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CALAR: Community Aware Location Assisted Routing Framework for Delay Tolerant Networks

Infrastructure less communication strategies havegreatly evolved and found its way to most of our real lifeapplications like sensor networks, terrestrial communications,military communications etc. The communication pattern for allthese scenarios being identical i.e. encounter basedcommunication,characteristics of each communication domainare distinct. Hence the protocols applied for each environmentshould be defined carefully by considering its owncommunication patterns. While designing a routing protocol themain aspects under consideration include delay, connectivity,cost etc. In case of applications having limited connectivity,concept of Delay tolerant network (DTN) is deployed, whichassists delivering messages even in partitioned networks withlimited connectivity by using store and forward architecture.Node properties like contact duration, inter contact duration,location, community, direction of movement, angle of contact etc.were used for designing different classes of routing protocols forDTN. This paper introduces a new protocol that exploits thefeatures of both community based as well as location basedrouting protocols to achieve higher data delivery ratio invehicular scenarios. Results obtained show that proposedalgorithms have much improved delivery ratio comparedtoexisting routing algorithms which use any one of the aboveproperty individually

High risk scoring for prediction of pregnancy outcome: a prospective study

Background: Objectives of current study were to detect high risk factors in pregnancy and to develop a simple scoring system to identify and categorize high risk pregnancies and to predict neonatal outcome by prospective multifactorial analysis of high risk factors.  Methods: In this prospective study, antepartum, intrapartum and neonatal parameters were integrated into the clinical records and the relationship of risk score to outcome was evaluated for 415 randomly selected pregnant patients over a period of 1 year. Risk scoring was applied on selected mothers more than 28 weeks of gestation who presented in labour.Results: Out of 415 women, 96 (59%) were High Risk, 191 (46%) were Low risk and 128 (31%) were No risk. In High risk group there were 59 perinatal deaths and perinatal mortality rate was very high (614 per 1000 live births).Conclusions: The risk scoring system can thus be used not only as a test for predicting perinatal mortality but also as a simple and cost effective screening tool for identifying pregnancies at higher risk of perinatal mortality and morbidity so that these are subjected to the special ‘high risk’ care they need

Cryptanalysis of Mono-Alphabetic Substitution Ciphers using Genetic Algorithms and Simulated Annealing

In this paper, we intend to apply the principles of genetic algorithms along with simulated annealing to cryptanalyze a mono-alphabetic substitution cipher. The type of attack used for cryptanalysis is a ciphertext-only attack in which we don’t know any plaintext. In genetic algorithms and simulated annealing, for ciphertext-only attack, we need to have the solution space or any method to match the decrypted text to the language text. However, the challenge is to implement the project while maintaining computational efficiency and a high degree of security. We carry out three attacks, the first of which uses genetic algorithms alone, the second which uses simulated annealing alone and the third which uses a combination of genetic algorithms and simulated annealing

Intrusion detection using clustering

In increasing trends of network environment every one gets connected to the system. So there is need of securing information, because there are lots of security threats are present in network environment. A number of techniques are available for intrusion detection. Data mining is the one of the efficient techniques available for intrusion detection. Data mining techniques may be supervised or unsuprevised.Various Author have applied various clustering algorithm for intrusion detection, but all of these are suffers form class dominance, force assignment and No Class problem. This paper proposes a hybrid model to overcome these problems. The performance of proposed model is evaluated over KDD Cup 1999 data set

Evaluation of Spray Based Routing Approaches in Delay Tolerant Networks

Delay Tolerant Networks (DTN) are mobile ad-hoc networks in which connections are often disruptive or discontinuous. Data forwarding using an appropriate routing strategy is a highly confronting issue in such networks. The traditional ad-hoc routing protocols which require end-to-end connectivity fail to function here due to frequent occurrences of network partitions. Spray and Wait (SaW) routing algorithm is a popular controlled replication based DTN protocol which provides a better delivery performance balancing the average delay and overhead ratio. An empirical analysis of various spray based approaches that have been proposed for DTN has been performed in this paper to compare and evaluate the basic Spray and Wait algorithms (Source Spray and Wait and Binary Sprayand Wait) with some of its major improvements (Spray andFocus, Average Delivery Probability Binary Spray and Wait and Composite methods to improve Spray and Wait). The main aim of this comparative study is to verify the effect of utility metrics in spray based routing protocols over simple spray based approaches. The ONE simulator has been used to provide a simulation environment to evaluate these algorithms and generate results. The performance metrics used are delivery ratio (DR), overhead ratio (OR) and average latency (ALat). The simulation results show that in terms of delivery ratio and average latency, Composite methods to improve Spray and Wait which incorporates delivery predictability metric in the wait phase and also acknowledgements to delete already deliveredmessages from a node’s buffer, outperforms all the other variants compared

Effect of frequent hand washing for COVID-19 prevention

Background: Coronavirus disease 2019 (COVID-19) has become a global public health concern. While dealing with COVID-19 pandemic, hand washing and the use of hand-hygiene products has been advocated, as a preventive measure. However, frequent hand washing leads to an increased risk of skin changes ranging from dryness and peeling of skin to itching, redness and blister formation. This study aims to understand the hand-hygiene practices and compare side effects between group using hand sanitizers with the group using soap with water.Methods: A 12-item self-administered close ended questionnaire assessing the hand washing habits and effect of the same on skin was used. A total of 60 cases were enrolled. The correlation between use of hand hygiene measures and the clinical changes was studied.Results: In our study, 32 out of the 60 reported a frequency of hand washing between 5-10 times a day. The awareness about using hand moisturizer was noted in 75% individuals. Side effects were more commonly observed in group B using alcohol-based sanitizers as compared to group A using soap with water. Dryness was the most common symptom, observed in 23.3% individuals using alcohol-based sanitizers and 10% individuals using soap with water.Conclusions: Hand-hygiene measures remain the cornerstone of prevention of COVID-19 transmission. However, the use of hand-hygiene products is associated with side effects especially dryness. Regular use of hand moisturizer is essential in preventing the unnecessary effects of frequent hand washing

MODBUS Protocol for Reading Parameter of AC Drive

This research paper is aimed for reading the parameters of AC Drive by using MODBUS Protocol. This communication protocol is used for Programmable Logic Controller (PLC) and this PLC is handling by controller. Hence in this we have checked and observed the parameters of AC Drive by PIC controller with Docklight Software. Here, Docklight software works exactly as AC Drive to read parameters by MODBUS Protocol. MODBUS module is a TTL to RS485 converter Module

Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks

Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow. © 2013 IEEE