Search Results - (( machine ((loading algorithm) OR (learning algorithm)) ) OR ( action research algorithm ))*

Distributed learning based energy-efficient operations in small cell networks by Mughees, Amna

“…Both user association and power allocation are solved in their respective action spaces. Simulation results demonstrate improved performance in power consumption, load, sum rate, utility, learning rate, convergence, and energy efficiency for small base stations (SBSs) and user equipment (UEs) compared to four benchmarked algorithms, including WMMSE, game theory, Q-learning, and DRL. …”
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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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Prediction of COVID-19 outbreak using Support Vector Machine / Muhammad Qayyum Mohd Azman by Mohd Azman, Muhammad Qayyum

“…In response to the unprecedented challenges posed by the COVID-19 pandemic, this research project presents a systematic approach to outbreak prediction, specifically advocating for the implementation of Support Vector Machine (SVM) algorithms. …”
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An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM) by Shamim, Akhtar

“…The scope of this study is tri folded, First, an exhaustive and parametric comparative study on a wide variety of machine learning algorithms is presented to evaluate the performance of machine learning algorithms in energy load prediction. …”
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An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms by Akhtar, Shamim, Muhamad Zahim, Sujod, Rizvi, Syed Sajjad Hussain

“…The comprehensive comparative study preparatory to the recommendation of the best candidate out of 24 machine learning algorithms on the SEIL dataset is presented in this work. …”
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Enhanced Q-Learning algorithm for potential actions selection in automated graphical user interface testing by Goh, Kwang Yi

“…We utilized the enhanced Q-Learning algorithm to compare actions, including context-based actions, to effectively achieve higher code coverage. …”
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Comparison Between the Bees Algorithm and Genetic Algorithm Model in Manpower Allocation on Cell Loading Problem by AlMahasneh, Hossam Sayel

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An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective by Mousavi, Seyed Mohsen, Sadeghi R., Kiarash, Lee, Lai Soon

“…To integrate machine learning and human interactions, this paper develops a new three-stage interactive algorithm in business analytics, called the interactive Nautilus-based algorithm, to address complex problems. …”
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Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer by Salleh N.S.M., Suliman A., J�rgensen B.N.

“…Brain; Errors; Forecasting; Learning algorithms; Mean square error; Memory architecture; Network architecture; Smart meters; Time series; Demand-side; Electricity load; Error values; Load predictions; Machine learning algorithms; Mean absolute error; Mean squared error; Prediction errors; Regression problem; Times series; Long short-term memory…”

Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia by Zaini, Farah Anishah, Sulaima, Mohamad Fani, Wan Abdul Razak, Intan Azmira, Othman, Mohammad Lutfi, Mokhlis, Hazlie

“…Least square support vector machines (LSSVM) are well suited to handle complex non-linear power load series. …”
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Improved bacterial foraging optimization algorithm with machine learning driven short term electricity load forecasting: A case study in Peninsular Malaysia by Sulaima, Mohamad Fani, Zaini, Farah Anishah, Wan Abdul Razak, Intan Azmira, Othman, Mohammad Lutfi, Mokhlis, Hazlie

“…Least square support vector machines (LSSVM) are well suited to handle complex non‑linear power load series. …”
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Anti-Obfuscation Techniques: Recent Analysis of Malware Detection by Gorment N.Z., Selamat A., Krejcar O.

“…Machine learning; Malware; Analysis tools; Anti virus; Anti-obfuscation; Comparatives studies; Machine learning algorithms; Malware analysis; Malware detection; Malwares; Obfuscation technique; Stealthy malware; Learning algorithms…”

Wearable based-sensor fall detection system using machine learning algorithm by Ishak, Anis Nadia, Habaebi, Mohamed Hadi, Yusoff, Siti Hajar, Islam, Md. Rafiqul

“…In this project, a wearable sensor-based fall detection system using a machine-learning algorithm had been developed. …”
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Machine learning using robust AI techniques / Prof. Madya Dr. Nordin Abu Bakar by Abu Bakar, Nordin (Prof. Madya Dr.)

“…Machine Learning is an area in AI that deals with cognitive ability to assess surrounding area for meaningful actions to pursue. …”
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Machine learning predictions of stock market pattern using Econophysics approach by Roslan, Nur Nadia Hani, Abdullah, Shahino Mah

“…By leveraging machine learning algorithms, such as Long Short-Term Memory (LSTM), the predictions generated closely follow the actual stock price movements for Inari Amertron Berhad. …”
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MOSELM approach for Voltage Stability Indicator using phasor measurement units by Abidin I.Z., Yap K.S., Saadun N., Abdullah S.K.S., Mohd Sarmin M.K.N.

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Detection and classification of conflict flows in SDN using machine learning algorithms by Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah

“…As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. …”
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Revolutionising pharmacy through machine learning: the progress and perils / Yuslina Zakaria by Zakaria, Yuslina

“…ML, a branch of artificial intelligence (AI), uses algorithms such as neural networks, decision trees, and support vector machines to learn from large datasets and make predictions. …”
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A Systematic Literature Review of Electricity Load Forecasting using Long Short-Term Memory by Salleh N.S.M., Suliman A., J�rgensen B.N.

“…Brain; Deregulation; Electric load forecasting; Electric power plant loads; Electric utilities; Learning algorithms; Statistical tests; Electricity load; Electricity load forecasting; Evaluation metrics; Load predictions; Long term planning; LSTM; Machine learning algorithms; Medium-term planning; Review papers; Systematic literature review; Long short-term memory…”

Cyberbullying detection: a machine learning approach by Yeong, Su Yen

“…Bag of Words model was used to convert text into numerical inputs. The machine learning algorithm, Support Vector Machine was chosen after comparing it with other algorithms such as Multinomial Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. …”
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