Detection of the diseases on tomatoes in advance and making early intervention and treating increases the production amount, efficiency and quality which will satisfy the consumer with a more affordable shelf price. In this way, the efforts of the farmers who are waiting for the harvest throughout the season will not be wasted. In this paper a compact convolutional neural network (CNN) is proposed for diseases identification task where the network is comprised of only 6 layers that is why it is computationally cheap in terms of parameters employed in the network. This network is trained by using PlantVillage’s tomato crops dataset which consists of 10 classes (9 diseases and 1 healthy). The proposed network is first compared with well-known pre-trained ImageNet deep networks using transfer learning approach. The results show that the proposed network performed better than pre-trained knowledge transferred deep network models and it is shown that there is no need to constitute very large, complicated network architectures to achieve a superior tomato diseases identification performance. Furthermore, to increase the performance of proposed network, data augmentation techniques are also employed during the network training. The proposed network achieves an accuracy, F₁ score, Matthews correlation coefficient, true positive rate and true negative rate of 99.70%, 98.49%, 98.31%, 98.49% and 99.81%, respectively using 9,077 unseen test images. Our results are better than or similar to the results of the state-of-the-art deep neural network approaches that used PlantVillage database and the proposed method employs the cheapest architecture.

Malaria is a blood disease caused by the Plasmodium parasite that is transmitted through the bite of female Anopheles mosquitoes. These mosquitoes can cross borders without passports or visas, making malaria a global health concern. To effectively treat malaria, infectious disease specialists must monitor the efficacy of the treatment by counting the number of parasites in a patient’s blood at various time intervals. However, this task is challenging because it involves examining thin or thick blood smear samples under a microscope, which can be tiring to the human eye, particularly when there are many infected patients or when there is a shortage of clinical experts. In such cases, rapid diagnosis is crucial. One approach is to capture microscopic images of blood smear samples using a camera and then employ deep learning-based object detection models to detect and count the infected red blood cells. In this study, state-of-the-art object detection models, including CenterNet, EfficientDet, Faster R-CNN, RetinaNet, and YOLOv8, were explored. The dataset was generated using thin blood smear images in the laboratory. The results revealed that YOLOv8s outperformed the other models, achieving an score of 0.9031 and an mAP@[0.50:0.05:0.95] score of 0.5957. This study also found that various model combinations and ensemble strategies could improve the detection of malaria parasites. Specifically, the weighted boxes fusion ensembling approach achieved an score of 0.9186 and an mAP@[0.50:0.05:0.95] score of 0.6196. In contrast, the non-maximum weighted method achieved an score of 0.9324 and an mAP@[0.50:0.05:0.95] score of 0.6214.

With the spread of SARS-CoV-2 variants with higher transmissibility and disease severity, rapid detection and isolation of patients remains a critical step in the control of the pandemic. RT-PCR is the recommended diagnostic test for the diagnosis of COVID-19. The current study aims to develop an artificial intelligence (AI)-driven COVID-19 RT-PCR detection system for rapid and reliable diagnosis, facilitating the heavy burden of healthcare workers. A multi-input deep convolutional neural network (DCNN) is proposed. A MobileNetV2 DCNN architecture was used to predict the possible diagnostic result of RT-PCR fluorescence data from patient nasopharyngeal sample analyses. Amplification curves in FAM (ORF1ab and N genes, SARS-CoV-2) and HEX (human RNAse P gene, internal control) channels of 400 samples were categorized as positive, weak-positive, negative or re-run (unspecific fluorescence). During the network training, HEX and FAM channel images for each sample were simultaneously presented to the DCNN. The obtained DCNN model was verified using another 160 new test samples. The proposed DCNN classified RT-PCR amplification curves correctly for all COVID-19 diagnostic categories with an accuracy, sensitivity, specificity, $F_1$-score, and AUC of the model reported to be 1. Furthermore, the performance of other pre-trained well-known DCNN models was also compared with the MobileNetV2 model using 5-fold cross-validation, and the results showed that there were no significant differences between the other models at the 5% significance level; however, the MobileNetV2 model outperformed others dramatically in terms of the training speed and fast convergence. The developed model can help rapidly diagnose COVID-19 patients and would be beneficial in tackling future pandemics.

The most extended form of a fuzzy set called the Bipolar Linear Diophantine Fuzzy Hypersoft Set is implemented with some basic operations. This is an extraordinary technique for handling uncertainty because it has a choice of reference parameters with auxiliary attributes. A widely used operator named Einstein aggregation operators was developed in our proposed context. This new operator will make the decision-making method consistent with advancements in our priorities. The prolongation of this advanced operator helps solve critical data in real life. Cancer is a common and challenging disease that is growing day by day, with the proliferation of cells uncontrollably. It often grows tumors along with a tendency to disseminate to many different regions of the body. Lung Carcinoma(Lung Cancer) is the most common and dangerous type and is comprehensively explored in this study. By considering different stages, traits, and a multitude of complications, the investigation vigilantly focuses on several facets of lung carcinoma. The best therapy to overcome this problem is concluded by incorporating our established Bipolar Linear Diophantine Fuzzy Weighted Aggregation Operators. In spite of researching the problem from the healthcare perspective, the analysis delves into innovative ways for bettering the outcomes from therapies.

This article considers an inverse problem of time fractional parabolic partial differential equations with the nonlocal boundary condition. Dirichlet-measured output data are used to distinguish the unknown coefficient. A finite difference scheme is constructed and a numerical approximation is made. Examples and numerical experiments, such as man-made noise, are provided to show the stability and efficiency of this numerical method.