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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science
Elatar, Said
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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.
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
Co-Authors Ait ider, Abdelouahed El Bazzi Mohamed, Salim Elhattab, Kamal Najdi, Lotfi Taha, Anass