The Effect of the Concentration Chang of Reinforcing Materials on the Values of Some Theoretical Attenuation Parameters

In this paper, some theoretical attenuation  parameters was calculated for shields  made up of composite materials basis polymer (Epoxy )  with added materials (C,Ni,PbO,Bi) with different concentrations (10,20,30,40,50)wt% , where the effect of concentrations variation on the attenuation coefficients values , effective atomic number and shields density has been studying. The computer program code X-Com was used to calculate the attenuation parameter values at different energies (0.662,1.173,1.332)MeV .Results shows that there is a clear change in the values of these parameters with the change of concentration ratios

Implementing an efficient expert system for services center management by fuzzy logic controller

Expert System (ES) is considered to be the prominent reasoning practices which are commonly employed towards various application domains. Considering expert systems, human understanding regarding specific proficiency in accomplishing specific tasks could be signified as facts and rules towards their knowledge base, which finds and employs the data delivered by means of a manipulator. Reasoning procedure has been further employed towards the specified expertise by means of heuristic methods for formulating the elucidation. Mechanisms which employ knowledge based approaches are considered to be more candid when compared to other conservative approaches. Knowledge could be signified clearly towards knowledge base, thereby capable in alteration with comparative easiness, which commonly employs the concept of rules. Inference engines employ knowledge base subjects for solving specific problems based on user responses by means of interface (for instance, specify the situations needed for car assessment). This inference unit deeds with knowledge for applying this knowledge for specific problems. There are numerous approaches for control systems that are applied in all the major areas in industry. In all these approaches for controlling the systems, fuzzy has been deemed to be the best methodology, mainly because of its increased speed and cost-efficiency. For machine regulation, fuzzy logic is found to be vividly employed. This paper mainly focuses in designing the simulation model for fuzzy logic regulator in advising the supervisor of service center in maintaining definite delay in service towards acceptable limit

Bandwidth And Gain Enhancement Of Ultra-Wideband Monopole Antenna Using MEBG Structure

Two designed of Ultra-Wide Band (UWB) monopole antenna with/without Electromagnetic Band Gap (MEBG) mushroom structure has been designed and analyzed. EBG structure is used in UWB monopole antenna to enhance the gain and the bandwidth as well, several main parameters of the proposed antenna are discussed such as return loos, gain, radiation pattern. First design of monopole antenna gave the bandwidth without EBG of 8.069 GHz (2.77-10.84 Ghz) while the maximum gain is 4.4 dB. Whereas second design with EBG gives wide huge bandwidth of 23.33 GHz (2.67-26 GHz) and higher maximum gain 5.8 dB. The highest impedance bandwidth attained is 161% considers VSWR 2:1. The proposed antenna will serve different applications such as Bluetooth, cellular systems and satellite communication and 5 G. The greater bandwidth also offers the antenna low mutual coupling rather than the antenna without EBG. FR4 substrate is used here with comparative permittivity 4.4. At the end of this study, physical model and measured results are presented and the measured results well match with the simulations

Pattern reconfigurable dielectric resonator antenna using capacitor loading for internet of things applications

This research study presents a cube dielectric resonator antenna (DRA) with four different radiation patterns for internet of things (IoT) applications. The various radiation patterns are determined by the grounded capacitor loading to reduce interference. The DRA is constructed of ceramic material with a dielectric constant of 30 and is fed via a coaxial probe located in the antenna’s center. Capacitors are used to load the four parasitic microstrip feed lines. Each pattern of radiation is adjustable by adjusting the capacitors loading on the feed line. The proposed antenna works at 3.5 GHz with -10 narrow impedance bandwidth of 74 MHz

Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution

Vehicle routing problem (VRP) is one of the many difficult issues that have no perfect solutions yet. Many researchers over the last few decades have established numerous researches and used many methods with different techniques to handle it. But, for all research, finding the lowest cost is very complex. However, they have managed to come up with approximate solutions that differ in efficiencies depending on the search space. Problem: In this study the problem is as follows: have a number of vehicles which are used for transporting applications to instance place. Each vehicle starts from a main location at different times every day. The vehicle picks up applications from start locations to the instance place in many different routes and return back to the start location in at specific times every day, starting from early morning until the end of official working hours, on the following conditions: (1) Every location will be visited once in each route, and (2) The capacity of each vehicle is enough for all applications included in each route. Objectives: Our paper attempt to find an optimal route result for VRP by using K-Nearest Neighbor Algorithm (KNNA). To achieve an optimal solution for VRP with the accompanying targets: (1) To reduce the distance and the time for all paths this leads to speedy the transportation of customers to their locations, (2) To implement the capacitated vehicle routing problem (CVRP) model for optimizing the solutions. Approach: The approach has been presented based on two phases: firstly, the algorithms have been adapted to solve the research problem, where its procedure is different than the common algorithm. The structure of the algorithm is designed so that the program does not require a large database to store the population, which speeds up the implementation of the program execution to obtain the solution; secondly, the algorithm has proven its success in solving the problem and finds a shortest route. For the purpose of testing the algorithm’s capability and reliability, it was applied to solve the same problem online validated and it achieved success in finding a shorter route. Finding: The findings outcome from this study have shown that: (1) A universal listed of dynamic KNNACVRP; (2) Identified and built up an assessment measure for KNNACVRP; (3) Highlight the strategies, based KNNA operations, for choosing the most ideal way (4) KNNA finds a shorter route for VRP paths. The extent of lessening the distance for each route is generally short, but the savings in the distance becomes more noteworthy while figuring the aggregate distances traveled by all transports day by day or month to month. This applies likewise to the time calculate that has been decreased marginally in view of the rate of reduction in the distances of the paths

Bio-inspired robotics enabled schemes in blockchain-fog-cloud assisted IoMT environment

Due to emerging developments in sports games, the usage of bio-ankle sensors has been growing progressively. Whereas, Internet of Medical Things (IoMT) is an emerging network that boosts bio-inspired sensors’ performances onto the fog-cloud network. However, a sequence of processes is required to complete the healthcare process for one sportsman. Therefore, workflow-enabled bio-inspired sensors tasks scheduled in IoMT postures different challenges. For instance, cost-efficient scheduling, security, and data validation in distributed hospitals to share their data. In this paper, we devise bio-inspired robotics-enabled schemes in the blockchain-fog-cloud-assisted IoMT environment. The goal is to minimize execution cost and blockchain of applications. Based on the proposed system, the study devises bio-inspired robotics function blockchain task scheduling (BIR-FBTS) schemes, determining the optimal assignment of tasks to the available nodes. The simulation results show that the proposed methods minimized 50% of the service cost and 40% of mined cost in the system compared to all existing bio-inspired healthcare systems

Review On Nasopharyngeal Carcinoma: Concepts, Methods Of Analysis, Segmentation, Classification, Prediction And Impact: A Review Of The Research Literature

Context: Nasopharyngeal Carcinoma (NPC) is the most famous type of tumor in the neck and started in the nasopharynx, the area at the top of the pharynx or “throat”, in which the participation of the relevant nose and tube sound including all upper respiratory tract. Purpose: The study is a reviewed literature on NPC Diagnosis. The objectives of the paper are to under-stand; (1) The conceptual definitions of Nasopharyngeal Carcinoma, (2) the descriptive nature of the structure of the prediction NPC, (3) The contextual usage of NPC, (4) who have access to the NPC, (5)the nature and components of the data used in NPC study, (6) what are the objectives of this field of research (7) what data collection techniques were employed in the previous researches (8) what were the outcome of the previous studies. Methodology: Until this time no systematic literature reviews (SLR) were conducted on NPC based on Segmentation, Classification, and Prediction. The aim of the study therefore, is to conduct a systematic review, classification and comparison of the previous methodology and approaches used in the studies on Nasopharyngeal Carcinoma based on Segmentation, Classification, and Prediction composition (published between 1970 and 2016). We therefore systematically reviewed available researches related to the NPC. We search for available literature using five electronics databases: ScienceDirect.com | Science, health and medical journals, IEEE – The world’s largest technical & professional organization, Springer –International Publisher Science, Technology, Medicine, ACM digital libraries and Google Scholar. Results: The concept of NPC encompasses vast information technologies, there are few papers that dwelt on explanations on the NPC structure or the terminology used, synopsis on the various methods and technologies involved in NPC, the contents of NPC, The various findings on NPC developmental work, Data Analysis, Segmentation, Classification, Prediction and Impacts of Nasopharyngeal Carcinoma. In this study, offered an explanation for NPC defined, how structure prediction Nasopharyngeal Carcinoma described, in what context NPC used, who has access to the NPC, the component data of the NPC used and studied, what is the purpose of research in this field, what methods of data collection was used in the review and whether the results of this study. Also an impression of all the several of methods and technologies involved in NPC, A synopsis of the details of NPC and the result that in NPC development work, segmentation of NPC, NPC diagnoses or the prognosis process

Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images

Breast cancer is considered to be one of the most threatening issues in clinical practice. However, existing breast cancer diagnosis methods face questions of complexity, cost, human-dependency, and inaccuracy. Recently, many computerized and interdisciplinary systems have been developed to avoid human errors in both quantification and diagnosis. A computerized system can be further improved to optimize the efficiency of breast tumour identification. The current paper presents an effort to automate characterization of breast cancer from ultrasound images using multi-fractal dimensions and backpropagation neural networks. In this study, a total of 184 breast ultrasound images (72 abnormal (tumour cases) and 112 normal cases) were examined. Various setups were employed to achieve a decent balance between positive and negative rates of the diagnosed cases. The obtained results manifested in high rates of precision (82.04%), sensitivity (79.39%), and specificity (84.75%)

Systematic Review of Computing Approaches for Breast Cancer Detection Based Computer Aided Diagnosis Using Mammogram Images

Breast cancer is one of the most prevalent types of cancer that plagues females. Mortality from breast cancer could be reduced by diagnosing and identifying it at an early stage. To detect breast cancer, various imaging modalities can be used, such as mammography. Computer-Aided Detection/Diagnosis (CAD) systems can assist an expert radiologist to diagnose breast cancer at an early stage. This paper introduces the findings of a systematic review that seeks to examine the state-of-the-art CAD systems for breast cancer detection. This review is based on 118 publications published in 2018–2021 and retrieved from major scientific publication databases while using a rigorous methodology of a systematic review. We provide a general description and analysis of existing CAD systems that use machine learning methods as well as their current state based on mammogram image modalities and classification methods. This systematic review presents all stages of CAD including pre-processing, segmentation, feature extraction, feature selection, and classification. We identify research gaps and outline recommendations for future research. This systematic review may be helpful for both clinicians, who use CAD systems for early diagnosis of breast cancer, as well as for researchers to find knowledge gaps and create more contributions for breast cancer diagnostics

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset