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Heartbeat Anomaly Detection Method Based on Electrocardiogram using Improved Certainty Cognitive Map by Sumiati, .

“…The test results of the MCM Method gave a Mean Squared Error (MSE) of 0.65 and Root Mean Squared Error (RMSE) of 0.80 and the test results of the CCM Method with a Mean Squared Error (MSE) of 0.15 and a Root Mean Squared Error (RMSE) of 0.39. …”
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Reduced rank technique for joint channel estimation and joint data detection in TD-SCDMA systems by Marzook, Ali Kamil, Ismail, Alyani, Mohd Ali, Borhanuddin, Sali, Aduwati, Khalaf, Mohannad H., Khatun, Sabira

“…The detectors: zero forcing block linear equalizer and minimum mean square error block linear equalizer algorithms are considered in this paper to recover the data. …”
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The development of a tracking algorithm for ambulance detection using squaring of RGB and HSV color processing techniques by Mohammad Syawaludin Syafiq, Hassan

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Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems by Sabira, Khatun, Ali K., Marzook, Alyani, Ismail, Aduwati, Sali, Mohannad Hamed, Khalaf, Borhan, M. Ali

“…The detectors: zero forcing block linear equalizer and minimum mean square error block linear equalizer algorithms are considered in this paper to recover the data. …”
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Enhancement of Space-Time Receiver Structure with Multiuser Detection for Wideband CDMA Communication Systems by Subramaniam, Jeevan Rao

“…We consider two different pilot symbol assisted adaptive beamforming algorithms, Least Mean Square (LMS) and Recursive Least Square (RLS). …”
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Automated plant classification system using a hybrid of shape and color features of the leaf by Hamid, Laith Emad

“…Automated plant leaf classification is a computerized approach that employs computer vision and machine learning algorithms to identify a plant based on the features of its leaf. …”
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Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods by Mohd Sharif, Zakaria, Mohammad Fadhil, Abas, Fatimah, Dg Jamil, Norhafidzah, Mohd Saad, Addie, Irawan, Pebrianti, Dwi

“…The fitness function with the Genetic Algorithm (GA) optimization method is tested and evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and detection time. 51 sets of data have been collected using software in the loop (SITL) methods and are used to determine the effectiveness of the proposed fitness function and GA. …”
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Flock optimization algorithm-based deep learning model for diabetic disease detection improvement by Balasubramaniyan, Divager, Husin, Nor Azura, Mustapha, Norwati, Mohd Sharef, Nurfadhlina, Mohd Aris, Teh Noranis

“…The entire research implementation result is carried out with the help of the MATLAB program and the results are analyzed with accuracy, precision, recall, computational time, reliability scalability, and error rate measures like root mean square error, mean square error, and correlation coefficients. …”
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Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods by Zakaria, Mohd Sharif, Abas, Mohammad Fadhil, Dg Jamil, Fatimah, Mohd Saad, Norhafidzah, Hashim, Addie Irawan, Pebrianti, Dwi

“…The fitness function with the Genetic Algorithm (GA) optimization method is tested and evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and detection time. 51 sets of data have been collected using software in the loop (SITL) methods and are used to determine the effectiveness of the proposed fitness function and GA. …”
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Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach by Bahadar A., Kanthasamy R., Sait H.H., Zwawi M., Algarni M., Ayodele B.V., Cheng C.K., Wei L.J.

“…Biomass; Coal; Complex networks; Errors; Forecasting; Gasification; Hydrogen production; Learning algorithms; Mean square error; Neural networks; Regression analysis; Sensitivity analysis; Support vector machines; Co-gasification; Gaussian process regression; Hydrogen-rich syngas; Machine learning algorithms; Machine-learning; Neural-networks; Process parameters; Regression model; Support vectors machine; Syn gas; Synthesis gas; coal; hydrogen; synfuel; biomass; chemical reaction; detection method; hydrogen; machine learning; multicriteria analysis; algorithm; Article; artificial neural network; biomass; controlled study; gasification; Gaussian processing regression; linear regression analysis; machine learning; mean absolute error; mean square error; parameters; prediction; root mean square error; sensitivity analysis; support vector machine; temperature; Bayes theorem; biomass; Bayes Theorem; Biomass; Coal; Hydrogen; Temperature…”

Leaf condition analysis using convolutional neural network and vision transformer by Yong, Wai Chun, Ng, Kok Why, Haw, Su Cheng, Naveen, Palanichamy, Ng, Seng Beng

“…Besides, existing leaf disease detection programs do not provide an optimized user’s experience. …”
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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

“…To rectify this problem, we propose a Dynamic Robust Bootstrap-LTS based (DRBLTS) algorithm where the percentage of outliers in each bootstrap sample is detected. …”
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Reduced-rank technique for joint channel estimation in TD-SCDMA systems. by Ismail, Alyani, Sali, Aduwati, Mohd Ali, Borhanuddin, Khatun, Sabira

“…The detectors: zero forcing block linear equalizer and minimum mean square error block linear equalizer algorithms are considered in this paper to recover the data. …”
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Plant leaf recognition algorithm using ant colony-based feature extraction technique by Ghasab, Mohammad Ali Jan

“…Then, based on the characteristics of each species, decision making is done by means of ant colony optimisation as a search algorithm to return the optimal subset of features regarding the related species. …”
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Improved Switching-Basedmedian Filter For Impulse Noise Removal by Teoh, Sin Hoong

“…Based on the evaluations from root mean square error (RMSE), false positive detection rate, false negative detection rate, mean structure similarity index (MSSIM), processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the art median filtering methods.…”
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Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory by Salleh N.S.M., Saripuddin M., Suliman A., Jorgensen B.N.

Dynamic transmit antenna shuffling scheme for hybrid multiple-input multiple-output in layered architecture by Chong, Jin Hui

“…The QR decomposition is a common signal processing technique for MIMO detection. The computational complexity (total number of arithmetic operations) of proposed LC-QR algorithm is significantly lower than the conventional QR decomposition, zero-forcing (ZF) and minimum mean square error (MMSE) detection algorithm. …”
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Performance of Levenberg-Marquardt neural network algorithm in power quality disturbances classification / Adibah I’zzah Mohamad Kasim by Mohamad Kasim, Adibah I’zzah

“…Results demonstrated that the LM algorithm outperformed Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) methods in terms of accuracy, convergence speed, and computational efficiency, achieving near-perfect regression values and minimal mean square error for most PQD types. …”
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A hybrid intrusion detection system based on different machine learning algorithms by Atefi, Kayvan, Yahya, Saadiah, Dak, Ahmad Yusri, Atefi, Arash

“…Hence this study aims to hybrid GA and forbids with high accuracy.The paper illustrates the benefit of hybrid SVM via GA also the paper has proven that by enhancing SVM with GA can reduce false alarms and mean square error (MSE) in detecting intrusion.…”
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Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

“…The error measurements of the proposed method such as Mean Absolute Percentage Error, Mean Absolute Error, And Root Mean Square Error for islanding detection are less than 0.02% for ideal and noisy conditions which shows that the algorithm is not sensitive to noise. …”
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