The SEMRCNN model is proposed for autonomously extracting prostate cancer locations from regions of multiparametric magnetic resonance imaging (MP-MRI). Feature maps are explored in order to provide fine segmentation based on the candidate regions. Two parallel convolutional networks retrieve these maps of apparent diffusion coefficient (ADC) and T2W images, which are then integrated to use the complimentary information in MP-MRI. By utilizing extrusion and excitation blocks, it is feasible to automatically increase the number of relevant features in the fusion feature map. The aim of this study is to study the current scenario of the SE Mask-RCNN and deep convolutional network segmentation model that can automatically identify prostate cancer in the MP-MRI prostatic region. Experiments are conducted using 140 instances. SEMRCNN segmentation of prostate cancer lesions has a Dice coefficient of 0.654, a sensitivity of 0.695, a specificity of 0.970, and a positive predictive value of 0.685. SEMRCNN outperforms other models like as V net, Resnet50-U-net, Mask-RCNN, and U network model for prostate cancer MP-MRI segmentation. This approach accomplishes fine segmentation of lesions by recognizing and finding potential locations of prostate cancer lesions, eliminating interference from surrounding areas, and improving the learning of the lesions’ features.

After soybean and palm oil, the world’s third largest oil seed crop is rapeseed mustard. Out of the seven consumable oil seed crops grown in India, rapeseed mustard is responsible for one-third of production. Mustard aphid (Lipaphis erysimi Kalt) is considered the primary pest causing mayhem in crop production. Understanding the genetics behind resistance will aid breeders in developing a resistant/tolerant strain. Appropriate parent selection and analyzing gene action contribute to economic benefit maximization. The aims of the current study are to use seven lines and five testers to determine the best-performing parents and crosses based on their general and specific capacity to combine and to examine the level of heterosis for yield and related features like mustard aphid resistance. Due to the self-pollination nature of Indian mustard, Kempthorne’s line X tester method is helpful to judge the combining ability. Therefore, seven lines and five testers of Indian mustard (Brassica juncea L.) were employed in the present study. The findings suggested that there was substantial genetic variation for all traits examined. The mean oil yield of R1 B2-26 × R1 B2-25, JD6 × R1 B2-25, and JD6 × R1 B2-29 hybrids was more significant than that of the ancestors. The results show that R1 B2-26 × R1 B2-25, JD6 × R1 B2-25, and JD6 × R1 B2-29 hybrids produced more oil than their parents. The variance explained by SCA was greater than that explained by GCA, as indicated by the Ϭ2 gca/Ϭ2 sca ratio being less than one for all characters, implying that nonadditive gene actions such as dominance, epistasis, and other interaction effects played an important role in the presence of these attributes. Punjab Local was discovered to be an excellent general merge for reducing crop duration, whereas JD6 was an excellent combiner for seed yield per crop, aphid infestation indicator, seed yield per crop, and oil production per plant. The predictability ratio was found to be less than 0.5 for almost all traits, denoting that the nonadditive gene measure is involved in controlling the nature except days to 50% flowering, aphid infestation index, oil content, and oil yield per plant. Thus, based on these four traits, selection for superior plants may be practiced in later generations.

Crop diseases, pest infestations, water shortages, weed infestations, and other issues affect the agriculture sector. Due to existing agricultural techniques, these issues result in significant crop loss, economic loss, and severe environmental hazards. Because agriculture is such a dynamic industry, robotics cannot solve all of its difficulties; instead, a single solution to a specific complex problem is supplied. To assist with these issues and provide a better approach globally, a variety of systems have been developed. Plant protection robots are characterized by complexity, constraint, and nonlinearity. In order to improve the accuracy and reliability of plant protection robots in agricultural job path planning, we propose a path planning method for agricultural plant protection robots based on a nonlinear algorithm. The ant colony algorithm was selected to plan the path distance index according to the working environment, and the feasibility of the simulation system was calculated. The results show that the fastest time used by the nonlinear algorithm is 5.3, and the path planning accuracy is up to 97.8%. Compared with the traditional algorithm, the algorithm has higher accuracy, less computing time, and higher computing efficiency.

Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prostate cancer tissue microarrays by pathologists using Gleason mode under many tissue microarray images is time-consuming, susceptible to subjective factors between different observers, and has low reproducibility. We have used the two most common technologies, deep learning, and computer vision, in this research, as the development of deep learning and computer vision has made pathology computer-aided diagnosis systems more objective and repeatable. Furthermore, the U-Net network, which is used in our study, is the most extensively used network in medical image segmentation. Unlike the classifiers used in previous studies, a region segmentation model based on an improved U-Net network is proposed in our research, which fuses deep and shallow layers through densely connected blocks. At the same time, the features of each scale are supervised. As an outcome of the research, the network parameters can be reduced, the computational efficiency can be improved, and the method’s effectiveness is verified on a fully annotated dataset.

COVID-19 pandemic caused global epidemic infections, which is one of the most severe infections in human medical history. In the absence of proper medications and vaccines, handling the pandemic has been challenging for governments and major health facilities. Additionally, tracing COVID-19 cases and handling data generated from the pandemic are also extremely challenging. Data privacy access and collection are also a challenge when handling COVID-19 data. Blockchain technology provides various features such as decentralization, anonymity, cryptographic security, smart contracts, and a distributed framework that allows users and entities to handle COVID-19 data better. Since the outbreak has made the moral crisis in the clinical and administrative centers worse than any other that has resulted in the decline in the supply of the exact information, however, it is vital to provide fast and accurate insight into the situation. As a result of all these concerns, this study emphasizes the need for COVID-19 data processing to acquire aspects such as data security, data integrity, real-time data handling, and data management to provide patients with all benefits from which they had been denied owing to misinformation. Hence, the management of COVID-19 data through the use of the blockchain framework is crucial. Therefore, this paper illustrates how blockchain technology can be implemented in the COVID-19 data handling process. The paper also proposes a framework with three main layers: data collection layer; data access and privacy layer; and data storage layer.