Breast mass detection remains a significant challenge in developing effective computer-aided diagnosis (CADx) systems to assist clinicians in differentiating between benign and malignant masses. This paper introduces a novel fuzzy rule-based CADx approach for mammographic mass classification, utilising Transformation-based Fuzzy Rule Interpolation with Mahalanobis matrices (MT-FRI). This method enables reliable and interpretable classification by transforming attributes into a new feature space and interpolating for unmatched cases, making it well-suited to limited-data scenarios. The proposed approach integrates a structured pipeline encompassing feature extraction, feature selection, fuzzy rule generation, and interpolation inference, all designed to enhance transparency in diagnostic decisions. The system implementing the approach is evaluated on four widely-used mammographic datasets—INbreast, CBIS-DDSM, BCDR-D01, and BCDR-F01. For the first time, comparative experiments demonstrate that state-of-the-art fuzzy rule interpolative methods, particularly MT-FRI, achieve superior classification performance over representative classical machine learning models and deep neural networks. Unlike deep learning models, which require extensive labelled data and function as ”black boxes”, MT-FRI produces transparent, human-readable rules, supporting clinical interpretability. This work underscores the potential of MT-FRI as an adaptable and interpretable CADx solution for mammographic diagnosis, especially valuable in sparse-data environments.

In recent years, with the rapid development of tourism, the abnormal events in the traffic fortress of tourist attractions not only endanger personal safety, but also cause a lot of negative effects on the society. In the face of the low accuracy of abnormal event detection in complex scenes and the low efficiency of manual observation, combining the advantages and characteristics of YOLOv5 and 3D convolutional neural network(C3D), an automatic abnormal event detection algorithm based on YOLOv5+C3D was proposed. Experimental data show that compared with other methods, the abnormal event detection method based on YOLOv5+C3D has a higher accuracy for various abnormal events detection, indicating that the trained model has a strong generalization ability for abnormal event detection.

In order to solve the problems of low signal-to-noise ratio, uncertain position, and blurred edges of mammography images, and to achieve full-view breast mass segmentation, a breast mass segmentation network based on improved UNet 3+ is proposed. The network provides prior knowledge of edge information for model segmentation by adding an edge-aware module and utilizing shallow and deep features in the network. At the same time, Hybrid loss function (HAM) is added to the network to obtain rich multi-scale information for handling lumps of different sizes and shapes. In addition, we add Dice loss and bce loss to the hybrid loss function to address the problem of pixel class imbalance in mammography images. The experimental results show that the improved UNet 3+ segmentation model has reached the Dice scores of 85.89% and 82.55% on the CBIS-DDSM and INbreast datasets, has improved aa and bb compared with the previous ones, and has a higher performance in the breast mass segmentation task. The accuracy rate is better than other classical models.

A lightweight object detection algorithm based on YOLOv5 is proposed to address the problem of deploying detection models for traffic targets. This method was proposed to reduce the number of channels in the backbone and introduced GSConv to improve performance. At the same time, GSConv was improved to cut the parameters. The C3 module was replaced in the Neck with C2f to obtain more comprehensive gradient flow information. Finally, the model parameters are only 1.41M. Experiment on BDD100K public traffic dataset shows that the lightweight model reduces the number of parameters while losing a small amount of Recalls, and its performance is better than mainstream lightweight networks.

In certain industries such as construction, high risks are often associated with the construction process, and safety helmets are crucial protective gear for workers on construction sites. To address the issues of missed and false detections of safety helmets in complex environments with current helmet detection methods, we propose an improved YOLOv5 object detection algorithm to detect the wearing of safety helmets. The proposed improvements include the addition of an attention mechanism and replacement of the loss function. The proposed method adds an efficient channel attention (ECA) mechanism to the YOLOv5 head network to enhance the model's ability to extract features related to safety helmets, thereby increasing precision without adding too much computational complexity. The loss function is replaced with NWD to effectively improve the detection progress of small safety helmets in YOLOv5.

Fuzzy rule interpolation (FRI) strongly supports approximate inference when a new observation matches no rules, through selecting and subsequently interpolating appropriate rules close to the observation from the given (sparse) rule base. Traditional ways of implementing the critical rule selection process are typically based on the exploitation of Euclidean distances between the observation and rules. It is conceptually straightforward for implementation but applying this distance metric may systematically lead to inferior results because it fails to reflect the variations of the relevance or significance levels among different domain features. To address this important issue, a novel transformation-based FRI approach is presented, on the basis of utilizing the Mahalanobis distance metric. The new FRI method works by transforming a given sparse rule base into a coordinates system where the distance between instances of the same category becomes closer while that between different categories becomes further apart. In so doing, when an observation is present that matches no rules, the most relevant neighboring rules to implement the required interpolation are more likely to be selected. Following this, the scale and move factors within the classical transformation-based FRI procedure are also modified by Choquet integral. Systematic experimental investigation over a range of classification problems demonstrates that the proposed approach remarkably outperforms the existing state-of-the-art FRI methods in both accuracy and efficiency.

Traditional fuzzy rule interpolation (FRI) methods typically utilise Euclidean distances between an observation and the rules in a given sparse rule base to select a set of rules closest to the observation to perform interpolation. However, simply applying the Euclidean distance metric may frequently lead to inferior results, because it cannot take into consideration the relevance degree of different features. To address this important issue, this paper presents an initial framework for a novel FRI approach which works based on exploiting a transformed rule base. Mahalanobis matrix learned by metric learning methods is used herein to transform the given sparse rule base to a new coordinates system where the distance between instances of the same category is closer, and instances of different categories is distant from each other. When a new observation is present which matches no rules, the selection of the nearest rules to implement the required interpolation is carried out in the transformed coordinates system. Then, the scale and move factors within the classical transformation-based FRI procedure are modified by Choquet integral. Experimental results obtained by employing different metric learning methods and Choquet integral over seven classification problems demonstrate that the proposed approach remarkably outperforms existing FRI methods.

Recently proposed methods of TSK inference extension (TSK+) and K closest rules based TSK (KCR) are able to potentially perform fuzzy rule-based inference with a sparse rule base, for regression problems. However, in certain real-world applications, observations may be rather far away from any rules that may be fired in order to implement the required regression and hence, these state-of-the-art techniques may not work satisfactorily. To investigate an alternative to address such practical problems, this paper presents a novel fuzzy rule interpolation (FRI) approach. It works based on integrating feature selection and feature aggregation to attain a rule-ranking list within the given sparse rule base. The process of acquiring an ordered rule list is accomplished offline, with an ordered rule base ready for use prior to the arrival of any observation online. It boosts the computational efficiency of closest rule selection during the FRI process. Initial experimental results demonstrate that the proposed approach is able to address the problems that TSK+ and KCR fail to do while having a similar time efficiency to them. Further, only two nearest rules to the observation are required to derive interpolated results, thereby significantly improving the automation level of the interpolative reasoning system.

Fuzzy rule-based systems are widely applied for real-world decision support, such as policy formation, public health analysis, medical diagnosis , and risk assessment. However, they face significant challenges when the application problem at hand suffers from the “curse of dimensionality” or “sparse knowledge base” . Combination of hierarchical fuzzy rule models and fuzzy rule interpolation offers a potentially efficient and effective approach to dealing with both of these issues simultaneously. In particular, backward fuzzy rule interpolation (B-FRI) facilitates approximate reasoning to be performed given a sparse rule base where rules do not fully cover all observations or the observations are not complete, missing antecedent values in certain available rules. This paper presents a hierarchical bidirectional fuzzy reasoning mechanism by integrating hierarchical rule structures and forward/backward rule interpolation. A computational method is proposed, building on the resulting hierarchical bidirectional fuzzy interpolation to maintain consistency in sparse fuzzy rule bases. The proposed techniques are utilised to address a range of decision support problems, successfully demonstrating their efficacy.

Finding the overall Pareto optimal front while addressing the effect of an increasing number of objectives has become an essential and challenging issue for multiobjective optimization in real-world applications. Preference information provided by a decision maker can guide the search for preferred regions of the Pareto front and accelerate the convergence of the population. In this paper, a new variant of the Pareto dominance relation, called preference angle and reference information-based dominance, is proposed to create a stricter partial order among nondominated solutions. In the proposed method, the Euclidean distance and angle information between candidate solutions and reference points are calculated to evaluate the degree of convergence and population diversity, respectively. In addition, an adaptive threshold is designed to adjust the judgment condition of ar-dominance using an iterative process in a prespecified interval. The proposed algorithm increases the convergence speed of the population and reduces the number of solutions in the nonpreferred region. Comparative evaluation experiments are presented with respect to two performance metrics for a variety of benchmark test problems and real-world aluminum electrolytic production cases. The results demonstrate that the proposed approach is effective for highly complex, multiobjective optimization problems when compared with five state-of-the-art evolutionary algorithms.

Intrusion detection system (IDS) is used to find malicious connections and protect networks from external or internal attacks. Various fuzzy or fuzzy intelligence approaches have been proposed in the development of IDS. In particular, the fuzzy interpolation technique guarantees the performance of IDS where only a sparse rule base is available. Furthermore, backward fuzzy interpolation allows interpolation to be carried out when certain antecedents of observation variables are absent, whereas conventional methods do not work. In this paper, a novel fuzzy association rules based classification intrusion detection system framework enhanced by a hierarchical bidirectional fuzzy rule interpolation technique is proposed for designing an IDS. Hierarchical bidirectional fuzzy rule interpolation is also employed to refine fuzzy rule base while which exists some consistency. This framework uses fuzzy association rules for building classifiers, and allows the generation of security alerts from situations which are not directly covered, missing values, or existing inconsistency by knowledge base. The proposed method is herein applied through integration with the Snort software to demonstrate the efficacy of this proposed approach.

Gas transportation pipes are important components in delivering gas to another stations. One drawback of these pipes is poor safety and weak maintenance, where leak identification provides a strong support of timely remedy to avoid escalated accidents. There are many techniques show their specific applications, in which acoustic emission (AE) is a particularly useful technique. However, the identification of small leak using AE becomes more difficult according to high noises and wider frequency domain. This paper proposes a MFCC-DSR approach to extract small leak features of gas pipes while the noises in circumstances are strong. In the approach, we explore a discrete stochastic resonance (DSR) model to improve signal noise ratio (SNR) levels of collected acoustic signals. Besides, we introduce a Mel frequency Cepstral coefficients (MFCC)-based scheme to extract features through calculating the gradient of power spectral density of acoustic signals. Experimental results indicate that our approach perform efficiently on small leak identification of gas pipes.

Using Chaotic characteristics of dynamic system is a promising direction to design cryptosystems that play a pivotal role in a very important engineering application of cognitive informatics, i.e., information assurance and security. However, encryption algorithms based on the lowdimensional chaotic maps face a potential risk of the keystream being reconstructed via return map technique or neural network method. In this paper, we propose a new digital image encryption algorithm that employs a hyper-chaotic cellular neural network. To substantiate its security characteristics, we conduct the following security analyses of the proposed algorithm: key space analysis, sensitivity analysis, information entropy analysis and correlation coefficients analysis of adjacent pixels. The results demonstrate that the proposed encryption algorithm has desirable security properties and can be deployed as a cornerstone in a sound security cryptosystem. The comparison of the proposed algorithm with five other chaos-based image encryption algorithms indicates that our algorithm has a better security performance.