With the help of machine learning (ML) techniques, the possible errors made by the pathologists and physicians, such as those caused by inexperience, fatigue, stress and so on can be avoided, and the medical data can be examined in a shorter time and in a more detailed manner. However, while the conventional ML techniques, such as classification, achieved excellent performance in classification accuracy when applied in medical diagnoses, they have a fatal shortcoming of poor performance since the imbalanced dataset, especially for the detection of the minority category. To tackle the shortcomings of conventional classification approaches, this study proposes a novel ensemble learning paradigm for medical diagnosis with imbalanced data, which consists of three phases: data pre-processing, training base classifier and final ensemble. In the first data pre-processing phase, we introduce the extension of Synthetic Minority Oversampling Technique (SMOTE) by integrating it with cross-validated committees filter (CVCF) technique, which can not only synthesize the minority sample and thereby balance the input instances, but also filter the noisy examples so as to perform well in the process of classification. In the classification phase, we introduce ensemble support vector machine (ESVM) classification technique, which were constructed by multiple diversity structures of SVM classifiers and thus has the advantages of strong generalization performance and classification precision. Additionally, in the last phase of the final ensemble strategy, we introduce the weighted majority voting strategy and introduce simulated annealing genetic algorithm (SAGA) to optimize the weight vector and thereby enhance the overall classification performance. The efficiency of our proposed ensemble learning method was tested on nine imbalanced medical datasets and the experimental results clearly indicate that the proposed ensemble learning paradigm outperforms other state-of-the-art classification models. Promisingly, our proposed ensemble learning paradigm can effectively facilitate medical decision making for physicians.

As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time. Nowadays, numerous classification methods have been utilized for breast cancer diagnosis. However, most of these classification models have concentrated on maximum the classification accuracy, failed to take into account the unequal misclassification costs for the breast cancer diagnosis. To the best of our knowledge, misclassifying the cancerous patient as non-cancerous has much higher cost compared to misclassifying the non-cancerous as cancerous. Consequently, in order to tackle this deficiency and further improve the classification accuracy of the breast cancer diagnosis, we propose an improved cost-sensitive support vector machine classifier (ICS-SVM) for the diagnosis of breast cancer. In the proposed approach, we take full account of unequal misclassification costs of breast cancer intelligent diagnosis and provide more reasonable results over previous works and conventional classification models. To evaluate the performance of the proposed approach, Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer datasets obtained from the University of California at Irvine (UCI) machine learning repository have been studied. The experimental results demonstrate that the proposed hybrid algorithm outperforms all the existing methods. Promisingly, the proposed method can be regarded as a useful clinical tool for breast cancer diagnosis and could also be applied to other illness diagnosis.

Aiming at the problem of automatic path planning for the whole safflower bulbs during the operation of safflower picking robots, an improved ant colony algorithm (ACA) was proposed to plan the three-dimensional path of the safflower picking points. The shortest time and distance were taken as the overall goal of path planning to comprehensively improve the working efficiency of safflower picking robots. First, in order to shorten time, the angle induction factor was introduced to reduce the angle rotation of the end-effector. Second, in order to shorten the length of the picking path, the picking track was optimized. Finally, the design of the secondary path optimization reduced the number of picking points, which not only shortened the length of the picking path, but also shortened the picking time. The simulation results show that the path planned by the improved ACA was reduced by three picking points, shortening the total length by 74.32%, and reducing the picking time by 0.957 s. The simulation results verify the feasibility of the improved ACA for safflower picking path planning, which provides theoretical reference and technical support for the picking path planning of dual roller safflower picking robots.

In response to the poor performance of long-distance small target recognition tasks and real-time intelligent monitoring, this paper proposes a deep learning-based recognition method aimed at improving the ability to recognize and monitor various behaviors of captive ewes. Additionally, we have developed a system platform based on ELFN-YOLO to monitor the behaviors of ewes. ELFN-YOLO enhances the overall performance of the model by combining ELFN with the attention mechanism CBAM. ELFN strengthens multiple layers with fewer parameters, while the attention mechanism further emphasizes the channel information interaction based on ELFN. It also improves the ability of ELFN to extract spatial information in small target occlusion scenarios, leading to better recognition results. The proposed ELFN-YOLO achieved an accuracy of 92.5%, an F1 score of 92.5%, and a mAP@0.5 of 94.7% on the ewe behavior dataset built in commercial farms, which outperformed YOLOv7-Tiny by 1.5%, 0.8%, and 0.7% in terms of accuracy, F1 score, and mAP@0.5, respectively. It also outperformed other baseline models such as Faster R-CNN, YOLOv4-Tiny, and YOLOv5s. The obtained results indicate that the proposed approach outperforms existing methods in scenarios involving multi-scale detection of small objects. The proposed method is of significant importance for strengthening animal welfare and ewe management, and it provides valuable data support for subsequent tracking algorithms to monitor the activity status of ewes.