This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota

A novel method for Network Intrusion Detection System (NIDS) has been proposed, based on the concept of how DNA sequence detects disease as both domains have similar conceptual method of detection. Three important steps have been proposed to apply DNA sequence for NIDS: convert the network traffic data into a form of DNA sequence using Cryptography encoding method; discover patterns of Short Tandem Repeats (STR) sequence for each network traffic attack using Teiresias algorithm; and conduct classification process depends upon STR sequence based on Horspool algorithm. 10% KDD Cup 1999 data set is used for training phase. Correct KDD Cup 1999 data set is used for testing phase to evaluate the proposed method. The current experiment results sh

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

This study presents experimental and numerical investigations on seven one-way, reinforced concrete (RC) slabs with a new technique of slab weight reduction using polystyrene-embedded arched blocks (PEABs). All slabs had the same dimensions, steel reinforcement, and concrete compressive strength. One of these slabs was a solid slab, which was taken as a control slab, while the other six slabs were cast with PEABs. The main variables were the ratio of the length of the PEABs to the length of the slab (lp/L) and the ratio of the height of the PEABs to the total slab depth (hP/H). The minimum decrease in the ultimate load capacity was about 6% with a minimum reduction in the slab weight of 15%. In contrast, the maximum decrease in the

Test results of eight reinforced concrete one way slab with lacing reinforcement are reported. The tests were designed to study the effect of the lacing reinforcement on the flexural behavior of one way slabs. The test parameters were the lacing steel ratio, flexural steel ratio and span to the effective depth ratio. One specimen had no lacing reinforcement and the remaining seven had various percentages of lacing and flexural steel ratios. All specimens were cast with normal density concrete of approximately 30 MPa compressive strength. The specimens were tested under two equal line loads applied statically at a thirds part (four point bending test) up to failure. Three percentage of lacing and flexural steel ratios wer

Astronomy image is regarded main source of information to discover outer space, therefore to know the basic contain for galaxy (Milky way), it was classified using Variable Precision Rough Sets technique to determine the different region within galaxy according different color in the image. From classified image we can determined the percentage for each class and then what is the percentage mean. In this technique a good classified image result and faster time required to done the classification process.

Accurate and simple techniques for measurement of fluid rheological properties are important for field operations in the oil industry. Marsh Funnels are popular qualitycontrol tools used in the field for drilling fluids and they offer a simple, practical alternative to viscosity measurement. In the normal measurements, a single point (drainage time) is used to determine an average viscosity; little additional information is extracted regarding the non-Newtonian behavior of the fluid. Here, a new model is developed and used to determine the rheological properties of drilling muds and other non-Newtonian fluids using data of fluid density and drainage time collected from a Marsh Funnel as a function of viscosity. The funnel results for viscos

Accurate and simple techniques for measurement of fluid rheological properties are important for field operations in the oil industry. Marsh Funnels are popular quality-control tools used in the field for drilling fluids and they offer a simple, practical alternative to viscosity measurement. In the normal measurements, a single point (drainage time) is used to determine an average viscosity; little additional information is extracted regarding the non-Newtonian behavior of the fluid.
Here, a new model is developed and used to determine the rheological properties of drilling muds and other non-Newtonian fluids using data of fluid density and drainage time collected from a Marsh Funnel as a function of viscosity. The funnel results for