Secure Data Aggregation Based on End-to-End Homomorphic Encryption in IoT-Based Wireless Sensor Networks.

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation.

Diet, Fluid Intake, Urine Output and Urinary Sodium/Potassium Ratios in Children With Urolithiasis.

We performed a cross-sectional study on 25 children (17 boys) with urolithiasis with normal glomerular functions at a tertiary care teaching hospital between March, 2018 to March, 2019. Dietary assessment showed that caloric intake was below recommended dietary allowance (RDA) in 68% patients while the median protein intake was 34.3% more. The fluid intake was below the recommended standards in 56%, and 48% of the children had urine output below 1.5 mL/kg/hour. The urinary sodium was elevated in 96% of the children, urinary potassium was low in 40%, and hypercalciuria was seen in 28%. While metabolic causes predominate in childhood urolithiasis, other factors like dietary changes, liberal fluid and low sodium intake are advised for prevention of recurrences as they have a contributory role too.

A CAD System for Alzheimer's Disease Classification Using Neuroimaging MRI 2D Slices.

Developments in medical care have inspired wide interest in the current decade, especially to their services to individuals living prolonged and healthier lives. Alzheimer's disease (AD) is the most chronic neurodegeneration and dementia-causing disorder. Economic expense of treating AD patients is expected to grow. The requirement of developing a computer-aided technique for early AD categorization becomes even more essential. Deep learning (DL) models offer numerous benefits against machine learning tools. Several latest experiments that exploited brain magnetic resonance imaging (MRI) scans and convolutional neural networks (CNN) for AD classification showed promising conclusions. CNN's receptive field aids in the extraction of main recognizable features from these MRI scans. In order to increase classification accuracy, a new adaptive model based on CNN and support vector machines (SVM) is presented in the research, combining both the CNN's capabilities in feature extraction and SVM in classification. The objective of this research is to build a hybrid CNN-SVM model for classifying AD using the MRI ADNI dataset. Experimental results reveal that the hybrid CNN-SVM model outperforms the CNN model alone, with relative improvements of 3.4%, 1.09%, 0.85%, and 2.82% on the testing dataset for AD vs. cognitive normal (CN), CN vs. mild cognitive impairment (MCI), AD vs. MCI, and CN vs. MCI vs. AD, respectively. Finally, the proposed approach has been further experimented on OASIS dataset leading to accuracy of 86.2%.

An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.

Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.

Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network.

Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.

Nutritional assessment in children with chronic kidney disease.

Growth failure is a major problem in pediatric patients with chronic kidney disease (CKD), and the onset of the condition in infancy is more likely to have an adverse impact on growth than its development in later childhood. This study was aimed to assess nutritional intake and anthropometry of children presenting with CKD in a developing country. In this cross-sectional observational study, children (1-18 years) with CKD visiting the outpatient services were enrolled. The age of onset, cause of CKD, and anthropometry were recorded. Dietary intakes from three 24 h dietary recall (2 mid-week and 1 weekend day) were recorded. A blood sample was taken from all subjects for biochemical parameters. A total of 45 children (forty males and five females) with CKD underwent nutritional assessment. The median age at assessment was 108 months (13-167). Twenty-seven (60%) subjects had CKD stage 1, 2, or 3 while the remaining 40% had CKD stage 4 or 5. Of the 45 children, 27 (60%) had moderate to severe malnutrition at assessment. The mean weight and height (standard deviation scores) were -2.77 ± 2.07 and -2.30 ± 1.38, respectively. The prevalence of growth retardation was much higher in late stages of CKD; the difference was statistically significant (P <0.01). The mean caloric deficit from recommended daily allowance was -40.33% for calories, +6.2% for proteins, and -10.51% for fats. The diet was highly deficient in iron (mean 48.9% deficit); deficient in calcium (mean -22.2%) and had excess phosphates (mean 18.3%). There was a progressive decrease in intake of nutrients in advanced stages of CKD. There was a high prevalence of malnutrition (60%) in children with CKD, especially in higher stages of CKD. An appropriate dietary assessment and nutritional counseling should be planned for all patients with CKD to prevent complications associated with malnutrition and anemia.