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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Elhattab, Kamal
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Intelligent agriculture system using low energy and based on the use of the internet of things Elhattab, Kamal; Abouelmehdi, Karim
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6346

Abstract

The field of smart agriculture is ranked among the top areas that uses the internet of things (IoT), whose goal is to increase the quantity and quality of agricultural productivity. The aim of this work is to realize a new device that will be cost-effective, reliable, and autonomous using a solar panel to provide electricity in large-scale agricultural fields, ESP32 to interconnect IoT sensors and the long range (LoRa) data transmission protocol to guarantee connectivity in places where there is no internet, whose objective is to monitor and irrigate agricultural fields only when there is a need for water. The data received by the sensors is sent to mobile app users via the Blynk cloud. The performance of our new approach is measured in terms of energy savings. This new model of irrigation and smart monitoring will improve the efficiency of farming techniques.
Survey of IoT and AI applications: future challenges and opportunities in agriculture Elhattab, Kamal; Elatar, Said
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1655-1663

Abstract

The internet of things (IoT) connects physical objects through sensors, software, and communication technologies, enabling efficient data collection and sharing. This interconnection promotes automation, real-time monitoring, and improved decision-making across various sectors. In agriculture, the integration of IoT with artificial intelligence (AI) is revolutionizing resource management by providing farmers with real-time information on crop health, climate conditions, and soil quality. This paper explores how IoT and AI are transforming traditional agricultural practices to enhance both efficiency and sustainability. Through an in-depth analysis of existing literature and practical applications in the sector, this study identifies significant advancements in crop management, reduction of losses, and resource optimization. Additionally, it highlights persistent challenges such as data security and interoperability. The aim is to address these challenges and propose innovative solutions to optimize agricultural processes. The results indicate that while IoT and AI offer substantial benefits, further advancements and solutions are needed to fully leverage these technologies for sustainable agricultural development.
Evaluating low-cost internet of things and artificial intelligence in agriculture Elhattab, Kamal; Elatar, Said
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp968-975

Abstract

This article investigates the transformative impact of low-cost internet of things (IoT) solutions on the agricultural sector, with a particular emphasis on integrating artificial intelligence (AI) and machine learning (ML) technologies. The study aims to illustrate how affordable IoT technologies, when combined with advanced AI and ML capabilities, can serve as a significant asset for small and medium-sized farms. It addresses the economic and technical barriers these farms face in adopting such technologies, including high initial costs and the complexity of implementation. By conducting a comprehensive evaluation of existing IoT hardware and software, the research identifies and highlights innovative, cost-effective solutions that have the potential to drive significant advancements in agricultural practices. The findings underscore how these integrated technologies can enhance operational efficiency, increase productivity, and support sustainable agricultural development. Additionally, the paper explores the potential challenges and limitations of adopting these technologies, offering insights into how they can be mitigated. Overall, the study demonstrates that the convergence of low-cost IoT with AI and ML presents a valuable opportunity for modernizing agriculture and improving farm management.
The future of healthcare: exploring internet of things and artificial intelligence applications, challenges, and opportunities Elhattab, Kamal; Naji, Driss; Ait ider, Abdelouahed; Abouelmehdi, Karim
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3075-3083

Abstract

The internet of things (IoT) refers to a network of physical devices embedded with sensors, software, and communication tools, which allow for seamless exchange and collection of data. This technology enables automation, continuous monitoring, and data-driven decision-making across a variety of fields. In the healthcare sector, the integration of IoT with artificial intelligence (AI) is transforming how patient care is delivered, providing real-time health monitoring, personalized treatment options, and more efficient management of healthcare resources. This study investigates the significant influence of the IoT and AI on the healthcare system, focusing on how these technologies improve patient outcomes and streamline healthcare operations. It also highlights emerging challenges in the adoption of these technologies and suggests potential solutions to address these obstacles and enhance healthcare delivery. The research is based on an in-depth review of AI and IoT applications in healthcare, uncovering advancements in patient monitoring, disease management, and operational efficiency, while also identifying key challenges such as data privacy concerns and issues with system interoperability.
Hybrid convolutional networks, hidden Markov models, and autoencoders for enhanced recognition Naji, Driss; Elhattab, Kamal; Joumad, Abdelali; Ait Ider, Abdelouahed; Ouisaadane, Abdelkbir; Idhmad, Azzeddine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp780-787

Abstract

Recognition problems, including object detection, scene understanding, and fine-grained categorisation, are popular subjects in computer vision. However, it is challenging to model spatial coherence and contextual dependencies in response to changes in configurations. Human Vs computers' ability in perception-although convolutional neural networks (CNNs) do well in the extraction of features, they have high dependence on local receptive fields and are not able to capture long-range spatial relationships and high-order interactions. To alleviate the shortcomings of the current approaches, we present an enhanced hybrid CNNs two dimensional hidden Markov model (2D-HMM) framework that combines 2D-HMM, Markov random fields (MRF) and variational autoencoders (VAEs) into a single model. The model employs 2D-HMMs for pairwise spatial modelling, MRFs for higher order context, and VAEs for stable latent representation learning. Tested on the MNIST and CIFAR-10 benchmark datasets, our approach consistently outperforms the state-of-the-art performance by 98.2% and 89.5%, respectively, with high robustness to noise and occlusion. Results from ablation studies further show that MRFs improve recall by 1.6% and VAEs improve precision by 1.3%, suggesting that they complement each other sufficiently with respect to overall testing performance. This work unifies deep learning and probabilistic graphical models, leading to more interpretable, scalable, and accurate recognition systems.
Co-Authors Abouelmehdi, Karim Ait ider, Abdelouahed Driss Naji Elatar, Said Idhmad, Azzeddine Joumad, Abdelali Ouisaadane, Abdelkbir