Search Results - (( java adaptation optimization algorithm ) OR ( _ implications learning algorithm ))

Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

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

Get full text
Get full text
Get full text

A comparative analysis of machine learning algorithms for diabetes prediction by Alansari, Waseem Abdulmahdi, Masnizah Mohd

“…The study contributes insights into the importance of pre-processing and feature selection in improving algorithm performance. The findings have implications for developing accurate predictive models and improving diabetes detection.…”
Get full text
Get full text
Get full text

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

“…Secondly, in solving every Machine Learning problem, there is no one algorithm superior to other algorithms. …”
Get full text
Get full text

A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection by Basheer G.S., Ahmad M.S., Tang A.Y.C.

“…We discuss both agent and multi-agent systems and focus on the implications of the theory of detecting learning styles that constitutes behaviors of learners when using online learning systems, learner's profile, and the structure of multi-agent learning systems. …”

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. …”
Get full text
Get full text
Get full text

Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
Get full text
Get full text
Get full text
Get full text

Deep learning algorithms for personalized services and enhanced user experience in libraries by Sa'ari, Haziah, Sahak, Mohd Dasuki, Skrzeszewskis, Stan

“…Through a comprehensive literature review, the study aims to uncover the benefits, challenges, and implications of integrating DL algorithms for user behaviour analysis and personalized services in library environments. …”
Get full text
Get full text

Deep learning algorithms for personalized services and enhanced user experience in libraries: a systematic review / Haziah Sa’ari, Mohd Dasuki Sahak and Stan Skrzeszewskis by Sa’ari, Haziah, Sahak, Mohd Dasuki, Skrzeszewskis, Stan

“…Through a comprehensive literature review, the study aims to uncover the benefits, challenges, and implications of integrating DL algorithms for user behaviour analysis and personalized services in library environments. …”
Get full text
Get full text

A new fuzzy peer assessment methodology for cooperative learning of students by Tay, Kai Meng, Kok, Chin Chai, Chee, Peng Lim

“…A case study to demonstrate the effectiveness of the proposed methodology is described. Implications of the results are analyzed and discussed. …”
Get full text
Get full text
Get full text

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. …”
Get full text
Get full text

Predicting the classification of heart failure patients using optimized machine learning algorithms by Ahmed, Marzia, Mohd Herwan, Sulaiman, Hassan, Md Maruf, Bhuiyan, Touhid

“…The optimized GBM model achieved a test accuracy of 94%, demonstrating superior performance compared to traditional machine learning models. The study underscores the importance of hyperparameter tuning through metaheuristic algorithms and highlights the potential of AIW-PSO in enhancing model performance for clinical prediction tasks. …”
Get full text
Get full text
Get full text

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. …”
Get full text
Get full text
Get full text

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.

Malware Classification and Detection using Variations of Machine Learning Algorithm Models by Andi Maslan, Andi Maslan, Abdul Hamid, Abdul Hamid

“…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. The purpose of the study is to detect, classify malware attacks using a variety of ML Algorithm models such as SVM, KNN and Neural Network and testing detection performance. …”
Get full text
Get full text
Get full text

Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar by Ja'afar, Nur Shahirah

“…To overcome these limitations, this research has proposed five machine learning algorithms namely Linear Regression, Lasso, Ridge, Random Forest and Decision Tree. …”
Get full text
Get full text

Algoritma pendaraban nombor perpuluhan dari perspektif pelajar Tingkatan Satu by Md. Yunus, Aida Suraya

“…Identification of students' algorithms has implications towards the teaching and learning activities and forms a basis in planning teaching strategies of multiplication involving decimals for mathematics educators.…”
Get full text
Get full text
Get full text

Analyzing enrolment patterns: Stacked ensemble statistical learning-based approach to educational decision making by Chuan, Zun Liang, Chong, Teak Wei, Japashov, Nursultan, Soon, Kien Yuan, Tan, Wei Qing, Noriszura, Ismail, Liong, Choong-Yeun, Tan, Ee Hiae

“…Moreover, the introduction of the novel stacked ensemble machine learning algorithm had improved predictive accuracy compared to traditional dichotomous logistic regression algorithms on average, particularly at optimal training-to-test ratios of 70:30, 80:20, and 90:10. …”
Get full text
Get full text
Get full text

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. …”
Get full text
Get full text

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

“…This systematic review aims to examine the existing literature on phonocardiography classification based on machine learning, focusing on algorithms, datasets, feature extraction methods, and classification models utilized. …”
Get full text
Get full text