SITI SOPHIAYATI BINTI YUHANIZ

@fai.utm.my

Assoc Professor at Faculty of Artificial Intelligence
UNIVERSITI TEKNOLOGI MALAYSIA

SITI SOPHIAYATI BINTI YUHANIZ


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https://researchid.co/sophiayuhaniz

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science
58

Scopus Publications

1921

Scholar Citations

18

Scholar h-index

28

Scholar i10-index

Scopus Publications

  • A COMPARATIVE STUDY OF FACIAL FEATURE EXTRACTION USING MTCNN, RETINAFACE AND DLIB FACE DETECTOR FOR PERSONALITY TRAITS RECOGNITION
    Zahra Shams Khoozani, Aznul Qalid Md Sabri (Corresponding Author), Woo Chaw Seng, Manjeevan Seera
    Malaysian Journal of Computer Science, 2026
    Generative Adversarial Networks (GANs) have advanced image synthesis and are widely used to augment training data for deep Convolutional Neural Networks (CNNs). However, in scientific domains like plant disease identification, interpretability and morphological control are essential. Existing GANs typically rely on pixel-level feedback from the discriminator and function as black-box architectures, often learning entangled representations that limit control over high-level morphological features like leaf shape and disease patterns. This study introduces CXAI-GAN, an explainable GAN that integrates Concept-based Explainable AI (C-XAI) to generate biologically realistic images with explicit morphological control. The generator is modified to disentangle and encode three interpretable concepts: leaf shape, surface texture, and disease pattern. Unlike post-hoc Explainable AI (XAI) methods (e.g., LIME, SHAP) that reveal what features matter numerically, CXAI-GAN explains why outputs are generated through concept learning. CXAI-GAN achieves strong performance with an FID of 19.64, SSIM of 0.955, and PSNR of 34.91. Fine-grained evaluations show high fidelity: shape similarity (HDS 0.973), texture alignment (VPS 0.999), and local SSIM 0.937. In a binary classification task of visually similar grape diseases, CXAI-GAN improved accuracy by 10% and reached 96.7% with synthetic training. These results demonstrate CXAI-GAN’s effectiveness in generating interpretable, high-quality images for downstream scientific tasks.
  • A Novel MCSUT Technique based on FastText Embedding for Improving Multi-URL Classification and Cybersecurity Performance
    Zafar Ali, Siti Sophiayati Yuhaniz, Wan Noor Hamiza, Jawaid Ahmed Siddiqui, Noureen Noureen, Husham M. Ahmed
    International Journal of Drug Delivery Technology, 2026
    The exponential growth of web content requires efficient URL-based classification. Current methodologies utilize public URL classification datasets that fall into two categories, including DMOZ, Web Proxy Data, and WebKB, which are considered a general category. Other dataset categories, such as phishing, OpenPhishing, URLNet, Web Spam, and malicious, are part of the cybersecurity datasets. The datasets face challenges of class imbalance, noise, and ambiguity, which affect the performance of the URL classification models. To address these limitations, this study proposes an innovative multiple contextual semantic URL tokens (MCSUT) augmented technique that improves the quality of the URL classification dataset by reducing the noise and ambiguity contained in the URLs. The strength of the MCSUT technique mainly relies on its utilization of contextual and semantic URL tokens derived from neural word embedding techniques, such as WordNet, Word2Vec, and FastText, which are based on original tokens. This significantly enhances the ability of deep neural networks to comprehend and interpret these contextual and semantically rich tokens. This study presents a series of experimental results based on three-word embeddings using two datasets (DMOZ and phishing Datasets) and the development of data schemes for the DMOZ and phishing datasets, utilizing contextual and semantic tokens. The innovative multiple contextual semantic URL tokens (MCSUT) based on FastText neural word embeddings have outperformed previous studies, achieving a 0.8625 F1 score compared to WordNet, Word2Vec embeddings, and baselines, and achieved an F1 score of 0.99% on the phishing dataset
  • Unmasking Online Hostility: Analysing and Mitigating Hate Speech in Social Media
    Jawaid Ahmed Siddiqui, Siti Sophiayati Yuhaniz, Zulfiqar Ali Memon
    Baghdad Science Journal, 2025
    تعمل منصات التواصل الاجتماعي على توليد كمية هائلة من البيانات في كل ثانية. تويتر، من الناحية العملية، ينتج الأفراد أكثر من ستمائة تغريدة في كل ثانية. أثناء نشر آراء المستخدمين وتعبيراتهم بحرية، من الصعب جدًا حصر خطاب الكراهية الذي يتم مشاركته ضد أي فرد أو دين أو أي مجموعة عرقية. وبالتالي، فإن الأشخاص المستهدفين بمثل هذا المحتوى الذي يحض على الكراهية يشعرون بالإحباط. وفي هذا الصدد، قامت الأساليب المختلفة بحل هذه المشكلة الخطيرة، ولكنها في بعض الأحيان لم تتمكن من تحقيق نتائج مرضية. ولذلك، نقترح نماذج مختلفة للتعلم الآلي لتصنيف البيانات المعطاة إلى فئتين، مسيئة أو غير مسيئة. تم إجراء التجارب على بيانات تويتر التي أنشأناها بأنفسنا باستخدام Twitter API ومكتبة Tweepy بواسطة Python. تم تقييم النتائج الناتجة بناءً على مقاييس مختلفة مثل الدقة والدقة والاستدعاء وقياس F1 واختبار MCNEMAR. بالمقارنة مع خوارزميات التعلم الآلي المختلفة، تفوق مصنف مجموعة الغابات العشوائية على الخوارزميات الأخرى، فإن حداثة ومساهمة ورقتنا البحثية هي: تطوير مجموعة بيانات تويتر التي تتكون من عدة تغريدات تحتوي على 11 متغير كائن مع أربعة متغيرات فئة مختلفة تظهر الهجوم المختلف المستويات، وتطبيق خوارزميات التعلم الآلي للكشف عن خطاب الكراهية، والتحليل المقارن لخوارزميات التعلم الآلي المختلفة مقابل مقاييس تقييم مختلفة بما في ذلك اختبار ماكنيمار. يتم شرح أهمية التقنية المقترحة جيدًا من خلال مجموعات بيانات Twitter التي تم إنشاؤها من خلال Twitter API ومكتبة Tweepy بواسطة Python.
  • Category-Based Sentiment Analysis of Sindhi News Headlines Using Machine Learning, Deep Learning, and Transformer Models
    Safdar Ali Soomro, Siti Sophiayati Yuhaniz, Mazhar Ali Dootio, Ghulam Mujtaba, Jawaid Ahmed Siddiqui
    IEEE Access, 2025
    The rapid growth of digital content has made sentiment analysis (SA) an essential tool for understanding public sentiment and classifying textual data. Despite significant progress in natural language processing (NLP), low-resource languages, particularly Sindhi, remain underexplored due to the lack of computational tools and annotated datasets. This study addresses this gap by introducing the Sindhi News Headlines Dataset (SNHD), a novel corpus annotated for both SA and category classification across eight categories: Crime, Economy, Entertainment, Health, Politics, Science & Technology, Social, and Sports. To evaluate the effectiveness of different machine learning (ML), deep learning (DL), and transformer-based approaches, we conduct a comparative analysis of various models on SA and category classification tasks. Furthermore, we leverage Explainable Artificial Intelligence (XAI) techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), to gain insights into model decision-making. Experimental results show that traditional ML models outperform DL and transformer-based models on the SNHD dataset. Specifically, Support Vector Machines with Radial Basis Function (SVM-RBF) achieves the highest performance for SA (0.74 accuracy and weighted F-score), while the Ridge Classifier (RC) delivers the best results for category classification (0.84 accuracy and weighted F-score). Among transformer models, XLM-RoBERTa demonstrates strong performance in category classification (0.82 accuracy and weighted F-score). These findings establish a benchmark for future research in Sindhi NLP and highlight the potential of hybrid approaches in tackling challenges associated with low-resource languages. This work provides a foundational resource for NLP researchers seeking to advance computational methods for Sindhi and similar underrepresented languages.
  • A Systematic Review on Sentiment Analysis for Sindhi Text
    Safdar Ali Soomro, Siti Sophiayati Yuhaniz, Mazhar Ali Dootio, Ghulam Murtaza, Muhammad Hussain Mughal
    Baghdad Science Journal, 2025
    نظرًا لتطبيقه في مجالات مثل عناوين الأخبار، وشراء المنتجات عبر الإنترنت، والتسويق، وإدارة السمعة، فقد ارتفعت أنشطة رفع الوعي في مجال استخراج الرأي بشكل ملحوظ. أصبحت مدونات الإنترنت والمواقع الاجتماعية ومتاجر التسوق الإلكترونية مرجعًا مهمًا للمعلومات التي ينتجها المستخدم. تتطلع شركات التصنيع والمبيعات والتسويق بشكل متزايد إلى هذا المورد للحصول على تعليقات عالمية حول ممارساتها وعناصرها. تتم مشاركة ملايين العبارات السندية يوميًا على مواقع الوسائط الإخبارية وTwitter وFacebook وSnapchat. إن تجاهل آراء الناس في اللغة السندية والتركيز فقط على اللغات الغنية بالموارد في العالم الغربي يؤدي إلى خسارة فادحة لهذه الكمية الكبيرة من البيانات. تركز هذه الدراسة على جمع وتقييم المنشورات المرتبطة باللغة السندية استجابة لمناهج التصنيف واستخراج الميزات والمعالجة المسبقة. تقدم الدراسة الحالية فحصًا شاملاً للعمل المنجز على كلمات اللغة السندية للعناصر أو تقييم العلامة التجارية. تركز الدراسة الحالية على الاستحواذ القائم على المجموعة، وتقنيات التصنيف، واستخراج الميزات، والمعالجة المسبقة للبيانات، والمنهجيات، والقيود. تم تقييم كل مقالة تمت مراجعتها وتصنيفها على أساس معايير معينة محددة. وبناء على النتائج، سوف تقترح هذه الدراسة عدة طرق مفيدة للتحقيق في المستقبل.
  • Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming
    Qi Li, Norshaliza Kamaruddin, Siti Sophiayati Yuhaniz, Hamdan Amer Ali Al-Jaifi
    Scientific Reports, 2024
    This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1128% and 5360% respectively when it is applied to fundamental indicators. For technical indicators, the hybrid model achieves a 206% increase in Rank IC and an impressive surge of 2752% in ICIR. Furthermore, the proposed hybrid SGP-LSTM model outperforms major Chinese stock indexes, generating average annualized excess returns of 31.00%, 24.48%, and 16.38% compared to the CSI 300 index, CSI 500 index, and the average portfolio, respectively. These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and financial analysts.
  • A Comparative Study of Automatic Hate Speech Detection Using Machine Learning
    Jawaid Ahmed Siddiqui, Siti Sophiayati Yuhaniz, Zulfiqar Ali Memon
    2024 IEEE 1st Karachi Section Humanitarian Technology Conference Khi Htc 2024, 2024
    There is no denying that social media’s ubiquitous use and the knowledge it seamlessly disseminates have improved humanity. But despite its many benefits, this growth has also given rise to urgent worries, including the spread of hate speech. Modern research have embraced a variety of feature engineering techniques and machine learning algorithms as a remedy to this increasing difficulty inside the world of social media platforms. These initiatives aim to automatically identify hate speech across several datasets, offering a promising way to lessen this pervasive problem. As far as we are aware that no research has directly compared different feature engineering methods with different machine learning algorithms to determine which method produces the best results on a representative public dataset. This article’s goal is to evaluate how well three different feature engineering approaches work with eight different machine learning algorithms using an open source free publicly available datasets that include 03 different classes. The results of the experiment revealed that the combination of bigram features and the (SVM) support vector machine algorithm performed the best overall, with an amazing accuracy rate of “79%”. Our research consequences touch on actual situations, making it a landmark study that potentially laid the foundation for future efforts aimed at automatically identifying hate speech. Further, the results of these comparisons will serve as state-of-the-art approaches against which further studies of automated text classification can be measured.
  • Decoding Digital Hostility: Examining and Addressing Hate Speech on Social Media Platforms
    Jawaid Ahmed Siddiqui, Siti Sophiayati Yuhaniz, Zulfiqar Ali Memon
    2024 IEEE 1st Karachi Section Humanitarian Technology Conference Khi Htc 2024, 2024
    There is no denying that the widespread usage of social media and the sharing of information have greatly benefited humanity. However, a number of issues have also emerged as a result of this increase in online engagement, most notably the spread of hate speech. Recent research has addressed this growing problem on social media platforms by automatically detecting hate speech in a variety of datasets using a variety of feature engineering approaches and machine/deep learning algorithms. It is interesting, though, that many of these studies—to the best of our knowledge—resort to identifying hate speech messages using traditional feature engineering techniques, which leads to less-than-ideal classification results. This is explained by the shortcomings of the feature engineering techniques now in use, specifically their vulnerability to the word order and word context problems. Therefore, more advanced strategies are desperately needed to address these issues and improve the precision of hate speech identification on social media platforms. To create fundamental lexical benchmarks is the goal. As distinguishing characteristics, our methodology makes use of the power of n-grams, which cover both character and word levels, and skip-grams, which cover both character and word levels. Notably, we successfully identify posts in all three defined categories with an impressive 78% accuracy. The results highlight how difficult it is to discriminate between vulgarities and hate speech. We also conduct a thorough exploration of potential directions for future research projects.
  • Fine-Grained Multilingual Hate Speech Detection Using Explainable AI and Transformers
    Jawaid Ahmed Siddiqui, Siti Sophiayati Yuhaniz, Ghulam Mujtaba Shaikh, Safdar Ali Soomro, Zafar Ali Mahar
    IEEE Access, 2024
    The detection of hate speech on online platforms is essential for maintaining safe and inclusive digital environments. Although significant progress has been made in binary classification for hate speech detection, challenges persist in multilingual and fine-grained classification. This study presents a comprehensive model for hate speech detection across English, Urdu, and Sindhi, utilizing advanced deep learning models like Bidirectional Encoder Representations from Transformers (BERT) and its multilingual variants. Additionally, the research employs Explainable Artificial Intelligence (XAI) techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), to gain insights into model performance. This work curated a multilingual hate speech detection dataset and a robust fine-grained hate speech detection model. The dataset includes non-hate and hate speech classes. Furthermore, the hate speech class is categorized into five fine-grained categories, including Disability, Gender, Nationality, Race, and Religion. The experimental findings of this study showed 91% F-score in binary class classification and 86% weighted F-score in fine-grained hate speech detection for multilingual datasets using XLM-RoBERTa technique. Notably, the Religion class achieved the highest F-score of 92%. It is believed that this study contributes to reducing the spread of hate speech (written in Either Urdu, English, or Sindhi) on various social media platforms.
  • Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme
    Amir Yasseen Mahdi, Siti Sophiayati Yuhaniz
    Computers Materials and Continua, 2023
    The extraction of features from unstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease. Furthermore, an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems. In this paper, an improved Term Weighting technique combined with Parts-Of-Speech (POS) Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease. Term Frequency-Inverse Document Frequency (TF-IDF) is the most often used term weighting scheme (TWS). However, TF-IDF has several developments to improve its drawbacks, in particular, it is not efficient enough to classify text by assigning effective weights to the terms in unstructured data. In this research, we proposed a modification term weighting scheme: RTF-C-IEF and compare the proposed model with four extraction methods: TF, TF-IDF, TF-IHF, and TF-IEF. The experiment was conducted on two new datasets for COVID-19 patients. The first dataset was collected from government hospitals in Iraq with 3053 clinical records, and the second dataset with 1446 clinical reports, was collected from several different websites. Based on the experimental results using several popular classifiers applied to the datasets of Covid-19, we observe that the proposed scheme RTF-C-IEF achieves is a consistent performer with the best scores in most of the experiments. Further, the modified RTF-C-IEF proposed in the study outperformed the original scheme and other employed term weighting methods in most experiments. Thus, the proper selection of term weighting scheme among the different methods improves the performance of the classifier and helps to find the informative term. © 2023 Tech Science Press. All rights reserved.
  • An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification
    Amir Yasseen Mahdi, Siti Sophiayati Yuhaniz, S Senanayake, N Baghdadi, A Malki, et al.
    International Journal of Intelligent Engineering and Systems, 2023
  • Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical text
    Amir Yasseen Mahdi, Siti Sophiayati Yuhaniz
    Mathematical Biosciences and Engineering, 2023
  • Exploring The Role of 5G Networks in Advancing IoT Enabled Smart Healthcare
    Rajeshwari Kanesin, Suriani Mohd Sam, Nilam Nur Amir Sjarif, Hafiza Abas, Siti Sophiayati Yuhaniz
    2nd IEEE National Biomedical Engineering Conference Nbec 2023, 2023
  • Genome assembly composition of the String "ACGT" array: a review of data structure accuracy and performance challenges
    Sherif Magdy Mohamed Abdelaziz Barakat, Roselina Sallehuddin, Siti Sophiayati Yuhaniz, Raja Farhana R. Khairuddin, Yasir Mahmood
    Peerj Computer Science, 2023
  • Modeling Orbital Propagation Using Regression Technique and Artificial Neural Network
    Nor'asnilawati Salleh, Siti Sophiayati Yuhaniz, Nurulhuda Firdaus Mohd Azmi
    International Journal on Advanced Science Engineering and Information Technology, 2022
  • An Adaptation of Deep Learning Technique in Orbit Propagation Model Using Long Short-Term Memory
    Nor'asnilawati Salleh, Nurulhuda Firdaus Mohd Azmi, Siti Sophiayati Yuhaniz
    3rd International Conference on Electrical Communication and Computer Engineering Icecce 2021, 2021
  • Evaluating the Masked and Unmasked Face with LeNet Algorithm
    Muhammad Haziq Rusli, Nilam Nur Amir Sjarif, Siti Sophiayati Yuhaniz, Steven Kok, Muhammad Solihin Kadir
    Proceeding 2021 IEEE 17th International Colloquium on Signal Processing and Its Applications Cspa 2021, 2021
  • Automatic Extraction of Knowledge for Diagnosing COVID-19 Disease Based on Text Mining Techniques: A Systematic Review
    Amir Yasseen Mahdi, Siti Sophiayati Yuhaniz
    Periodicals of Engineering and Natural Sciences, 2021
  • Cyberbullying detection: Current trends and future directions
    Journal of Theoretical and Applied Information Technology, 2020
  • Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: A review
    Nor’asnilawati Salleh, Siti Sophiayati Yuhaniz, Sharizal Fadlie Sabri, Nurulhuda Firdaus Mohd Azmi
    International Journal on Advanced Science Engineering and Information Technology, 2020
  • Big Data Deep Learning Tools
    Nur Farhana Hordri, Siti Sophiayati Yuhaniz, Siti Mariyam Shamsuddin, Nurulhuda Firdaus Mohd Azmi
    Encyclopedia of Big Data Technologies, 2019
  • Enhancing simplified general perturbations-4 model for orbit propagation using deep learning: A review
    Nor'asnilawati Salleh, Siti Sophiayati Yuhaniz, Nurulhuda Firdaus Mohd. Azmi, Sharizal Fadlie Sabri
    ACM International Conference Proceeding Series, 2019
  • Handling class imbalance in credit card fraud using resampling methods
    Nur Farhana Hordri, Siti Sophiayati, Nurulhuda Firdaus, Siti Mariyam
    International Journal of Advanced Computer Science and Applications, 2018
  • Online Islamic business enhancer tool (OIBET) for young entrepreneurs
    Proceedings of the 31st International Business Information Management Association Conference Ibima 2018 Innovation Management and Education Excellence Through Vision 2020, 2018
  • SMS spam classification using Vector Space Model and Artificial Neural Network
    International Journal of Advances in Soft Computing and Its Applications, 2018
  • An overview of cross-document coreference resolution
    Aliakbar Keshtkaran, Siti Sophiayati Yuhaniz, Suhaimi Ibrahim
    1st International Conference on Computer and Drone Applications Ethical Integration of Computer and Drone Technology for Humanity Sustainability Iconda 2017, 2017
  • Energy harvesting in wireless sensor networks: A survey
    Kamarul Zaman Panatik, Kamilia Kamardin, Sya Azmeela Shariff, Siti Sophiayati Yuhaniz, Noor Azurati Ahmad, Othman Mohd Yusop, SaifulAdli Ismail
    2016 IEEE 3rd International Symposium on Telecommunication Technologies Istt 2016, 2017
  • Improving accuracy of decoding process with pore-based fingerprint fuzzy vault in biometric cryptosystem
    Siti Munawwarah Mahmod, Salwani Mohd Daud, Siti Sophiayati Yuhaniz, Azizul Azizan, Nilam Nur Amir Sjarif
    Advanced Science Letters, 2017
  • A systematic literature review on features of deep learning in big data analytics
    International Journal of Advances in Soft Computing and Its Applications, 2017
  • Hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis
    N. F Hordri, S. S Yuhaniz, S. M Shamsuddin, A Ali
    Advanced Science Letters, 2017
  • Review of environmental wireless sensor networks system and design
    Journal of Telecommunication Electronic and Computer Engineering, 2017
  • Design of a terminal node controller hardware for CubeSat tracking applications
    Y A Ahmad, N J Nazim, S S Yuhaniz
    Iop Conference Series Materials Science and Engineering, 2016
  • A parametric study of textile artificial magnetic conductor with wire dipole at 2.45GHZ and 5.8GHZ
    Arpn Journal of Engineering and Applied Sciences, 2016
  • Designing a low cost cubesat's command and data handling subsystem kit
    Arpn Journal of Engineering and Applied Sciences, 2016
  • Particle swarm optimization for ANFIS interpretability and accuracy
    Dian Palupi Rini, Siti Mariyam Shamsuddin, Siti Sophiayati Yuhaniz
    Soft Computing, 2016
  • Development of mission control station software for a CubeSat mission
    S. S. Yuhaniz, N. Hamzah
    International Conference on Space Science and Communication Iconspace, 2015
  • Enhancing security and privacy protection for MapReduce processing: The initial simulation work flow
    International Journal of Advances in Soft Computing and Its Applications, 2015
  • Data quality in big data: A review
    International Journal of Advances in Soft Computing and Its Applications, 2015
  • Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis
    Zahra Beheshti, Siti Mariyam Hj. Shamsuddin, Ebrahim Beheshti, Siti Sophiayati Yuhaniz
    Soft Computing, 2014
  • Support vector machine (SVM) in handwritten character recognition using freeman chain code (FCC)
    Dewi Nasien, Habibollah Haron, Aini Najwa Azmi, Siti Sophiayati Yuhaniz
    Advanced Science Letters, 2014
  • Performance of meta-heuristic techniques in freeman chain code (FCC) extraction for handwritten character recognition
    Dewi Nasien, Habibollah Haron, Siti Sophiyati Yuhaniz, Aini Najwa Azmi, Haswadi Hassan
    Advanced Science Letters, 2014
  • Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems
    Zahra Beheshti, Siti Mariyam Shamsuddin, Siti Sophiayati Yuhaniz
    Journal of Global Optimization, 2013
  • A comparison study of biogeography based optimization for optimization problems
    International Journal of Advances in Soft Computing and Its Applications, 2013
  • Balanced the trade-offs problem of ANFIS using particle swarm optimization
    Dian Palupi Rini, Siti Mariyam Shamsuddin, Siti Sophiayati Yuhaniz
    Telkomnika, 2013
  • Fusing modalities in forensic identification with score discretization
    Ceur Workshop Proceedings, 2013
  • Detecting floods using an object based change detection approach
    B. Faiza, S. S. Yuhaniz, S. Z. Mohd Hashim, A. K. Kalema
    2012 International Conference on Computer and Communication Engineering Iccce 2012, 2012
  • A new framework in solving tailing and necking problems of thinned binary image
    Sabarina Abu Bakar, SitiSophiayati Yuhaniz, HairudinAbd Majid, Habibollah Haron
    Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering, 2012
  • Crowd analysis and its applications
    Nilam Nur Amir Sjarif, Siti Mariyam Shamsuddin, Siti Zaiton Mohd Hashim, Siti Sophiayati Yuhaniz
    Communications in Computer and Information Science, 2011
  • An intelligent decision-making system for flood monitoring from space
    Tanya Vladimirova, Siti Yuhaniz
    Soft Computing, 2011
  • Recognition of isolated handwritten latin characters using one continuous route of freeman chain code representation and feedforward neural network classifier
    World Academy of Science Engineering and Technology, 2010
  • Statistical learning theory and support vector machines
    Dewi Nasien, Siti S. Yuhaniz, Habibollah Haron
    2nd International Conference on Computer Research and Development Iccrd 2010, 2010
  • Support Vector Machine (SVM) for english handwritten character recognition
    Dewi Nasien, Habibollah Haron, Siti Sophiayati Yuhaniz
    2010 2nd International Conference on Computer Engineering and Applications Iccea 2010, 2010
  • Metaheuristics methods (GA & ACO) for minimizing the length of freeman chain code from handwritten isolated characters
    World Academy of Science Engineering and Technology, 2010
  • An onboard automatic change detection system for disaster monitoring
    Siti Sophiayati Yuhaniz, Tanya Vladimirova
    International Journal of Remote Sensing, 2009
  • An intelligent decision-making system for flood monitoring from space
    G. Howells, K. D. McDonald-Maier, T. Binzegger, M. P. Young
    Proceedings 2007 Ecsis Symposium on Bio Inspired Learning and Intelligent Systems for Security Bliss 2007, 2007
  • Intelligent imaging on board small observation satellites
    T. Vladimirova, S. Yuhaniz, M. Meerman, P. Stephens, D. Hodgson
    International Geoscience and Remote Sensing Symposium IGARSS, 2006
  • Embedded intelligent imaging on-board small satellites
    Siti Yuhaniz, Tanya Vladimirova, Martin Sweeting
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005
  • Flood detection of tsunami affected areas using multispectral images
    Asian Association on Remote Sensing 26th Asian Conference on Remote Sensing and 2nd Asian Space Conference Acrs 2005, 2005

RECENT SCHOLAR PUBLICATIONS

  • Key frame selection for personality traits recognition
    NA Mahamad Amin, NN Amir Sjarif, SS Yuhaniz
    Engineering Computations, 1-17 , 2025
    2025
    Citations: 1
  • A comparative study of facial feature extraction using mtcnn, retinaface and dlib face detector for personality traits recognition
    NAM Amin, NNA Sjarif, SS Yuhaniz
    Malaysian Journal of Computer Science 38 (2), 22-38 , 2025
    2025
    Citations: 2
  • Category-Based Sentiment Analysis of Sindhi News Headlines Using Machine Learning, Deep Learning, and Transformer Models
    SA Soomro, SS Yuhaniz, MA Dootio, J Siddiqui
    IEEE Access , 2025
    2025
    Citations: 6
  • A review of convolutional neural network model for audio-visual features extraction in personality traits recognition
    NAM Amin, NNA Sjarif, SS Yuhaniz
    International Journal of Innovative Computing 15 (1), 45-52 , 2025
    2025
    Citations: 1
  • A Systematic Review on Sentiment Analysis for Sindhi Text
    SA Soomro, SS Yuhaniz, MA Dootio, G Murtaza, MH Mughal
    Baghdad Science Journal 22 (5), 1676-1691 , 2025
    2025
    Citations: 2
  • Unmasking Online Hostility: Analysing and Mitigating Hate Speech in Social Media
    JA Siddiqui, SS Yuhaniz, ZA Memon
    Baghdad Science Journal 22 (4), 1393-1408 , 2025
    2025
  • Fine-grained multilingual hate speech detection using explainable AI and transformers
    JA Siddiqui, SS Yuhaniz, GM Shaikh, SA Soomro, ZA Mahar
    IEEE Access 12, 143177-143192 , 2024
    2024
    Citations: 28
  • RETRACTED: Integration of GPT3 and Dialog GPT Framework in Mental Health Chatbot-A Systematic Literature Research
    DA Nandurkar, SS Yuhaniz, M Sahu
    2024
  • A Comparative Study of Automatic Hate Speech Detection Using Machine Learning
    JA Siddiqui, SS Yuhaniz, ZA Memon
    2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC), 1-7 , 2024
    2024
    Citations: 3
  • Decoding Digital Hostility: Examining and Addressing Hate Speech on Social Media Platforms
    JA Siddiqui, SS Yuhaniz, ZA Memon
    2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC), 1-5 , 2024
    2024
  • Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming
    Q Li, N Kamaruddin, SS Yuhaniz, HAA Al-Jaifi
    Scientific reports 14 (1), 422 , 2024
    2024
    Citations: 43
  • Convolutional Neural Network Model for Facial Feature Extraction in Personality Traits Recognition
    NAM Amin, NNA Sjarif, SS Yuhaniz
    Open International Journal of Informatics 11 (2), 133-140 , 2023
    2023
    Citations: 1
  • Influancing Factors To Adopt M-LearningDuring Covid-19 For Schools In Pakistan
    SM Bilal, SS Yuhaniz, NH Hassan
    Open International Journal of Informatics 11 (2), 141-152 , 2023
    2023
  • Exploring The Role of 5G Networks in Advancing IoT Enabled Smart Healthcare
    R Kanesin, SM Sam, NNA Sjarif, H Abas, SS Yuhaniz
    2023 IEEE 2nd National Biomedical Engineering Conference (NBEC), 66-71 , 2023
    2023
    Citations: 2
  • Genome assembly composition of the String “ACGT” array: a review of data structure accuracy and performance challenges
    SMMA Barakat, R Sallehuddin, SS Yuhaniz, RFR Khairuddin, ...
    PeerJ Computer Science 9, e1180 , 2023
    2023
  • An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification.
    AY Mahdi, SS Yuhaniz
    International Journal of Intelligent Engineering & Systems 16 (4) , 2023
    2023
    Citations: 3
  • Low-cost IoT-Based Smart Notification System for Rural Agriculture
    MU Diginsa, YM Yusof, A Azizan, SM Sam, NA Ahmad, H Abas, ...
    Open International Journal of Informatics 11 (1), 8-22 , 2023
    2023
    Citations: 2
  • PTHP: Index for Optimizing Genome Assembly Overlapping and Read Alignment
    SMMA Barakat, R Sallehuddin, SS Yuhaniz, RFR Khairuddin, Y Yusoff
    International Journal 10 (1), 958-972 , 2023
    2023
  • Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical text
    AY Mahdi, SS Yuhaniz
    Math. Biosci. Eng 20, 5268-5297 , 2023
    2023
    Citations: 11
  • Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme
    AY Mahdi, SS Yuhaniz
    Computers, Materials & Continua 74 (1), 1375-1392 , 2023
    2023
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Particle swarm optimization: technique, system and challenges
    DP Rini, SM Shamsuddin, SS Yuhaniz
    International journal of computer applications 14 (1), 19-26 , 2011
    2011
    Citations: 712
  • Energy harvesting in wireless sensor networks: A survey
    KZ Panatik, K Kamardin, SA Shariff, SS Yuhaniz, NA Ahmad, OM Yusop, ...
    2016 IEEE 3rd international symposium on Telecommunication Technologies … , 2016
    2016
    Citations: 119
  • Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis
    Z Beheshti, SMH Shamsuddin, E Beheshti, SS Yuhaniz
    Soft Computing 18 (11), 2253-2270 , 2014
    2014
    Citations: 115
  • Support Vector Machine (SVM) for English handwritten character recognition
    D Nasien, H Haron, SS Yuhaniz
    2010 Second international conference on computer engineering and … , 2010
    2010
    Citations: 98
  • Particle swarm optimization for ANFIS interpretability and accuracy
    DP Rini, SM Shamsuddin, SS Yuhaniz
    Soft Computing 20 (1), 251-262 , 2016
    2016
    Citations: 77
  • Binary accelerated particle swarm algorithm (BAPSA) for discrete optimization problems
    Z Beheshti, SM Shamsuddin, SS Yuhaniz
    Journal of Global optimization 57 (2), 549-573 , 2013
    2013
    Citations: 63
  • Deep learning and its applications: A review
    NF Hordri, SS Yuhaniz, SM Shamsuddin
    Conference on postgraduate annual research on informatics seminar, 1-5 , 2016
    2016
    Citations: 57
  • A systematic literature review on features of deep learning in big data analytics
    NF Hordri, A Samar, SS Yuhaniz, SM Shamsuddin
    Int J Adv Soft Comput Appl 9 (1), 32-49 , 2017
    2017
    Citations: 56
  • Handling class imbalance in credit card fraud using resampling methods
    NF Hordri, SS Yuhaniz, NFM Azmi, SM Shamsuddin
    Int. J. Adv. Comput. Sci. Appl 9 (11), 390-396 , 2018
    2018
    Citations: 48
  • Statistical learning theory and support vector machines
    D Nasien, SS Yuhaniz, H Haron
    2010 Second International Conference on Computer Research and Development … , 2010
    2010
    Citations: 47
  • Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming
    Q Li, N Kamaruddin, SS Yuhaniz, HAA Al-Jaifi
    Scientific reports 14 (1), 422 , 2024
    2024
    Citations: 43
  • Balanced the trade-offs problem of ANFIS using particle swarm optimization
    DP Rini, SM Shamsuddin, SS Yuhaniz
    TELKOMNIKA (Telecommunication Computing Electronics and Control) 11 (3), 611-616 , 2013
    2013
    Citations: 34
  • Fine-grained multilingual hate speech detection using explainable AI and transformers
    JA Siddiqui, SS Yuhaniz, GM Shaikh, SA Soomro, ZA Mahar
    IEEE Access 12, 143177-143192 , 2024
    2024
    Citations: 28
  • Embedded intelligent imaging on-board small satellites
    S Yuhaniz, T Vladimirova, M Sweeting
    Asia-Pacific Conference on Advances in Computer Systems Architecture, 90-103 , 2005
    2005
    Citations: 28
  • A review of market basket analysis on business intelligence and data mining
    NNA Sjarif, NFM Azmi, SS Yuhaniz, DHT Wong
    International journal of business intelligence and data mining 18 (3), 383-394 , 2021
    2021
    Citations: 27
  • Crowd analysis and its applications
    NN Amir Sjarif, SM Shamsuddin, SZ Mohd Hashim, SS Yuhaniz
    International conference on software engineering and computer systems, 687-697 , 2011
    2011
    Citations: 27
  • An onboard automatic change detection system for disaster monitoring
    S Sophiayati Yuhaniz, T Vladimirova
    International Journal of Remote Sensing 30 (23), 6121-6139 , 2009
    2009
    Citations: 26
  • An adaptation of deep learning technique in orbit propagation model using long short-term memory
    N Salleh, NFM Azmi, SS Yuhaniz
    2021 International Conference on Electrical, Communication, and Computer … , 2021
    2021
    Citations: 21
  • A comparison study of biogeography based optimization for optimization problems
    NF Hordri, SS Yuhaniz, D Nasien
    Int. J. Advance. Soft Comput. Appl 5 (1) , 2013
    2013
    Citations: 16
  • The Heuristic extraction algorithms for freeman chain code of handwritten character
    D Nasien, H Haron, SS Yuhaniz
    International Journal of Experimental Algorithms-IJEA 1 (1), 1-20 , 2011
    2011
    Citations: 16