An internet of things-based medication validity monitoring system

Mohialden, Yasmin Makki; Hussien, Nadia Mahmood; Jabbar, Qabas Abdal Zahraa; Mohammed, Mostafa Abduhgafoor; Sutikno, Tole

doi:10.11591/ijeecs.v26.i2.pp932-938

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…Instead, it creates a discriminative model, similar to many of the classifiers that have been studied in the past (i.e., neural networks and decision trees). KNN plots nonlinear decision boundaries (Xie) on a graph and assists in assigning data points to one of the areas plotted on the graph [90]. It is the fundamental concept of KNN that if a test sample, which is an n-dimensional vector, is similar to a training sample, then it belongs to the training sample class [91].…”

Section: Methodsmentioning

confidence: 99%

Big data classification based on improved parallel k-nearest neighbor

Ali

Mohammed²,

Hasan³

et al. 2023

TELKOMNIKA

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In response to the rapid growth of many sorts of information, highway data has continued to evolve in the direction of big data in terms of scale, type, and structure, exhibiting characteristics of multi-source heterogeneous data. The k-nearest neighbor (KNN) join has received a lot of interest in recent years due to its wide range of applications. Processing KNN joins is time-consuming and inefficient due to the quadratic structure of the join method. As the number of applications dealing with vast amounts of data develops, KNN joins get more sophisticated. The authors seek to save money on computer resources by leveraging a large number of threads and multiprocessors. Six popular datasets are used to apply the method and evaluate the sequential and parallel performance of the KNN technique. These datasets are used to compare the sequential and parallel performance of the KNN method. When compared to a matching multi-core solution, the final implementation saves computing resources. It has been optimized to utilize as little RAM as possible, allowing it to manage high-resolution photo data without sacrificing efficiency. The authors will use the technique they presented using Spark Radoop. Our performance research validates the supplied method's efficacy and scalability.

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Section: Methodsmentioning

confidence: 99%

Big data classification based on improved parallel k-nearest neighbor

Ali

Mohammed²,

Hasan³

et al. 2023

TELKOMNIKA

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“…To determine the function and choose the subset of first 20 features, this method employs built-in function selection and filtering approaches, especially the F test, the light gradient amplification algorithm, and the random forest (RF) algorithm. The proposed model was then tested on more attacks after being trained using the records for a specific type of attack [26]- [28]. The AE-SVM model is intended to quickly identify attacks.…”

Section: Introductionmentioning

confidence: 99%

Distributed denial of service attack defense system-based auto machine learning algorithm

Aljanabi

Altaie²,

Talib³

et al. 2023

Bulletin EEI

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The use of network-connected gadgets is rising quickly in the internet age, which is escalating the number of cyberattacks. The detection of distributed denial of service (DDoS) attacks is a tedious task that has necessitated the development of a number of models for its identification recently. Nonetheless, because of major fluctuations in subscriptions and traffic rates, it continues to be a difficult challenge. A novel automatic detection technique was created to address this issue in this work, which reduces the feature space and consequently minimizes the computational time and model overfitting. Data preprocessing is done first to increase the model's generalizability; then, a feature selection method is used to choose the most pertinent features to increase the accuracy of the classification process. Additionally, hyperparameter tuning-choosing the proper parameters for the learning approach-improved model performance. Finally, the support vector machine (SVM) is compatible with the optimization and the hyperparameters offered by supervised learning methods. The CICDDoS2019 dataset was used to evaluate each of these assays, and the experimental findings demonstrated that, with an accuracy of 99.95%, the suggested model performs well when compared to more modern techniques.

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“…The IoT enables individuals to live and work more effectively, as well as having total control over their life. IoT is critical for businesses, in addition to delivering smart gadgets for home automation [7], [58], [66], [73], [78], [122]. IoT provides organizations with a real-time perspective of how their systems are doing, providing insights into machine performance, supply chain, and logistical operations [71], [97].…”

Section: Introductionmentioning

confidence: 99%

“…The IoT allows businesses to automate operations and save labor costs. It also reduces waste and enhances service delivery by reducing the cost of making and transporting goods and by providing customers with more transaction-related information [68], [73], [90], [116], [134]. Due to the fact that more businesses are realizing the value of connected devices for their ability to compete, IoT has grown to become one of the most important technologies in modern life.…”

Section: Introductionmentioning

confidence: 99%

Insights on the internet of things: past, present, and future directions

Sutikno

Thalmann

2022

TELKOMNIKA

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The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process's ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.

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An internet of things-based medication validity monitoring system

Cited by 3 publications

References 12 publications

Big data classification based on improved parallel k-nearest neighbor

Big data classification based on improved parallel k-nearest neighbor

Distributed denial of service attack defense system-based auto machine learning algorithm

Insights on the internet of things: past, present, and future directions

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