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Novel direct and self-regulating approaches to determine optimum growing multi-experts network structure by Loo, C.K., Rajeswari, M., Rao, M.V.C.

“…However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts self-adaptive learning rates for gradient-descent learning rules. …”
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Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models by Quadros, Jaimon Dennis, Khan, Sher Afghan, Aabid, Abdul, Baig, Muneer

“…The data for training and testing the algorithms was derived using the regression equation developed using the Box-Behnken Design (BBD). …”
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Edge assisted crime prediction and evaluation framework for machine learning algorithms by Adhikary, Apurba, Murad, Saydul Akbar, Munir, Md Shirajum, Choong Seon, Hong Seong

“…To anticipate occurrences, ML methods such as Decision Trees, Neural Networks, K-Nearest Neighbors, and Impact Learning are being utilized, and their performance is compared based on the data processing and modification used. …”
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A deep reinforcement learning hybrid algorithm for the computational discovery and characterization of small proteins utilizing mycobacterium tuberculosis as a model by Ouwabunmi, Babalola AbdulHafeez

“…The accurate prediction and characterization of small open reading frames (smORFs) are critical for understanding their functional roles in gene regulation and cellular processes. This study presents the development and evaluation of a novel hybrid machine learning algorithm that integrates the strengths of Random Forest and Gradient Boosting models to enhance the prediction of smORFs. …”
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Nonlinear dynamic system identification and control via self-regulating modular neural network by Kiong, L.C., Rajeswari, M., Rao, M.V.C.

“…In addition, the Fully Self-Organized Simplified Adaptive Resonance Theory (FOSART) is modified and adopted to generate an induced Delaunay triangulation that is highly desired for optimal function approximation. Self-adaptive learning rates Gradient Descent learning rules are employed in a supervised learning phase. …”
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Artificial Intelligence (AI) in the art and design industry / Fahmi Samsudin by Samsudin, Fahmi

“…It encompasses different types, such as rule-based AI using if-then statements for decision-making, machine learning which employs algorithms to analyze and learn from data, and deep learning utilizing artificial neural networks to learn from extensive datasets. …”
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Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO) by Ahmed, Ehab Ali

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DNA enhancer prediction using machine learning techniques with novel feature representation by Fong, Pui Kwan

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A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications by Uddin I., Awan H.H., Khalid M., Khan S., Akbar S., Sarker M.R., Abdolrasol M.G.M., Alghamdi T.A.H.

“…Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. …”

ANN-Based Binary Backtracking Search Algorithm for VPP Optimal Scheduling and Cost-Effective Evaluation by Hannan M.A., Abdolrasol M.G., Mohamed R., Al-Shetwi A., Ker P., Begum R., Muttaqi K.

Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization by Wong, Yong Jie, Arumugasamy, Senthil Kumar, Jewaratnam, Jegalakshimi

“…This paper compares mean absolute error, mean square error, and mean absolute percentage error (MAPE) in the PCL biopolymerization process for 11 different training algorithms that belong to six classes, namely (1) additive momentum, (2) self-adaptive learning rate, (3) resilient backpropagation, (4) conjugate gradient backpropagation, (5) quasi-Newton, and (6) Bayesian regulation propagation. …”
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Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review by Dehkordi, Iman Farhadian, Manochehri, Kooroush, Aghazarian, Vahe

“…For example, it could be used to keep an eye on and regulate industrial services, or it could be used to improve corporate operations. …”
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Base pressure control through micro jets at supersonic Mach numbers using experimental and machine learning approach by Aabid, Abdul, Khan, Sher Afghan, Yasir, Javed

“…This study presents active control methods using microjets to regulate base pressure, employing experimental and machine learning approaches. …”
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Enhancing Aquilaria species classification using agarwood oil analysis and k-Nearest Neighbors (KNN) as machine learning / Amir Hussairi Zaidi ... [et al.] by Zaidi, Amir Hussairi, Hasnu Al-Hadi, Anis Hazirah ‘Izzati, Ahmad Sabr, Noor Aida Syakira, Nik Kamaruzaman, Nik Fasha Edora, Ismail, Nurlaila, Mohd Yusoff, Zakiah, Taib, Mohd Nasir

“…This study employs machine learning, specifically the k-Nearest Neighbors (kNN) algorithm, to classify Aquilaria species based on chemical compounds features extracted from agarwood oil. …”
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Comparative analysis of danger theory variants in measuring risk level for text spam messages by Zainal K., Jali M.Z., Hasan A.B.

Tap Settings Optimization in Power Systems: Economic Efficiency Enhancement Through Cuckoo Search Algorithm While Ensuring Voltage Stability by Raizal N.I., Mansor M.H., Musirin I., Shaaya S.A., Ismail S.A., Ismail N.L.

Voltage tracking of DC-DC CUK converter using neural network control by Bidin, Mohamed Noor Azman

“…The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. …”
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Implementation of DSP-based optimal fuzzy logic speed controller for IM drive using quantum lightning search algorithm by Abd. Ali J., Hannan M.A., Mohamed A., Pin Jern K., Abdolrasol M.G.

Understanding the sentiment on gig economy: good or bad? by Norazmi, Fatin Aimi Naemah, Mazlan, Nur Syazwani, Said, Rusmawati, Ok Rahmat, Rahmita Wirza

“…Based on the three algorithms used in this research, LGBM has been the best model with the highest accuracy of 85%, while SVM has 84% and LR 82%. …”
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A conceptual Malaysian private healthcare university-of-the-future business model: staying relevant in the digital and post-covid era by Saiid, Muhammad, Bah, Ahmadou Pathe, Ahmad Dahlan, Abdul Rahman

“…The results from the analysis and design of the BMC and SC indicate that the principal changes required to digitally transform the university to a UotF include: the adoption of an open learning system for flexible and lifelong learning; transitioning the campus to a more 'smart', eco-friendly and sustainable campus; incorporating the latest artificial intelligence, machine learning algorithms, and data analytics to improve the university‘s management systems and enhance delivery mechanisms for teaching and learning; and to improve community engagement relationships and partnerships to deliver value to the economic, health and safety of the society, as well as to leverage the open learning system to provide curated courses to niche categories of students locally and globally.…”
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