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All Journal Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic
Shafaatunnur Hasan
Universiti Teknologi Malaysia
Computer Science & IT

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Enhanced Self Organizing Map (SOM) and Particle Swarm Optimization (PSO) for Classification Shafaatunnur Hasan; Siti Mariyam Shamsuddin; Bariah binti Yusob
Generic Vol 5 No 2 (2010): Vol 5, No 2 (2010)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Hybrid technique for Self Organizing Map and Particle Swarm Optimization approach is commonly implemented in clustering area. In this paper, a hybrid approach that is based on Enhanced Self Organizing Map and Particle Swarm Optimization (ESOM/PSO) for classification is proposed. Enhanced Self Organization map which based on Kohonen network structure is to improve the quality of the data classification and labeling. New formulation of hexagonal lattice area is used for the enhancement Self Organizing Map structure. The proposed hybrid ESOM/PSO algorithm uses PSO to evolve the weights for ESOM. The weights are trained by ESOM in the first stage. In the second stage, they are optimized by PSO. In the proposed algorithm, the result is measured by using a classification accuracy and quantization error techniques.
Big Data Platforms and Techniques Salisu Musa Borodo; Siti Mariyam Shamsuddin; Shafaatunnur Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 1, No 1: January 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v1.i1.pp191-200

Abstract

Data is growing at unprecedented rate and has led to huge volume generated; the data sources include mobile, internet and sensors. This voluminous data is generated and updated at high velocity by batch and streaming platforms. This data is also varied along structured and unstructured types. This volume, velocity and variety of data led to the term big data. Big data has been premised to contain untapped knowledge, its exploration and exploitation is termed big data analytics. This literature reviewed platforms such as batch processing, real time processing and interactive analytics used in big data environments. Techniques used for big data are machine learning, Data Mining, Neural Network and Deep Learning. There are big data architecture offerings from Microsoft, IBM and National Institute of Standards and Technology. Big data potentials can transform economies and reduce running cost of institutions. Big data has challenges such as storage, computation, security and privacy
An enhanced NLP approach for BI-RADS extraction in breast ultrasound reports using deep learning Ahmed Sahl; Shafaatunnur Hasan; Maie M. Aboghazalah
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp191-199

Abstract

Breast cancer stands as one of the top causes of death around the globe, making the accurate interpretation of breast ultrasound reports vital for early diagnosis and treatment. Unfortunately, key findings in these reports are often buried in unstructured text, complicating automated extraction. This study presents a deep learning-based natural language processing (NLP) approach to extract breast imaging reporting and data system (BI-RADS) categories from breast ultrasound data. We trained a recurrent neural network (RNN) model, specifically using a BiLSTM architecture, on a dataset of reports that were manually annotated from a hospital in Saudi Arabia. Our approach also incorporates uncertainty estimation techniques to tackle ambiguous cases and uses data augmentation to boost model performance. The experimental results indicate that our deep learning method surpasses traditional rule-based and machine-learning techniques, achieving impressive accuracy in classification tasks. This research plays a significant role in automating radiology reporting, aiding clinical decision-making, and pushing forward the field of breast cancer research.
Co-Authors Ahmed Sahl Bariah binti Yusob Maie M. Aboghazalah Salisu Musa Borodo Siti Mariyam Shamsuddin