The cloud-users are getting impatient by experiencing the delays in loading the content of the web applications over the internet, which is usually caused by the complex latency while accessing the cloud datacenters distant from the cloud-users. It is becoming a catastrophic situation in availing the services and applications over the cloud-centric network. In cloud, workload is distributed across the multiple layers which also increases the latency. Time-sensitive Internet of Things (IoT) applications and services, usually in a cloud platform, are running over various virtual machines (VM’s) and possess high complexities while interacting. They face difficulties in the consolidations of the various applications containing heterog

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Mak

    In data mining and machine learning methods, it is traditionally assumed that training data, test data, and the data that will be processed in the future, should have the same feature space distribution. This is a condition that will not happen in the real world. In order to overcome this challenge, domain adaptation-based methods are used. One of the existing challenges in domain adaptation-based methods is to select the most efficient features so that they can also show the most efficiency in the destination database. In this paper, a new feature selection method based on deep reinforcement learning is proposed. In the proposed method, in order to select the best and most appropriate features, the essential policies

A novel robust finite time disturbance observer (RFTDO) based on an independent output-finite time composite control (FTCC) scheme is proposed for an air conditioning-system temperature and humidity regulation. The variable air volume (VAV) of the system is represented by two first-order mathematical models for the temperature and humidity dynamics. In the temperature loop dynamics, a RFTDO temperature (RFTDO-T) and an FTCC temperature (FTCC-T) are designed to estimate and reject the lumped disturbances of the temperature subsystem. In the humidity loop, a robust output of the FTCC humidity (FTCC-H) and RFTDO humidity (RFTDO-H) are also designed to estimate and reject the lumped disturbances of the humidity subsystem. Based on Lyapunov theo

Abstract

This study investigates the mechanical compression properties of tin-lead and lead-free alloy spherical balls, using more than 500 samples to identify statistical variability in the properties in each alloy. Isothermal aging was done to study and compare the aging effect on the microstructure and properties.

The results showed significant elastic and plastic anisotropy of tin phase in lead-free tin based solder and that was compared with simulation using a Crystal Plasticity Finite Element (CPEF) method that has the anisotropy of Sn installed. The results and experiments were in good agreement, indicating the range of values expected with anisotropic properties.

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Multi-spectral satellite images of the Landsat satellite by the tow sensitive Thematic Mapper (TM) and Thematic Mapper Enhancement (ETM+), which covered the study area located south east of Iraq. In this research; used the sixth thermal spectral band (Thermal Band) for study the water cover in the AlRazzaza Lake located within the province of Karbala. We intended to study the cover a case of the study area, used satellite images showing the status of region during the period from 1990 to 2001 and 2007. From this study we conclude that cover the water of the study area change in sequence case to decrease during these years.

This research deals with unusual approach for analyzing the Simple Linear Regression via Linear Programming by Two - phase method, which is known in Operations Research: “O.R.”. The estimation here is found by solving optimization problem when adding artificial variables: Ri. Another method to analyze the Simple Linear Regression is introduced in this research, where the conditional Median of (y) was taken under consideration by minimizing the Sum of Absolute Residuals instead of finding the conditional Mean of (y) which depends on minimizing the Sum of Squared Residuals, that is called: “Median Regression”. Also, an Iterative Reweighted Least Squared based on the Absolute Residuals as weights is performed here as another method to

     As a result of the pandemic crisis and the shift to digitization, cyber-attacks are at an all-time high in the modern day despite good technological advancement. The use of wireless sensor networks (WSNs) is an indicator of technical advancement in most industries. For the safe transfer of data, security objectives such as confidentiality, integrity, and availability must be maintained. The security features of WSN are split into node level and network level. For the node level, a proactive strategy using deep learning /machine learning techniques is suggested. The primary benefit of this proactive approach is that it foresees the cyber-attack before it is launched, allowing for damage mitigation. A cryptography algorithm is put