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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Knowledge Engineering and Data Science Indonesian Journal of Electrical Engineering and Computer Science
Rayner Alfred
Universiti Malaysia Sabah
Computer Science & IT Engineering

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Network Traffic Time Series Performance Analysis Using Statistical Methods Purnawansyah Purnawansyah; Haviluddin Haviluddin; Rayner Alfred; Achmad Fanany Onnilita Gaffar
Knowledge Engineering and Data Science Vol 1, No 1 (2018)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (605.856 KB) | DOI: 10.17977/um018v1i12018p1-7

Abstract

This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting.  
Ensemble deep learning for tuberculosis detection Mohd Hanafi Ahmad Hijazi; Leong Qi Yang; Rayner Alfred; Hairulnizam Mahdin; Razali Yaakob
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 2: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i2.pp1014-1020

Abstract

Tuberculosis (TB) is one of the deadliest infectious disease in the world. TB is caused by a type of tubercle bacillus called Mycobacterium Tuberculosis. Early detection of TB is pivotal to decrease the morbidity and mortality. TB is diagnosed by using the chest x-ray and a sputum test. Challenges for radiologists are to avoid confused and misdiagnose TB and lung cancer because they mimic each other. Semi-automated TB detection using machine learning found in the literature requires identification of objects of interest. The similarity of tissues, veins and small nodules presenting the image at the initial stage may hamper the detection. In this paper, an approach to detect TB, that does not require segmentation of objects of interest, based on ensemble deep learning, is presented. Evaluation on publicly available datasets show that the proposed approach produced a model that recorded the best accuracy, sensitivity and specificity of 91.0%, 89.6% and 90.7% respectively.
Modified framework for sarcasm detection and classification in sentiment analysis Mohd Suhairi Md Suhaimin; Mohd Hanafi Ahmad Hijazi; Rayner Alfred; Frans Coenen
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp1175-1183

Abstract

Sentiment analysis is directed at identifying people's opinions, beliefs, views and emotions in the context of the entities and attributes that appear in text. The presence of sarcasm, however, can significantly hamper sentiment analysis. In this paper a sentiment classification framework is presented that incorporates sarcasm detection. The framework was evaluated using a non-linear Support Vector Machine and Malay social media data. The results obtained demonstrated that the proposed sarcasm detection process could successfully detect the presence of sarcasm in that better sentiment classification performance was recorded. A best average F-measure score of 0.905 was recorded using the framework; a significantly better result than when sentiment classification was performed without sarcasm detection.
RGB-D salient object detection with local feature and semantic segmentation Zhang Wang; Kim On Chin; Rayner Alfred; Junyi Chai; Rundong Zhang; Soo See Chai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2774-2785

Abstract

Red, green, blue–depth (RGB-D) salient object detection (SOD) focuses on identifying visually prominent objects by simulating human visual perception. While existing RGB-D SOD methods have demonstrated results, there remain challenges in effectively leveraging extrinsic cues and enhancing feature representation. To address these limitations, novel RGB-D SOD model with local feature extraction and semantic segmentation (LFSS) is introduced, which is built on an encoder-decoder architecture. The encoder preprocesses the input images by merging RGB and depth data through a channel and spatial attention (CSA) module. A local feature extraction module further refines this fusion. The decoder consists of three key modules: i) the multi-feature extraction (MFE) module enhances base features through diverse convolutional operations; ii) the semantic segmentation enhancement (SSE) module optimizes features via spatial pyramid pooling and atrous convolution; and iii) the local/global agreement and edge detection (LGE) module that enables multi-level feature interaction and edge detection. These modules work sequentially to enhance and extract salient objects. LFSS is evaluated on six standard RGB-D SOD datasets (NJU2K, NLPR, STERE, LFSD, SSD, SIP) by four metrics, outperforming the comparison models with up to 1.2% F-measure improvement. LFSS is found to be a versatile model, offering valuable applications in engineering.
Co-Authors Achmad Fanany Onnilita Gaffar Frans Coenen Hairulnizam Mahdin Haviluddin Haviluddin Junyi Chai Kim On Chin Leong Qi Yang Mohd Hanafi Ahmad Hijazi Mohd Suhairi Md Suhaimin Purnawansyah Purnawansyah Razali Yaakob Rundong Zhang Soo See Chai Zhang Wang