Load balancing is a standard feature of EFI XF 3.1 and later and is described in the manual provided with the product. However, in EFI XF 3.0, load balancing is not an officially supported feature and can only be achieved by manual ticket editing. Manual ticket editing is only recommended for experienced users. This document describes the steps necessary to achieve load balancing in EFI XF 3.0. NOTE: This method of load balancing cannot be applied to versions of EFI XF prior to 3.0.

Load Balancing

Advances in large-scale integrated logic and communication technology coupled with the applications explosion have led to distributed architectures. A distributed computer system is basically an interconnection of processing elements, each having certain capabilities, communicating with other elements through a network, and working on a set of related or unrelated jobs. DCS architecture is implemented in global systems such as Arpanet, local computer networks such as C,,*, and office information systems. One important DCS characteristic is localized information processing by a subset of the processing elements. A piece of information may be processed repeatedly within a local subsystem before it changes locality. For example, a seat assignment file in an airline reservation system is very heavily used first at the airport where passengers are preparing to board. Then, as the plane flies to another airport, the use locality changes. To maximize the efficiency of the system, the file should be distributed so that it is accessible at the locality of first use and should be allowed to migrate as the need changes. The distribution of DCS information is the distribution design problem. In general, information is partitioned into fragments (files) and placed in appropriate locations. In some systems, the files are uniquely defined, and no partitioning is necessary. But often databases must be partitioned horizontally, when the instances of an object are divided into fragments, or vertically when the attributes are divided into possibly overlapping fragments. After the fragments are partitioned, the FAP, or (optimal) file allocation problem, distributes files and fragments on a DCS to satisfy system objectives, such as availability, reliability, and delay constraints, and if the constraints and needs change dynamically, the files are allowed to migrate. The optimal file migration problem adjusts the file migration sequence to assure efficient adaptation to changing needs. Solutions to the distribution design problems depend on the operations performed on the information. In the simplest form, an operation is a file access from a specified origin. In a database distributed on a DCS, an operation or query may originate in a program located anywhere in the system. It may access multiple files assembled at a single node before it is processed, or the query and the intermediate results created in each step may be sent sequentially through the files. A combination of the two strategies is also possible. In addition, query processing is related to file placement, concurrency control, and communication network design, as shown in Figure 1, since files are partitioned and placed according to the network characteristics and query characteristics in the system. It is very difficult to solve these problems as whole units for real databases, so the designer usually decomposes them into independent problems. The FAP was originally isolated and investigated by Chu2 who studied it with respect to multiple files on a multiprocessor system. Current work lies in the integration of the query processing, file partitioning, concurrency control, and network design problems with the file placement problem. This article examines recent developments in these areas.

File placement on distributed computer systems

Artificial intelligence - enabled soft sensor and internet of things for sustainable agriculture using ensemble deep learning architecture

The majority of countries rely largely on agriculture for employment. Irrigation accounts for a sizable amount of water use. Crop irrigation is an important step in crop yield prediction. Field harvesting is very reliant on human supervision and experience. It is critical to safeguard the field’s water supply. The shortage of fresh water is a major challenge for the world, and the situation will deteriorate further in the next years. As a result of the aforementioned challenges, smart irrigation and precision farming are the only viable solutions. Only with the emergence of the Internet of Things and machine learning have smart irrigation and precision agriculture become economically viable. Increased efficiency, expense optimization, energy maximization, forecasting, and general public convenience are all benefits of the Internet of Things (IoT). As systems and data processing become more diversified, security issues arise. Security and privacy concerns are impeding the growth of the Internet of Things. This article establishes a framework for detecting and classifying intrusions into IoT networks used in agriculture. Security and privacy are major concerns not only in agriculture-related IoT networks but in all applications of the Internet of Things as well. In this framework, the NSL KDD data set is used as an input data set. In the preprocessing of the NSL-KDD data set, first all symbolic features are converted to numeric features. Feature extraction is performed using principal component analysis. Then, machine learning algorithms such as support vector machine, linear regression, and random forest are used to classify preprocessed data set. Performance comparisons of machine learning algorithms are evaluated on the basis of accuracy, precision, and recall parameters.

https://downloads.hindawi.com/journals/jfq/2022/3955514.pdf

Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming

One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of fruits and other foods has increased significantly. This is primarily because these technologies make significant advancements in areas where the human eye falls short. This means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. This article discusses how to check and assess food using picture segmentation and machine learning. It is capable of classifying fruits and determining whether a piece of fruit is rotten. To begin, Gaussian elimination is used to remove noise from images. Then, photos are subjected to histogram equalization in order to improve their quality. Segmentation of the image is carried out using the K-means clustering technique. Then, fruit photos are classified using machine learning methods such as KNN, SVM, and C4.5. These algorithms determine if a fruit is damaged or not.

Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System

The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyberbullying; however, because these attempts rely on the victims’ interactions, they are not practical. Therefore, detection of cyberbullying without the involvement of the victims is necessary. In this study, we attempted to explore this issue by compiling a global dataset of 37,373 unique tweets from Twitter. Moreover, seven machine learning classifiers were used, namely, Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Stochastic Gradient Descent (SGD), Random Forest (RF), AdaBoost (ADB), Naive Bayes (NB), and Support Vector Machine (SVM). Each of these algorithms was evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers’ recognition rates applied to the global dataset. The experimental results show the superiority of LR, which achieved a median accuracy of around 90.57%. Among the classifiers, logistic regression achieved the best F1 score (0.928), SGD achieved the best precision (0.968), and SVM achieved the best recall (1.00).

A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter

: With the rise of new technologies, such as the Internet of Things, raising the productivity of agricultural and farming activities is critical to improving yields and cost-effectiveness. IoT, in particular, can improve the efficiency of agriculture and farming processes by eliminating human intervention through automation. The fast rise of Internet of Things (IoT)-based tools has changed nearly all life sectors, including business, agriculture, surveillance, etc. These radical developments are upending traditional agricultural practices and presenting new options in the face of various obstacles. IoT aids in collecting data that is useful in the farming sector, such as changes in climatic conditions, soil fertility, amount of water required for crops, irrigation, insect and pest detection, bug location disruption of creatures to the sphere, and horticulture. IoT enables farmers to effectively use technology to monitor their forms remotely round the clock. Several sensors, including distributed WSNs (wireless sensor networks), are utilized for agricultural inspection and control, which is very important due to their exact output and utilization. In addition, cameras are utilized to keep an eye on the field from afar. The goal of this research is to evaluate smart agriculture using IoT approaches in depth. The paper demonstrates IoT applications, benefits, current obstacles, and potential solutions in smart agriculture. This smart agricultural system aims to find existing techniques that may be used to boost crop yield and save time, such as water, pesticides, irrigation, crop, and fertilizer management.

A Revisit of Internet of Things Technologies for Monitoring and Control Strategies in Smart Agriculture

The COVID-19 pandemic has limited people’s mobility to a certain extent, making it difficult to purchase goods and services offline, which has led the creation of a culture of increased dependence on online services. One of the crucial issues with using credit cards is fraud, which is a serious challenge in the realm of online transactions. Consequently, there is a huge need to develop the best approach possible to using machine learning in order to prevent almost all fraudulent credit card transactions. This paper studies a total of 66 machine learning models based on two stages of evaluation. A real-world credit card fraud detection dataset of European cardholders is used in each model along with stratified K-fold cross-validation. In the first stage, nine machine learning algorithms are tested to detect fraudulent transactions. The best three algorithms are nominated to be used again in the second stage, with 19 resampling techniques used with each one of the best three algorithms. Out of 330 evaluation metric values that took nearly one month to obtain, the All K-Nearest Neighbors (AllKNN) undersampling technique along with CatBoost (AllKNN-CatBoost) is considered to be the best proposed model. Accordingly, the AllKNN-CatBoost model is compared with related works. The results indicate that the proposed model outperforms previous models with an AUC value of 97.94%, a Recall value of 95.91%, and an F1-Score value of 87.40%.

/pdf/enhanced-credit-card-fraud-detection-model-using-machine-3cjnq632.pdf

Enhanced Credit Card Fraud Detection Model Using Machine Learning

House combustion is one of the main concerns for builders, designers, and property residents Singular sensors were used for a long time in the event of detection of a fire, but these sensors can not measure the amount of fire to alert the emergency response units To address this problem, this study aims to implement a smart fire detection system that would not only detect the fire using integrated sensors but also alert property owners, emergency services, and local police stations to protect lives and valuable assets simultaneously The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the system To get real-life data without putting human lives in danger, an IoT technology has been implemented to provide the fire department with the necessary data Finally, the main feature of the proposed system is to minimize false alarms, which, in turn, makes this system more reliable The experimental results showed the superiority of our model in terms of affordability, effectiveness, and responsiveness as the system uses the Ubidots platform, which makes the data exchange faster and reliable

/pdf/a-smart-fire-detection-system-using-iot-technology-with-19zrgg0pdl.pdf

A smart fire detection system using iot technology with automatic water sprinkler

Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.

https://www.mdpi.com/2071-1050/13/15/8379/pdf

Suliman Mohamed Fati

Papers

A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter

A Revisit of Internet of Things Technologies for Monitoring and Control Strategies in Smart Agriculture

Enhanced Credit Card Fraud Detection Model Using Machine Learning

A smart fire detection system using iot technology with automatic water sprinkler

The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees