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Objectives: To determine and compare the diagnostic performance of doppler sonography of fetal umbilical artery (UA) and middle cerebral artery (MCA) in prediction of adverse perinatal outcome in suspected intrauterine growth retardation (IUGR). Methodology: One hundred singleton pregnancies between 28 and 40 weeks of gestation complicated by intrauterine growth restriction were prospectively examined with doppler ultrasonography of the UA and MCA and were correlated with fetal outcome. Results: Of the 100 cases, 52 had elevated umbilical systolic/diastolic (S/D) ratio, 58 had elevated umbilical resistance index (RI), 63 had elevated umbilical pulsatality index ( PI). 58 had abnormal MCA S/D, 47 had abnormal MCA RI, 43 had abnormal MCA PI and 57 had cerebroplacental ratio (CPR) 48 hrs was maximum (56.1%) in the CPR

Data is incredibly significant in today's digital age because data represents facts and numbers from our regular life transactions. Data is no longer arriving in a static form; it is now arriving in a streaming fashion. Data streams are the arrival of limitless, continuous, and rapid data. The healthcare industry is a major generator of data streams. Processing data streams is extremely complex due to factors such as volume, pace, and variety. Data stream classification is difficult owing to idea drift. Concept drift occurs in supervised learning when the statistical properties of the target variable that the model predicts change unexpectedly. We focused on solving various forms of concept drift problems in healthcare data streams in this research, and we outlined the existing statistical and machine learning methodologies for dealing with concept drift. It also emphasizes the use of deep learning algorithms for concept drift detection and describes the various healthcare datasets utilized for concept drift detection in data stream categorization.

Introduction: Diastolic dysfunction is a common complication of diabetic cardiomyopathy, and it is often present in patients with type 2 diabetes, even in the absence of systolic ventricular failure. Tissue Doppler imaging is a valuable technique for detecting subclinical diastolic dysfunction. Early diagnosis of diastolic dysfunction is crucial for timely intervention and prevention of long-term cardiovascular complications. The Tissue Doppler method, combined with 2nd ECHO, was used to examine diastolic dysfunction in diabetics and non-diabetics. Material and methods: A total of 129 subjects (case) with type 2 diabetes were compared with 333 non-diabetic subjects. 2D-ECHO including Tissue Doppler method was performed to assess left ventricular diastolic function. Results: 76% from diabetic group had diastolic dysfunction, and 3.3% in non-diabetic group had diastolic dysfunction (P < 0.05). We found mitral valve and tricuspid valve regurgitation, aortic valve stenosis and LVH was more common among diabetics compared to non-diabetics (p<0.05).We found LV diastolic dysfunction, PAH (Pulmonary arterial hypertension) and PE (Pulmonary embolism) was more common among diabetics compared to non-diabetics (p<0.05). Conclusion: In the current investigation, the overall prevalence of diastolic dysfunction in asymptomatic type 2 diabetes patients was 76%. When comparing asymptomatic type 2 diabetes to healthy individuals, diastolic dysfunction was far more common. When diabetic individuals are thought to have healthy hearts, routine screening for diastolic dysfunction may be justified by the use of tissue Doppler imaging. [ABSTRACT FROM AUTHOR]

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information. Precision agriculture uses datamining to advance agricultural developments. Many farmers aren't getting the most out of their land because they don't use precision agriculture. They harvest crops without a well-planned recommendation system. Future crop production is calculated by combining environmental conditions and management behavior, yielding numerical and categorical data. Most existing research still needs to address data preprocessing and crop categorization/classification. Furthermore, statistical analysis receives less attention, despite producing more accurate and valid results. The study was conducted on a dataset about Karnataka state, India, with crops of eight parameters taken into account, namely the minimum amount of fertilizers required, such as nitrogen, phosphorus, potassium, and pH values. The research considers rainfall, season, soil type, and temperature parameters to provide precise cultivation recommendations for high productivity. The presented algorithm converts discrete numerals to factors first, then reduces levels. Second, the algorithm generates six datasets, two fromCase-1 (dataset withmany numeric variables), two from Case-2 (dataset with many categorical variables), and one from Case-3 (dataset with reduced factor variables). Finally, the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation. The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions, soil nutrients, and geo-location. Finally, the prepared dataset solves the classification problem, leading to a model evaluation that selects the best dataset for precise crop prediction. [ABSTRACT FROM AUTHOR]

In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms. [ABSTRACT FROM AUTHOR]