Search Results - (( developing negative means algorithm ) OR ( java implication based algorithm ))

Automated negative lightning return strokes characterization using brute-force search algorithm by Abdul Haris, Faranadia, Ab. Kadir, Mohd Zainal Abidin, Sudin, Sukhairi, Jasni, Jasronita, Johari, Dalina, Zaini, Nur Hazirah

“…Hence, this study proposed the development of an automated negative lightning return strokes characterization using a brute-force search algorithm. …”
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Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms by Lei, X., Mohamad, U.H., Sarlan, A., Shutaywi, M., Daradkeh, Y.I., Mohammed, H.O.

“…For the objective of classifying FI in terms of fraud or not, the Intelligent Information System for Financial Institutions (IISFI) relying on Supervised ML (SML) Algorithms has been created in this work. Bayesian Belief Network, Neural Network, Decision trees, Naïve Bayes, and Nearest Neighbor has been compared for the purpose of classifying FI risks using the performance measures asfalse positive rate, true positive rate, true negative rate, false negative rate, accuracy, F-Measure, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Med AE, Receiver Operating Characteristic (ROC) area,Precision Recall Characteristic (PRC) area, and measures of PC. …”
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Automated negative lightning return strokes characterization using brute-force search algorithm by Abdul Haris, Faranadia, Ab. Kadir, Mohd Zainal Abidin, Sudin, Sukhairi, Jasni, Jasronita, Johari, Dalina, Zaini, Nur Hazirah

“…Hence, this study proposed the development of an automated negative lightning return strokes characterization using a brute-force search algorithm. …”
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Automated Negative Lightning Return Strokes Characterization Using Brute-Force Search Algorithm by Faranadia Abdul Haris, Mohd Zainal Abidin Ab. Kadir, Sukhairi Sudin, Jasronita Jasni, Dalina Johari, Nur Hazirah Zaini

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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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Improved criteria determination of an automated negative lightning return strokes characterisation using Brute-Force search algorithm by Abdul Haris, Faranadia

“…A total of 206 negative lightning return strokes waveforms were analysed and automatically characterised using the proposed algorithm. …”
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Development Of Fall Risk Clustering Algorithm In Older People by Wong, Kam Kang

“…Therefore, the aim of this study is to develop a clustering-based fall risk algorithm which can provide assistances for clinician in management of falls. …”
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Sustainable Management Of River Water Quality Using Artificial Intelligence Optimisation Algorithms by Chia, See Leng

“…The performance was benchmarked using root mean squared error (RMSE), mean absolute error (MAE), Coefficient of Determination (R2 ), mean absolute percentage error (MAPE) and Global Performance Index (GPI) as well as their time cost. …”
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Automatic document clustering and indexing of multiple documents using KNMF for feature extraction through Hadoop and lucene on big data by Laxmi Lydia E., Sharmili N., Nguyen P.T., Hashim W., Maseleno A.

TOPSIS-based Regression Algorithms Evaluation by Abu-Shareha, Ahmad Adel

“…The results showed that different preferences led to varying algorithm rankings, but top-ranked algorithms were distinguished using a specific dataset. …”
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Breast cancer diagnosis through an optimization-driven multispectral gamma correction (ODMGC) by Raj A, Arul Edwin, Ahmad, Nabihah, Durai S, Ananiah

“…This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

An automated cervical pre-cancerous diagnostic system by Nor Ashidi, Mat-Isa, Mohd Yusoff Mashor, Nor Hayati, Othman

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Sentiment analysis of restaurant reviews in Kuala Terengganu based on K-Nearest Neighbor/ Siti Syazwana Jafri by Jafri, Siti Syazwana

“…The chosen technique is classification and the algorithm that will be applied in the classification process is K- Nearest Neighbor (KNN). …”
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Parameter-driven count time series models / Nawwal Ahmad Bukhari by Nawwal , Ahmad Bukhari

“…In this study, a parameter-driven count time series model with three different distributions that are Poisson, zero-inflated Poisson and negative binomial was developed. A key property of our model is that the distributions of the observed count data are independent, conditional on the latent process, although the observations are correlated marginally. …”
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A generalized laser simulator algorithm for optimal path planning in constraints environment by Aisha, Muhammad

“…The mean path cost generated by the LS algorithm, on the other hand, is 14% higher than that generated by the PRM. …”
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Comparison of CPU damage prediction accuracy between certainty factor and forward chaining techniques by Tri Ginanjar Laksana, Ade Rahmat Iskandar, Wan Nooraishya Wan Ahmad

“…The suggested algorithm yields the mean accuracy of the certainty factor approach in diagnosing computer damage utilizing the constructed system. …”
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Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms by Rachmad, Iqbal, Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Tata, Sutabri

“…The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. …”
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Statistical analysis engine capacity, weight, and torque on MPV fuel consumption using regression and correlation algorithms by Hafidz Salafuddin, Nanang K. Pradipta, Farrah Anis Fazliatul Adnan, Jong Soo Rhee, Dianta Ginting

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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari by Satari, Siti Zanariah

“…Here, we introduce a measure of similarity based on the circular distance and obtain a cluster tree using the single linkage clustering algorithm. Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. …”
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