Our proposed work, SculptorGAN, represents a novel advancement in the domain of medical imaging, for accurate and automatic diagnosis renal tumors, using techniques principles Generative Adversarial Network (GAN). This dichotomous framework forms contrast to normal segmentation models like that U-Net model but, instead, founded on strategy is aimed towards reconstruction CT images, particularly malignancies. The core SculptorGAN methodology GAN-based approach precise three-dimensional...

In this work, we develop and trained deep learning models for the segmenting classification of cassava leaf disease as Blight or Mosaic. As second-largest provider carbohydrates in Africa, is a key food security crop grown by smallholder farmers because it can withstand harsh conditions. At least 80% household farms Sub-Saharan Africa grow starchy root, but viral diseases are major sources poor yields.  Our emphasis here was on two that occur Nigeria which Cassava Mosaic Disease (CMD)...

Chronic Kidney Disease (CKD) represents a considerable global health challenge, emphasizing the need for precise and prompt prediction of disease progression to enable early intervention enhance patient outcomes. As per this study, we introduce an innovative fusion deep learning model that combines Graph Neural Network (GNN) tabular data predicting CKD by capitalizing on strengths both graph-structured representations. The GNN processes data, uncovering intricate relationships between...

Kidney tumors represent a significant medical challenge, characterized by their often-asymptomatic nature and the need for early detection to facilitate timely effective intervention. Although neural networks have shown great promise in disease prediction, computational demands limited practicality clinical settings. This study introduces novel methodology, UNet-PWP architecture, tailored explicitly kidney tumor segmentation, designed optimize resource utilization overcome complexity...

Background: Accurate semantic segmentation of kidney tumors in computed tomography (CT) images is difficult because feature varied forms and occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances tumor segmentation. Methods: We present weight pruning (WP)-UNet, a deep network model that lightweight with small scale; it involves few parameters quick assumption time low floating-point computational complexity. Results: trained evaluated CT from 210 patients....

Abstract Background: The major challenge in medical imaging is to achieve high accuracy output during semantic image segmentation tasks biomedical while having fewer computational operations and faster inference. It necessary modalities such as kidney tumor CT scan activities, assist radiologists. Several previous studies have carried out a complex deep network that requires resources. However, on of scans with flops parameters has not yet been evaluated. Methods: This research paper...

To establish biological exposure index (BEI) of occupational to arsenic and its inorganic compounds through epidemiology the regression analysis internal external workers.

Summary Energy‐efficient data collection in wireless sensor networks (WSNs) is crucial due to the limited battery capacity of nodes (SNs). Using a mobile sink (MS) for can lower energy consumption SNs avoid relaying WSNs. However, single MS not feasible solution large‐scale WSNs, so it was necessary use multiple MSs collect data. A synchronous scheduling strategy (SMS2DC) proposed this paper, which uses two types MS, local from SN and global MS. In process, we begin by partitioning network...

Сложившаяся нестабильная экономическая ситуация диктует поиск новых ресурсов в пищевой промышленности и формирует структуру питания населения. Поэтому актуальная научная государственная проблема – обеспечение населения функциональными, экологически чистыми качественными продуктами питания. Основной целью данной исследовательской работы стало производство качественного продукта из чистого сырья растительного производства. Новизна состоит приготовлении варенья с использованием плодов томатов...

To provide relevant recommendations for clients, a recommendation system is essential in online commerce, streaming services, and news article websites. Existing methods systems are limited by the cold start problem. The Deep Neural Network (DNN) – Long Short-Term Memory (LSTM) technique developed this study to improve efficiency of systems. DNN method used predict new user ratings based on prior ratings, while LSTM recommend movie user. user-item similarity was calculated algorithm offer...

Objective: The method was established for the detection of whole blood indium and serum indium. By comparing results two samples, it is possible to explore significance in population exposed compounds. Methods: According GBZ/T 295-2017 GBZ 294-2017, samples were diluted 20 times by 0.5% nitric acid solution (including 0.05% Triton X-100) . Under standard mode inductively coupled plasma mass spectrometry (ICP-MS) , indirect exposure group, low group high an mine detected with μg/L rhodium as...

This study deals with the systematic of mining data and medical image-based CAD to classify or predict Kidney Renal (KRCC) tumors. tumors are different types having characteristics have methodologies tumor its stages. KRCC is most common type cancer kidney, but there others. Several factors may increase risk a person developing disease like smoking, obesity, High blood pressure, many more. In almost all cases, only single kidney affected, in rare both can be affected by KRCC. As grows, it...

Abstract Background Accurate semantic segmentation of kidney tumours in computed tomography (CT) images is difficult because feature varied forms and, occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances tumour segmentation. Methods We present WP-UNet, a deep network model that lightweight with small scale; it involves few parameters quick assumption time and low floating-point computational complexity. Results trained evaluated CT from 300 patients....

Motivation: It is essential for the diagnosis and treatment of renal cancers to segment kidney tumours precisely effectively. In medical image segmentation tasks, deep learning models have demonstrated promising results, UNet model widely employed in this field. However, optimising tumour further can improve its efficacy deployment feasibility. Related Works: Previous works explored various techniques efficiency segmentation. Image partitioning methods divides input into smaller regions,...

В статье дана оценка использования племенных ресурсов голштинской породы в отечественном молочном скотоводстве за 2007-2017годы. Показана численность и продуктивность породы, разводимого на территории Российской Федерации, разрезе регионов. настоящее время голштинская порода скота разводится 55 регионах пяти Федеральных округов РФ, ее племенные ресурсы представлены 144 племенными стадами 18 страны. Поголовье голштинского возросло к 2017 году до 456 тыс. голов при удельном весе 16,3% среди...

This research was conducted with the goals of developing models that have an accurate classification chronic kidney disease (CKD) and locating significant prognostic factors within a clinical dataset. In disease, identification major risk contribute to improved prognoses provide assistance nephrologists. The data source is not balanced enough serve as benchmark for any machine learning or deep due privacy concerns other factors. As result, it difficult achieve consistent accuracy imbalanced...

The objective of this study was to develop a system for chronic kidney disease (CKD) and identify relevant prognostic features using clinical dataset. Accurate classification major risk factors in lead better prognosis assist nephrologists. Due privacy other factors, the data source is not balanced trail any models. Therefore, it difficult achieve consistent accuracy with an imbalanced dataset, there will be variance results different machine learning In proposed study, GAN's generated...

Nowadays, many companies sell their products and services on social media; they can get ideas directly from the end-users these media. Manually reading each text is time-consuming, so by analyzing emotions of all text, roughly know how positive or negative users there are a particular topic. It also helps estimate effect organizations' promoting systems distinguishing public view an item item-related occasion. A large portion exploration done as such far centers around getting profound...

Objective: To investigate the role of renal tubular epithelial cells in cadmium-induced fibrosis. Methods: Established a sub-chronic cadmium exposure mouse model and analyzed progress fibrosis induced by through Masson staining immunohistochemistry, then co-culture system fibroblasts was established, levels proliferation activation were detected Reverse Transcription-Polymerase Chain Reaction (RT-PCR) Western blotting. Results: Sub-chronic led to weight loss mice (P<0.05) , β-microglobulin...