The IIUM Engineering Journal, published tri-annually (January, May and September), is a double-blind peer-reviewed open-access journal of the Kulliyyah of Engineering, International Islamic University Malaysia (IIUM). Our next issue will be on May 2026 We are currently indexed in: Scopus (Since 2016) MyCite WoS(Emerging Sources Citation)
It is with great pleasure that we present Volume 27, Number 1 of the IIUM Engineering Journal, an issue that reflects the breadth, depth, and evolving priorities of contemporary engineering research. The papers published in this volume collectively demonstrate how rigorous theoretical foundations, advanced computational techniques, and practical engineering solutions converge to address pressing technological, environmental, and societal challenges. This issue opens with contributions from Chemical and Biotechnology Engineering, where research on the growth kinetics of Pleurotus pulmonarius advances sustainable biotechnology practices through innovative methods to revive spawn culture. This work highlights how process optimization and biological engineering can contribute to food security and value-added agro-industrial applications. A substantial portion of this volume is devoted to Electrical, Computer, and Communications Engineering, underscoring the rapid progress in intelligent systems, data-driven modeling, and energy-aware technologies. Several papers explore the integration of artificial intelligence and machine learning into diverse domains, including graph neural networks for large-scale skyline query processing, transformer-based malware detection at the byte level, deep learning with explainability for skin care detection, and advanced sentiment prediction models. Complementing these are applied engineering studies on inertial sensor self-calibration for autonomous underwater vehicles, LoRaWAN-based IoT systems for electric fence monitoring, optimized PID control for energy-efficient induction motors, and vision-transformer-based groundwater-level forecasting for sustainable water-resource management. Together, these works illustrate how AI-enhanced engineering is reshaping decision-making, automation, and system reliability. In the Materials and Manufacturing Engineering section, the focus shifts to next-generation materials and devices that support sustainability and high-performance applications. Contributions on eco-friendly tin-based perovskite solar cells and graphene–silver hybrid inks provide valuable insights into material characterization, performance optimization, and the development of greener energy and electronic solutions. The Mechanical and Aerospace Engineering contribution in this issue addresses surrogate model-based optimization of unmanned aerial vehicle wings, demonstrating how computational intelligence and response surface methodologies can accelerate design cycles while maintaining aerodynamic efficiency and structural integrity. Research in Mechatronics and Automation Engineering further enriches this volume, covering a wide spectrum of intelligent and autonomous systems. Papers in this section examine humanoid robot stability on uneven terrain, vehicle identification using YOLO-based deep learning, autonomous underwater vehicles for subaquatic exploration, FSM-based MBIST controller implementation, adaptive energy balance control for solar-powered hydroponics, and advanced medical image segmentation using attention-enhanced neural architectures. Collectively, these studies highlight the increasing role of autonomy, robustness, and sustainability in modern engineered systems. Taken as a whole, Vol. 27 No. 1 exemplifies the journal’s commitment to publishing high-quality, interdisciplinary engineering research that bridges theory and practice. The diversity of topics and methodologies presented here reflects not only the dynamic nature of engineering as a discipline but also its critical role in supporting sustainable development, digital transformation, and technological innovation. We extend our sincere appreciation to the authors for their valuable contributions, to the reviewers for their rigorous and constructive evaluations, and to the editorial team for their dedication in bringing this issue to publication. We hope that the articles in this volume will stimulate further research, inspire collaboration across disciplines, and serve as a meaningful reference for researchers, practitioners, and policymakers alike. Prof. Ir. Ts. Dr. Teddy Surya Gunawan Executive Editor IIUM Engineering Journal ISSN: 1511-788X E-ISSN: 2289-7860 Published by: IIUM Press, International Islamic University Malaysia Jalan Gombak, 53100 Kuala Lumpur, Malaysia Phone (+603) 6421-5014, Fax: (+603) 6421-6298
Copyright (c) 2026 IIUM Press https://creativecommons.org/licenses/by-nc/4.0
Stability and Control of Humanoid Robots during Walking and Sudden Stops on Uneven Terrain using Inverted Pendulum Modeling and Fuzzy-enhanced Linear Quadratic Regulator
Humanoid robots, designed to resemble human structure with a head, arms, and legs, stand out among various robot types due to their ability to interact with human-designed environments. Their kinematic structure, composed of links connected by joints, enables humanoid robots to perform tasks such as walking on uneven terrain, lifting objects, and opening doors. Walking is a critical function for humanoid robots, but it shifts their center of mass (CoM), which can lead to instability and falls. The inverted pendulum model is a widely used approach to humanoid robot walking, particularly on uneven surfaces, enabling CoM projection and balance adjustment. However, disturbances from floor unevenness, mechanical issues, and other factors can still cause the robot to fall. This underscores the need for effective control systems to stabilize movement and maintain balance. The Linear Quadratic Regulator (LQR) is a suitable control approach for humanoid robots, particularly in multiple-input, multiple-output systems. It stabilizes the robot while managing computational load, generating complex humanoid movements through linear approximations. To address uncertainties and adapt control gains, fuzzy control can be integrated, thereby enabling smoother transitions and improved disturbance handling. When walking on uneven terrain, a humanoid robot's body orientation may shift, causing the CoM to exceed tolerance limits and leading to falls. Thus, a control system that adjusts the walking pattern to maintain CoM stability is essential. In conclusion, humanoid robots, with their human-like structure, can perform complex tasks in human environments. Control systems, such as LQR and fuzzy control, are critical for maintaining balance and stability, even under challenging conditions, thereby enabling humanoid robots to keep their trajectory and prevent falls. ABSTRAK: Robot seperti manusia (humanoid), direka menyerupai struktur manusia dengan kepala, lengan, dan kaki, menonjol dalam pelbagai jenis robot kerana keupayaannya berinteraksi dengan persekitaran yang direka oleh manusia. Struktur kinematiknya, terdiri daripada pautan yang dihubungkan oleh sendi, membolehkan robot humanoid melakukan tugas seperti berjalan di permukaan tidak rata, mengangkat objek, dan membuka pintu. Berjalan adalah fungsi penting robot humanoid, tetapi pergerakan mengubah pusat jisim (CoM), menyebabkan ketidakstabilan dan jatuh. Model bandul terbalik adalah pendekatan biasa digunakan bagi robot humanoid berjalan, terutama pada permukaan tidak rata, membolehkan unjuran CoM dan pelarasan keseimbangan. Namun, gangguan ketidaksamaan permukaan lantai, masalah mekanikal, dan faktor lain masih boleh menyebabkan robot jatuh. Ini menunjukkan keperluan pada sistem kawalan berkesan yang menstabilkan pergerakan dan mengekalkan keseimbangan. Regulator Kuadratik Linear (LQR) adalah pendekatan kawalan sesuai untuk robot humanoid, terutama dalam sistem masukan dan keluaran berganda. Ia menstabilkan robot sambil menguruskan beban pengiraan, menghasilkan pergerakan humanoid yang kompleks melalui pendekatan linear. Bagi menangani ketidakpastian dan menyesuai keuntungan kawalan, kawalan kabur boleh diintegrasikan, menjadikan peralihan lebih lancar dan pengendalian gangguan lebih baik. Apabila berjalan di permukaan tidak rata, orientasi badan robot humanoid mungkin berubah, menyebabkan CoM bergerak melebihi had toleransi, yang mengakibatkan jatuh. Oleh itu, sistem kawalan penyesuaian corak berjalan bagi mengekalkan kestabilan CoM adalah penting. Kesimpulannya, robot humanoid, dengan struktur seperti manusia, dapat melakukan tugas kompleks dalam persekitaran manusia. Sistem kawalan seperti LQR dan kawalan kabur adalah penting bagi memastikan keseimbangan dan kestabilan, walaupun dalam keadaan mencabar, membolehkan robot humanoid mengekalkan trajektori mereka dan mencegah jatuh.
Vehicle Identification and Classification Using YOLO Algorithm
Vehicle identification and classification are among the challenging activities for the management and control of a large number of different vehicles moving in the inner city. Among many identification and classification systems, the YOLO algorithm stands out for its ability to analyze at high speed and with high accuracy. The algorithm is continually evolving, with notable versions including YOLOv8. This research presents a method for identifying and classifying vehicles using the YOLOv8 algorithm. The assessment of the proposed method's effectiveness was conducted using two COCO datasets (328,000 images) and a real-world dataset from Ho Chi Minh City (HCMC) with more than 1,000 images. The findings indicate that the proposed method can be applied to identify and classify vehicles with an accuracy of 93%-98%. Comparative results with prior studies also demonstrate the superiority of the YOLOv8 algorithm. ABSTRAK: Pengecaman dan pengelasan kenderaan adalah salah satu aktiviti mencabar dalam pengurusan dan kawalan sejumlah besar kenderaan berbeza yang bergerak di bandar. Di antara kebanyakan sistem pengecaman dan pengelasan, algoritma YOLO menonjol kerana keupayaannya menganalisa pada kelajuan berketepatan tinggi. Algoritma ini terus dibangunkan dengan penambahbaikan yang banyak dan versi yang ketara ialah YOLOv8. Penyelidikan ini membentangkan kaedah mengenal pasti dan mengelaskan kenderaan menggunakan algoritma YOLOv8. Penemuan penilaian keberkesanan kaedah yang dicadangkan telah dijalankan menggunakan dua set data COCO dengan 328,000 imej dan set data sebenar di Ho Chi Minh City (HCMC) lebih daripada 1,000 imej. Dapatan kajian menunjukkan kaedah ini diaplikasikan dalam mengenal pasti dan pengelasan kenderaan berketepatan 93% hingga 98%. Hasil perbandingan dengan kajian lepas juga menunjukkan keunggulan algoritma YOLOv8.
Deep Learning Framework for Sentiment Prediction using Residual Connections in Bidirectional – Gated Recurrent Unit
Sentiment analysis plays an essential role in Natural Language Processing (NLP) for differentiating emotions and opinions expressed in various pieces of text. However, existing algorithms face challenges in handling complex language patterns and capturing long-term dependencies, thereby increasing overall computational cost. This research aims to design an improved sentiment analysis model that enhances accuracy and efficiency while addressing gradient-related limitations in deep networks. This research proposes a Residual Bidirectional Gated Recurrent Unit (RBi-GRU) algorithm for effective sentiment analysis, leveraging residual connections to improve accuracy and efficiency. Residual connections are incorporated into the Bi-GRU network to facilitate gradient flow across layers and mitigate the vanishing gradient problem during training. It also enables deeper networks by protecting data from earlier layers, which further enhances feature representation. Additionally, tokenization, stemming, and global vector-based word representations (GloVe) are employed during preprocessing to capture the semantic relationships and meanings of words, thereby improving contextual understanding in sentiment analysis. The developed RBi-GRU algorithm achieves 98.74% accuracy, 98.99% precision, 98.32% sensitivity, and 98.64% F1-score on the Sentiment140 dataset, compared with the Rectified Linear Unit-based Gated Recurrent Unit (ReLU-GRU). ABSTRAK: Analisis sentimen memainkan peranan penting dalam Pemprosesan Bahasa Semula Jadi (NLP) bagi membezakan emosi dan pendapat yang dizahirkan dalam teks; namun, algoritma sedia ada menghadapi cabaran pengendalian corak bahasa yang kompleks serta kebergantungan jangka panjang, sekaligus meningkatkan masa pemprosesan. Kajian ini bertujuan mereka bentuk model analisis sentimen berketepatan tinggi dan cekap sambil menangani kekangan berkaitan kecerunan rangkaian mendalam. Sebuah algoritma Unit Kawalan Berulang Baki Dua Arah (RBi-GRU) dicadangkan dengan gabungan baki ke dalam rangkaian Bi-GRU bagi memudahkan aliran kecerunan antara lapisan dan mengurangkan masalah lenyap kecerunan semasa latihan, di samping membolehkan pembinaan rangkaian lebih mendalam dan meningkatkan perwakilan ciri. Selain itu, teknik prapemprosesan seperti penandaan token, pengakaran (stemming), serta penggunaan Representasi Kata Vektor Global (GloVe) diaplikasi bagi menangkap hubungan semantik dan makna kontekstual perkataan dengan lebih berkesan. Dapatan kajian eksperimen menunjukkan bahawa algoritma RBi-GRU mencapai ketepatan 98.74%, kejituan 98.99%, kepekaan 98.32%, dan skor F1 sebanyak 98.64% pada set data Sentimen140, sekaligus mengatasi prestasi model Unit Kawalan Berulang berasaskan Unit Pembetulan Linear (ReLU-GRU).
ORCA: AI-powered Autonomous Underwater Vehicle for Subaquatic Exploration
This paper presents research and development of an Autonomous Underwater Vehicle (AUV), named ORCA, to perform underwater missions independently without human intervention. ORCA plays a vital role in challenges that test robots' abilities in navigation, exploration, and interaction with the aquatic environment. Using advanced design tools, the AUV is meticulously designed in Inventor and manufactured via CNC machining, laser cutting, and 3D printing. We concentrate on the vehicle's design, manufacturing processes, control systems, PID controllers, and vision systems. Subsequently, the research and development effort is expanded to incorporate critical functionalities, including environmental perception, object detection, deep learning algorithms, and path-planning strategies. The culmination of this research has produced an AUV capable of autonomous underwater navigation, effective obstacle avoidance, efficient object detection, and precise payload manipulation using a gripper mechanism. ORCA has a comprehensive sensor suite comprising a BNO055 IMU, an MS5803-14BA pressure sensor for depth measurement, and a Logitech C525 camera for image processing. The system integration not only enhances the vehicle's operational capabilities but also represents a significant advancement in underwater robotics. ABSTRAK: Kajian ini membentangkan penyelidikan dan pembangunan Kenderaan dalam Air Berautonomi (AUV) yang dikenali sebagai ORCA, dibangunkan bagi tujuan misi bawah air secara autonomi tanpa sebarang campur tangan manusia. ORCA memainkan peranan penting dalam menghadapi cabaran menguji kebolehan robot dalam aspek navigasi, penerokaan, serta interaksi dengan persekitaran akuatik. Dengan memanfaatkan alat reka bentuk yang canggih, AUV ini direka bentuk dengan teliti menggunakan perisian Inventor dan dihasilkan melalui teknik pemesinan CNC, pemotongan laser, dan percetakan 3D. Penekanan utama dalam penyelidikan ini adalah pada reka bentuk kenderaan, proses pembuatan, sistem kawalan, pengawal PID, serta sistem penglihatan. Selain itu, penyelidikan dan pembangunan ini turut diperluaskan bagi menyertakan fungsi-fungsi kritikal seperti persepsi persekitaran, pengesanan objek, algoritma pembelajaran mendalam, dan strategi perancangan laluan. Dapatan kajian ini telah menghasilkan AUV yang mampu melaksanakan navigasi bawah air secara autonomi, menghindari halangan dengan berkesan, mengesan objek dengan cekap, serta mengendali beban dengan tepat menggunakan mekanisme pemegang. ORCA dilengkapi dengan suit pengesan yang komprehensif, termasuk pengesan IMU BNO055, pengesan tekanan MS5803-14BA bagi pengukuran kedalaman, serta kamera Logitech C525 bagi pemprosesan visual. Integrasi sistem ini bukan sahaja meningkatkan keupayaan operasi kenderaan tetapi juga mewakili satu langkah penting dalam perkembangan robotik bawah air.
FSM-Based MBIST Controller Implementation using the March AZ2 Test Algorithm
An efficient on-chip memory testing requires the use of a Memory Built-In Self-Test (MBIST) that applies a low-complexity test algorithm that offers excellent fault coverage, to ensure high test quality at a minimal cost. The March AZ2 algorithm, with 14N complexity, was previously established to balance the fault coverage and test complexity. It was previously implemented in an MBIST controller through an automatic generation process using an Electronic Design Automation (EDA) tool, which limits its customizability and optimization potential. This paper presents the implementation of an MBIST controller that employs the March AZ2 test algorithm and is based on a Finite-State Machine (FSM) architecture. The developed MBIST controller was implemented on a Field-Programmable Gate Array (FPGA) to validate its functionality and fault coverage. A comparison with an equivalent MBIST controller automatically generated using Siemens EDA Tessent MemoryBIST tool demonstrates that the proposed FSM-Based MBIST controller achieves 84% lower circuit area and 32% power consumption, while offering similar fault coverage. ABSTRAK: Pengujian memori atas cip yang cekap memerlukan penggunaan Ujian-Kendiri Terbina-Dalam Memori (MBIST) menggunakan algoritma ujian berkompleksiti rendah dan menawarkan liputan kesalahan yang tinggi, bagi memastikan kualiti ujian baik pada kos minimum. Algoritma March AZ2, dengan kekompleksan 14N, telah diperkenalkan sebelum ini bagi mengimbangi antara liputan kesalahan dan kompleksiti ujian. Ia telah dilaksanakan dalam pengawal MBIST melalui proses penjanaan automatik menggunakan Automasi Reka Bentuk Elektronik (EDA), yang menghadkan tahap kebolehsuaian dan potensi pengoptimuman. Kajian ini membentangkan pelaksanaan pengawal MBIST yang menggunakan algoritma ujian March AZ2, berasaskan seni bina Mesin Keadaan-Terhingga (FSM). Pengawal MBIST yang dibangunkan telah dilaksanakan pada Litar Terpadu-Serba Guna (FPGA) bagi mengesahkan fungsi dan liputan kesalahan. Perbandingan dengan pengawal MBIST yang dijana secara automatik menggunakan Siemens EDA Tessent MemoryBIST membuktikan bahawa pengawal MBIST berasaskan FSM yang dicadangkan mengurangkan keluasan litar sebanyak 84% dan penggunaan kuasa 32% lebih rendah, sambil mengekalkan liputan kesalahan yang sama.
Adaptive Energy Balance Control System via State of Charge (SoC) for a Sustainable Solar-Powered Outdoor-Hydroponics in Tropical Islands
Efficient energy management is essential for sustaining outdoor hydroponics systems powered by solar energy, particularly in tropical island environments where sunlight and rainfall vary throughout the day. To address this, a solar-powered hydroponics system was developed with an adaptive energy balance control strategy based on the State of Charge (SoC). The system requires reliable real-time monitoring and decision-making, achieved by integrating voltage, current (ACS712), light-dependent resistors (LDR), and flow sensors, along with an ESP32 microcontroller for data acquisition and control logic. The adaptive control method dynamically regulates power consumption by adjusting the water pump's operation in response to SoC levels, solar radiation, and rainfall. Experimental validation shows the system maintains the battery’s SoC above 55%, ensuring power availability while optimizing energy use. Pump operation is disabled during rainfall and minimized at night to prevent deep discharge, enhancing overall system stability. Daytime solar charging is complemented by controlled discharge during non-solar hours, improving energy sustainability. The results confirm the effectiveness of the proposed strategy in reducing unnecessary energy consumption, improving system reliability, and supporting continuous hydroponic cultivation under varying tropical conditions. ABSTRAK: Pengurusan tenaga yang cekap amat penting bagi memastikan kelestarian sistem hidroponik luar yang menggunakan tenaga solar, terutama di kawasan pulau tropika yang mempunyai corak cahaya matahari dan perubahan hujan sepanjang hari. Bagi memenuhi keperluan ini, satu sistem hidroponik berkuasa solar telah dibangunkan dengan strategi kawalan imbangan tenaga adaptif berasaskan State of Charge (SoC). Sistem ini memerlukan pemantauan masa nyata dan keupayaan membuat keputusan terpercayai, dicapai melalui integrasi penderia voltan, arus (ACS712), LDR (Rintangan Peka Cahaya), dan aliran, serta mikropengawal ESP32 bagi pemerolehan data dan logik kawalan. Kaedah kawalan adaptif ini mengatur penggunaan tenaga secara dinamik dengan melaras operasi pam air berdasarkan tahap SoC, intensiti cahaya matahari, dan keadaan hujan. Dapatan kajian menunjukkan sistem ini mampu mengekalkan SoC bateri melebihi 55%, sekaligus memastikan bekalan kuasa yang stabil sambil mengoptimum penggunaan tenaga. Operasi pam dihentikan semasa hujan dan dikurangkan pada waktu malam bagi mengelakkan nyahcas bateri berlebihan, seterusnya meningkatkan kestabilan sistem. Pengecasan bateri pada waktu siang dilengkapi dengan penyahcasan terkawal semasa tanpa cahaya matahari, sekaligus memperkukuh kemampanan tenaga. Dapatan kajian membuktikan bahawa strategi kawalan ini berkesan dalam mengurangkan penggunaan tenaga tidak diperlukan, meningkatkan kebolehpercayaan sistem, dan menyokong penanaman hidroponik berterusan dalam persekitaran tropika yang dinamik.
ToxoSegFusion: Attention-enhanced Dual-backbone Neural Architecture for Retinal Lesion Segmentation
Ocular toxoplasmosis (OT) often presents a diagnostic dilemma in clinics, with retinal lesions that are not only varied in appearance but also frequently subtle and underrepresented in fundus images. Current automated segmentation tools, though promising, are often hampered by class imbalance and a lack of robust testing across real-world scenarios. To address these gaps, we developed ToxoSegFusion, a dual-backbone deep learning framework that capitalizes on the complementary strengths of DenseNet121 and ResNet101, enhanced with attention modules. Unlike typical single-backbone models, this hybrid approach was specifically tuned for the intricate challenges of OT lesion segmentation, using a combined Dice and binary cross-entropy loss to better balance rare lesion pixels. We trained and validated on 149 image-mask pairs from the OTFID-Version 3 dataset, achieving an intersection over union of 0.858 and a Dice coefficient of 0.795, both exceeding the current MobileNetV2/U-Net baseline. The model also demonstrated reliable performance on the DRIVE dataset for vessel segmentation, indicating practical flexibility. By facilitating accurate lesion localization, ToxoSegFusion enables more timely interventions in ophthalmology. Future directions include larger multi-center trials and streamlined models for routine deployment. ABSTRAK: Toksoplasmosis okular (OT) sering menimbulkan cabaran diagnostik di klinik, dengan lesi retina halus pelbagai rupa dan kurang terwakili pada imej fundus. Alat segmentasi automatik semasa, walaupun memberi harapan, sering terhad pada ketidakseimbangan kelas dan kekurangan ujian di peringkat perubatan. Bagi mengatasi kekurangan ini, kajian ini membangunkan ToxoSegFusion, sebuah rangka kerja pembelajaran mendalam berkomponen dua yang memanfaatkan kekuatan saling melengkapi DenseNet121 dan ResNet101, diperkaya dengan mekanisme perhatian. Tidak seperti model komponen tunggal biasa, pendekatan hibrid ini dirancang khusus bagi cabaran kompleks segmentasi lesi OT, menggunakan kehilangan Dice dan entropi silang binari gabungan bagi keseimbangan terbaik antara piksel lesi yang jarang. Kajian ini melatih dan mengesahkan 149 pasangan imej-topeng dari set data OTFID-Versi 3, mencapai persilangan atas kesatuan 0.858 dan pekali Dice 0.795, keduanya melebihi garis dasar MobileNetV2/U-Net semasa. Model juga menunjukkan prestasi terbaik pada DRIVE bagi segmentasi salur darah, mencadangkan fleksibiliti praktis. Melalui pengesanan lokasi lesi yang tepat, ToxoSegFusion membuka jalan bagi intervensi lebih tepat pada masa oftalmologi. Pada masa hadapan, cadangan bagi penyebaran rutin adalah melalui ujian berbilang pusat yang lebih besar dan perkemasan model.
Performance Investigation and Efficiency Enhancement of Eco-Friendly Tin-Based CH3NH3SnI3 Perovskite Solar Cell via SCAPS-1D
Halide perovskite materials, particularly lead-based CH3NH3PbI3, have garnered significant attention in the PV industry for their exceptional efficiency in solar cell applications. However, due to the toxicity of lead, research interest has shifted toward Sn-based alternatives. This study explores a lead-free Sn-based perovskite solar cell (PSC) with the structure ITO/TiO2/CH3NH3SnI3/CBTS/Ni, where CH3NH3SnI3 (MASnI3) serves as the absorber material, TiO? as the electron transport layer (ETL), Cu2BaSnS4 (CBTS) as the hole transporting layer (HTL). Device performance is analyzed using the SCAPS-1D simulation software. The impact of key performance-determining parameters, including the thickness, doping density, and defect density of the absorber, ETL, and HTL, has been accounted for. The proposed PSC architecture, optimized for key performance-determining parameters, achieves a power conversion efficiency PCE (?) of 27.28%, an open-circuit voltage (VOC) of 1.0283 V, a fill factor (FF) of 83.62%, and a short-circuit current density (JSC) of 31.72 mA/cm2. This study examines the influence of interface defect density, shunt and series resistances, back-contact metal work function, and operating temperature on the performance of PSCs. Furthermore, the analysis includes current density-voltage (J-V) and quantum efficiency (QE) characteristics to provide a comprehensive evaluation of the effectiveness of the proposed PSC. ABSTRAK: Bahan perovskit halida, khususnya CH?NH?PbI? berasaskan plumbum, telah menarik perhatian besar dalam industri fotovolta (PV) berikutan kecekapan tinggi dalam aplikasi sel suria; namun, isu ketoksikan plumbum telah mengalih tumpuan penyelidikan kepada alternatif berasaskan timah (Sn). Kajian ini meneroka sel suria perovskit (PSC) bebas plumbum berasaskan Sn dengan seni bina ITO/TiO?/CH?NH?SnI?/CBTS/Ni, di mana CH?NH?SnI? (MASnI?) bertindak sebagai bahan penyerap, TiO? sebagai lapisan pengangkut elektron (ETL), dan Cu?BaSnS? (CBTS) sebagai lapisan pengangkut lubang (HTL). Prestasi peranti dianalisa menggunakan perisian simulasi SCAPS-1D dengan mengambil kira parameter penentu prestasi utama, termasuk ketebalan, ketumpatan pendopan, dan ketumpatan kecacatan bagi lapisan penyerap, ETL dan HTL. Seni bina PSC yang dicadangkan, selepas pengoptimuman parameter, mencapai kecekapan penukaran kuasa (PCE) sebanyak 27.28%, voltan litar terbuka (V_OC) 1.0283 V, faktor pengisian (FF) 83.62%, dan ketumpatan arus litar pintas (J_SC) 31.72 mA/cm². Kajian ini turut menilai pengaruh ketumpatan kecacatan antara muka, rintangan siri dan pirau, fungsi kerja logam sentuhan belakang, serta suhu operasi terhadap prestasi PSC. Di samping itu, analisis ciri ketumpatan arus–voltan (J–V) dan kecekapan kuantum (QE) disertakan bagi memberikan penilaian menyeluruh terhadap keberkesanan sel suria perovskit yang dicadangkan.
Evaluating Mechanical and Conductivity of Graphene-Silver Hybrid Inks on Copper Substrate at Elevated Temperature
This study investigates the effect of temperature on the damping behaviour, stiffness, and natural frequency of hybrid conductive ink (HCI) printed on a copper (Cu) substrate. The HCI comprises graphene nanoplatelets (GNPs), silver flakes (SF), and silver acetate (SA). The objective is to evaluate the HCI’s electrical conductivity and mechanical properties under varying thermal conditions. The HCI paste was formulated with a specified ratio of organic solvents, terpineol, and 1-butanol and cured in an oven at 260°C for 3 hours. The baseline droplet ratio was set at 1:1, and three Cu samples with varied HCl compositions were printed using a 60 µm mesh stencil process. The terpineol concentrations were changed while the 1-butanol droplet remained constant. The samples were then tested for electrical conductivity at room temperature using a two-point probe, in accordance with IEEE Std 118-1978, followed by mechanical behaviour testing using an ASTM E756-05 impact test. Furthermore, the samples were exposed to a range of temperature tests to evaluate mechanical and electrical conductivity under thermal stress. The results showed that the baseline composition exhibited minimal resistance and resistivity across the temperature range, with an average resistance of ? 0.2 ? and a resistivity of ? 0.8 ? · mm, respectively. The baseline composition also exhibited reduced stiffness and damping, with natural frequencies of 10.90, 1.40 kN/m, and 37.51 Hz across the samples. Therefore, the baseline composition exhibits relatively good electrical and mechanical properties for applications in flexible electronics. ABSTRAK: Kajian ini meneliti kesan suhu terhadap sifat redaman, kekakuan, dan frekuensi semula jadi bagi dakwat konduktif hibrid (HCI) yang dicetak pada substrat kuprum (Cu). HCI ini terdiri daripada graphene platelet nano (GNP), serpihan perak (SF), dan perak asetat (SA). Objektif kajian ini adalah bagi menilai kekonduksian elektrik dan sifat mekanikal HCI di bawah keadaan haba berbeza. Pes HCI dibangunkan dengan nisbah tertentu pelarut organik, terpineol dan 1-butanol, dan dikeringkan dalam ketuhar pada suhu 260?C selama tiga jam. Nisbah titisan asas ditetapkan pada 1:1, dan tiga sampel Cu dengan komposisi HCI berbeza telah dicetak menggunakan proses stensil jaring 60 µm. Kepekatan terpinol diubah manakala titisan 1-butanol dikekalkan pada kadar yang sama. Sampel-sampel tersebut kemudiannya diuji bagi kekonduksian elektrik pada suhu bilik menggunakan probe Dua-Titik mengikut kod ujian piawaian IEEE Std 118-1978 dan diteruskan dengan ujian sifat mekanikal menggunakan ujian impak ASTM E756-05. Tambahan, sampel telah terdedah kepada pelbagai ujian suhu bagi menilai kekonduksian mekanikal dan elektrik di bawah tekanan haba. Dapatan kajian menunjukkan bahawa komposisi asas menunjukkan rintangan dan kerintangan minimum sepanjang julat suhu, dengan purata rintangan ? 0.2 ? dan kerintangan ? 0.8 ?/mm. Komposisi asas juga menunjukkan sifat mekanikal dengan kekakuan berkurangan, tingkah laku redaman, dan frekuensi semula jadi masing-masing sebanyak 10.90, 1.40 kN/m, dan 37.51 Hz merentasi sampel. Oleh itu, komposisi asas menunjukkan sifat elektrik dan mekanikal yang agak baik bagi aplikasi elektronik fleksibel.
Inertial Sensor Self-Calibration Module Using Attitude Heading And Reference System For Autonomous Underwater Vehicle Navigation
This research addresses the complex task of enhancing navigation accuracy in Autonomous Underwater Vehicles (AUVs), a self-propelled robotic system used for ocean exploration, environmental monitoring, and underwater interventions. A core component of AUV navigation is an Inertial Measurement Unit (IMU), a sensor suite that tracks orientation and motion by measuring accelerations and angular rates. However, the IMU is highly susceptible to noise interference, which degrades accuracy and reliability. To address these challenges, this study introduces an innovative Inertial Sensor Self-Calibration Module that dynamically adjusts calibration parameters in real time, thereby compensating for sensor drift and inaccuracies. The research further conducts a comparative analysis of several calibration and filtering techniques integrated into the AUV's Inertial Navigation System (INS), including Magnetic Calibration, the Extended Kalman Filter (EKF), the EKF with Measurement Noise, the EKF with Process Noise, and the Attitude and Heading Reference System (AHRS) Filter. Among these, the AHRS filter demonstrated superior precision, achieving the lowest average error of 1.06 degrees with a standard deviation of 0.345 degrees in angle measurements, an improvement of up to 97.81% compared to raw data. These findings highlight the effectiveness of the AHRS filter in improving navigation accuracy in complex underwater environments. The insights gained from this research not only deepen the understanding of noise impact and sensor calibration in AUV systems but also pave the way for future innovations in oceanic exploration, environmental monitoring, and underwater interventions. ABSTRAK: Kajian ini adalah berkenaan menangani tugas kompleks meningkatkan ketepatan navigasi Kenderaan Bawah Air Autonomi (AUV), iaitu sistem robotik berkuasa sendiri digunakan bagi penerokaan lautan, pemantauan alam sekitar, dan intervensi bawah air. Komponen utama navigasi AUV bergantung pada Unit Pengukuran Inersia (IMU), iaitu rangkaian pengesan orientasi dan pergerakan dengan mengukur pecutan dan kadar sudut. Namun, IMU sangat terdedah kepada gangguan bunyi, di mana ianya mengurangkan ketepatan dan kebolehpercayaan. Oleh itu, kajian ini memperkenalkan Modul Kalibrasi Diri Pengesan Inersia yang inovatif, iaitu secara dinamik menyesuaikan parameter kalibrasi pada masa nyata, secara efektif mengimbangi hanyutan sensor dan ketidaktepatan. Kajian ini juga membuat analisis perbandingan beberapa teknik kalibrasi dan penapisan yang diintegrasikan pada Sistem Navigasi Inersia (INS) AUV, termasuk Kalibrasi Magnetik, Penapis Kalman Lanjutan (EKF), EKF dengan Bunyi Pengukuran, EKF dengan Bunyi Proses, dan Penapis Sistem Rujukan Sikap dan Haluan (AHRS). Antara teknik-teknik ini, penapis AHRS menunjukkan ketepatan terbaik, dengan ralat purata terendah iaitu 1.06 darjah dan sisihan piawai pengukuran sudut 0.345 darjah dan peningkatan sehingga 97.81% berbanding data mentah. Penemuan ini menunjukkan keberkesanan penapis AHRS dalam meningkatkan ketepatan navigasi pada persekitaran bawah air yang kompleks. Dapatan kajian ini bukan sahaja memperdalam pemahaman tentang kesan bunyi dan kalibrasi pengesan dalam sistem AUV, tetapi turut membuka ruang terhadap inovasi masa depan dalam penerokaan lautan, pemantauan alam sekitar, dan intervensi bawah air.
GNN-Based Skyline Query Processing for Large-Scale and Incomplete Graphs
Skyline queries are crucial in database management, selecting optimal points from multi-dimensional datasets based on dominance relationships. They are widely used in decision-making, recommendation systems, and data filtering. However, traditional skyline algorithms struggle with large volumes and missing data, leading to high computational costs and inefficiencies. This research proposes a hybrid approach that integrates the ISkyline dominance graph technique with Graph Neural Networks (GNNs) to improve skyline query performance under such conditions. The GNN component is utilized to predict skyline tuples in the presence of missing or incomplete data. Evaluation on both synthetic and real-world datasets demonstrates improved accuracy and efficiency compared with established methods such as ISkyline, SIDS, and OIS. This research demonstrates the potential to improve query processing efficiency and to support applications in e-commerce, finance, and smart data systems. ABSTRAK: Pertanyaan latar langit adalah penting dalam pengurusan pangkalan data, iaitu dengan memilih titik optimum daripada set data berbilang dimensi berdasarkan hubungan dominasi. Ia digunakan secara meluas dalam membuat keputusan, sistem pengesyoran, dan penapisan data. Walau bagaimanapun, algoritma latar langit tradisional bergelut dengan kuantiti data yang besar dan data menghilang, membawa kepada peningkatan kos pengiraan dan ketidakcekapan. Kajian ini mencadangkan pendekatan hibrid yang mengintegrasi teknik graf penguasaan ISkyline dengan Rangkaian Graf Neural (GNNs) bagi meningkatkan prestasi pertanyaan latar langit berkeadaan sedemikian. Komponen GNN digunakan bagi meramalkan tupel latar langit dengan kehadiran data menghilang atau tidak lengkap. Penilaian pada kedua-dua set data sintetik dan dunia nyata menunjukkan peningkatan ketepatan dan kecekapan jika dibandingkan dengan kaedah sedia ada seperti ISkyline, SIDS dan OIS. Kajian ini menunjukkan potensi bagi mencipta pemprosesan pertanyaan yang lebih cekap, menyokong aplikasi e-dagang, kewangan dan sistem data pintar.
Development of Electric Fence Fault Sensing and Monitoring System with LoRaWAN IoT
The persistent challenge of Human-Elephant Conflict (HEC) in regions like Malaysia necessitates robust and efficient mitigation strategies. Electric fences are effective but often face maintenance inefficiencies due to delayed fault detection. This study presents a smart electric fence monitoring system designed for real-time fault diagnosis and localisation. The system employs IoT-enabled in-place sensor nodes comprising 10 kV voltage sensors, short-circuit detection sensors, and 3-axis gyroscope sensors. Sensor data is transmitted via a LoRaWAN network, selected for its long-range, low-power characteristics, which are well-suited to rural, low-bandwidth environments where electric fences are typically deployed. Field validation of a 50 m, 10 kV, 6 A electric fence segment achieved 100% voltage and short-circuit detection rates and 99.91% gyroscope tilt accuracy. Reliable data transmission was maintained up to 1.3 km, with an RSSI of -110 dBm, in campus environments with concrete obstructions. Supplementary testing at the same positions using antennas at increased height yielded an RSSI of -79 dBm, with a 41 dB link margin, highlighting the potential for range scaling in future work. The system has so far been validated on a short 50 m electric fence segment with a practical LoRaWAN range of 1.3 km under campus conditions, indicating the need for further optimisation and field-scale trials. The system provides a practical solution for the Department of Wildlife and National Parks, Peninsular Malaysia (PERHILITAN), with direct application to their Sistem Pagar Elektrik Gajah (SPEG) for HEC mitigation. Beyond this application, the approach demonstrates potential for future use of IoT-based Structural Health Monitoring (SHM) concepts in resource-constrained rural infrastructures. ABSTRAK: Cabaran berterusan Konflik Manusia-Gajah (HEC) di kawasan seperti Malaysia memerlukan strategi mitigasi yang kukuh dan berkesan. Pagar elektrik berkesan dalam menangani konflik ini, tetapi sering berhadapan masalah penyelenggaraan akibat kelewatan pengesanan kerosakan pada pagar. Kajian ini membentangkan satu sistem pemantauan pagar elektrik pintar yang direka bagi mengdiagnosis penentuan lokasi kerosakan secara masa nyata. Sistem ini menggunakan nod pengesan dengan keupayaan internet benda terdiri daripada pengesan voltan 10 kV, pengesan pengesanan litar pintas, dan pengesan giroskop. Data pengesan dihantar melalui rangkaian LoRaWAN, yang dipilih atas dasar rangkaian jarak jauh dan penggunaan tenaga minimal. Ciri-ciri ini dianggap bersesuaian bagi persekitaran luar bandar di mana pagar elektrik biasanya dipasang, kerana kawasan ini seringkali tidak mendapat rangkaian telekomunikasi komersial. Ujian lapangan pada segmen pagar elektrik sepanjang 50 m, 10 kV 6 A telah berjaya mengesan voltan dan litar pintas dengan ketepatan 100%, manakala pengesan giroskop pula mampu mengesan kecondongan pada kadar 99.91%. Penghantaran data daripada pengesan pula berjaya mencapai jarak maximum 1.3 km dengan RSSI -110 dBm dalam persekitaran kampus dengan penghalang konkrit. Ujian sampingan penghantaran data yang dilaksanakan menggunakan antena berkedudukan tinggi pada lokasi dan jarak yang sama pula, menunjukkan RSSI -79 dBm dengan margin pautan 41 dB. Ini menunjukkan potensi peningkatan jarak penghantaran data untuk kajian seterusnya. Pasa masa ini, sistem ini telah disahkan kepenggunaanya bagi segmen pagar elektrik pendek dengan kepanjangan 50 m, manakala penghantaran data secara berkesan dihadkan pada 1.3 km dalam persekitaran kampus. Had ini menunjukkan, wujud keperluan bagi kerja-kerja penambah baikan bagi meningkatkan kadar keberkesanan melalui ujian berskala lapangan pada masa depan. Sistem ini menyediakan penyelesaian kejuruteraan yang praktikal untuk Jabatan Perlindungan Hidupan Liar dan Taman Negara Semenanjung Malaysia (PERHILITAN), dengan aplikasi langsung pada Sistem Pagar Elektrik Gajah (SPEG) bagi mitigasi HEC. Kajian ini juga menunjukkan potensi bagi penggunaan konsep Pemantauan Kesihatan Struktur (SHM) berasaskan IoT pada masa hadapan dalam infrastruktur luar bandar.
Mohammad Syarifuddin Bin Mohammad Sufian
Enhanced Beach Photo Translation using Modified Unsupervised GAN with Regularization
To optimize time and cost, tourists often require tools to modify the sky background and atmosphere in beach photos, such as replacing blue-sky views with sunsets or vice versa. Independent modification of the sky and sea is difficult because their color palettes are similar. Another problem that often occurs in image translation is the scarcity of paired datasets, and beach photo datasets are particularly limited in lighting conditions, weather variations, and viewing perspectives. This limitation can cause Generative Adversarial Networks (GAN) models to lose their generalization ability, become prone to overfitting, and produce visual artifacts in the outputs. Therefore, this study proposes an unsupervised GAN approach using a modified CycleGAN and improves its performance for beach image translation by integrating identity mapping, -parameter optimization, a multiscale kernel, and regularization techniques. CycleGAN consists of two generators and two discriminators. The sunset generator translates a blue sky into a sunset sky; the generated output is then passed to the sunset discriminator to determine whether it is real or fake. The generator input image is resized and normalized through preprocessing. The generator architecture is structured to enhance image reconstruction and feature extraction. The details of the translation results are fine-tuned using a 30x30 PatchGAN discriminator and a multiscale kernel convolutional layer. The effect of the hyperparameter , which strikes a balance between cycle consistency, structural preservation, and color fidelity, is also investigated in this work. The findings indicate that while higher values increase generator loss, they also improve consistency, making it harder to handle dark objects and white clothing. To overcome this issue, regularization techniques, namely photometric augmentation and spectral normalization (SN), together with multiscale kernel convolutional (MSCov), have been applied. Photometric augmentation and MSCov are used to enhance the model's robustness to photographic variations, while SN improves its efficiency and stability. The results of the study show that the proposed method improves image translation accuracy as measured by Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). ABSTRAK: Bagi mengoptimum masa dan kos, pelancong sering memerlukan alat menukar foto latar belakang langit dan suasana pantai, seperti mengganti pemandangan langit biru dengan matahari terbenam atau sebaliknya. Pengubahsuaian bebas langit dan laut sukar dilakukan kerana palet warna langit dan laut adalah serupa. Masalah lain sering berlaku dalam menterjemah imej adalah ketiadaan set data foto berpasangan dan foto pantai sering mengalami kepelbagaian terhad, terutama dari segi pencahayaan, variasi cuaca dan perspektif tontonan. Had ini boleh menyebabkan model Rangkain Generatif Adversari (GAN) ??kehilangan keupayaan generalisasi, terdedah kepada terlebih muat dan penghasilan artifak visual dalam hasil terjemahan. Oleh itu, kajian ini mencadangkan pendekatan GAN tanpa pengawasan menggunakan CycleGAN yang diubah suai bagi meningkatkan prestasi terjemahan imej pantai melalui penyepaduan pemetaan identiti, pengoptimuman parameter , kernel berbilang skala dan menggunakan teknik regularisasi. CycleGAN terdiri daripada dua generator dan dua rangkaian neural diskriminator. Generator matahari terbenam digunakan dalam menterjemah langit biru kepada langit matahari terbenam, kemudian imej terhasil dimajukan kepada diskriminator matahari terbenam bagi menentukan sama ada imej terhasil dikelaskan sebagai imej sebenar atau imej palsu. Imej input generator diubah saiz dan dinormalkan melalui prapemprosesan. Seni bina generator distrukturkan dengan meningkatkan pembinaan semula imej dan pengekstrakan ciri. Butiran hasil terjemahan diperhalusi menggunakan diskriminator PatchGAN 30x30 dan lapisan konvolusi kernel berbilang skala. Kesan hiper parameter turut dikaji bagi mencapai keseimbangan antara ketekalan kitaran, pemeliharaan struktur, dan kesetiaan warna. Dapatan kajian menunjukkan bahawa walaupun nilai lebih tinggi ianya meningkatkan kehilangan generator dan konsistensi, menjadikannya lebih sukar dalam mengendali objek gelap dan pakaian putih. Bagi mengatasi isu ini, teknik regularization iaitu pembesaran fotometrik dan normalisasi spektral (SN) bersama Konvulasi Kernel Skala Berbilang (MSCov) telah digunakan. Pembesaran fotometrik dan MSCov dilaksanakan dalam meningkatkan keteguhan model pada variasi fotografi, manakala SN digunakan bagi meningkatkan kecekapan dan kestabilan model. Hasil kajian menunjukkan kaedah ini mampu meningkatkan ketepatan hasil terjemahan imej berdasarkan Ralat Purata Kuasa Dua (MSE), Indeks Persamaan Struktur (SSIM) dan Persamaan Tampalan Perseptual Imej Terpelajar (LPIPS).
Advanced Groundwater Level Forecasting using QSO-based Vision Transformer Model for Sustainable Water Resource Management
The reliable predictions of groundwater levels are crucial for long-term management of water resources, and they are an excellent source for human well-being. While conventional ML approaches work well for low-dimensional data, they are optimized for hyperparameters on high-dimensional inputs and also capture complex temporal correlations. To address these restrictions, this research presents a new framework for predicting groundwater-level changes up to five months in advance, built on the Vision Transformer (ViT) and optimised with Quokka Swarm Optimisation (QSO). ViT enables strong global feature extraction and long-range dependency modelling by processing time-series data as sequential image-like patches, in contrast to traditional neural networks. Drawing on quokka’s adaptive survival behaviour, the QSO procedure optimises the transformer's hyperparameters, such as patch size, attention heads, and depth, in real time to enhance prediction accuracy. The ViT+QSO outperformed baseline deep learning methods on groundwater datasets from Southern Africa in terms of RMSE, MAE, correlation coefficient (R), and Nash-Sutcliffe Efficiency (NSE). Quantile regression uncertainty quantification further improves the model's reliability for water resource planning. Hydrological variables, in addition to climate indices, influence groundwater fluctuations, as confirmed by ablation research. The proposed ViT-QSO achieved 93% R and 0.118 MAE, whereas the basic ViT achieved only 89.1% R and 0.152 MAE for groundwater-level prediction. Scalability, interpretability, and suitability for areas with limited monitoring infrastructure are hallmarks of the proposed methodology. This research provides valuable insights into how to better withstand the effects of climate change, in addition to human activities, on groundwater supplies. ABSTRAK: Ramalan paras air bawah tanah yang boleh dipercayai adalah penting bagi pengurusan jangka panjang sumber air dan kesejahteraan manusia; namun, pendekatan pembelajaran mesin konvensional berhadapan kekangan pengendalian input berdimensi tinggi serta model korelasi temporal kompleks. Kajian ini mencadangkan satu rangka kerja baharu berasaskan Pengubah Visi (ViT) yang dioptimum menggunakan Optimisasi Kawanan Quokka (QSO) dalam meramal perubahan paras air bawah tanah pada lima bulan lebih awal. ViT memproses data siri masa sebagai tampalan jujukan menyerupai imej bagi membolehkan pengekstrakan ciri global dan model kebergantungan jarak jauh, manakala QSO mengoptimumkan hiperparameter pengubah secara adaptif dalam meningkatkan ketepatan ramalan. Model ViT-QSO menunjukkan prestasi unggul berbanding kaedah pembelajaran mendalam asas pada set data air bawah tanah di Afrika Selatan, dengan peningkatan ketara dari segi RMSE, MAE, pekali korelasi (R), dan Kecekapan Nash–Sutcliffe (NSE), serta mencapai nilai R sebanyak 93% dan MAE 0.118 berbanding ViT asas masing-masing mencatatkan 89.1% dan 0.152. Pengkuantitian ketidakpastian melalui regresi kuantil meningkatkan kebolehpercayaan model bagi perancangan sumber air, manakala kajian ablasi mengesahkan peranan pembolehubah hidrologi dan indeks iklim terhadap turun naik paras air bawah tanah. Secara keseluruhan, metodologi yang dicadangkan adalah berskala, boleh ditafsir, dan sesuai pada kawasan infrastruktur pemantauan terhad, serta memberikan sumbangan penting dalam menangani kesan perubahan iklim dan aktiviti manusia terhadap sumber air bawah tanah.
Optimizing Energy Efficiency in Three-Phase Induction Motors via GWO-Tuned PID Control
Induction motors consume a significant share of industrial electricity, making their efficiency a crucial aspect of sustainable energy management. Traditional Proportional–Integral–Derivative (PID) controllers are commonly used to regulate motor performance; however, their fixed parameters often fail to maintain optimal control under varying load conditions. Although optimization methods, such as Genetic Algorithms and Particle Swarm Optimization, have been introduced to enhance PID tuning, they often encounter challenges, including premature convergence and limited adaptability. This creates a clear need for an optimization strategy that is both robust and dynamically responsive to ensure energy-efficient motor operation. To address this gap, this study introduces a Grey Wolf Optimization (GWO)-based PID tuning strategy that distinguishes itself from existing methods by achieving a superior adaptive balance between exploration and exploitation. This characteristic enables the controller to maintain stable, responsive performance even under fluctuating load conditions. Experimental results confirm that the proposed GWO-PID controller successfully reduces motor current from 285.21 A to 164.2 A and lowers power consumption from 150 kW to 84.5 kW, achieving a 42.4% reduction in current and a 44.7% improvement in energy efficiency. Additionally, electricity costs decrease by 43.5%, demonstrating strong economic potential. The novelty of this research lies in integrating GWO’s adaptive intelligence with PID control, yielding a more effective, reliable, and energy-efficient solution than existing optimization-based controllers for industrial induction motor systems. ABSTRAK: Motor aruhan menggunakan sebahagian besar tenaga elektrik industri, menjadikan kecekapan operasi satu aspek penting dalam pengurusan tenaga mampan. Pengawal konvensional Kadar-Integral-Pembezaan (PID) lazim digunakan bagi mengawal prestasi motor; namun, parameter tetapnya sering gagal mengekalkan kawalan optimum di bawah keadaan beban berubah. Walaupun kaedah pengoptimuman seperti Algoritma Genetik dan Pengoptimuman Kawanan Partikel telah diperkenalkan bagi menambah baik talaan PID, pendekatan ini sering menghadapi masalah penumpuan awal dan keupayaan adaptasi yang terhad. Hal ini memerlukan satu strategi pengoptimuman yang lebih mantap dan responsif secara dinamik bagi memastikan operasi motor cekap tenaga. Bagi menangani masalah ini, kajian ini memperkenalkan satu strategi talaan PID berasaskan Pengoptimuman Serigala Kelabu (Grey Wolf Optimization, GWO) yang menonjol dari kaedah sedia ada melalui keseimbangan adaptifnya yang unggul pada penerokaan dan pengeksploitasian. Ciri ini membolehkan pengawal mengekalkan prestasi stabil dan responsif walaupun berkeadaan beban yang berubah. Dapatan kajian melalui eksperimen menunjukkan bahawa pengawal GWO–PID yang dicadangkan ini berjaya mengurangkan arus motor daripada 285.21 A kepada 164.2 A dan menurunkan penggunaan kuasa daripada 150 kW kepada 84.5 kW—mencapai pengurangan arus sebanyak 42.4% dan peningkatan kecekapan tenaga sebanyak 44.7%. Selain itu, kos elektrik turut menurun sebanyak 43.5%, sekaligus membuktikan potensi ekonomi yang kukuh. Keunikan kajian ini terletak pada integrasi kecerdasan adaptif GWO dengan kawalan PID, menawarkan penyelesaian lebih berkesan, boleh dipercayai, dan cekap tenaga berbanding pengawal berasas pengoptimuman lain bagi sistem motor aruhan industri.
Development of a Model for Malware Detection and Classification at the Byte Level Based on Transformer
Malware threats have been a critical concern in cybersecurity, particularly due to the increasing complexity and constantly evolving variants that are difficult to detect using conventional signature-based or static rule-based methods. This research focused on developing a Transformer-based model at the byte level to detect and classify malware effectively and adaptively, thereby streamlining the analytical process without requiring specialized feature tokenization. The primary objective was to design and evaluate a Transformer model that captures universal and adaptive malware patterns directly from raw byte representations, enabling cross-platform applicability. A quantitative experimental approach was employed using three public datasets: Malware Detection PE-Based Analysis, MC-dataset-binary, and Malware.zip. Data processing involved byte embedding, dilated 1D convolution, multi-head self-attention, and attention pooling. Model optimization was conducted using AdamW with a combined scheduler, Stochastic Weight Averaging (SWA), random byte masking augmentation, and Mixup Embedding. Experimental results showed that the byte-level Transformer model achieved high classification accuracy across the three datasets, namely 99%, 92%, and 94%, respectively. These results demonstrate that a byte-level Transformer can effectively capture universal malware patterns in binary data, offering a flexible and highly accurate approach to developing resilient defenses against modern cyber threats. ABSTRAK: Ancaman perisian perosak (malware) merupakan isu kritikal dalam keselamatan siber, terutama apabila disebabkan oleh peningkatan kerumitan dan variasi yang sentiasa berevolusi, sekaligus menyukarkan pengesanan menggunakan kaedah konvensional berasaskan tandatangan atau peraturan statik. Kajian ini memfokuskan kepada pembangunan model berasaskan Transformasi pada peringkat bait (byte-level) bagi mengesan dan mengklasifikasikan perisian perosak secara berkesan dan adaptif, tanpa memerlukan pengekstrakan atau penandaan ciri khusus. Objektif utama kajian adalah bagi mereka bentuk dan menilai keberkesanan model Transformasi yang mampu menangkap corak perisian perosak bersifat universal dan adaptif secara langsung daripada representasi bait mentah, seterusnya membolehkan kebolehgunaan merentas platform. Pendekatan eksperimen kuantitatif digunakan dengan melibatkan tiga set data awam, iaitu Analisis Berdasarkan Pengesanan Perisian Perosak PE, Set Data Binari MC, dan Malware.zip. Pemprosesan data merangkumi pembenaman bait, konvolusi 1D terlarut (dilated), perhatian kendiri berbilang kepala, serta pengumpulan berasaskan perhatian. Pengoptimuman model dilaksanakan menggunakan AdamW dengan penjadual gabungan, Purata Pemberat Stokastik (SWA), penambahan data melalui penyamaran bait rawak, dan Penggabungan Embedding. Dapatan kajian melalui eksperimen menunjukkan bahawa model Transformasi peringkat bait mencapai ketepatan pengelasan tertinggi, iaitu masing-masing 99%, 92%, dan 94% bagi ketiga-tiga set data. Dapatan ini membuktikan bahawa Transformasi peringkat bait berupaya menangkap corak perisian perosak universal daripada data binari, sekaligus menawarkan pendekatan fleksibel dan berketepatan tinggi bagi membangunkan pertahanan yang lebih berdaya tahan terhadap ancaman siber moden.
Deep Learning-Based Skin Care Detection with Multi-method Explainability: Grad-CAM, Lime, and Occlusion Sensitivity
Skin cancer is one of the most common malignancies worldwide, where early detection significantly improves treatment outcomes. While deep learning models show promise for automated skin lesion classification, their lack of interpretability limits clinical adoption. This study presents a comprehensive comparative analysis of three convolutional neural networks, ResNet-50, GoogLeNet, and SqueezeNet, for binary skin lesion classification (benign vs. malignant), integrating three explainable AI (XAI) methods (Grad-CAM, LIME, and Occlusion Sensitivity) to enhance clinical interpretability. We trained and evaluated these architectures on the Kaggle Skin Cancer dataset, which contains 2,637 dermoscopic images (1,440 benign, 1,197 malignant). Transfer learning employed ImageNet pre-trained weights with two-stage fine-tuning. Performance was assessed using accuracy, precision, recall, F1-score, specificity, and AUC-ROC metrics. ResNet-50 achieved the highest accuracy of 91.36% with an excellent AUC of 0.9721, demonstrating superior balanced performance. GoogLeNet achieved 88.94% accuracy with 73% fewer parameters, offering an optimal accuracy-efficiency trade-off. The proposed lightweight CNN, despite having the fewest parameters (1.2M), achieved 85.45% accuracy and a malignancy detection sensitivity of 92.7%, making it well-suited for screening applications. Training times ranged from 1.5 minutes (SqueezeNet) to 3 minutes 39 seconds (ResNet-50), demonstrating feasibility for resource-constrained settings. All XAI methods successfully generated clinically meaningful explanations, with models consistently focusing on lesion centers, color variations, and irregular borders. This study demonstrates that combining deep learning with XAI enables accurate and interpretable skin cancer detection. ResNet-50 is well-suited to well-resourced clinical settings, GoogLeNet offers balanced performance for resource-constrained deployments, and SqueezeNet enables mobile telemedicine applications with superior sensitivity. ABSTRAK: Kanser kulit merupakan antara malignansi yang paling lazim di seluruh dunia, dan pengesanan awal terbukti dapat meningkatkan keberkesanan rawatan secara signifikan. Walaupun model pembelajaran mendalam menunjukkan potensi tinggi dalam pengelasan automatik lesi kulit, kekurangan kebolehinterpretasian telah mengehadkan penerimaan klinikal. Kajian ini membentangkan analisis perbandingan menyeluruh terhadap tiga rangkaian neural konvolusi, iaitu ResNet-50, GoogLeNet, dan SqueezeNet, bagi pengelasan binari lesi kulit (jinak vs. malignan), digabungkan dengan tiga kaedah kecerdasan buatan boleh jelas (XAI), iaitu Grad-CAM, LIME, dan Kepekaan Halangan, bagi menyokong interpretasi klinikal. Model dilatih dan dinilai menggunakan set data Kanser Kulit Kaggle yang mengandungi 2,637 imej dermoskopi, dengan menggunakan pembelajaran pindahan berasaskan pemberat pralatih ImageNet dan penalaan halus dua peringkat. Penilaian prestasi menggunakan metrik ketepatan, ketepatan ramalan, kepekaan, skor F1, pengkhususan, dan AUC-ROC menunjukkan bahawa ResNet-50 mencapai prestasi tertinggi dengan ketepatan 91.36% dan AUC 0.9721, manakala GoogLeNet menawarkan keseimbangan optimum antara ketepatan dan kecekapan dengan pengurangan parameter sebanyak 73%. SqueezeNet, walaupun paling ringan, mencapai kepekaan pengesanan malignan tertinggi sebanyak 92.7%, menjadikannya sesuai untuk aplikasi saringan dan teleperubatan mudah alih. Semua kaedah XAI berjaya menghasilkan penjelasan bermakna secara klinikal, dengan fokus konsisten pada pusat lesi, variasi warna, dan sempadan tidak sekata. Secara keseluruhan, kajian ini membuktikan bahawa penggabungan pembelajaran mendalam dan XAI membolehkan pengesanan kanser kulit yang tepat, boleh ditafsir, dan sesuai dalam pelbagai kekangan sumber klinikal.
Surrogate Model-based Optimization of a Biplane Unmanned Aerial Vehicle Wing Using Genetic Algorithm and Response Surface Methodology
Multi-wing aerial vehicle designs have existed since the early days of aviation; however, they are not commonly employed in modern designs, as advances in materials technology have enabled the structurally feasible high-aspect-ratio monoplane configurations. Nevertheless, the application has the potential to serve as a solution in the field of mini unmanned aerial vehicle (UAV) design, offering the advantage of a smaller wing span. The most popular multi-wing application, the biplane, features two wings mounted in parallel and is typically characterized by the geometric terms decalage, stagger, and gap, which should be optimized during the design process. This study focuses on aerodynamic optimization of a biplane wing to maximize the lift-to-drag ratio (CL/CD) and the CL3/2/CD by optimizing the aforementioned geometric parameters. To this end, the aerodynamic analysis method was initially validated by comparing it with wind-tunnel data for a monoplane wing reported in the literature. The Response Surface Method (RSM) was used to assess the effects of the design parameters and to generate surrogate models for CL/CD and CL3/2/CD. The optimal design was determined using the genetic algorithm. The results indicated that decalage and gap distance primarily determine range improvement, whereas gap and stagger are the primary parameters for improving endurance. The applied optimization approach resulted in improvements of 10.93% in range and 46.63% in endurance relative to the base biplane configuration. ABSTRAK: Reka bentuk kenderaan udara berbilang sayap telah wujud sejak awal perkembangan penerbangan; namun, ia jarang digunakan dalam reka bentuk moden berikutan kemajuan teknologi bahan yang membolehkan nisbah-aspek-tinggi konfigurasi monosayap lebih berdaya struktur. Namun, aplikasi berbilang sayap berpotensi menjadi penyelesaian reka bentuk kenderaan udara tanpa pemandu bersaiz kecil (mini UAV), khususnya dengan kelebihan bentangan sayap lebih kecil. Konfigurasi berbilang sayap iaitu paling lazim, sayap berkembar (biplane), merujuk kepada dua sayap yang disusun selari dan dicirikan oleh parameter geometri utama seperti sela, aturan, dan jurang, perlu ditentukan secara optimum dalam proses reka bentuk. Kajian ini memfokuskan kepada pengoptimuman aerodinamik sayap berkembar bagi memaksimumkan nisbah angkat kepada seretan (CL/CD) dan CL3/2/CD melalui pemilihan optimum parameter geometri tersebut. Kaedah analisis aerodinamik terlebih dahulu disahkan melalui perbandingan data terowong angin bagi monosayap yang dilaporkan dalam literatur. Kaedah Permukaan Tindak Balas (Response Surface Method, RSM) digunakan bagi menilai kesan parameter reka bentuk dan model pengganti bagi CL/CD dan CL3/2/CD, manakala reka bentuk optimum ditentukan menggunakan algoritma genetik. Keputusan menunjukkan bahawa sela dan jurang merupakan parameter utama dalam peningkatan jarak terbang, manakala jurang dan aturan lebih dominan dalam meningkatkan daya tahan penerbangan. Pendekatan pengoptimuman yang dicadangkan menghasilkan peningkatan sebanyak 10.93% dari segi jarak dan 46.63% dari segi daya tahan berbanding konfigurasi sayap berkembar asas.
Growth Kinetics of Grey Oyster Mushroom (Pleurotus pulmonarius) Spawn Culture Revival from Spray Drying Application
Spray drying represents a promising approach for preserving fungal mycelium liquid cultures, with the assurance of successful revival from powder form being a critical factor for its application. Although liquid culture of grey oyster mushroom (Pleurotus pulmonarius) has been well established, its potential for drying and subsequent revival remains underexplored, thereby limiting its use in long-term storage. In this study, the effect of various spray dryer inlet temperatures (80, 90, 100, 110, 120, 130, 140, and 150 °C) on the revival and growth kinetics of P. pulmonarius powder cultures was evaluated using maltodextrin and Arabic gum (10%) as protective agents. Growth kinetics were modelled using the Contois model, which revealed that powder prepared at 80 °C with maltodextrin exhibited the highest µmax value (6.0205), indicating superior revival and growth capacity. With Arabic gum, the highest µmax (5.7604) was observed at 90 °C. At higher inlet temperatures, revival and growth were still observed with both protectants, albeit at reduced rates. Metabolic activity, reflected by glucose consumption and citric acid production, further confirmed successful revival, with citric acid reaching the highest level of 4.958 ppm. Overall, this study demonstrates that spray-dried powder cultures of P. pulmonarius retain viability and metabolic activity, highlighting their potential for long-term preservation. ABSTRAK: Teknik pengeringan sembur merupakan satu inovasi berguna untuk kultur cecair miselium kulat, dan jaminan bahawa bentuk serbuk kultur tersebut boleh dihidupkan semula merupakan faktor penting dalam pengeringan. Walaupun kultur cecair miselium cendawan tiram kelabu telah berjaya dibangunkan, potensinya untuk dikeringkan dan seterusnya dihidupkan semula masih kurang diterokai, sekaligus menhadkan aplikasinya dalam penyimpanan jangka panjang. Kesan suhu masuk pengering sembur yang berbeza-beza (80, 90, 100, 110, 120, 130, 140 dan 150°C) terhadap kebolehan hidup semula dan kinetik pertumbuhan kultur serbuk tiram kelabu menggunakan 10% maltodekstrin dan gam Arab sebagai agen pelindung telah dikaji. Model Cointois telah digunakan dan nilai yang diperoleh daripada persamaan tersebut menunjukkan bahawa kultur serbuk yang disediakan pada 80°C dengan perlindungan maltodekstrin mempunyai nilai µmax tertinggi iaitu 6.0205 dan menunjukkan kebolehan hidup semula serta pertumbuhan yang paling baik. Dengan penggunaan gam Arab, nilai µmax tertinggi (5.7604) dicapai pada suhu 90°C. Bagi kedua-dua agen pelindung, walaupun pada suhu yang lebih tinggi, kultur serbuk masih menunjukkan kebolehan untuk hidup semula dan berkembang tetapi pada kadar yang lebih rendah. Dari aspek metabolisme, penggunaan glukosa dan penghasilan asid sitrik yang tertinggi iaitu 4.958 ppm menunjukkan bahawa kultur serbuk mempunyai aktiviti metabolik dalam tempoh hidup semula . Kajian ini membuktikan bahawa kultur serbuk P. pulmonarius mampu dibangunkan dan berpotensi untuk dipelihara dalam tempoh yang lebih lama.