Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segmentation of the organs, diseases, or abnormalities in medical images has become demanding. The segmentation of medical images helps in checking the growth of disease like tumour, controlling the dosage of medicine, and dosage of exposure to radiations. Medical image segmentation is really a challenging task due to the various artefacts present in the images. Recently, deep neural models have shown application in various image segmentation tasks. This significant growth is due to the achievements and high performance of the deep learning strategies. This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks. The paper examines the various widely used medical image datasets, the different metrics used for evaluating the segmentation tasks, and performances of different CNN based networks. In comparison to the existing review and survey papers, the present work also discusses the various challenges in the field of segmentation of medical images and different state-of-the-art solutions available in the literature.

COVID-19 is likely to pose a significant threat to healthcare, especially for disadvantaged populations due to the inadequate condition of public health services with people's lack of financial ways to obtain healthcare. The primary intention of such research was to investigate trend analysis for total daily confirmed cases with new corona virus (i.e., COVID-19) in the countries of Africa and Asia. The study utilized the daily recorded time series observed for two weeks (52 observations) in which the data is obtained from the world health organization (WHO) and world meter website. Univariate ARIMA models were employed. STATA 14.2 and Minitab 14 statistical software were used for the analysis at 5% significance level for testing hypothesis. Throughout time frame studied, because all four series are non-stationary at level, they became static after the first variation. The result revealed the appropriate time series model (ARIMA) for Ethiopia, Pakistan, India, and Nigeria were Moving Average order 2, ARIMA(1, 1, 1), ARIMA(2, 1, 1), and ARIMA (1, 1, 2), respectively.

In this digital world, a set of information about the real-world entities is collected and stored in a common place for extraction. When the information generated has no meaning, it will convert into meaningful information with a set of rules. Those data have to be converted from one form to another form based on the attributes where it was generated. Storing these data with huge volume in one place and retrieving from the repository reveals complications. To overcome the problem of extraction, a set of rules and algorithms was framed by the standards and researchers. Mining the data from the repository by certain principles is called data mining. It has a lot of algorithms and rules for extraction from the data warehouses. But when the data is stored under a common structure on the repository, the values derived from that huge volume are complicated. Computing statistical data using data mining provides the exact information about the real-world applications like population, weather report, and probability of occurrences.

Cognitive learning is progressively prospering in the field of Internet of Things (IoT). With the advancement in IoT, data generation rate has also increased, whereas issues like performance, attacks on the data, security of the data, and inadequate data resources are yet to be resolved. Recent studies are mostly focusing on the security of the data which can be handled by blockchain. Blockchain technology records the learned data into the block which is generated after completing proper consensus mechanism. In this paper, Hetero Federated Learning approach is used to apply cognitive learning on data produced by Internet of Thing devices. Security on cognitiveIoT data is provided by blockchain using Proof of Work consensus mechanism. By applying blockchain over heteroFL approach, we have conducted various simulations to check the performance of our proposed framework. Parameters taken into consideration during performance evaluation are effect of number of blocks on memory utilization and impact of data sample size on accuracy according to different learning rates.

Covid-19 pandemic has been a catalyst for mandatory use of technology to deliver class room learning to students who have been forced to Learn From Home (LFH). Learn From Home requires Technology Enhanced Learning (TEL) method if students are to be satisfied with online delivery of lessons. Technology Enhanced Learning is used to describe the integration of technology to teaching and learning; the demand for Technology Enhanced Learning has been increased as a result of the lockdown. The study explores various online teaching platforms, techniques, online examination, and Technology Enhanced Learning methods needed to be used by university instructors to ensure that educating the students goes on unhindered. The identified variables were empirically tested with the aid of a structured questionnaire. The study concludes that there is a positive relationship between satisfaction with the delivery of online study and the perceived effectiveness of TEL among higher education students.

Background: Anemia in children is a serious public health issue in low‐income settings, particularly sub‐Saharan Africa. In the Democratic Republic of Congo (DRC), evidence on the geographic distribution and multilevel determinants of childhood anemia remains limited. This study aimed to examine the spatial pattern and identify individual, household, and community‐level determinants of anemia among children aged 6–59 months in the DRC. Methods: A secondary analysis was conducted using data from the 2023/2024 DRC‐DHS. A weighted sample of 9449 children aged 6–59 months was included. Spatial analyses were performed using Global Moran’s I, Getis‐Ord Gi∗, and Kriging interpolation techniques to assess geographic clustering and spatial patterns of childhood anemia. Multilevel binary logistic regression models were fitted to identify factors associated with childhood anemia, and adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. Results: Of the 9449 children, 6092 (64.5%) were anemic. Spatial analysis showed significant geographic clustering of anemia in the DRC (Moran’s I = 0.516, p = 0.0078), with hotspot regions in western‐central and cold spots in eastern provinces. Our findings showed that several factors were associated with lower odds of childhood anemia. Specifically, we observed reduced odds among children aged 24–35 months (AOR = 0.48; 95% CI: 0.34–0.68), females (AOR = 0.89; 95% CI: 0.81–0.98), and those from households in the middle wealth index (AOR = 0.86; 95% CI: 0.75–0.98). In contrast, higher odds of childhood anemia were observed among children with severe stunting (AOR = 1.19; 95% CI: 1.04–1.37), severe underweight (AOR = 1.58; 95% CI: 1.31–1.92), fever (AOR = 1.24; 95% CI: 1.10–1.40), those born to anemic mothers (AOR = 1.39; 95% CI: 1.26–1.53), children from households with female household head (AOR = 1.21; 95% CI: 1.08–1.35), and those residing in communities with higher levels of poverty (AOR = 1.24; 95% CI: 1.05–1.47). Conclusion: Childhood anemia in the DRC remains highly prevalent and spatially clustered, with western and northern provinces particularly affected. Individual, household, and community factors were identified as significant determinants of anemia risk. Targeted interventions focusing on high‐risk regions, nutritional support, maternal health, and socioeconomic improvements are critical to reducing childhood anemia in the DRC.