Search Results - (( data application using algorithm ) OR ( code classification learning algorithm ))

Network Traffic Classification Analysis on Differentiated Services Code Point Using Deep Learning Models for Efficient Deep Packet Inspection by Ahmed Khan, Fazeel, Abubakar, Adamu

“…The data was gathered using real-time packet capturing tools which were then processed and moved with model development using different deep learning algorithms such as, LSTM, MLP, RNN and Autoencoders. …”
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Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach by Qayyum A., Saeed Malik A., Saad N.M., Iqbal M., Abdullah M.F., Rasheed W., Abdullah T.A.B.R., Bin Jafaar M.Y.

“…Aerial photography; Aircraft detection; Antennas; Codes (symbols); Discrete cosine transforms; Discrete wavelet transforms; Glossaries; Image classification; Image coding; Image enhancement; Learning algorithms; Learning systems; Object recognition; Remote sensing; Satellite imagery; Satellites; Unmanned aerial vehicles (UAV); Discrete tchebichef transforms; Discriminative features; Finite Ridgelet Transform; Histogram of oriented gradients; Image processing and computer vision; Scale invariant feature transforms; SIFT; Sparse coding; Classification (of information)…”

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

“…Then, a Machine Learning Algorithm (MLA) is used to train and test the data before a classifier is used to classify the new incoming dataset. …”
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Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

“…The features extracted of time domain and frequency domain in vibration and acoustic will use as database of a Support Vector Machine (SVM) algorithms by using MATLAB R2021a. The result from the SVM algorithms will be used as database for the machine learning in Microsoft Azure. …”
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Deep learning based emotion recognition for image and video signals: matlab implementation by Ashraf, Arselan, Gunawan, Teddy Surya, Kartiwi, Mira

“…This book is carried out to develop an image and video-based emotion recognition model using CNN for automatic feature extraction and classification with Matlab sample codes. Five emotions are considered for recognition: angry, happy, neutral, sad, and surprise, compared to previous algorithms. …”
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Systematic review for phonocardiography classification based on machine learning by Altaf, Abdullah, Mahdin, Hairulnizam, Alive, Awais Mahmood, Ninggal, Mohd Izuan Hafez, Altaf, Abdulrehman, Javid, Irfan

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Improving hand written digit recognition using hybrid feature selection algorithm by Wong, Khye Mun

“…Therefore, many researchers have applied and developed various machine learning algorithms that could efficiently tackle the handwritten digit recognition problem. …”
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Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

“…Several machine learning techniques based on supervised learning have been adopted in the classification of malware. …”
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Modelling of clinical risk groups (CRGs) classification using FAM by Mohd. Asi, Salina, Saad, Puteh

“…FAM is a fast learning algorithm and used less epoch training [4]. …”
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Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

“…However, supervised learning technique has limitations for malware classification task. …”
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Phishing image spam classification research trends: Survey and open issues by John Abari, Ovye, Mohd Sani, Nor Fazlida, Khalid, Fatimah, Mohd Yunus Bin Sharum, Mohd Yunus, Mohd Ariffin, Noor Afiza

“…Furthermore, several public data sets, features set, classification methods, and measuring metrics are found and the popular once were pinpointed. …”
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Dynamic android malware category classification using semi-supervised deep learning by Mahdavifar, Samaneh, Kadir, Andi Fitriah Abdul, Fatemi, Rasool, Alhadidi, Dima, Ghorbani, Ali A

“…We evaluate our proposed model on CICMalDroid2020 and conduct a comparison with Label Propagation (LP), a well-known semi-supervised machine learning technique, and other common machine learning algorithms. …”
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Enhancing teaching and learning through data-driven optimization of servicing code demand and lecturer allocation using WEKA analysis by Rochin Demong, Nur Atiqah, Mohamed Razali, Murni Zarina, Kamaruddin, Juliana Noor, Shamsuddin, Sazwan, Awang, Nor Ain, Kamarudin, Norjuliatie, Wan Othman, Noor Faradilla

“…This study applies machine learning techniques using the WEKA analytical tool to explore, cluster and classify servicing code applications using a dataset gathered from multiple faculties, and campuses within the Faculty of Business and Management in a selected public university in Malaysia. …”
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Improving brain tumor segmentation in MRI images through enhanced convolutional neural networks by Ayomide, Kabirat Sulaiman, Mohd Aris, Teh Noranis, Zolkepli, Maslina

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Source code classification using latent semantic indexing with structural and frequency term weighting by Yusof, Yuhanis, Alhersh, Taha, Mahmuddin, Massudi, Mohamed Din, Aniza

“…Furthermore,it is also learned that the use of structural information in the weighting scheme contribute to a better classification.…”
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Machine learning-based enhanced deep packet inspection for IP packet priority classification with differentiated services code point for advance network management by Khan, Fazeel Ahmed, Abubakar, Adamu

“…This study presents an approach to enhance intelligent packet forwarding priority classification on Differentiated Services Code Point (DSCP), leveraging classifiers from machine learning algorithms for Deep Packet Inspection (DPI). …”
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XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection by Bouke, Mohamed Aly, Alramli, Omar Imhemed, Abdullah, Azizol

“…The methodology incorporates data balancing through Hybrid Random Sampling, feature selection using the Gini Index, and a two-layer model explainability via Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) techniques. …”
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Object-Oriented Programming semantics representation utilizing agents by Mohd Aris, Teh Noranis

“…Learning programming from source code examples is a common behavior among novices. …”
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Maldroid- attribute selection analysis for malware classification by Rahiwan Nazar, Romli, Mohamad Fadli, Zolkipli, Mohd Zamri, Osman

“…Hence, the objective of this paper is to find the most effective and efficient attribute selection and classification algorithm in malware detection. Moreover, in order to get the best combination between attribute selection and classification algorithm, eight attributes selection and seven categories machine learning algorithm are applied in this study. …”
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Classification of machine learning engines using latent semantic indexing by Yusof, Yuhanis, Alhersh, Taha, Mahmuddin, Massudi, Mohamed Din, Aniza

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