Search Results - (( data implication learning algorithm ) OR ( data internalization based algorithm ))

Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

“…The best algorithm will not be the same for all the data sets. …”
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Virtual reality in algorithm programming course: practicality and implications for college students by Dewi, Ika Parma, Ambiyar, Mursyida, Lativa, Effendi, Hansi, Giatman, Muhammad, Efrizon, Hanafi, Hafizul Fahri, Ali, Siti Khadijah

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Enhancing project completion date prediction using a hybrid model: rule-based algorithm and machine learning algorithm by Abd Rahman, Mohd Shahrizan, Jamaludin, Nor Azliana Akmal, Zainol, Zuraini, Tengku Sembok, Tengku Mohd

“…The study employs a hybrid predictive model that combines Big Data technologies, Extract Load Transfer (ELT) processes, rule-based algorithms (RBA), machine learning (ML), and Power BI visualizations. …”
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Application of machine learning and artificial intelligence in detecting SQL injection attacks by Md Sultan, Abu Bakar, Agiliga, Nwabudike Augustine, Osman, Mohd Hafeez Bin, Sharif, Khaironi Yatim

“…More recently, cyber-attacks have also been on the rise and SQL injection attacks are some of major threats to data security. AI and machine learning have come a long way, however their usage in cybersecurity is still somewhat nascent. …”
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Systematic review for phonocardiography classification based on machine learning by Altaf, Abdullah, Mahdin, Hairulnizam, Alive, Awais Mahmood, Ninggal, Mohd Izuan Hafez, Altaf, Abdulrehman, Javid, Irfan

“…Deep learning, in particular, leverages layered neural networks to process data in complex ways, mimicking how the human brain works. …”
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Systematic Review for Phonocardiography Classification Based on Machine Learning by Abdullah Altaf, Abdullah Altaf, Hairulnizam Mahdin, Hairulnizam Mahdin, Awais Mahmood Alive, Awais Mahmood Alive, Mohd Izuan Hafez Ninggal, Mohd Izuan Hafez Ninggal, Abdulrehman Altaf, Abdulrehman Altaf, Irfan Javid, Irfan Javid

“…Deep learning, in particular, leverages layered neural networks to process data in complex ways, mimicking how the human brain works. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection by Bouke, Mohamed Aly, Alramli, Omar Imhemed, Abdullah, Azizol

“…Traditional machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM) have been employed to mitigate this challenge. …”
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Innovating communication adaptation on social media using AI-based decision support systems by Zakaria, Noor Azura, Adriana, Dadi, Budi, Danang Surya, Mardiana, Mardiana, Rafika, Ageng Setiani, Asmawati, Ari

“…By integrating data riven insights and machine learning models, the system supports users in crafting adaptive and persuasive communication strategies that resonate with their network. …”
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Why coding? Why now? From coding to computational thinking through computational mathematics problem based learning (CM-PBL) by Ku, Soh Ting, Talib, Othman, Md. Yunus, Aida Suraya, Zolkepli, Maslina

“…Rather than emphasizing student learning passively through listening, watching, practicing exercises and imitating isolated skills, the CM-PBL learning framework allowing students to simulate and build their own computational models to support their self-learning and understanding of mathematic concepts. …”
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Accelerating color theory-based design through artificial intelligence: a conceptual welcome by Khairudin, Khayril Anwar, Yahaya, Mohamad Hafiz, Abdul Jamil, Siti Ermi Syahira, Rosli, Hazrul Marzan, Abu, Muhammad Badrulzzaman, Abd Rahman, Muh Sarip

“…By leveraging machine learning and generative algorithms, designers can automate tasks such as palette generation, contrast evaluation, and emotional tone mapping, thus significantly reducing the time and cognitive load required in early design stages (Luo & Moroney, 2021). …”
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A comparative analysis of machine learning algorithms for diabetes prediction by Alansari, Waseem Abdulmahdi, Masnizah Mohd

“…The methodology involves data collection, pre-processing, and training the algorithms using k-fold cross-validation. …”
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Prediction of payment method in convenience stores using machine learning by Pratondo, Agus, Novianty, Astri, Pudjoatmodjo, Bambang

“…This study explores the application of machine learning techniques, specifically the Random Forest algorithm, to predict payment modes in the context of the Indonesian community. …”
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Malware Classification and Detection using Variations of Machine Learning Algorithm Models by Andi Maslan, Andi Maslan, Abdul Hamid, Abdul Hamid

“…Types of attacks can be Ping of Death, flooding, remote-controlled attacks, UDP flooding, and Smurf Attacks. Attack data was obtained from the ClaMP dataset, which has an unbalanced data set, and has very high noise, so it is necessary to analyze data packets in network logs and optimize feature extraction which is then analyzed statistically with machine learning algorithms. …”
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Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion by Ahmed Osman A.I., Latif S.D., Wee Boo K.B., Ahmed A.N., Huang Y.F., El-Shafie A.

Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions by Shamshad, H., Ullah, F., Ullah, A., Kebande, V.R., Ullah, S., Al-Dhaqm, A.

“…Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. …”
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A study of density-grid based clustering algorithms on data streams by Amini, A., Saybani, M.R., Sahaf Yazdi, S.R.A.

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Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms by Raheem, Ajiboye Adeleke, Hauwau, Isah-Kebbe, O., Oladele Tinuke

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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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A hybrid deep learning-based unsupervised anomaly detection in high dimensional data by Muneer, A., Taib, S.M., Fati, S.M., Balogun, A.O., Aziz, I.A.

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