Search Results - (( java application optimization algorithm ) OR ( early identification learning algorithm ))

Early Detection Of ADHD Among Children Using Machine Learning by Nur Atiqah, Kamal

“…This abstract explores the significance of early ADHD detection, the potential of fMRI for ADHD diagnosis, and the role of machine learning in facilitating early identification. …”
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Optimization of blood vessel detection in retina images using multithreading and native code for portable devices by Tran, Duc Ngoc, Hussin, Fawnizu Azmadi, Yusoff, Mohd Zuki

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Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things by Mohd Arfian, Ismail, Siti Nur Fathin Najwa, Mustaffa, Abed, Munther H.

“…This work highlights the significance of leveraging deep transfer learning and IoT in achieving early identification of suspected COVID-19 patients. …”
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Route Optimization System by Zulkifli, Abdul Hayy

“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques by Muhammad Afnan, Mohammad Nasir

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Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

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Methods for identification of the opportunistic gut mycobiome from colorectal adenocarcinoma biopsy tissues by Aisyah, Yunus, Norfilza, Mohd Mokhtar, Raja Affendi, Raja Ali *, Siti Maryam, Ahmad Kendong, Hajar, Fauzan Ahmad

“…•Application of machine learning algorithms to the identification of potential mycobiome biomarkers for non-invasive colorectal cancer screening. …”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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Analysis of banana plant health using machine learning techniques by Thiagarajan, Joshva Devadas, Kulkarni, Siddharaj Vitthal, Jadhav, Shreyas Anil, Waghe, Ayush Ashish, Raja, S.P., Rajagopal, Sivakumar, Poddar, Harshit, Subramaniam, Shamala

“…Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. …”
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Comparative study on leaf disease identification using Yolo v4 and Yolo v7 algorithm by Wang, Xinming, Tang, Sai Hong, Mohd Ariffin, Mohd Khairol Anuar, Ismail, Mohd Idris Shah

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Predictive Modelling of Stroke Occurrence among Patients using Machine Learning by Sures, Narayasamy, Thilagamalar, Maniam

“…Early identification of high-risk patients enables timely intervention and implementation of preventive measures, potentially reducing the burden of stroke-related complications. …”
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Deep learning-based colorectal cancer classification using augmented and normalised gut microbiome data / Mwenge Mulenga by Mwenge , Mulenga

“…While the complex relations that exist between the microbiome and host phenotypes make machine learning algorithms suitable for analysing the microbiome data, deep learning methods are becoming more popular due to their outstanding performance in related fields. …”
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Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy by Rahman, Sam Matiur, Ali, Md. Asraf, Altwijri, Omar, Alqahtani, Mahdi, Ahmed, Nasim, Ahamed, Nizam Uddin

“…Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. …”
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Comparative study of informative acoustic features for VTOL UAV faulty prediction using machine learning by Mohd Sani, Fareisya Zulaikha, Makhtar, Siti Noormiza, Mohd Nor, Elya, Kamarudin, Nur Diyana, Md Ali, Syaril Azrad, Md Ali, Kurnianingsih

“…Pitch, zero-crossing and short-time energy are selected as the significant audio features for the machine learning classification algorithm. UAV sounds collected in the experiment will be analysed and divided into a 60:40 ratio for training and testing datasets. …”
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Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

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Keylogger detection analysis using machine learning algorithm / Muhammad Faiz Hazim Abdul Rahman by Abdul Rahman, Muhammad Faiz Hazim

“…Early identification of a keylogger malware attack could prevent hackers from accessing personal user data and reduce the likelihood of infiltration, which could reveal account information, credit cards, usernames, passwords, and other data. …”
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Advancing machine learning for identifying cardiovascular disease via granular computing by Ku Muhammad Naim, Ku Khalif, Noryanti, Muhammad, Mohd Khairul Bazli, Mohd Aziz, Mohammad Isa, Irawan, Mohammad Iqbal, ., Muhammad Nanda, Setiawan

“…The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for CVD identification. …”
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Dyslexia handwriting detection using Convolutional Neural Network (CNN) algorithm / Sofea Najihah Mohd Zaki by Mohd Zaki, Sofea Najihah

“…Convolutional Neural Network (CNN) algorithm was chosen as one of the possible solutions after a thorough analysis of many algorithms for dyslexic handwriting identification. …”
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Classification of Learner Retention using Machine Learning Approaches by Nur Amalina Diyana Suhaimi , Norshaliza Kamaruddin, Thirumeni T Subramaniam, Nilam Nur Amir Sjarif, Maslin Masrom, Nurazean Maarop

“…The benefit of performing Machine Learning is that it enables the identification of at-risk learners at the earliest opportunity and therefore implement the earliest interventions to retain them. …”
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