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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Electronics, Electromedical Engineering, and Medical Informatics
Ait ider, Abdelouahed
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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.
Optimizing Medical Logistics Networks: A Hybrid Bat-ALNS Approach for Multi-Depot VRPTW and Simultaneous Pickup-Delivery Taha, Anass; Elatar, Said; El Bazzi Mohamed, Salim; Ait Ider, Abdelouahed; Najdi, Lotfi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1054

Abstract

This paper tackles the multi-depot heterogeneous-fleet vehicle-routing problem with time windows and simultaneous pickup and delivery (MDHF-VRPTW-SPD), a variant that mirrors he growing complexity of modern healthcare logistics. The primary purpose of this study is to model this complex routing problem as a mixed-integer linear program and to develop and validate a novel hybrid metaheuristic, B-ALNS, capable of delivering robust, high-quality solutions. The proposed B-ALNS combines a discrete Bat Algorithm with Adaptive Large Neighborhood Search, where the bat component supplies frequency-guided diversification, while ALNS adaptively selects destroy and repair operators and exploits elite memory for focused intensification. Extensive experiments were conducted on twenty new benchmark instances (ranging from 48 to 288 customers), derived from Cordeau’s data and enriched with pickups and a four-class fleet. Results show that B-ALNS attains a mean cost 1.15 % lower than a standalone discrete BA and 2.78 % lower than a simple LNS, achieving the best average cost on 17/20 instances and the global best solution in 85% of test instances. Statistical tests further confirm the superiority of the hybrid B-ALNS, a Friedman test and Wilcoxon signed-rank comparisons give p-value of 0.0013 versus BA and p-value of 0.0002 versus LNS, respectively. Although B-ALNS trades speed for quality (182.65 seconds average runtime versus 54.04 seconds for BA and 11.61 seconds for LNS), it produces markedly more robust solutions, with the lowest cost standard deviation and consistently balanced routes. These results demonstrate that the hybrid B-ALNS delivers statistically significant, high-quality solutions within tactical planning times, offering a practical decision-support tool for secure, cold-chain-compliant healthcare logistics
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 Driss Naji El Bazzi Mohamed, Salim Elatar, Said Elhattab, Kamal Idhmad, Azzeddine Joumad, Abdelali Najdi, Lotfi Ouisaadane, Abdelkbir Taha, Anass