Search

Your search keyword '"Abdullah, Muhammad"' showing total 29 results
Start Over Author "Abdullah, Muhammad" Topic machine learning

Refine your results

more »

more »

more »
29 results on '"Abdullah, Muhammad"'

1. Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning.

Author

Kingston BR, Syed AM, Ngai J, Sindhwani S, and Chan WCW

Subjects
Animals, Female, Liver Neoplasms pathology, Lung Neoplasms pathology, Mice, Mice, Inbred BALB C, Image Interpretation, Computer-Assisted methods, Imaging, Three-Dimensional methods, Liver Neoplasms diagnostic imaging, Lung Neoplasms diagnostic imaging, Machine Learning, Nanoparticles metabolism, Neoplasm Micrometastasis diagnostic imaging
Abstract

Metastasis of solid tumors is a key determinant of cancer patient survival. Targeting micrometastases using nanoparticles could offer a way to stop metastatic tumor growth before it causes excessive patient morbidity. However, nanoparticle delivery to micrometastases is difficult to investigate because micrometastases are small in size and lie deep within tissues. Here, we developed an imaging and image analysis workflow to analyze nanoparticle-cell interactions in metastatic tumors. This technique combines tissue clearing and 3D microscopy with machine learning-based image analysis to assess the physiology of micrometastases with single-cell resolution and quantify the delivery of nanoparticles within them. We show that nanoparticles access a higher proportion of cells in micrometastases (50% nanoparticle-positive cells) compared with primary tumors (17% nanoparticle-positive cells) because they reside close to blood vessels and require a small diffusion distance to reach all tumor cells. Furthermore, the high-throughput nature of our image analysis workflow allowed us to profile the physiology and nanoparticle delivery of 1,301 micrometastases. This enabled us to use machine learning-based modeling to predict nanoparticle delivery to individual micrometastases based on their physiology. Our imaging method allows researchers to measure nanoparticle delivery to micrometastases and highlights an opportunity to target micrometastases with nanoparticles. The development of models to predict nanoparticle delivery based on micrometastasis physiology could enable personalized treatments based on the specific physiology of a patient's micrometastases., Competing Interests: The authors declare no conflict of interest.

Published
2019
Full Text
View/download PDF

2. Geo-Smart City Flood Mitigation and Monitoring Using Geospatial Technology

Author

Muhamad, Nurfashareena, Abed, Marwa Sultan, Shariff, Abdul Rashid Mohamed, Abdullah, Muhammad Firdaus Ammar, Lim, Choun-Sian, Al-Habshi, Mohammed, Sathyamoorthy, Dinesh, Shaharuddin, Nur Akma, Ahmad, Noordin, Negm, Abdelazim M., Series Editor, Chaplina, Tatiana, Series Editor, Yadava, Ram Narayan, editor, and Ujang, Muhamad Uznir, editor

Published
2024
Full Text
View/download PDF

3. The Classification of Badminton Strokes: A Feature Importance Investigation

Author

Li, Qiyang, P. P. Abdul Majeed, Anwar, Musa, Rabiu Muazu, Abdullah, Muhammad Amirul, Teh, Sze-Hong, Liu, Chenguang, Yap, Eng Hwa, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tan, Andrew, editor, Zhu, Fan, editor, Jiang, Haochuan, editor, Mostafa, Kazi, editor, Yap, Eng Hwa, editor, Chen, Leo, editor, Olule, Lillian J. A., editor, and Myung, Hyun, editor

Published
2024
Full Text
View/download PDF

4. Various Type of Crops and Trees Detection Using Clustering Technique Through Image Processing

Author

Rahman, Mohd Izzat Mohd, Razman, Mohd Azraai Mohd, Khairuddin, Ismail Mohd, Majeed, Anwar P. P. Abdul, Abdullah, Muhammad Amirul, Isa, Wan Hasbullah Mohd, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Abdullah, Muhammad Amirul, editor, Khairuddin, Ismail Mohd., editor, Ab. Nasir, Ahmad Fakhri, editor, Mohd. Isa, Wan Hasbullah, editor, Mohd. Razman, Mohd. Azraai, editor, Rasid, Mohd. Azri Hizami, editor, Zainal, Sheikh Muhammad Hafiz Fahami, editor, Bentley, Barry, editor, and Liu, Pengcheng, editor

Published
2023
Full Text
View/download PDF

5. Crop-saving with AI: latest trends in deep learning techniques for plant pathology

Author

Zafar Salman, Abdullah Muhammad, Md Jalil Piran, and Dongil Han

Subjects
deep learning, disease detection, computer vision, machine learning, plant disease, vision transformers, Plant culture, SB1-1110
Abstract

Plant diseases pose a major threat to agricultural production and the food supply chain, as they expose plants to potentially disruptive pathogens that can affect the lives of those who are associated with it. Deep learning has been applied in a range of fields such as object detection, autonomous vehicles, fraud detection etc. Several researchers have tried to implement deep learning techniques in precision agriculture. However, there are pros and cons to the approaches they have opted for disease detection and identification. In this survey, we have made an attempt to capture the significant advancements in machine-learning based disease detection. We have discussed prevalent datasets and techniques that have been employed as well as highlighted emerging approaches being used for plant disease detection. By exploring these advancements, we aim to present a comprehensive overview of the prominent approaches in precision agriculture, along with their associated challenges and potential improvements. This paper delves into the challenges associated with the implementation and briefly discusses the future trends. Overall, this paper presents a bird’s eye view of plant disease datasets, deep learning techniques, their accuracies and the challenges associated with them. Our insights will serve as a valuable resource for researchers and practitioners in the field. We hope that this survey will inform and inspire future research efforts, ultimately leading to improved precision agriculture practices and enhanced crop health management.

Published
2023
Full Text
View/download PDF

6. Classification of Sepak Takraw Kicks Using Machine Learning

Author

Tan, Fu Yang, Hassan, Mohd Hasnun Arif, P. P. Abdul Majeed, Anwar, Mohd Razman, Mohd Azraai, Abdullah, Muhammad Amirul, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, di Mare, Francesca, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Hassan, Mohd Hasnun Arif, editor, Ahmad (a) Manap, Zulkifli, editor, Baharom, Mohamad Zairi, editor, Johari, Nasrul Hadi, editor, Jamaludin, Ummu Kulthum, editor, Jalil, Muhammad Hilmi, editor, Mat Sahat, Idris, editor, and Omar, Mohd Nadzeri, editor

Published
2022
Full Text
View/download PDF

7. The Classification of Skateboarding Tricks: A Support Vector Machine Hyperparameter Evaluation Optimisation

Author

Ibrahim, Muhammad Ar Rahim, Shapiee, Muhammad Nur Aiman, Abdullah, Muhammad Amirul, Razman, Mohd Azraai Mohd, Musa, Rabiu Muazu, Majeed, Anwar P. P. Abdul, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Ab. Nasir, Ahmad Fakhri, editor, Ibrahim, Ahmad Najmuddin, editor, Ishak, Ismayuzri, editor, Mat Yahya, Nafrizuan, editor, Zakaria, Muhammad Aizzat, editor, and P. P. Abdul Majeed, Anwar, editor

Published
2022
Full Text
View/download PDF

8. The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning Models and K-Nearest Neighbors

Author

Shapiee, Muhammad Nur Aiman, Ibrahim, Muhammad Ar Rahim, Mohd Razman, Mohd Azraai, Abdullah, Muhammad Amirul, Musa, Rabiu Muazu, Abu Osman, Noor Azuan, P. P. Abdul Majeed, Anwar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Ab. Nasir, Ahmad Fakhri, editor, Ibrahim, Ahmad Najmuddin, editor, Ishak, Ismayuzri, editor, Mat Yahya, Nafrizuan, editor, Zakaria, Muhammad Aizzat, editor, and P. P. Abdul Majeed, Anwar, editor

Published
2022
Full Text
View/download PDF

9. The Identification of Significant Mechanomyography Time-Domain Features for the Classification of Knee Motion

Author

Mohamed, Tarek Mohamed Mahmoud Said, Abdullah, Muhammad Amirul, Alqaraghuli, Hasan, Musa, Rabiu Muazu, Nasir, Ahmad Fakhri Ab., Mohd Razman, Mohd Azraai, Bajuri, Mohd Yazid, P. P. Abdul Majeed, Anwar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Ab. Nasir, Ahmad Fakhri, editor, Ibrahim, Ahmad Najmuddin, editor, Ishak, Ismayuzri, editor, Mat Yahya, Nafrizuan, editor, Zakaria, Muhammad Aizzat, editor, and P. P. Abdul Majeed, Anwar, editor

Published
2022
Full Text
View/download PDF

10. The Effect of Image Input Transformation from Inertial Measurement Unit Data on the Classification of Skateboarding Tricks

Author

Abdullah, Muhammad Amirul, Ibrahim, Muhammad Ar Rahim, Shapiee, Muhammad Nur Aiman, Zakaria, Muhammad Aizzat, Mohd Razman, Mohd Azraai, Musa, Rabiu Muazu, P. P. Abdul Majeed, Anwar, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, di Mare, Francesca, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Chew, Esyin, editor, P. P. Abdul Majeed, Anwar, editor, Liu, Pengcheng, editor, Platts, Jon, editor, Myung, Hyun, editor, Kim, Junmo, editor, and Kim, Jong-Hwan, editor

Published
2021
Full Text
View/download PDF

11. Gearbox Fault Diagnostics: An Examination on the Efficacy of Different Feature Extraction Techniques

Author

Hasan, Md Jahid, Rashid, Mamunur, Nasir, Ahmad Fakhri Ab., Abdullah, Muhammad Amirul, Razman, Mohd Azraai Mohd, Musa, Rabiu Muazu, P. P. Abdul Majeed, Anwar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Mat Jizat, Jessnor Arif, editor, Khairuddin, Ismail Mohd, editor, Mohd Razman, Mohd Azraai, editor, Ab. Nasir, Ahmad Fakhri, editor, Abdul Karim, Mohamad Shaiful, editor, Jaafar, Abdul Aziz, editor, Hong, Lim Wei, editor, Abdul Majeed, Anwar P. P., editor, Liu, Pengcheng, editor, Myung, Hyun, editor, Choi, Han-Lim, editor, and Susto, Gian-Antonio, editor

Published
2021
Full Text
View/download PDF

12. AESA Antennas using Machine Learning with Reduced Dataset.

Author

ZAIB, Alam, MASOOD, Abdur Rehman, ABDULLAH, Muhammad Asad, KHATTAK, Shahid, SALEEM, Aasim Bin, and ULLAH, Irfan

Subjects
ARTIFICIAL neural networks, PHASED array antennas, COMPUTER engineering, ANTENNAS (Electronics), MACHINE learning
Abstract

This paper proposes a deep neural network (DNN)-based approach for radiation pattern synthesis of 8 elements phased array antenna. For this purpose, 181 points of a desired radiation pattern are fed as input to the DNN and phases of array elements are extracted as the outputs. Existing DNN techniques for radiation pattern synthesis are not directly applicable to higher-order arrays as the dataset size grows exponentially with array dimensions. To overcome this bottleneck, we propose novel and efficient methods of generating datasets for DNN. Specifically, by leveraging the constant phase-shift characteristic of the phased array antenna, dataset size is reduced by several orders of magnitude and made independent of the array size. This has considerable advantages in terms of speed and complexity, especially in real-time applications as the DNN can immediately learn and synthesize the desired patterns. The performance of the proposed methods is validated by using an ideal square beam and an optimal array pattern as reference inputs to the DNN. The results generated in MATLAB as well as in CST, demonstrate the effectiveness of the proposed methods in synthesizing the desired radiation patterns. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

13. Cdascaler: a cost-effective dynamic autoscaling approach for containerized microservices.

Author

Shafi, Numan, Abdullah, Muhammad, Iqbal, Waheed, Erradi, Abdelkarim, and Bukhari, Faisal

Subjects
*WEB-based user interfaces, *STIMULUS & response (Psychology), *MACHINE learning, *CONTAINERS, *COST
Abstract

Microservices are containerized, loosely coupled, interactive smaller units of the application that can be deployed, reused, and maintained independently. In a microservices-based application, allocating the right computing resources for each containerized microservice is important to meet the specific performance requirements while minimizing the infrastructure cost. Microservices-based applications are easy to scale automatically based on incoming workload and resource demand automatically. However, it is challenging to identify the right amount of resources for containers hosting microservices and then allocate them dynamically during the auto-scaling. Existing auto-scaling solutions for microservices focus on identifying the appropriate time and number of containers to be added/removed dynamically for an application. However, they do not address the issue of selecting the right amount of resources, such as CPU cores, for individual containers during each scaling event. This paper presents a novel approach to dynamically allocate the CPU resources to the containerized microservice during the autoscaling events. Our proposed approach is based on the machine learning method, which can identify the right amount of CPU resources for each container, dynamically spawning for the microservices over time to satisfy the application's response time requirements. The proposed solution is evaluated using a benchmark microservices-based application based on real-world workloads on the Kubernetes cluster. The experimental results show that the proposed solution outperforms by yielding a 40% to 60% reduction in violating the response time requirements with 0.5 × to 1.5 × less cost compared to the state-of-art baseline methods. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

14. The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models

Author

Shapiee, Muhammad Nur Aiman, Ibrahim, Muhammad Ar Rahim, Razman, Mohd Azraai Mohd, Abdullah, Muhammad Amirul, Musa, Rabiu Muazu, Abdul Majeed, Anwar P. P., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Mohd Razman, Mohd Azraai, editor, Mat Jizat, Jessnor Arif, editor, Mat Yahya, Nafrizuan, editor, Myung, Hyun, editor, Zainal Abidin, Amar Faiz, editor, and Abdul Karim, Mohamad Shaiful, editor

Published
2020
Full Text
View/download PDF

15. The Classification of Skateboarding Tricks by Means of Support Vector Machine: An Evaluation of Significant Time-Domain Features

Author

Abdullah, Muhammad Amirul, Ibrahim, Muhammad Ar Rahim, Shapiee, Muhammad Nur Aiman, Abdul Majeed, Anwar P. P., Mohd Razman, Mohd Azraai, Musa, Rabiu Muazu, Zakaria, Muhammad Aizzat, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Mohd Razman, Mohd Azraai, editor, Mat Jizat, Jessnor Arif, editor, Mat Yahya, Nafrizuan, editor, Myung, Hyun, editor, Zainal Abidin, Amar Faiz, editor, and Abdul Karim, Mohamad Shaiful, editor

Published
2020
Full Text
View/download PDF

16. The Classification of Skateboarding Trick Manoeuvres: A Frequency-Domain Evaluation

Author

Ibrahim, Muhammad Ar Rahim, Shapiee, Muhammad Nur Aiman, Abdullah, Muhammad Amirul, Razman, Mohd Azraai Mohd, Musa, Rabiu Muazu, Abdul Majeed, Anwar P. P., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Mohd Razman, Mohd Azraai, editor, Mat Jizat, Jessnor Arif, editor, Mat Yahya, Nafrizuan, editor, Myung, Hyun, editor, Zainal Abidin, Amar Faiz, editor, and Abdul Karim, Mohamad Shaiful, editor

Published
2020
Full Text
View/download PDF

17. The Classification of Skateboarding Trick Manoeuvres Through the Integration of Image Processing Techniques and Machine Learning

Author

Shapiee, Muhammad Nur Aiman, Ibrahim, Muhammad Ar Rahim, Mohd Razman, Mohd Azraai, Abdullah, Muhammad Amirul, Musa, Rabiu Muazu, Hassan, Mohd Hasnun Arif, Abdul Majeed, Anwar P. P., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Kasruddin Nasir, Ahmad Nor, editor, Ahmad, Mohd Ashraf, editor, Najib, Muhammad Sharfi, editor, Abdul Wahab, Yasmin, editor, Othman, Nur Aqilah, editor, Abd Ghani, Nor Maniha, editor, Irawan, Addie, editor, Khatun, Sabira, editor, Raja Ismail, Raja Mohd Taufika, editor, Saari, Mohd Mawardi, editor, Daud, Mohd Razali, editor, and Mohd Faudzi, Ahmad Afif, editor

Published
2020
Full Text
View/download PDF

18. The Classification of Skateboarding Trick Manoeuvres: A K-Nearest Neighbour Approach

Author

Ibrahim, Muhammad Ar Rahim, Abdullah, Muhammad Amirul, Shapiee, Muhammad Nur Aiman, Mohd Razman, Mohd Azraai, Musa, Rabiu Muazu, Zakaria, Muhammad Aizzat, Abu Osman, Noor Azuan, P. P. Abdul Majeed, Anwar, Lovell, Nigel H., Advisory Editor, Oneto, Luca, Advisory Editor, Piotto, Stefano, Advisory Editor, Rossi, Federico, Advisory Editor, Samsonovich, Alexei V., Advisory Editor, Babiloni, Fabio, Advisory Editor, Liwo, Adam, Advisory Editor, Magjarevic, Ratko, Advisory Editor, Hassan, Mohd Hasnun Arif, editor, Che Muhamed, Ahmad Munir, editor, Mohd Ali, Nur Fahriza, editor, Lian, Denise Koh Choon, editor, Yee, Kok Lian, editor, Safii, Nik Shanita, editor, Yusof, Sarina Md, editor, and Fauzi, Nor Farah Mohamad, editor

Published
2020
Full Text
View/download PDF

20. Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

Author

Alina Cornelia Pacurari, Sanket Bhattarai, Abdullah Muhammad, Claudiu Avram, Alexandru Ovidiu Mederle, Ovidiu Rosca, Felix Bratosin, Iulia Bogdan, Roxana Manuela Fericean, Marius Biris, Flavius Olaru, Catalin Dumitru, Gianina Tapalaga, and Adelina Mavrea

Subjects
artificial intelligence, lung cancer, machine learning, diagnostic imaging, Medicine (General), R5-920
Abstract

The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures currently in use, and evaluate the clinical relevance of these diagnostic imaging methods. A systematic search of PubMed, Web of Science, Cochrane, and Scopus databases was conducted in February 2023, encompassing the literature published up until December 2022. The review included nine studies, comprising five case–control studies, three retrospective cohort studies, and one prospective cohort study. Various ML architectures were analyzed, including artificial neural network (ANN), entropy degradation method (EDM), probabilistic neural network (PNN), support vector machine (SVM), partially observable Markov decision process (POMDP), and random forest neural network (RFNN). The ML architectures demonstrated promising results in detecting and classifying lung cancer across different lesion types. The sensitivity of the ML algorithms ranged from 0.81 to 0.99, while the specificity varied from 0.46 to 1.00. The accuracy of the ML algorithms ranged from 77.8% to 100%. The AI architectures were successful in differentiating between malignant and benign lesions and detecting small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This systematic review highlights the potential of ML AI architectures in the detection and classification of lung cancer, with varying levels of diagnostic accuracy. Further studies are needed to optimize and validate these AI algorithms, as well as to determine their clinical relevance and applicability in routine practice.

Published
2023
Full Text
View/download PDF

21. A Combination of Light Pre-trained Convolutional Neural Networks and Long Short-Term Memory for Real-Time Violence Detection in Videos.

Author

Nizam bin Abdullah, Muhammad Shahril, Karim, Hezerul Abdul, and AlDahoul, Nouar

Subjects
CONVOLUTIONAL neural networks, DEEP learning, VIRTUAL networks, VIOLENCE, AUTOMATIC identification, RASPBERRY Pi, MACHINE learning
Abstract

Machine learning techniques have been used widely to analyze videos that have scenes of violence for censorship or surveillance purposes. Violence detection plays a crucial role in preventing underage and teenage exposure to violent acts and ensuring a safer viewing environment. The automatic identification of violent scenes is significant to classify videos into two classes, including violence and non-violence. The existing violence detection models suffer from several problems, including memory inefficiency and low-speed inference, and thus make them unsuitable to be implemented on embedding systems with limited resources. This article aims to propose a novel combination of light Convolutional Neural Networks (CNN), namely EfficientNet- B0 and Long Short-Term Memory (LSTM). The public dataset which consists of two different datasets, was utilized to train, evaluate, and compare the deep learning models used in this study. The experimental results show the superiority of EfficientNet B0-LSTM, which outperform other models in terms of accuracy (86.38%), F1 score (86.39%), and False Positive Rate (13.53%). Additionally, the proposed model has been deployed to a low-cost embedding device such as Raspberry Pi for real-time violence detection. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

22. Development of a mobile application for user to learn new hobbies/skills using similarity learning tool (machine learning).

Author

Hwang, Lu, Lazam, Nor Azlinah Md., and Abdullah, Muhammad Naim

Subjects
MOBILE learning, MOBILE apps, MACHINE learning, INFORMATION overload, HOBBIES, DECISION making
Abstract

One of the greatest issues in the past which is the lack of available information and difficulty in finding information has ironically taken a different turn in today's context of ever increasingly available information. Within the current day and age, information has become much easier to find and acquire. However, due to the availability and access of information, a new problem arises in the form of information overload. Information overload occurs when there is too much information available, which causes a difficulty in understanding a certain topic or issue. This results in difficulty when making decisions, confusion, frustration or overall lack of motivation to further pursuing the topic or issue. This can prove to be an issue for someone who is new to a certain field or someone who lacks a clear perspective on how to continue from where they are at currently. This research aims to develop a mobile application in order to reduce information overload and tackle the issues of facing a stagnating hobby, to make it more interesting by connecting users with each other to allow for discussion and socializing. Information overload will be dealt with using machine learning algorithms in order to understand the users' grasp on their hobbies while making sure that they are able to keep up and stay interested in the hobbies [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

23. Improved detection of Facebook anomalies and abnormalities using graph-based and machine learning techniques.

Author

Elghanuni, Ramzi Hamid, Helmi, Rabab Alayham Abbas, and Abdullah, Muhammad Irsyad

Subjects
MACHINE learning, CLASSIFICATION algorithms, ONLINE social networks, SUPPORT vector machines, FEATURE selection
Abstract

The explosive growth of online-Social-Networks and the vast number of their subscribers' data has played a major role at this time to the trend toward interest to detect suspicious and anomalous behaviours on this OSNs. Moreover, the attracted attackers and impostors were intercepted personal data, share false news and promote malignant activities. Thus, the researchers were encouraged by the graphical structure of social networks to use different graph metrics and classification algorithms to detect anomalous activities. However, some of those classification algorithms have been a negative effect or no effect on the end-results, nor do they always achieve satisfactory results by using standalone classification algorithms. This article introduces a new algorithm, called PCA-NN which suggest efficient detection of anomalies and non-anomalies for Facebook. Also, the feature selection and Principal Component Analysis (PCA) techniques have applied. furthermore, classification algorithms for machine learning have used to assess the anomalies and non-anomalies such as supporting vector machine (SVM), neural network (NN), logistic regression (LR) and our built algorithm PCA-NN. Lastly, a minimum number of features have used by the proposed algorithm (PCA-NN) to identify the anomalies on online social networks with the highest accuracy and sensitivity. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

24. Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning

Author

Warren C. W. Chan, Abdullah Muhammad Syed, Shrey Sindhwani, Benjamin R. Kingston, and Jessica Ngai

Subjects
0301 basic medicine, Lung Neoplasms, Tumor cells, 02 engineering and technology, Metastatic tumor, 3d microscopy, Machine learning, computer.software_genre, Metastasis, Machine Learning, 03 medical and health sciences, Mice, Imaging, Three-Dimensional, Image Interpretation, Computer-Assisted, Medicine, Animals, Mice, Inbred BALB C, Multidisciplinary, Tissue clearing, business.industry, Micrometastasis, Liver Neoplasms, Cancer, Patient survival, 021001 nanoscience & nanotechnology, medicine.disease, 030104 developmental biology, PNAS Plus, Neoplasm Micrometastasis, Nanoparticles, Female, Artificial intelligence, 0210 nano-technology, business, computer
Abstract

Metastasis of solid tumors is a key determinant of cancer patient survival. Targeting micrometastases using nanoparticles could offer a way to stop metastatic tumor growth before it causes excessive patient morbidity. However, nanoparticle delivery to micrometastases is difficult to investigate because micrometastases are small in size and lie deep within tissues. Here, we developed an imaging and image analysis workflow to analyze nanoparticle–cell interactions in metastatic tumors. This technique combines tissue clearing and 3D microscopy with machine learning-based image analysis to assess the physiology of micrometastases with single-cell resolution and quantify the delivery of nanoparticles within them. We show that nanoparticles access a higher proportion of cells in micrometastases (50% nanoparticle-positive cells) compared with primary tumors (17% nanoparticle-positive cells) because they reside close to blood vessels and require a small diffusion distance to reach all tumor cells. Furthermore, the high-throughput nature of our image analysis workflow allowed us to profile the physiology and nanoparticle delivery of 1,301 micrometastases. This enabled us to use machine learning-based modeling to predict nanoparticle delivery to individual micrometastases based on their physiology. Our imaging method allows researchers to measure nanoparticle delivery to micrometastases and highlights an opportunity to target micrometastases with nanoparticles. The development of models to predict nanoparticle delivery based on micrometastasis physiology could enable personalized treatments based on the specific physiology of a patient’s micrometastases.

Published
2019

25. Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces.

Author

Koziel, Slawomir and Abdullah, Muhammad

Subjects
*RADAR cross sections, *UNIT cell, *GLOBAL optimization
Abstract

Popularity of metasurfaces has been continuously growing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite potential benefits, a practical obstacle hindering widespread metasurface utilization is the lack of systematic design procedures. Conventional approaches are largely intuition-inspired and demand heavy designer’s interaction while exploring the parameter space and pursuing optimum unit cell geometries. Not surprisingly, these are unable to identify truly optimum solutions. In this article, we introduce a novel machine-learning-based framework for automated and computationally efficient design of metasurfaces realizing broadband RCS reduction. Our methodology is a three-stage procedure that involves global surrogate-assisted optimization of the unit cells, followed by their local refinement. The last stage is direct EM-driven maximization of the RCS reduction bandwidth, facilitated by appropriate formulation of the objective function involving regularization terms. The appealing feature of the proposed framework is that it optimizes the RCS reduction bandwidth directly at the level of the entire metasurface as opposed to merely optimizing unit cell geometries. Computational feasibility of the optimization process, especially its last stage, is ensured by high-quality initial designs rendered during the first two stages. To corroborate the utility of our procedure, it has been applied to several metasurface designs reported in the literature, leading to the RCS reduction bandwidth improvement by 15%–25% when compared with the original designs. Furthermore, it was used to design a novel metasurface featuring over 100% of relative bandwidth. Although the procedure has been used in the context of RCS design, it can be generalized to handle metasurface development for other application areas. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

26. Training Feedforward Neural Networks Using Arithmetic Optimization Algorithm for Medical Classification

Author

Ang, Koon Meng, Lim, Wei Hong, Tiang, Sew Sun, Rahman, Hameedur, Ang, Chun Kit, Natarajan, Elango, Khan, Mohamed Khan Afthab Ahamed, Pan, Li, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Abdullah, Muhammad Amirul, editor, Khairuddin, Ismail Mohd., editor, Ab. Nasir, Ahmad Fakhri, editor, Mohd. Isa, Wan Hasbullah, editor, Mohd. Razman, Mohd. Azraai, editor, Rasid, Mohd. Azri Hizami, editor, Zainal, Sheikh Muhammad Hafiz Fahami, editor, Bentley, Barry, editor, and Liu, Pengcheng, editor

Published
2023
Full Text
View/download PDF

27. The Classification of Impact Signal of 6 DOF Cobot by Means of Machine Learning Model

Author

Kai, Gavin Lim Jiann, Nasir, Ahmad Fakhri Ab., Majeed, Anwar P. P. Abdul, Razman, Mohd Azraai Mohd, Khairuddin, Ismail Mohd, Li, Lim Thai, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Khairuddin, Ismail Mohd., editor, Abdullah, Muhammad Amirul, editor, Ab. Nasir, Ahmad Fakhri, editor, Mat Jizat, Jessnor Arif, editor, Mohd. Razman, Mohd. Azraai, editor, Abdul Ghani, Ahmad Shahrizan, editor, Zakaria, Muhammad Aizzat, editor, Mohd. Isa, Wan Hasbullah, editor, and Abdul Majeed, Anwar P. P., editor

Published
2022
Full Text
View/download PDF

28. Universiti Malaysia Pahang Autonomous Shuttle Development: Lane Classification Analysis Using Convolutional Neural Network (CNN)

Author

Yee, Lee Yin, Zakaria, Muhammad Aizzat, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Khairuddin, Ismail Mohd., editor, Abdullah, Muhammad Amirul, editor, Ab. Nasir, Ahmad Fakhri, editor, Mat Jizat, Jessnor Arif, editor, Mohd. Razman, Mohd. Azraai, editor, Abdul Ghani, Ahmad Shahrizan, editor, Zakaria, Muhammad Aizzat, editor, Mohd. Isa, Wan Hasbullah, editor, and Abdul Majeed, Anwar P. P., editor

Published
2022
Full Text
View/download PDF

29. Chili Plant Classification Using Transfer Learning Models Through Object Detection

Author

Shapiee, Muhammad Nur Aiman, Abdul Manan, Amirul Asyraf, Mohd Razman, Mohd Azraai, Mohd Khairuddin, Ismail, P. P. Abdul Majeed, Anwar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Khairuddin, Ismail Mohd., editor, Abdullah, Muhammad Amirul, editor, Ab. Nasir, Ahmad Fakhri, editor, Mat Jizat, Jessnor Arif, editor, Mohd. Razman, Mohd. Azraai, editor, Abdul Ghani, Ahmad Shahrizan, editor, Zakaria, Muhammad Aizzat, editor, Mohd. Isa, Wan Hasbullah, editor, and Abdul Majeed, Anwar P. P., editor

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results