Data Mining Practical Machine Learning Tools and Techniques

With the exponential growth of the Industrial Internet of Things (IIoT), multiple outlets are constantly producing a vast volume of data. It is unwise to locally store all the raw data in the IIoT devices since the energy and storage spaces of the end devices are strictly constrained. self-organization and short-range Internet of Things (IoT) networking also support outsourced data and cloud computing, independent of the distinctive resource constraint properties. For the remainder of the findings, there is a sequence of unfamiliar safeguards for IoT and cloud integration problems. The delivery of cloud computing is highly efficient, storage is becoming more and more current, and some groups are now altering their data from in house records Cloud Computing Vendors' hubs. Intensive IoT applications for workloads and data are subject to challenges while utilizing cloud computing tools. In this report, we research IoT and cloud computing and address cloud-compatible problems and computing techniques to promote the stable transition of IoT programs to the cloud.

https://journal.qubahan.com/index.php/qaj/article/download/36/21

IoT and Cloud Computing Issues, Challenges and Opportunities: A Review

New research into human-computer interaction seeks to consider the consumer's emotional status to provide a seamless human-computer interface. This would make it possible for people to survive and be used in widespread fields, including education and medicine. Multiple techniques can be defined through human feelings, including expressions, facial images, physiological signs, and neuroimaging strategies. This paper presents a review of emotional recognition of multimodal signals using deep learning and comparing their applications based on current studies. Multimodal affective computing systems are studied alongside unimodal solutions as they offer higher accuracy of classification. Accuracy varies according to the number of emotions observed, features extracted, classification system and database consistency. Numerous theories on the methodology of emotional detection and recent emotional science address the following topics. This would encourage studies to understand better physiological signals of the current state of the science and its emotional awareness problems.

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Multimodal Emotion Recognition using Deep Learning

The distributed energy system (DES) architecture is subject to confusion about renewable energy limits, primary energy supply and energy carriers' costs. For the grid to use unreliable electricity sources, the end-user's on-demand presence in the intelligent energy management context is essential. The participation of end-users could influence the management of the system and the volatility of energy prices. By delivering auxiliary services using demand side-resource to increase system reliability, robust planning, constraint control and scheduling, consumers may support grid operators. The optimized approach to managing energy resources enhances demand response to renewable energy sources integrally, controls the demand curve with load versatility as the system requires it. The opportunity to adjust/regulate the charging profile by choosing a particular device. This article discusses a literature and policy analysis that looks at the role of energy management system aggregators and the end-users participating in subsidiary systems within Smart Grid programmers and technologies. In the implementation of aggregators for energy management systems, the objective is to understand the patterns, threats, obstacles and potential obstacles.

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Energy Management for Internet of Things via Distributed Systems

The cloud is the best method used for the utilization and organization of data. The cloud provides many resources for us via the internet. There are many technologies used in cloud computing systems; each one uses a different kind of protocols and methods. Many tasks can execute on different servers per second, which cannot execute on their computer. The most popular technologies used in the cloud system are Hadoop, Dryad, and another map reducing framework. Also, there are many tools used to optimize the performance of the cloud system, such as Cap3, HEP, and Cloudburst. This paper reviews in detail the cloud computing system, its used technologies, and the best technologies used with it according to multiple factors and criteria such as the procedure cost, speed cons and pros. Moreover, A comprehensive comparison of the tools used for the utilization of cloud computing systems is presented.

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Comparison Among Cloud Technologies and Cloud Performance

Optical fibers are utilized widely for data transmission systems because of their capacity to carry extensive information and dielectric nature. Network architectures utilizing multiple wavelengths per optical fiber are used in central, metropolitan, or broad‐area applications to link thousands of users with a vast range of transmission speeds and capacities. A powerful feature of an optical communication link is sending several wavelengths through the 1300‐to‐1600‐ nm range of a fibre simultaneously. The technology of integrating several wavelengths onto a similar fiber is called wavelength division multiplexing (WDM). The principle of WDM utilized in concurrence with optical amplifiers has an outcome in communication links that permit rapid communications among users in the world's countries. This paper presents an overview of the challenges of fibre optic Review Article Kareem et al.; AJRCOS, 7(4): 48-58, 2021; Article no.AJRCOS.67395 49 communication. This paper offers an outline of the areas to be the most relevant for the future advancement of optical communications. The invention of integrated optics and modern optical fibers takes place in the field of optical equipment and components.

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A Survey of Optical Fiber Communications: Challenges and Processing Time Influences

The software development in different fields leads to increase the requirements for effective, efficient and complicated software. Due to the huge amounts of software requirements, it is possible some errors to occur in the certain part of the programs and this means a real challenge for the software producer. The need for an effective test system is necessary for designing reliable programs and avoiding the errors that may appear during the software product. In this review, many techniques are discussed for the process of generating test cases which are a group of conditions that determine whether the designed programs are able to satisfy the user’s requirements or not. Fuzzy logic utilizes an operational profile in the process of allocating test case to improve the software quality, as well as the design of fault propagation path to predicts the software defects during the test operation, also the automatic generation for PLC test cases that produce a new track through the program code in order to minimize the test cases needed for large size program.

custom: The Impact of Test Case Generation Methods on the Software Performance: A Review

In December 2019, SARS-CoV-2 caused coronavirus disease (COVID-19) distributed to all countries, infecting thousands of people and causing deaths. COVID-19 induces mild sickness in most cases, although it may render some people very ill. Therefore, vaccines are in various phases of clinical progress, and some of them being approved for national use. The current state reveals that there is a critical need for a quick and timely solution to the Covid-19 vaccine development. Non-clinical methods such as data mining and machine learning techniques may help do this. This study will focus on the COVID-19 World Vaccination Progress using Machine learning classification Algorithms. The findings of the paper show which algorithm is better for a given dataset. Weka is used to run tests on real-world data, and four output classification algorithms (Decision Tree, K-nearest neighbors, Random Tree, and Naive Bayes) are used to analyze and draw conclusions. The comparison is based on accuracy and performance period, and it was discovered that the Decision Tree outperforms other algorithms in terms of time and accuracy.

https://journal.qubahan.com/index.php/qaj/article/download/53/38

COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms

Nowadays, heart diseases are considered to be the primary reasons for unexpected deaths. Thus, various medical devices have been developed by engineers to diagnose and scrutinize various diseases. Healthcare has become one of the most substantial issues for both individuals and government due to brisk growth in human population and medical expenditure. Many patients suffer from heart problems causing some critical threats to their life, therefore they need continuous monitoring by a traditional monitoring system such as Electrocardiographic (ECG) which is the most important technique used in measuring the electrical activity of the heart, this technique is available only in the hospital which is very costly and far for remote patients. The development of wireless technologies enables to build a network of connected devices via the internet. The proposed ECG monitoring system consists of AD8382 ECG sensor to read patient's data, Arduino Uno, ESP8266 Wi-Fi module, and IoT Blynk application. The implementation of the proposed ECG healthcare system enables the doctor to monitor the patient's remotely using IoT Blynk application installed on his smartphone for processing and visualizing the patient's ECG signal. The monitoring process can be done at any time and anywhere without the need for the hospital.

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Designing ECG Monitoring Healthcare System Based on Internet of Things Blynk Application

Diabetes Mellitus is a chronic disease that spreads quickly worldwide. It results from increasing the blood glucose level and causes complications in the heart, kidney, and eyes. Diabetic Retinopathy (DR) is an eye disease that refers to the bursting of blood vessels in the retina as Diabetes exacerbates. It is considered the main reason for blindness because it appears without showing any symptoms in the primitive stages. Earlier detection and classification of DR cases is a crucial step toward providing the necessary medical treatment. Recently, machine learning plays an efficient role in medical applications and computer-aided diagnosis due to the accelerated development in its algorithms. In this paper, we aim to study the performance of various machine learning algorithms-based DR detection and classification systems. These systems are trained and tested using massive amounts of retina fundus and thermal images from various publicly available datasets. These systems proved their success in tracking down the warning signs and identifying the DR severity level. The reviewed systems' results indicate that ResNet50 deep convolutional neural network was the most effective algorithm for performance metrics. The Resnet50 contains a set of feature extraction kernels that can analyze retina images to extract wealth information. We conclude that machine learning algorithms can support the physician in adopting appropriate diagnoses and treating DR cases.

Dathar Abas Hasan

Papers

A Survey of Optical Fiber Communications: Challenges and Processing Time Influences

custom: The Impact of Test Case Generation Methods on the Software Performance: A Review

COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms

Designing ECG Monitoring Healthcare System Based on Internet of Things Blynk Application

Machine Learning-based Diabetic Retinopathy Early Detection and Classification Systems- A Survey