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  1. 1

    Investigation of block matching algorithm for video coding by Faizul Hadi Mohamad Jamil

    Published 2013
    “…The Spatial model exploits the redundancy in a single video frame (I frame) while the temporal model exploits the redundancy among frames (P frame). …”
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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

    Published 2025
    “…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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    Thesis
  4. 4

    Development of finite element model for soil-structure interaction by Shoaei, Mohammad Dalili

    Published 2014
    “…The developed system, discussed above, includes an ANN algorithm to predict ultimate bearing capacity of two-layer soil and a finite element model (2DSSI) for analysis of interaction between soil and framed structures through utilization of the developed interface element along with all included aspects of SSI. …”
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  5. 5

    An enhanced adaptive grey verhulst prediction model for network security situation by Leau, Yu Beng, Selvakumar Manickam

    Published 2016
    “…The proposed model applied Kalman Filtering algorithm to predict the residual in the next time-frame and closer the deviation between the predicted and actual network security situation. …”
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    Article
  6. 6

    Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems by Salih, Dhiadeen Mohammed

    Published 2015
    “…On the other hand, an adaptive model predictive controller (WNMPC) based on LWN-OSELM modelling method is proposed for nonlinear system control applications. …”
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    An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

    Published 2020
    “…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
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  9. 9

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    Published 2013
    “…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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  10. 10

    An adaptive block-based matching algorithm for crowd motion sequences by Kajo, I., Kamel, N., Malik, A.S.

    Published 2018
    “…The proposed algorithm is based on block-based matching, particle advection, and social force model. …”
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  11. 11

    An adaptive block-based matching algorithm for crowd motion sequences by Kajo, I., Kamel, N., Malik, A.S.

    Published 2018
    “…The proposed algorithm is based on block-based matching, particle advection, and social force model. …”
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    Article
  12. 12

    REGION-BASED ADAPTIVE DISTRIBUTED VIDEO CODING CODEC by ABDELRAHMAN ELAMIN, ABDELRAHMAN ELAMIN

    Published 2011
    “…Due to the limitation of compensation method, the predicted frame, or the side information, is expected to have varying degrees of success. …”
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    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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  15. 15

    Two level aggregation scheme with optimization in subframes headers for IEEE 802.11n wireless networks by Noma, Adamu Muhammad

    Published 2014
    “…Furthermore, a new two-level aggregation model is developed by integrating the eA-MSDU into A-MPDU and designing suitable operating algorithm. …”
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  16. 16

    Bio-signal identification using simple growing RBF-network (OLACA) by Asirvadam , Vijanth Sagayan, McLoone, Sean, Palaniappan, R

    Published 2007
    “…The main goal of this paper is to develop a simple back-propagation based (SBP) resource allocation network (RAN), or also known as sequential learning technique using Radial Basis Function by incorporating Gaussian kernel, in order to identify (model) EEG and ECG signals. Simulation results show the modeled data show good representation of the original signals with less prediction error.…”
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    AI powered asthma prediction towards treatment formulation: an android app approach by Murad, Saydul Akbar, Adhikary, Apurba, Md Muzahid, Abu Jafar, Sarker, Md Murad Hossain, Khan, Md. Ashikur Rahman, Hossain, Md. Bipul, Bairagi, Anupam Kumar, Masud, Mehedi, Kowsher, Md

    Published 2022
    “…TensorFlow is utilized to integrate machine learning with an Android application. We accomplished asthma therapy using an Android application developed in Java and running on the Android Studio platform.…”
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  19. 19

    A Reinforcement Learning Based Adaptive ROI Generation for Video Object Segmentation by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

    Published 2021
    “…Our approach encodes the important information during the segmentation phase by a simultaneous process of various reference frames that are subsequently utilized to predict the persistent and conspicuous objects in the foreground. …”
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  20. 20

    A vision-based deep learning approach for non-contact vibration measurement using (2+1)D CNN and optical flow by Harold Harrison, Mazlina Mamat, Farah Wong, Hoe Tung Yew, Racheal Lim, Wan Mimi Diyana Wan Zaki

    Published 2025
    “…The proposed model achieved an average Mean Absolute Percentage Error (MAPE) of 7.51 %, with acceleration predictions exhibiting the lowest error at 4.84 % and displacement the highest at 8.80 % across varying brightness levels and object-camera distances. …”
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