demonstrate that this approach not only enhances the classification accuracy of the FKNN algorithm but also achieves a balanced performance in activity recognition using multisensor data fusion.