Fairness Comparison of TCP Variants over Proactive and Reactive Routing Protocol in MANET

Mobile ad hoc networks (MANETs) are applicable in an infrastructureless environment where the mobile devices act as routers and intermediate nodes are used to transfer segments to their final destination. As Transmission control protocol (TCP) was originated for Internet with fundamentally different properties, faces serious challenges when used in mobile ad hoc networks. TCP functionality degrades, due to special properties of MANET such as route failure because of significant change of network topology and link errors. TCP uses Congestion Control Algorithms; TCP Vegas is one of them which claim to have better throughput comparing with other TCP variants in a wired network. Fairness issues of TCP Variants in MANET including existing routing protocol are still unsolved. To determine the best TCP Variants in MANET environment over renowned routing protocol is the main objective of this paper. A Study on the throughput fairness of TCP Variants namely, Vegas, Reno, New Reno, SACK, FACK, and Cubic are performed via simulation experiment using network simulator (ns-2) over existing routing protocol, named, AODV, AOMDV, DSDV, and DSR. This fairness evaluation of TCP flows arranged a contrast medium for the TCP Variants using stated routing protocol in MANET. However, TCP Vegas obtain unfair throughput in MANET. The simulation results show that TCP Reno outperforms other TCP variants under DSDV routing protocol

Text to Emotion Extraction Using Supervised Machine Learning Techniques

Proliferation of internet and social media has greatly increased the popularity of text communication. People convey their sentiment and emotion through text which promotes lively communication. Consequently, a tremendous amount of emotional text is generated on different social media and blogs in every moment. This has raised the necessity of automated tool for emotion mining from text. There are various rule based approaches of emotion extraction form text based on emotion intensity lexicon. However, creating emotion intensity lexicon is a time consuming and tedious process. Moreover, there is no hard and fast rule for assigning emotion intensity to words. To solve these difficulties, we propose a machine learning based approach of emotion extraction from text which relies on annotated example rather emotion intensity lexicon. We investigated Multinomial Naïve Bayesian (MNB) Classifier, Artificial Neural Network (ANN) and Support Vector Machine (SVM) for mining emotion from text. In our setup, SVM outperformed other classifiers with promising accuracy

An effective feature extraction method for rice leaf disease classification

Our society is getting more and more technology dependent day by day. Nevertheless, agriculture is imperative for our survival. Rice is one of the primary food grains. It provides sustenance to almost fifty percent of the world population and promotes huge amount of employments. Hence, proper mitigation of rice plant diseases is of paramount importance. A model to detect three rice leaf diseases, namely bacterial leaf blight, brown spot, and leaf smut is proposed in this paper. Backgrounds of the images are removed by saturation threshold while disease affected areas are segmented using hue threshold. Distinctive features from color, shape, and texture domain are extracted from affected areas. These features can robustly describe local and global statistics of such images. Trying a couple of classification algorithms, extreme gradient boosting decision tree ensemble is incorporated in this model for its superior performance. Our model achieves 86.58% accuracy on rice leaf diseases dataset from UCI, which is higher than previous works on the same dataset. Class-wise accuracy of the model is also consistent among the classes

A Fast and Reliable Approach for COVID-19 Detection from CT-Scan Images

Background: COVID-19 is a highly contagious respiratory disease with multiple mutant variants, an asymptotic nature in patients, and with potential to stay undetected in common tests, which makes it deadlier, more transmissible, and harder to detect. Regardless of variants, the COVID-19 infection shows several observable anomalies in the computed tomography (CT) scans of the lungs, even in the early stages of infection. A quick and reliable way of detecting COVID-19 is essential to manage the growing transmission of COVID-19 and save lives. Objective: This study focuses on developing a deep learning model that can be used as an auxiliary decision system to detect COVID-19 from chest CT-scan images quickly and effectively. Methods: In this research, we propose a MobileNet-based transfer learning model to detect COVID-19 in CT-scan images. To test the performance of our proposed model, we collect three publicly available COVID-19 CT-scan datasets and prepare another dataset by combining the collected datasets. We also implement a mobile application using the model trained on the combined dataset, which can be used as an auxiliary decision system for COVID-19 screening in real life. Results: Our proposed model achieves a promising accuracy of 96.14% on the combined dataset and accuracy of 98.75%, 98.54%, and 97.84% respectively in detecting COVID-19 samples on the collected datasets. It also outperforms other transfer learning models while having lower memory consumption, ensuring the best performance in both normal and low-powered, resource-constrained devices. Conclusion: We believe, the promising performance of our proposed method will facilitate its use as an auxiliary decision system to detect COVID-19 patients quickly and reliably. This will allow authorities to take immediate measures to limit COVID-19 transmission to prevent further casualties as well as accelerate the screening for COVID-19 while reducing the workload of medical personnel. Keywords: Auxiliary Decision System, COVID-19, CT Scan, Deep Learning, MobileNet, Transfer Learnin

Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action

Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or “golden rules,” for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice

Security Analysis of Peer-to-Peer based Soft System Bus based Systems

Summary Asynchronous message queuing middleware is a dependable solution for reliable communication in distributed computing environment. Soft System Bus (SSB) is an asynchronous messaging middleware that facilitates availability, reliability and continuity to SSB based Systems (SSBBSs). An SSBBS can be maintained, upgraded and reconfigured during run time without stopping its reactions even when it had some trouble or it is being attacked. A peer-to-peer (P2P) based implementation of an SSBBS was designed for large scale enterprise. However, the security analysis was not sufficient for P2P based SSBBSs. This paper gives a security analysis for P2P-based SSBBSs, and lists up security threats including new types of security threats for P2P-based SSBBSs. The paper also shows security requirements and technical issues for P2P-based SSBBSs

A Novel Approach of Implementing an FFT Algorithm on OFDM System

In this paper, we make insightful assessment of the computational performance of orthogonal frequency division multiplexing (OFDM) system in terms of complex calculations required using different Fourier transform techniques. We briefly introduce the different transform technique, viz. discrete Fourier transform (DFT) and various types of fast Fourier transform (FFT) as 2-radix FFT, 4-radix FFT etc. Underpinning the existing techniques, we propose a new FFT approach that can be applied to an OFDM system with less computation complexity

Privacy preserving support vector machines in wireless sensor networks

It is important to achieve energy efficient data mining in Wireless Sensor Networks (WSN) while preserving privacy of data. In this paper, we present a privacy preserving data mining based on Support Vector Machines (SVM). We review the previous approach in privacy preserving data mining in distributed system. And we also review energy efficient data mining in WSN. We then propose an energy efficient privacy preserving data mining in WSN. We use SVM because it has been shown best classification accuracy and sparse data presentation using support vectors. We show security analysis and energy estimation of our proposed approach

Ten golden rules for optimal antibiotic use in hospital settings : the WARNING call to action

Abstract: Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or "golden rules," for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice