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In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.

The impact of deep learning (DL) is substantial across numerous domains, particularly in agriculture. Within this context, our study focuses on the classification of problematic soybean seeds. The dataset employed encompasses five distinct classes, totaling 5513 images. Our model, based on the InceptionV3 architecture, undergoes modification with the addition of five supplementary layers to enhance efficiency and performance. Techniques such as transfer learning, adaptive learning rate adjustment (to 0.001), and model checkpointing are integrated to optimize accuracy. During initial evaluation, the InceptionV3 model achieved 88.07% accuracy in training and 86.67% in validation. Subsequent implementation of model tuning strategies significantly improves performance. Augmenting the architecture with additional layers, including Average Pooling, Flatten, Dense, Dropout, and Softmax, plays a pivotal role in enhancing accuracy. Evaluation metrics, including precision, recall, and F1-score, underscore the model's effectiveness. Precision ranges from 0.9706 to 1.0000, while recall values demonstrate a high capture rate across all classes. The F1-score, reflecting a balance between precision and recall, exhibits remarkable performance across all classes, with values ranging from 0.9851 to 1.0000. Comparative analysis with existing studies reveals competitive accuracy of 98.73% achieved by our proposed model. While variations exist in specific purposes and datasets among studies, our model showcases promising performance in soybean seed classification, contributing to advancements in agricultural technology for crop health assessment and management.

Attackers are increasingly targeting Internet of Things (IoT) networks, which connect industrial devices to the Internet. To construct network intrusion detection systems (NIDSs), which can secure Agriculture 4.0 networks, powerful deep learning (DL) models have recently been deployed. An effective and adaptable intrusion detection system may be implemented by using the architectures of long short-term memory (LSTM) and convolutional neural network combined with long short-term memory (CNN–LSTM) for detecting DDoS attacks. The CIC-DDoS2019 dataset was used to design a proposal for detecting different types of DDoS attacks. The dataset was developed using the CICFlowMeter-V3 network. The standard network traffic dataset, including NetBIOS, Portmap, Syn, UDPLag, UDP, and normal benign packets, was used to test the development of deep learning approaches. Precision, recall, F1-score, and accuracy were among the measures used to assess the model’s performance. The suggested technology was able to reach a high degree of precision (100%). The CNN–LSTM has a score of 100% with respect to all the evaluation metrics. We used a deep learning method to build our model and compare it to existing systems to determine how well it performs. In addition, we believe that this proposed model has highest possible levels of protection against any cyber threat to Agriculture 4.0.

Background Melanoma is one of the deadliest tumors in the world. Early detection is critical for first-line therapy in this tumor pathology and it remains challenging due to the need for histological analysis to ensure correctness in diagnosis. Therefore, multiple computer-aided diagnosis (CAD) systems working on melanoma images were proposed to mitigate the need of a biopsy. However, although the high global accuracy is declared in literature results, the CAD systems for the health fields must focus on the lowest false negative rate (FNR) possible to qualify as a diagnosis support system. The final goal must be to avoid classification type 2 errors to prevent life-threatening situations. Another goal could be to create an easy-to-use system for both physicians and patients. Results To achieve the minimization of type 2 error, we performed a wide exploratory analysis of the principal convolutional neural network (CNN) architectures published for the multiple image classification problem; we adapted these networks to the melanoma clinical image binary classification problem (MCIBCP). We collected and analyzed performance data to identify the best CNN architecture, in terms of FNR, usable for solving the MCIBCP problem. Then, to provide a starting point for an easy-to-use CAD system, we used a clinical image dataset (MED-NODE) because clinical images are easier to access: they can be taken by a smartphone or other hand-size devices. Despite the lower resolution than dermoscopic images, the results in the literature would suggest that it would be possible to achieve high classification performance by using clinical images. In this work, we used MED-NODE, which consists of 170 clinical images (70 images of melanoma and 100 images of naevi). We optimized the following CNNs for the MCIBCP problem: Alexnet, DenseNet, GoogleNet Inception V3, GoogleNet, MobileNet, ShuffleNet, SqueezeNet, and VGG16. Conclusions The results suggest that a CNN built on the VGG or AlexNet structure can ensure the lowest FNR (0.07) and (0.13), respectively. In both cases, discrete global performance is ensured: 73% (accuracy), 82% (sensitivity) and 59% (specificity) for VGG; 89% (accuracy), 87% (sensitivity) and 90% (specificity) for AlexNet.

Background The incidence rates of cervical cancer in developing countries have been steeply increasing while the medical resources for prevention, detection, and treatment are still quite limited. Computer-based deep learning methods can achieve high-accuracy fast cancer screening. Such methods can lead to early diagnosis, effective treatment, and hopefully successful prevention of cervical cancer. In this work, we seek to construct a robust deep convolutional neural network (DCNN) model that can assist pathologists in screening cervical cancer. Methods ThinPrep cytologic test (TCT) images diagnosed by pathologists from many collaborating hospitals in different regions were collected. The images were divided into a training dataset (13,775 images), validation dataset (2301 images), and test dataset (408,030 images from 290 scanned copies) for training and effect evaluation of a faster region convolutional neural network (Faster R-CNN) system. Results The sensitivity and specificity of the proposed cervical cancer screening system was 99.4 and 34.8%, respectively, with an area under the curve (AUC) of 0.67. The model could also distinguish between negative and positive cells. The sensitivity values of the atypical squamous cells of undetermined significance (ASCUS), the low-grade squamous intraepithelial lesion (LSIL), and the high-grade squamous intraepithelial lesions (HSIL) were 89.3, 71.5, and 73.9%, respectively. This system could quickly classify the images and generate a test report in about 3 minutes. Hence, the system can reduce the burden on the pathologists and saves them valuable time to analyze more complex cases. Conclusions In our study, a CNN-based TCT cervical-cancer screening model was established through a retrospective study of multicenter TCT images. This model shows improved speed and accuracy for cervical cancer screening, and helps overcome the shortage of medical resources required for cervical cancer screening.

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.

Grain size analysis is used to study grain size and distribution. It is a critical indicator in sedimentary simulation experiments (SSEs), which aids in understanding hydrodynamic conditions and identifying the features of sedimentary environments. Existing methods for grain size analysis based on images primarily focus on scenarios where grain edges are distinct or grain arrangements are regular. However, these methods are not suitable for images from SSEs. We proposed a deep learning model incorporating histogram layers for the analysis of SSE images with fuzzy grain edges and irregular arrangements. Firstly, ResNet18 was used to extract features from SSE images. These features were then input into the histogram layer to obtain local histogram features, which were concatenated to form comprehensive histogram features for the entire image. Finally, the histogram features were connected to a fully connected layer to estimate the grain size corresponding to the cumulative volume percentage. In addition, an applied workflow was developed. The results demonstrate that the proposed method achieved higher accuracy than the eight other models and was highly consistent with manual results in practice. The proposed method enhances the efficiency and accuracy of grain size analysis for images with irregular grain distribution and improves the quantification and automation of grain size analysis in SSEs. It can also be applied for grain size analysis in fields such as soil and geotechnical engineering.

Deep learning is a powerful tool that became practical in 2008, harnessing the power of Graphic Processing Unites, and has developed rapidly in image, video, and natural language processing. There are ongoing developments in the application of deep learning to medical data for a variety of tasks across multiple imaging modalities. The reliability and repeatability of deep learning techniques are of utmost importance if deep learning can be considered a tool for assisting experts, including physicians, radiologists, and sonographers. Owing to the high costs of labeling data, deep learning models are often evaluated against one expert, and it is unknown if any errors fall within a clinically acceptable range. Ultrasound is a commonly used imaging modality for breast cancer screening processes and for visually estimating risk using the Breast Imaging Reporting and Data System score. This process is highly dependent on the skills and experience of the sonographers and radiologists, thereby leading to interobserver variability and interpretation. For these reasons, we propose an interobserver reliability study comparing the performance of a current top-performing deep learning segmentation model against three experts who manually segmented suspicious breast lesions in clinical ultrasound (US) images. We pretrained the model using a US thyroid segmentation dataset with 455 patients and 50,993 images, and trained the model using a US breast segmentation dataset with 733 patients and 29,884 images. We found a mean Fleiss kappa value of 0.78 for the performance of three experts in breast mass segmentation compared to a mean Fleiss kappa value of 0.79 for the performance of experts and the optimized deep learning model. [Display omitted] •Automatic breast mass segmentation statistical performance was on par with that of human expert.•Proposed Model had a mean Dice coefficient comparable to that of human expert.•Proposed Model had a mean Hausdorff distance comparable to that of human expert.•Proposed a focal Matthew's correlation coefficient for loss function.

In the current era of the digital world, the hash of any digital means considered as a footprint or fingerprint of any digital term but from the ancient era, human fingerprint considered as the most trustworthy criteria for identification and it also cannot be changed with time even up to the death of an individual. In the court of law, fingerprint-proof is undeniably the most dependable and acceptable evidence to date. Fingerprint designs are exclusive in each human and the chance of two individuals having identical fingerprints is an exceptional case about one in sixty-four thousand million also the fingerprint minutiae patterns of the undistinguishable twins are different, and the ridge pattern of each fingertip remain unchanged from birth to till death. Fingerprints can be divided into basic four categories i.e. Loop, whorl, arch, and composites, nevertheless, there are more than 100 interleaved ridge and valleys physiognomies, called Galton’s details, in a single rolled fingerprint. Due to the immense potential of fingerprints as an effective method of identification, the present research paper tries to investigate the problem of blood group identification and analysis of diseases those arises with aging like hypertension, type 2-diabetes and arthritis from a fingerprint by analyzing their patterns correlation with blood group and age of an individual. The work has been driven by studies of anthropometry, biometric trademark, and pattern recognition proposing that it is possible to predict blood group using fingerprint map reading. Dermatoglyphics as a diagnostic aid used from ancient eras and now it is well established in number of diseases which have strong hereditary basis and is employed as a method for screening for abnormal anomalies. Apart from its use in predicting the diagnosis of disease; dermatoglyphics is also used in forensic medicine in individual identification, physical anthropology, human genetics and medicine. However, the Machine and Deep Learning techniques, if used for fingerprint minutiae patterns to be trained by Neural Network for blood group prediction and classification of common clinical diseases arises with aging based on lifestyle would be an unusual research work.