Search Results - (( progressive evaluation learning algorithm ) OR ( java application mining algorithm ))

Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

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Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms by Lee, Quan

“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

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Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine by Band, S.S., Taherei Ghazvinei, P., bin Wan Yusof, K., Hossein Ahmadi, M., Nabipour, N., Chau, K.-W.

“…This study shows that the application of the new procedure resulted in confident generality performance and learns faster than orthodox learning algorithms. In conclusion, the assessment indicated that the advanced Extreme Learning Machine simulation was capable as a promising alternative to existing numerical methods for computing the coefficient of performance for turbines. …”
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Intent-IQ: customer’s reviews intent recognition using random forest algorithm by Mazlan, Nur Farahnisrin, Ibrahim Teo, Noor Hasimah

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A review on object detection algorithms based deep learning methods / Wan Xing ... [et al.] by Wan Xing, Sultan Mohd, Mohd Rizman, Johari, Juliana, Ahmat Ruslan, Fazlina

“…Deep learning-based object detection algorithms can be categorized into three main types: end-to-end algorithms, two-stage algorithms, and one-stage algorithms. …”
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Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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An artificial intelligence approach to monitor student performance and devise preventive measures by Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.

“…The fact is that the modern-day educational institutes tend to collect enormous amount of data concerning their students from various sources, however, the institutes are craving novel procedures to utilize the data to magnify their prestige and improve the education quality. This research evaluates the effectiveness of machine learning algorithms to monitor students� academic progress and informs the instructor about the students at the risk of ending up with unsatisfactory result in a course. …”

Dual optimization approach in discrete Hopfield neural network by Guo, Yueling, Zamri, Nur Ezlin, Mohd Kasihmuddin, Mohd Shareduwan, Alway, Alyaa, Mansor, Mohd. Asyraf, Li, Jia, Zhang, Qianhong

“…Quantitative evaluations show that the proposed model successfully enhances the optimization of both phases, ranking first compared to 10 recent algorithms for all metrics. …”
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Benchmarking Robust Machine Learning Models Under Data Imperfections in Real-World Data Science Scenarios by Marlindawati, ., Mohammad, Azhar, Esha, Sabir

“…Multiple classical machine learning algorithms and deep learning models were assessed across diverse benchmark datasets. …”
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Modeling total quality management in higher education with case-based reasoning by Jani H.M.

“…This paper emphasizes on the mastery learning approach, which is in line with TQM. The learning process contains the following steps: Plan, teach (Do), Check (formative evaluation), revised teaching (Act), and Test (summative evaluation). …”

Comparison of different deep learning object detection algorithms on fruit drying characterization by Umair, Mohammad Yamin, Norazlianie, Sazali, Kettner, Maurice, Mohd Azraai, Mohd Razman, Weiβ, Robert

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Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique by Jais, Fatin Nabihah, Che Azemin, Mohd Zulfaezal, Hilmi, Mohd Radzi, Mohd Tamrin, Mohd Izzuddin, Mohd. Kamal, Khairidzan

“…Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.…”
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Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning by Amin, I., Hassan, S., Jaafar, J.

“…Deep Learning is progressively becoming popular for computer based automated diagnosis of diseases. …”
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State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission by Gholizadeh, S., Leman, Z., Baharudin, B.T.H.T.

“…Acoustic emission (AE) is a passive structural health monitoring (SHM) technique that provides real-time damage detection based on stress waves generated by cracks in the structure. This study evaluates the damage progression on glass fiber reinforced polyester composite specimens using different approaches of machine learning. …”
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Advances in materials informatics: A review by Sivan, Dawn, Kumar, K. Satheesh, Aziman, Abdullah, Raj, Veena, Izan Izwan, Misnon, Ramakrishna, Seeram, Jose, Rajan

“…The advancements in computational intelligence via machine learning and deep learning algorithms in different fields of materials science are discussed. …”
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A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures by Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.

“…This research evaluates the effectiveness of machine learning classifiers to monitor students� academic progress and informs the instructor about the students at the risk of producing unsatisfactory final result in a course. …”