Power transformer diagnostic methods based on traditional intelligent learning are affected by the scarcity of transformer fault data, which hinders their further application and prevents them from obtaining high diagnostic accuracy. To solve this problem, a few-shot method based on Gaussian Prototype Network (GPN) is proposed to achieve an effective and accurate diagnosis of power transformers using even a small number of fault samples. The method is an organic combination of embedding network and distance metric. The proposed approach is verified by datasets of dissolved gas and literature, which come from real power transformers and historical data. The results show that the method can achieve up to 96.7% accuracy, which is suitable for the field of power transformer fault diagnosis.

This chapter focuses on the issue of selecting a blood glucose meter for diabetic patients. To address this problem, the authors have developed a comprehensive evaluation model that combines triangular fuzzy (TF) logic, multi-granularity (MG), three-way decision (TWD), and rough set theory. The evaluation model takes into consideration the performance indicators of the blood glucose meter and user requirements. The authors utilize TF logic to model the indicators and determine their weights, while integrating the TWD method to handle incomplete and uncertain information. Subsequently, they introduce the PROMETHEE method to comprehensively evaluate and rank various alternatives. Finally, within this evaluation model, they select the optimal choice based on its score. Experimental results demonstrate the effectiveness of our proposed method in resolving the blood glucose meter selection problem and providing personalized recommendations for diabetic patients.

Diabetes, a chronic metabolic disease prevalent in real world, poses challenges due to its complex ambiguity and uncertainty in diagnosis and treatment. To address these issues, this paper proposes an intelligent decision-making framework that combines triangular fuzzy sets (TFSs), multigranularity (MG) three-way decision (TWD) and MABAC (Multi-Attribute Border Approximation Area Comparison). First, granular computing is applied to cope with the uncertainty of diabetes disease data by transforming it into an MG representation. Next, the MG TWD method is utilized to categorize the diagnosis and treatment of the disease into different levels, leading to a comprehensive evaluation and the final diagnosis result. Additionally, TFSs are employed to describe the fuzzy characteristics of diabetes and remedy plans, whereas the MABAC method is used to compare and evaluate different plans, ultimately selecting the optimal treatment strategy. By applying these methods, we effectively address the ambiguity and uncertainty associated with diabetes diagnosis and treatment. The intelligent decision-making framework offers more accurate and reliable diagnosis results, serving as a scientific foundation for doctors to develop personalized treatment plans. Moreover, this study holds significant value in advancing the field of diabetes diagnosis and treatment, offering new ideas and methods to improve the quality of life and health outcomes for diabetes patients.

The rise of the digital economy and e-commerce has fostered a movement towards efficient low-resource medical information processing, a trend that holds great importance in the healthcare sector. Diabetes, being a widespread chronic condition, has witnessed the introduction of glucometers, which offer patients a convenient method of monitoring their blood sugar levels. However, it is worth noting that a considerable proportion of online comments may be subject to emotional bias or contain inaccurate information. Furthermore, the performance of glucometers can be influenced by several attributes, including price, accuracy and portability, thereby potentially complicating the decision-making process for consumers. Semantic analysis can be employed to acquire valuable information, aiding consumers in reasonably choosing the suitable glucometer. This paper utilizes the benefits of granular computing, an emerging computing paradigm, to effectively handle incomplete and uncertain medical information. It employs generalized fuzzy sets, rough sets and three-way decisions (TWD) techniques to boost the accuracy and reliability of medical information fusion. Subsequently, the MABAC (Multi-Attribute Border Approximation Area Comparison) method is utilized to evaluate the reviews of every glucometer, calculate their aggregated scores, and rank and compare them. Ultimately, in light of consumers’ needs and trade-offs, the glucometer with the highest score can be selected. The proposed approach comprehensively considers the weight and priority of multiple attributes, reduces information overload and mitigates selection difficulties, thereby enhancing the accuracy and reliability of low-resource medical information processing.

In the realm of industrial production, maintaining continuous monitoring and implementing precise diagnostics of mine ventilators (MVs) holds a critical role in minimizing faults and accidents. Hence, it becomes imperative to devise an efficient and precise fault diagnosis (FD) technique for MVs. This paper endeavors to address the FD challenge of MVs by integrating federated learning (FL) with granular computing models. FL offers a partial solution to the issues of security and privacy associated with data sharing, which ensures data security while establishing an FD system for MVs. This system harnesses the adjustable multi-granularity (MG) triangular fuzzy (TF) probabilistic rough set (PRS) model to enhance the model's interpretability. In this study, the TF concept is introduced into the structure of three-way decisions (TWD) to tackle uncertainty and multi-performance attributes. We introduce the notion of an MG TF information system (IS) and propose an adjustable MG TF PRS model. The ELECTRE (Elimination Et Choice Translating Reality) method is employed to determine the optimal threshold. Furthermore, to validate the efficiency of the proposed model, we establish a TF multi-attribute group decision-making (MAGDM) approach using MV data within the MG and TWD frameworks. Finally, we verify the method's applicability through comparative analysis experiments. The experimental outcomes demonstrate the method's effectiveness and practicality in diagnosing faults for MVs.

In recent years, many sentiments classification models, such as deep learning models and traditional machine learning models, claim that they can achieve state-of-the-art performance in sentiment analysis problems. Admittedly, this is based on the premise that the training samples are class balanced. However, in the real world, the training data sets we can get are often imbalanced, which will cause the trained classifier to tend to predict the test samples into a majority, making the recall of minority very low. In order to minimize the influence of the imbalanced data class on the model performance, a transfer learning method based on a convolution neural network is proposed in this paper. First, we use a CNN-based model for pre-training in the class-balanced source domain data set, before transferring the model to the target domain for fine-tuning to improve the recall of minority class; furthermore, we propose a transfer learning-based under-sampling technique, which can under-sample the majority class in the target domain. In the data set after under-sampling, we again fine-tune the pre-trained model, so that the recall and precision of the minority class have been greatly improved. The experiments on real-world data sets show that our proposed under-sampling method has obvious advantages compared with others.

CoolCube™ is a monolithic 3D technology which has the potential to solve the interconnection density limitation of the existing TSV-based 3D integration processes. Since the active devices are fabricated on extremely this die substrates, heat dissipation has been pointed as a potential showstopper issue for this emerging technology. This work provides a comparative study of the thermal performance of the CoolCube and TSV-based 3D integration processes for a range of technology parameters and application scenarios. Results show that CoolCube exhibits thermal performance similar to or even better than the TSV-based technologies thanks to its very tight die-to-die thermal coupling.