The early detection and diagnosis of breast cancer may increase survival rates and reduce overall treatment costs. The cancer of the breast is a severe and potentially fatal disease that impacts individuals worldwide. Mammography is a widely utilized imaging technique for breast cancer surveillance and diagnosis. However, images produced with mammography frequently contain noise, poor contrast, and other anomalies that hinder radiologists from interpreting the images. This study develops a novel deep-learning technique for breast cancer detection using mammography images. The proposed procedure consists of two primary steps: region-of-interest (ROI) (1) extraction and (2) classification. At the beginning of the procedure, a YOLOX model is utilized to distinguish breast tissue from the background and to identify ROIs that may contain lesions. In the second phase, the EfficientNet or ConvNeXt model is applied to the data to identify benign or malignant ROIs. The proposed technique is validated using a large dataset of mammography images from various institutions and compared to several baseline methods. The pF1 index is used to measure the effectiveness of the technique, which aims to establish a balance between the number of false positives and false negatives, and is a harmonic mean of accuracy and recall. The proposed method outperformed existing methods by an average of 8.0%, obtaining superior levels of precision and sensitivity, and area under the receiver operating characteristics curve (ROC AUC) and the precision–recall curve (PR AUC). In addition, ablation research was conducted to investigate the effects of the procedure’s numerous components. According to the findings, the proposed technique is another choice that could enhance the detection and diagnosis of breast cancer using mammography images.

This research investigates the critical role of frequency selection in Electrical Impedance Tomography (EIT), a non-invasive imaging technique that reconstructs internal conductivity distributions through injected electrical currents. Empirical frequency selection is paramount to maximizing the fidelity and specificity of EIT images. The study explores the impact of distinct frequency ranges—low, medium, and high—on image contrast and clarity, particularly focusing on differentiating conductive materials from non-conductive materials. The findings reveal distinct empirical frequency bands for enhancing the respective contrasts: 15–38 kHz for conductive materials (copper) and 45–75 kHz for non-conductive materials (acrylic resin). These insights shed light on the frequency-dependent nature of material contrast in EIT images, guiding the selection of empirical operating ranges for various target materials. This research paves the way for improved sensitivity and broader applicability of EIT in diverse areas.

In light of the negative effects of mental stress on health, there is growing scientific attention placed on understanding it better. This study aims to introduce a method that uses biological indicators such as skin surface temperature and skin resistance using easily accessible data to detect and predict stress levels. The publicly available dataset used in this study included the physiological signals of a group of university students whose signals were recorded by a wearable device during three exam sessions (two midterm and one final exam). Features were extracted from heart rate, electrodermal activity, and physiological and skin surface temperature and were input for the classification. In the labeling process, the stress levels were classified as high, low, or moderate. Machine learning algorithms such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) with classifiers were trained and tested with the labeled data The stress level of a subject was predicted with the highest F1 score of up to 0.8 and accuracy of higher than 70%. Such findings highlight the important role of skin conductance and surface temperature in estimating stress levels.

Breast cancer is a common cancer that affects women all over the world. Therefore, detection at an early stage is crucial for reducing the mortality rate linked to this disease. Mammography is the primary screening method for breast cancer. However, it has drawbacks, including high rates of false-positive and negative results, inter-observer variability, and limited sensitivity with dense breast tissue. To solve such problems, breast cancer was analyzed and classified using mammography images and deep learning models from the Radiological Society of North America (RSNA) database. This database contains processed and raw images from the RSNA that consist of annotated malignancies and clinical data. Using deep learning models based on convolutional neural network (CNN) models such as visual geometry group (VGG), Googlenet, EfficientNet, and Residual Networks, mammograms were classified into cancer or non-cancer categories. In this study, a novel architecture was proposed by combining CNNs and attention mechanisms, which extracted and highlighted the relevant features. A dataset of 8000 patients with 47,000 photographs was used to train and assess the model via 5-fold cross-validation. The results outperformed prior methods using the same database and reached an average accuracy rate of 95%. The results showed that mammography with deep learning methods considerably improved breast cancer detection and diagnosis.

In the food industry, the need to assess and predict the ripening process of fruits plays an important role in optimizing storage and transportation strategies, ensuring the product quality when reaching consumers. The study proposes a method using microwave spectrum based on vector network analyzer combined with machine learning models. This method is used to evaluate among numerous machine learning models and predict the number of days required for an unripe banana to turn to semi ripe and then ripe banana. Data is collected through scattering parameters, including reflection parameter S11 and transmission parameter S21, in the frequency range from 1 GHz to 5 GHz. The S-parameters are processed, analyzed and extracted characteristic data and fed into the machine learning models to perform the comparison and prediction process.