39 research outputs found
Fusion-features and visual-dictionary image recognition methods for apple classification in smart manufacturing / Ahsiah Ismail
No full textSmart manufacturing enables an efficient manufacturing process to optimize
production. The optimization is performed through data analytics that requires reliable
and informative data as input. Therefore, in this research, two image recognition feature
extraction methods namely Curvelet Wavelet-Gray Level Co-occurrence Matrix (CWGLCM)
and Fuzzy-Spatial Pyramid Matching (F-SPM) are proposed to provide reliable
inputs for vision-based apple classification in smart manufacturing. Feature extraction is
one of the major steps that could influent the efficiency of the manufacturing process.
The CW-GLCM method is a feature extraction of fusion-features with Decision Tree
classifier, while the F-SPM method uses a visual-dictionary based method to extract
features of visual pattern and the output is process by Support Vector Machine (SVM)
classifier. To evaluate the performance of the proposed methods, they are compared with
five existing methods, which are Bag of Words (BOW), Spatial Pyramid Matching
(SPM), Gray Level Co-occurrence Matrix (GLCM) Texture analysis, Convolutional
Neural Network (CNN) and Contrast‐Limited Adaptive Histogram Equalization + GLCM
+ Extreme Learning Machine (CLAHE+GLCM+ELM). Three datasets which are NDDA,
NDDAW and DA datasets with a total of 1310 apple images are collected to test the
proposed methods. The NDDA and NDDAW datasets are both binary-class of defective
and non-defective apple dataset, with NDDAW contains more low-quality region images
compared to the NDDA. Conversely, the DA dataset comprised of five different types of
defective apples to be used in multi-class tests. The proposed methods are trained and
evaluated using 10-fold cross-validation. Their classification accuracy, precision and
recall rate are then measured. Training and testing times are also recorded. From the
evaluation, the proposed F-SPM method attained 98.15% classification accuracy, 96.30% precision and 100% recall for NDDA, 91.07% for accuracy, 100% precision and 84.85%
recall for NDDAW, 86.33% for accuracy, 91.43% precision and 85.00% recall for DA
dataset. The F-SPM method outperformed the existing methods especially for NDDAW
and DA datasets. Alternatively, the CW-GLCM method able to obtain 98.15% accuracy,
96.30% precision and 100% recall for NDDA, 89.11% accuracy, 86.79% precision and
91.01% recall for NDDAW, 85.20% of accuracy, 88.33% precision and 85.00% recall for
DA dataset. The proposed CW-GLCM also shows the highest percentage (100%) for all
measurements (accuracy, precision and recall) and it even outperform others in
recognizing the Bruise defect. These results indicate that both proposed methods are
reliable and have the potential to be used for vision classification in smart manufacturing
M-Kitchen’s tapai pulut and cookies workshop
No full textM-kitchen has organized a workshop to make Tapai Pulut and baking Cookies on the 17th of March 2023. The
workshop has taken place at the CITA pantry, Kuliyyah of Information and Communication Technology (KICT),
International Islamic University Malaysia (IIUM). The workshop was attended by Dr. Dini Oktarina Dwi
Handayani, Dr. Maznah Ahmad, Dr. Ahsiah Ismail, Dr. Noor Azian, Dr. Izyani Zulkifli, Noor Azizah Mohamadali and
the students from Usrah in Action subject.It was a full day workshop where two types of dishes, which are
Tapai Pulut and cookies being taught to the participants. The Tapai Pulut Making was taught by Dr. Dini
Oktarina Dwi Handayani and the cookies baking class was taught by Dr. Maznah Ahmad. The participants
were looking forward to trying all the recipes taught by the instructor
Postgraduate Colloquium 2024: innovating for a sustainable future: interdisciplinary approaches in the digital era
No full textThe Postgraduate Colloquium 2024, held as a special event in conjunction with the 3rd International Interdisciplinary Conference on Research & Opportunities (IICRO 2024), took place on 7th August 2024 at the Kulliyyah of Information and Communication Technology (KICT), International Islamic University Malaysia (IIUM). This event was proudly hosted by Universitas Raharja, Indonesia, the Indonesian Lecturer Association, and the International Islamic University Malaysia, under the theme "Innovating for a Sustainable Future: Interdisciplinary Approaches in the Digital Era."
The colloquium provided a unique platform for postgraduate students across diverse fields—such as computer science, information systems, software engineering, data science, cybersecurity, and creative multimedia—to share their research. It emphasized the importance of exploring not only research findings but also their broader implications for academic and professional communities. Participants were encouraged to present challenges encountered in their work, fostering dialogue and collaboration among peers and experts.
This hybrid-mode event welcomed students at all stages of their research journey. Early-stage researchers gained valuable insights into postgraduate research, while those in the middle or final stages received constructive critique to refine their projects. Out of 14 submissions, 8 papers were selected for publication, reflecting the innovation and academic rigor of the participants.
Aligned with the overarching theme of IICRO 2024, this colloquium underscored the importance of interdisciplinary collaboration and highlighted the pivotal role of postgraduate research in addressing the challenges of a sustainable future in the digital era.
We extend our deepest gratitude to all participants, reviewers, and judges, as well as to the hosting institutions for their unwavering support. We hope this collection of selected works inspires future research and collaboration across disciplines
An evaluation of convolutional neural network (CNN) model for copy-move and splicing forgery detection
No full textImage forgeries such as copy-move and splicing are very common due to the availability of the advancement in software editing techniques. However, most of the existing methods for forgery detection consider only one type of image forgery due to the reason that both forgeries have different traits. In this paper, a Convolutional Neural Network (CNN) model which is one of the deep learning approaches is simulated and analyzed to detect any forged image without knowing their types of forgeries. In the model, three phases are involved: Data Preprocessing, Feature Extraction, and Classification. The model learns to extract features from convolutional, pooling, and Rectified Linear unit layer, and classified the image whether it is original or forged using fully connected layer. For the experimental works, three datasets namely MICC-F2000 (2000 images), CASIA 1 (1721 images), and CASIA 2 (12615 images) are tested and compared with existing deep learning-based methods. The results show that the CNN model achieved the highest performance with accuracy of 79% for CASIA 1 and 89% for CASIA 2
Cyber security awareness model based on NIST (national institute of standards and technology) for secondary school students in Malaysia
No full textAs cybersecurity issues surge, it highlights the pressing need for increased awareness. In our digitally interconnected world, where information technology underpins the lives of individuals, businesses, and even national security, the threat of cyberattacks looms large. To enhance national cybersecurity preparedness, there is a critical call to action: we must promote public awareness of cybersecurity issues and bolster the ranks of trained cybersecurity professionals. This research introduces a novel cybersecurity awareness model based on the National Institute of Standards and Technology (NIST). Its primary goal is to make NIST accessible to secondary school students pursuing ICT courses and their invaluable resource, the school counselling units. The model aims to instil cybersecurity awareness in secondary school students and guide them in choosing the most suitable higher education paths, particularly in the ICT and cybersecurity fields. The study comprises three phases. In the first phase, a systematic literature review (SLR) examines globally applicable cybersecurity education models. The second phase investigates the implementation of cybersecurity education for secondary school students in Malaysia. This includes interviews with school counselling units responsible for guiding students toward higher education and surveys of students in grades four and five enrolled in ICT courses. The third and pivotal phase focuses on developing the proposed model. It involves analysing and implementing the results of the SLR, mapping them to the NIST framework, and incorporating themes from interviews and surveys. As a tangible representation of the model, a mobile application prototype has been developed. The Cybersecurity Awareness Model empowers school counselling units to seamlessly integrate career exploration with career decision-making for secondary students at any stage of their development. Moreover, it offers a means to create strategies for after-school programs, particularly in the dynamic field of cybersecurity. This model is expected to guide students in exploring academic majors and careers, preparing for higher education post-high school, initiating internships or job searches, and adapting to a world in constant flux. In doing so, it not only empowers individuals but also strengthens the nation's resilience in the face of cybersecurity challenges
Vision-based vehicle classification using deep learning model
No full textVehicle classification offers intelligent solutions for road traffic monitoring by enabling future prediction planning and decision making. Predictive analytics can be used to predict traffic congestion based on the types of vehicles on the road. In this research, the reliability of deep learning based models for vision-based vehicle classification is investigated. Four models of You Only Look Once (YOLO) are investigated, namely YOLOv5s, YOLOv5x, YOLOv10n, and YOLOv12n. These models were trained and evaluated on a vehicle dataset comprising five vehicle classes, which are Ambulance, Bus, Car, Motorcycle, and Truck, with a total number of 1103 images. From the experiment conducted, YOLOv10n achieved the highest performance measure of [email protected] with 0.859 across all vehicle classes, including per-class evaluation, demonstrating superior detection compared to the other models. Finally, the results indicate that the YOLOv10n model can be used in vision-based vehicle classificatio
Investigation of convolutional neural network model for vehicle classification in smart city
No full textSmart city optimize efficiency by integrating advanced digital technologies, real-time data analytics, and intelligent automation. With the evolution of big data, smart cities enhance infrastructure and provide intelligent solutions for transportation with the integration of high-level adaptability of computer technologies including artificial intelligence (AI). The optimization can be achieved through predictive analytics in providing intelligent solutions for transportation. However, this requires reliable and accurate informative data as input for predictive analytics. Therefore, in this paper, five models of Convolutional Neural Network (CNN) deep learning method are investigated to determine the most accurate model for classification; namely Single Shot Detector (SSD) Resnet50, SSD Resnet152, SSD MobileNet, You Only Look Once (YOLO) YOLOv5 and YOLOv8. A total of 1324 vehicle images are collected to test these CNN models. The images consist of five different categories of vehicles, which are ambulance, car, motorcycle, bus and truck. The performances of all the models are compared. From the evaluation, the model YOLOv8 attained 0.956 of precision, 0.968 of recall and 0.968 of F1 score and outperformed the others. In terms of computational time, YOLOv5 is the fastest. However, a minimal computational time difference is observed between the YOLOv5 and YOLOv8, which were separated by only 20 minutes
Investigation of fusion features for apple classification in smart manufacturing
No full textSmart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called CurveletWavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAWwith a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy. © 2019 by the authors
