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Scientific Journal of Engineering Research
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31091725     DOI : https://doi.org/10.64539/sjer
Core Subject : Engineering,
Engineering
The Scientific Journal of Engineering Research (SJER) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The journal is committed to publishing high-quality articles in all fundamental and interdisciplinary areas of engineering, with a particular emphasis on advancements in Information Technology. It encourages submissions that explore emerging fields such as Machine Learning, Internet of Things (IoT), Deep Learning, Artificial Intelligence (AI), Blockchain, and Big Data, which are at the forefront of innovation and engineering transformation. SJER welcomes original research articles, review papers, and studies involving simulation and practical applications that contribute to advancements in engineering. It encourages research that integrates these technologies across various engineering disciplines. The scope of the journal includes, but is not limited to: Mechanical Engineering Electrical Engineering Electronic Engineering Civil Engineering Architectural Engineering Chemical Engineering Mechatronics and Robotics Computer Engineering Industrial Engineering Environmental Engineering Materials Engineering Energy Engineering All fields related to engineering By fostering innovation and bridging knowledge gaps, SJER aims to contribute to the development of sustainable and intelligent engineering systems for the modern era.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 24 Documents
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The Role of Atomic-Scale Disorder in Tailoring the Functional Properties of Crystalline Materials: A Comprehensive Review Rahman, Md Sultanur; Uddin, Md Jasim; Hasan, Rakib; Mia, Md Mehedi Hasan
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): October Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.326

Abstract

It has long been believed that crystalline solids will always have atomic-scale disorder, which includes vacancies, interstitials, andesite defects, local strain fields, short-range compositional changes, and amorphous pockets. The functional qualities of materials can be controlled by redefining disorder as a flexible and adjustable design parameter. Across classes of crystalline materials (oxides, chalcogenides, perovskites, semiconductors, and two-dimensional crystals), we synthesize experimental and theoretical advances demonstrate how particular types and distributions of atomic-scale disorder alter charge-carrier dynamics, optical absorption and emission, magnetic ordering, ionic conductivity, thermal transport, and mechanical response. Mechanistic relationships are highlighted, including how correlated defect complexes and local strain mediate polaron generation and carrier mobility, how interface disorder and grain-boundary structure control ion transport and catalytic activity, and how point defects alter electronic band edges and trap states. From total-scattering PDF analysis and advanced spectroscopies to aberration-corrected TEM, atom probe tomography, and scanning probe microscopies, we go over characterization tools and how data-driven models, large-scale molecular dynamics, and first-principles calculations are coming together to predict and direct disorder engineering. Successful methods for improving device performance such as defect-enabled light emission, dopant-activated ionic conductors, and disorder-stabilized phases are highlighted in case studies. We conclude with useful recommendations for intentional disorder design and point out unresolved issues, such as in-operando characterization, multiscale modelling, and controlled defect synthesis, providing a roadmap for utilizing atomic-scale disorder to develop next-generation functional materials.
Ensemble Learning Framework for Image-Based Crop Disease Detection Using CNN Models Betrand, Chidi Ukamaka; Benson-Emenike, Mercy Eberechi; Kelechi, Douglas Allswell; Onukwugha, Chinwe Gilean; Oragba, Nneka Martina
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): October Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.330

Abstract

Crop diseases pose a significant threat to global food security, causing substantial yield losses estimated at 10-40% annually. Traditional methods of disease identification, reliant on visual inspection by farmers or experts, are often subjective, time-consuming, and limited by the availability of specialists. This study proposes an ensemble learning framework for robust image-based crop disease detection, specifically designed to address the challenges of heterogeneous, non-Independent and Identically Distributed (non-IID) agricultural datasets in decentralized environments. Utilizing the Plant Village dataset, we implement a stacking ensemble model integrating diverse Convolutional Neural Networks (CNNs) such as VGG (Visual Geometry Group), ResNet, and Inception as base learners, with a meta-learner to optimize prediction fusion. The system employs comprehensive data preprocessing, including resizing, normalization, noise removal, segmentation, and augmentation, to enhance robustness against real-world variability. Transfer learning with ResNet50 was adopted as a baseline model. The baseline ResNet50 achieved 59% test accuracy across seven grape and potato disease classes. The ensemble model improved performance, attaining 63% accuracy with average precision, recall, and F1-scores of 56%, 52%, and 52% respectively. Class imbalance remained a limiting factor for certain categories. The ensemble learning approach outperformed individual models, demonstrating improved generalization across diverse datasets. Although computational demands and imbalance challenges persist, the system provides a promising AI-driven pipeline for accurate crop disease diagnosis, supporting sustainable agricultural practices.
Hybrid Fuzzy–Multi-Objective Particle Swarm Optimization Control for Real-Time Energy Management in PV-Powered Fast Charging Infrastructure for Electric Vehicles Elgammal, Adel
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): October Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.339

Abstract

This paper proposes an innovative Fuzzy–Multi-Objective Particle Swarm Optimization (Fuzzy-MOPSO) based hybrid control strategy for real-time energy management in PV-integrated fast charging systems for EVs. The developed approach combines fuzzy logic control and multi-objective optimization algorithm to achieve dynamic balance between charge rate, power quality, grid stability, and cost of energy. This fuzzy controller can be flexibly used in the presence of variable and uncertainty conditions (e.g., fluctuated solar irradiance, changing EV charging request, grid voltage disturbance) since it has gradual control operations by adjusting converter duty ratios and charging current values. The MOPSO algorithm simultaneously optimizes the multiple antagonistic objectives such as minimization of THD, unity PF with less charging time and increased PV utilization efficiency by adjust fuzzy membership functions and rule weights in real-time. Simulation results in MATLAB/Simulink show that the hybrid controller performs better than classical PI controllers or single fuzzy or PSO based control system. The Fuzzy-MOPSO controller also limits the THD 0.995, and charging efficiency enhancement of (8–12%) with stochastic PV and load changes, in conformity to IEEE-519. Excessively generated energy cost are reduced as well by 15% through the optimal control on the power flow between PV generation, storage and grid. The hybridization of fuzzy reasoning and swarm-based optimization provides for fast transient response, renewable intermittency robustness, and grid integration sustainability. These findings validate that the proposed Fuzzy-MOPSO technique is an appropriate approach to intelligent, efficient and eco-friendly FCI of fast charging in REN smart cities.
Incremental Development of a Framework for Mitigating Adversarial Attacks on CNN Models Nisar, Maaz; Fayyaz, Nabeel; Ahmed, Muhammad Abdullah; Shams, Muhammad Usman; Fareed, Bushra
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): October Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.349

Abstract

This work explores the vulnerability of Convolutional Neural Networks (CNNs) to adversarial attacks, particularly focusing on the Fast Gradient Sign Method (FGSM). Adversarial attacks, which subtly manipulate input images to deceive machine learning models, pose significant threats to the security and reliability of CNN-based systems. The research introduces an enhanced methodology for identifying and mitigating these adversarial threats by incorporating an anti-noise predictor to separate adversarial noise and images, thereby improving detection accuracy. The proposed method was evaluated against multiple adversarial attack strategies using the MNIST dataset, demonstrating superior detection performance compared to existing techniques. Additionally, the study highlights the integration of Fourier domain-based noise accommodation, enhancing robustness against attacks. The findings contribute to the development of more resilient CNN models capable of effectively countering adversarial manipulations, emphasizing the importance of continuous adaptation and multi-layered defense strategies in securing machine learning systems.

Page 3 of 3 | Total Record : 24

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