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Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machineto-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%

Breast cancer is a major cause of death among women in both developed and underdeveloped countries. Early detection and diagnosis of breast cancer are crucial for patients to receive proper treatment and increase their chances of survival. To improve the automatic detection and diagnosis of breast cancer, a new deep learning model called "Breast Cancer Prognosis Based Transfer Learning (BCP-TL)" has been developed. This model uses transfer learning, which applies the knowledge gained from solving one problem to another relevant problem. The model is based on a pre-trained convolutional neural network (CNN) that extracts features from the mammographic image analysis society (MIAS) dataset. Four different CNN architectures were used in this model: AlexNet, Xception, ResNeXt, and Channel Boosted CNN. The performance of the model was evaluated using six metrics, including accuracy, sensitivity, specificity, precision, F1-score, and the area under the ROC curve (AUC). The combination of Xception and Channel Boosted CNN showed excellent performance. By combining essential features from multiple iterations, the Channel Boosted CNN can achieve higher accuracy in breast cancer diagnosis, with an overall accuracy of 98.96%. This highlights the potential of the BCP-TL model in effectively detecting and diagnosing breast cancer. [ABSTRACT FROM AUTHOR]

Nowadays, a massive amount of data leads to cause network traffic and inflexible mobility in future mobile networks. A new Group Mobility Model (GMM) named MoMo is introduced that addresses the issue of the aforementioned problems. Even though, software defined network (SDN) is functional with network-rooted mobility protocols that enhance the network efficiency. Some existing network-rooted mobility administration methods still undergo handover delay, packet loss, and high signaling cost through handover processing. In this research work, SDN-based fast handover for GMM is proposed. Here, the neighbor number of evolving node transition probabilities of the mobile node (MN) and their obtainable resource probabilities are estimated. This makes a mathematical framework to decide the preeminent number of the evolving nodes and then allot these to mobile nodes virtually with all associations finished by the exploit of Open-Flow tables. The performance examination demonstrates that the proposed SDN rooted GMM technique has the enhanced performance than the conventional handover process and further technique by handover latency, signaling cost, network throughput, and packet loss. [ABSTRACT FROM AUTHOR]

BACKGROUND/OBJECTIVES: A multiple micronutrient-fortified drink could be an effective strategy to combating micronutrient deficiencies in school going children. To assess the efficacy of a multiple micronutrient-fortified drink in reducing iron deficiency (ID), ID anemia (IDA), anemia and improving micronutrient status among schoolchildren with low iron stores. The study employed a school-based, randomized, double-blind, placebo-controlled design. SUBJECTS/METHODS: Schoolchildren with low serum ferritin (SF RESULTS: Study groups at baseline were comparable, and compliance to the intervention was similar. The overall prevalence of ID, IDA and anemia was 64%, 19% and 24%, respectively. The prevalence of ID, IDA, vitamin C and vitamin [B.sub.12] deficiencies significantly reduced by 42%, 18%, 21% and 5%, respectively, in the intervention arm (P CONCLUSIONS: The multi micronutrient-fortified drink was efficacious in reducing the prevalence of ID, IDA, vitamin C and vitamin [B.sub.12] deficiency and improved micronutrient status in schoolchildren. European Journal of Clinical Nutrition (2013) 67, 36-41; doi: 10.1038/ejcn.2012.188; published online 12 December 2012 Keywords: fortification; micronutrients; children; iron deficiency; iron deficiency anemia; anemia, INTRODUCTION Micronutrient deficiencies are common among populations in the developing world, particularly among children owing to their higher physiological requirements and lower consumption of nutrient-rich foods. (1) Available data from [...]

Urine microscopy for detecting pus cells is a common investigation ordered in hospitalized general medical patients as part of routine care. A few previous studies have shown that sterile pyuria is not uncommon in this population. We studied the prevalence of pyuria among patients hospitalized with non-urinary tract infection (UTI) diagnosis in the medical wards. We excluded patients with asymptomatic bacteriuria (ASB). Pyuria was quantified in uncentrifuged urine using the chamber counting method, and ≥ 10 pus cells per mm3 was considered significant. We also compared this method with the commonly used but less accurate method of counting pus cells/high power field using centrifuged urine (routine method). We studied 196 patients; 113 (57.7%) were males. Most (175[89.3%]) patients were hospitalized for an infection. We found that 18.4% of the study group had sterile pyuria, and it was strongly associated with the presence of concomitant microscopic hematuria (unadjusted odds ratio, 3.74 [1.65 to 8.50]; P=0.002). We found no association of pyuria with female gender, diabetes, acute kidney injury, or current antibiotic use. By routine method, 56 (28.6 %) patients had significant pyuria. In comparison to the chamber counting method, the routine method was 69.4(63—75.8) % sensitive and 80.6(75.1—86.2) % specific. The positive and negative predictive values were 44.6 (37.7— 51.6) % and 92.1 (88.4 — 95.9) %. We concluded that sterile pyuria and microscopic hematuria could be present in a proportion of hospitalized general medical patients without UTI or ASB. Clinical judgment is essential in interpreting the significance of abnormal urinalysis reports.

Content Based Image Retrieval (CBIR) is a way of querying image databases. CBIR looks at visual properties of an image as "search terms" and returns pictures from a database that share the same or almost similar visual properties. Most CBIR systems in the literature works by extracting the image color, texture and shape features before comparing them with those in the database and then compute the distance between features of images for retrieval purposes. In this proposed work, we use a MapReduce model framework to index the large-scale images and Spark has been used as a proportionate method of retrieving the index, which runs on the higher layer of MapReduce and Hadoop distributed file system (HDFS) environment. HDFS provides an in-memory data storage and fast retrieval mechanism using the indexing process. The image retrieval is performed in alignment with the K-Nearest Neighbour's model using Apache implementation. The processing time has been evaluated with the Hadoop framework in CBIR. The proposed approach takes 10% less time to index images than the distributed image segmentation method discussed in the literature. [ABSTRACT FROM AUTHOR]

Content Based Image retrieval systems (CBIR) retrieve the image features from the massive databases using the retrival set [15]. The Extracted image set will be maintained in a secured repository to enhance the approach of submissive storage. The Stored image suspects a variance in the colour festo and colour shaping using the texture classification. The stored image has been retrieved formally from the massive databases using the traditional algorithms, but the secure futuristic behavior of image storage can be done from cluster formation it maintains the original copy of the image termed as cluster data image(CDI) [32]. Apart from former traditional algorithms to capture the image set features the proposed algorithm induces a novel scheme of metaheuristic algorithm named as bees algorithm has been surveyed for the retrieval sector. The efficiency can be best measured in cluster set images using the precision value measurement and the accuracy has been established with efficient diffusion algorithm based on normalization and medical applications are considered for same data set retrieval. [ABSTRACT FROM AUTHOR]