Search Results - (( intelligence model learning algorithm ) OR ( intelligence systematic learning algorithm ))

Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review by Yafouz A., Ahmed A.N., Zaini N., El-Shafie A.

“…Decision trees; Forecasting; Multilayer neural networks; Ozone; Predictive analytics; Support vector machines; Artificial intelligence techniques; Machine learning techniques; Multi layer perceptron; Optimization approach; Ozone concentration forecasting; Prediction accuracy; Stand-alone algorithm; Tropospheric ozone concentration; Learning systems; ozone; air quality; algorithm; concentration (composition); machine learning; optimization; ozone; prediction; theoretical study; air pollutant; air quality; artificial intelligence; artificial neural network; concentration (parameter); decision tree; feed forward neural network; forecasting; fuzzy system; human; measurement accuracy; multilayer perceptron; prediction; random forest; recurrent neural network; Review; support vector machine; systematic review…”

A systematic review of machine learning techniques and applications in soil improvement using green materials by Saad, Ahmed Hassan, Nahazanan, Haslinda, Yusuf, Badronnisa, Toha, Siti Fauziah, Alnuaim, Ahmed, El-Mouchi, Ahmed, Elseknidy, Mohamed, Mohammed, Angham Ali

“…The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. …”
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A systematic review of machine learning techniques and applications in soil improvement using green materials by Saad, Ahmed Hassan, Nahazanan, Haslinda, Yusuf, Badronnisa, Toha, Siti Fauziah, Alnuaim, Ahmed, El-Mouchi, Ahmed, Elseknidy, Mohamed, Mohammed, Angham Ali

“…The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. …”
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Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis by Baashar Y., Alkawsi G., Alhussian H., Capretz L.F., Alwadain A., Alkahtani A.A., Almomani M.

Synergizing intelligence and knowledge discovery: Hybrid black hole algorithm for optimizing discrete Hopfield neural network with negative based systematic satisfiability by Rusdi, Nur ‘Afifah, Zamri, Nur Ezlin, Kasihmuddin, Mohd Shareduwan Mohd, Romli, Nurul Atiqah, Manoharam, Gaeithry, Abdeen, Suad, Mansor, Mohd. Asyraf

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Reverse migration prediction model based on machine learning / Azreen Anuar by Anuar, Azreen

“…And the third objective is to evaluate reverse migration prediction model based on machine learning analysis. For this purpose, three (3) algorithms have been assessed, namely, the Random Forest, Decision Tree, and Gradient Boosted Tree. …”
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Reimagining English language learning: a systematic review of AI integration in classroom practice (2019–2024) by Ibrahim Brian, Muhammad Shyazzwan

“…This Systematic Literature Review (SLR) examines the integration of Artificial Intelligence (AI) in English Language Learning (ELL) with the aim of reimagining classrooms as creative, inclusive, and technology-enhanced spaces. …”
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Artificial Intelligence Integration in Mobile Applications: Innovation and Challenges in Supporting Quran Memorization and Review by Khairulnazrin Nasir, Rahim Kamarul Zaman, Ahmad Bazli Ahmad Hilmi, Abdul Hakim Mahadzir, Ahmad Mukhlis bin Mustaffa

A systematic literature review of deep learning neural network for time series air quality forecasting by Zaini N., Ean L.W., Ahmed A.N., Malek M.A.

Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis by Baashar, Y., Alkawsi, G., Alhussian, H., Capretz, L.F., Alwadain, A., Alkahtani, A.A., Almomani, M.

“…Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30 of all fatalities in the United States and 45 in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. …”
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Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis by Baashar, Y., Alkawsi, G., Alhussian, H., Capretz, L.F., Alwadain, A., Alkahtani, A.A., Almomani, M.

“…Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30 of all fatalities in the United States and 45 in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. …”
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A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science by Balogun, A.-L., Tella, A., Baloo, L., Adebisi, N.

“…The study also revealed that machine learning algorithms such as random forest, gradient boosting machine, and classification and regression trees (CART) accurately predict air pollution hazard when integrated with spatial models. …”
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The framework of inventory prediction model using ABC analysis for inventory management / Umi Safierah Masdiki and Rozita Kadar by Masdiki, Umi Safierah, Kadar, Rozita

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Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study by Abu-Samah A., Abdul Razak N.N., Mohamad Suhaimi F., Jamaludin U.K., Chase G.

Systematic review and meta-analysis on the classification metrics of machine learning algorithm based radiomics in hepatocellular carcinoma diagnosis by Mohd Haniff, Nurin Syazwina, Ng, Kwan Hoong, Kamal, Izdihar, Mohd Zain, Norhayati, Abdul Karim, Muhammad Khalis

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An artificial intelligence approach to monitor student performance and devise preventive measures by Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.

Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination by Nallakukkala, Sirisha, Tackie-Otoo, Bennet Nii, Aliyu, Ruwaida, Lal, Bhajan, Nallakukkala, Jagadish Ram Deepak, Devi, Gayathri

“…In this context. ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. …”
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Artificial intelligence-driven anticancer peptide discovery by Wu, Junrui, Ji, Shuaiqi, Sahibzada, Kashif Iqbal, Lou, Mengxue, An, Feiyu, Li, Wenqian, Guo, Jiawei, Zhang, Taowei, Zhang, Xinyi, Chou, Yilin, Zhang, Henan, Jin, Hao, Ma, Teng, Liu, Weichi, Alikulov, Begali, Golovneva, Natalia Alekseevna, Foo, Hooi Ling, Kuralay, Issayeva, Sun, Zhihong, Wei, Dongqing, Wu, Rina

“…However, traditional screening methods are constrained by their low efficiency, high costs, and technical complexity, limiting their capacity to meet the demands of high-throughput applications. Artificial intelligence (AI) has provided new methods to address these challenges, significantly improving the efficiency and accuracy of ACP screening through the application of machine learning and deep learning algorithms. …”
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A systematic literature review on the application of artificial intelligence in enhancing care for kidney diseases patients by Rahman, Md Saidur, Md Nor, Nor Saadah

“…Fourteen studies published from 2018 to 2024 were reviewed in the systematic review to learn how AI technology was incorporated to improve care for CKD patients. …”
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A systematic and critical review on effective utilization of artificial intelligence for bio-diesel production techniques by Ahmad, Junaid, Awais, Muhammad, Rashid, Umer, Ngamcharussrivichai, Chawalit, Raza Naqvi, Salman, Ali, Imtiaz

“…The AI-enabled biodiesel prediction methods consist of several stages, i.e., biodiesel data collection, biodiesel data preprocessing, developing, and tuning machine learning (ML) algorithm on biodiesel data, and predicting unknown biodiesel properties. …”
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