In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurement

In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these

HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

XML is being incorporated into the foundation of E-business data applications. This paper addresses the problem of the freeform information that stored in any organization and how XML with using this new approach will make the operation of the search very efficient and time consuming. This paper introduces new solution and methodology that has been developed to capture and manage such unstructured freeform information (multi information) depending on the use of XML schema technologies, neural network idea and object oriented relational database, in order to provide a practical solution for efficiently management multi freeform information system.

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci