Analysis of Elliptic Curve Cryptography (ECC) for Energy Efficiency in Wireless Sensor Networks

Rapid growth of wireless sensor networks (WSN) in recent times has resulted in greater security requirements. One of the primary concerns in wireless sensor networks is energy efficiency and security mechanisms are no different.  Currently, security in wireless sensor networks is often implemented by symmetric key cryptography due to its low-power implementation. Public Key Cryptography (PKC), on the other hand, is advantageous as it requires less overhead information during transmission of packets that ultimately lessens overall size of the protocol. In addition, Public Key Cryptography provides better data confidentiality and authentication in wireless sensor networks. In this study, we focus on Public Key Cryptography for greater efficiency in key distribution, low protocol overhead and efficient hardware implementation on the sensor nodes. Considering the constraints of energy efficient wireless sensor networks, we analyze and compare some well known Public Key algorithms, their implementation in wireless sensor networks, and how these algorithms can benefit the fundamental security services. We also evaluate energy consumption parameters for encryption as well as data transmission and suggest energy efficient encryption mechanisms

Uncertainty assisted robust tuberculosis identification with Bayesian convolutional neural networks

Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty

Frequency of Diabetic and Non-diabetic patients having fetal anomalies at 3rd trimester using ultrasound

Background: Pre-gestational or gestational diabetes in pregnancy is now more common among pregnant mothers as a result of the obesity pandemic. Objective: To determine frequency of diabetic and non-diabetic patients having fetal anomalies at 3rd trimester using ultrasound. Methodology: Descriptive study was conducted at radiology department Chughtai Lab, Lahore. About 250 Diabetic and Non-Diabetic pregnant women of all age were included in this study. Consecutive sampling technique was used Data was analyzed by SSPS version 24.0. All quantitative variables were reported in mean ± S.D were presented in frequency and percentage and bar charts were presented. Results: The mean age of 250 participants was 28±5.1 with minimum age of 15 years and maximum age of 45 years. Out of 250 participants, 210(84%) had no Gestational Diabetes Mellitus and 40(16%) had Gestational Diabetes Mellitus. Out of 250 patients, 204(81.6%) had adequate Amniotic Fluid Index value, 27(10.8%) had Oligohydramnios and 19(7.6%) had Polyhydramnios. In our study 16(6.4%) diabetic patients and 26(10.4%) non-diabetic patients have anomalies. Conclusion: The study concluded that frequency of anomalies doesn’t depend on patients being diabetic or non-diabetic. As in our study diabetic patients are lesser anomalies than the non-diabetic patients. &nbsp

Prevalence of Multiplicity and Appropriate Adjustments Among Cardiovascular Randomized Clinical Trials Published in Major Medical Journals.

IMPORTANCE: Multiple analyses in a clinical trial can increase the probability of inaccurately concluding that there is a statistically significant treatment effect. However, to date, it is unknown how many randomized clinical trials (RCTs) perform adjustments for multiple comparisons, the lack of which could lead to erroneous findings. OBJECTIVES: To assess the prevalence of multiplicity and whether appropriate multiplicity adjustments were performed among cardiovascular RCTs published in 6 medical journals with a high impact factor. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, cardiovascular RCTs were selected from all over the world, characterized as North America, Western Europe, multiregional, and rest of the world. Data were collected from past issues of 3 cardiovascular journals (Circulation, European Heart Journal, and Journal of the American College of Cardiology) and 3 general medicine journals (JAMA, The Lancet, and The New England Journal of Medicine) with high impact factors published between August 1, 2015, and July 31, 2018. Supplements and trial protocols of each of the included RCTs were also searched for multiplicity. Data were analyzed December 20 to 27, 2018. EXPOSURES: Data from the selected RCTs were extracted and verified independently by 2 researchers using a structured data instrument. In case of disagreement, a third reviewer helped to achieve consensus. An RCT was considered to have multiple treatment groups if it had more than 2 arms; multiple outcomes were defined as having more than 1 primary outcome, and multiple analyses were defined as analysis of the same outcome variable in multiple ways. Multiplicity was examined only for the analysis of the primary end point. MAIN OUTCOMES AND MEASURES: Outcomes of interest were percentages of primary analyses that performed multiplicity adjustment of primary end points. RESULTS: Of 511 cardiovascular RCTs included in this analysis, 300 (58.7%) had some form of multiplicity; of these 300, only 85 (28.3%) adjusted for multiplicity. Intervention type and funding source had no statistically significant association with the reporting of multiplicity risk adjustment. Trials that assessed mortality vs nonmortality outcomes were more likely to contain a multiplicity risk in their primary analysis (66.3% [177 of 267] vs 50.4% [123 of 244]; P < .001), and larger trials vs smaller trials were less likely to make any adjustments for multiplicity (35.6% [52 of 146] vs 21.4% [33 of 154]; P = .001). CONCLUSIONS AND RELEVANCE: Findings from this study suggest that cardiovascular RCTs published in medical journals with high impact factors demonstrate infrequent adjustments to correct for multiple comparisons in the primary end point. These parameters may be improved by more standardized reporting