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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Pailus, Rayner Henry Alfred, Rayner Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

Many researchers are interested in finding solutions for face recognition against low light illumination, many succeeded using Histogram Equalization, Retinex Theory, Illumination map estimation model, Frequency domain and Deep learning methods. Nevertheless, not all these mentioned methods can improve all types of face detection and face recognition challenges. Many researchers of face recognition agree and believe that a better-quality input image used for face detection, feature extraction, feature selection, training and testing will produce better detection and recognition. Thus, this paper will evaluate other established and published algorithms and our proposed method called Multiple Adaptive Derivative Passive Image Processing (MADPIP). Our solution does not use only one method but multiple and does not have a single output but multiple outputs. When an image goes through multiple adaptive changes multiple times, it is no longer appropriate to call it an output but rather a multiple-processed output or a multiple adaptive derivative. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification enhancement Convolutional neural network Image restoration Low light images

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Multiple Adaptive Derivative Passive Image Processing Approach to Unsharp Masking Restoration for CNN on Low Light Images 4th

Multiple Adaptive Derivative Passive Image Processing Approa...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Pailus, Rayner Henry Alfred, Rayner Faculty of Computing and Informatics Creative Advanced Machine Intelligence Research Centre Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

Most researchers thought face detection challenges are distinguishing faces against the background images and coping with multiple face poses whereby every face will only have an eye, half of a nose and half of a mouth when a face is taken from one side profile image. The actual challenge of face detection is when there is no light or luminant to shine on the face when the image is taken. This challenge is also known as low light illumination. These days, CNNs have been used as the basis of deep learning frameworks in various research works. This paper compares our restoration process against existing non-CNN-based and CNN-based methods. It evaluates whether Retinex and Bilateral theory still can be applied in enhancing and solving face detection in low light. When an image goes through multiple adaptive changes, multiple times as input and output, it is no longer appropriate to call it an output but rather a derivative. Thus, our proposed output is labelled as Multiple Adaptive Derivative and this paper evaluates the performance of the proposed Multiple Adaptive Derivative Passive Image Processing (MADPIP) restoration and enhancement processes for images taken in low light using Logarithmic, CLAHE and Unsharp Masking. Apart from contrast and edge quality, MADPIP’s integrity, image entropy, visual edge and time processing are also measured. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Comparative Study on Embedding Models for Keyword Extraction Using KeyBERT Method 13

A Comparative Study on Embedding Models for Keyword Extracti...

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13th IEEE International Conference on System Engineering and Technology, ICSET 2023

作者： Issa, Bayan Jasser, Muhammed Basheer Chua, Hui Na Hamzah, Muzaffar University of Aleppo Faculty of Informatics Engineering Syria Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350340891

KeyBERT is a method for keywords/keyphrases extraction, which has three steps. The first step is selecting candidate keywords from a text using sklearn library, the second step is the embedding operation of the text and its candidate keywords;this operation is done by BERT to get a numerical representation that represents the meanings. The third step is calculating the cosine similarity between individual keywords vectors and document vector. In this paper, we focus on the second step of KeyBERT (embedding step). Although KeyBERT has a lot of supported models for the embedding operation, there are no extensive previous comparative studies to analyze and study the effect of using different supported models in KeyBERT. We introduce a comparative study of two commonly used groups of models;the first group is sentence-transformers pretrained models, supported via the sentence-transformers library, and the second group includes the Longformer model, supported via the Hugginface Transformers library. We conduct the comparative study of models on benchmark datasets, which contain English text documents of multi-domains with different text lengths. Based on the study, we found that the Paraphrase-mpnet-base-v2 model provides the best results among all other models in keyword extraction in terms of effectiveness (f1-score, recall, precision, MAP) on all datasets, with higher efficiency (time) on short text compared with using it on long text;accordingly, we recommend using it in that context. On the other hand, the Longformer model is the most efficient/fastest in keyword extraction among all other models on all datasets and this superiority has been evident, especially in long text;accordingly, we recommend using it in that context. © 2023 IEEE.

关键词： Embeddings

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Rielkies Nearby Sales Agents Finder Using Haversine Method

Rielkies Nearby Sales Agents Finder Using Haversine Method

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Sulaiman, Mohd Suffian Aziz, Ahmad Ajad Azmi, Azri Derasit, Zuraidah Ismail, Azlan Nor Abdul Rahim, Siti Khatiiah College of Computing Informatics & Mathematics Universiti Teknologi Mara Malaysia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Malaysia

ISBN: (纸本)9798350302684

With their new culinary products, Rielkies Choco jars, which come in a variety of flavors, the chocolate-based company Rielkies, which was created by one of Malaysia's most well-known actors, has been able to gain popularity in Malaysia. A system that enables Rielkies's customers to locate their preferred and reliable sales agents is crucial given the large number of people registered as their product sales agents in each state throughout Malaysia. Due to privacy and security concerns, many customers steer clear of doing business online. Many customers are also concerned with the sales representatives' delivery strategies because they often favor cash on delivery due to its security. The development of a web-based tool to locate salespeople nearby was suggested in this article. The tool would give customers or users access to the contact information of salespeople as well as information on their location and preferred method of delivery. The Haversine technique is chosen as the most appropriate algorithm to employ for this project to calculate the distance. The system's functions include locating local sales representatives for customers and managing sales representatives for admin to confirm the sales representatives to ensure they are reliable and secure. The result demonstrates that the system's functionality has been attained. To make their transactions more secure and simple than before, the project will assist customers in locating their favorite sales representatives. © 2023 IEEE.

关键词： Sales

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A Robust Multiple Adaptive Derivative Face Recognition System on Pose and Illumination 4th

A Robust Multiple Adaptive Derivative Face Recognition Syste...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Pailus, Rayner Henry Alfred, Rayner Faculty of Computing and Informatics Creative Advanced Machine Intelligence Research Centre Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

Computer system using CNN visions has proven to be flawed as it fails to capture all the different angles and positions of the image. The main problem with CNN is that it has is no proper system in localizing a person’s head without depending on the person image appearance. CNN based models can memorise images that are augmented and linked together after trained them with supervised positive and negative from the ground truth annotated images marked with landmarks but does not conduct face localization. Thus, the objective of this paper is to investigate, design a face recognition system that is able to recognise a person regardless of his or her head poses. This paper’s task is to investigate a proper system that will be able to be classify face localization. First, MAD Passive Image Processing (MADPIP) is used to perform restoring and enhancing all images. Second, MAD Face Detection (MADFD) is used to process face image with the optimization of Zhu Ramanan (OZR) algorithm to locate landmarks position accurately. Then, MAD Localization and Landmarking (MADLL) is used to build OZR model by performing landmarking classification. Next, the Optimized Zhu Ramanan (OZR) is designed to improve Zhu Ramanan (ZR) processing time by replacing HOG with OZR and finally all the extracted features are normalized. The results showed that the performance of the proposed Multiple adaptive derivative Face Recognition System (MADFRS)is acceptable in which the proposed MADFRS approach proposed a solution with accuracy percentage of 99.80% which is the third highest for LFW Dataset Benchmark. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Advanced deep learning for masked individual surveillance

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International Journal of Cognitive computing in Engineering 2024年第1期5卷 406-415页

作者： Elhoseny, Mohamed Hassan, Ahmed Shehata, Marwa H. Kayed, Mohammed College of Computing and Informatics University of Sharjah United Arab Emirates Faculty of Computers and Information Mansoura University Egypt Faculty of Science Beni-Suef University Beni-Suef62511 Egypt Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef62511 Egypt

During Covid-19 pandemic, face masks have become a ubiquitous protective measure. This poses new challenges for surveillance systems that heavily rely on facial recognition. To address this critical issue, we present a novel enhanced surveillance system that leverages deep learning techniques to tackle two crucial tasks simultaneously: anomaly detection of masked individuals' activities and masked face completion for accurate recognition. For anomaly detection, we employ a custom-designed deep neural network capable of processing real-time video streams. Finding a dataset of anomaly events of masked individuals is a big challenge for us. We handle this challenge using efficient techniques such as Dlib library and other image processing techniques. The network is trained on a diverse dataset encompassing normal and abnormal activities of masked individuals, enabling it to identify suspicious behaviors effectively. The surveillance cameras will exchange information, using a suitable network protocol, about detected anomalies and share relevant image data to aid in decision-making and choose the best images for further processing. In the context of masked face completion, we present a novel architecture called CCGAN network that is a combination of convolutional neural network (CNN) and conditioned generative adversarial network (CGAN) to generate the hidden parts of the face in a form that is accurate and close to the original face shape, as shown in our results. We conduct extensive experiments on publicly available datasets, demonstrating superior performance in both anomaly detection and masked face completion tasks. We have achieved 90% accuracy for anomaly detection of masked people. © 2024

关键词： Generative adversarial networks

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Investigating the Effects of Applying Different Text Pre-processing on the Performance of Sentiment Analysis for Malay Document Corpus 4th

Investigating the Effects of Applying Different Text Pre-p...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Alfred, Rayner Rahim, Elly Mazlin Binti Pailus, Rayner Henry Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

In this era where social media networking and internet applications keep growing, people can easily express their opinions and feelings towards anything, either on products or services, in social media. These online reviews are the primary source in text analysis tasks, such as a sentiment analysis which is used to understand people’s sentiments towards something. Usually, online reviews are raw data that come in unstructured form and have a lot of noise. It needs to be processed and cleaned so that it can be used for further analysis. So far, there are limited works conducted on investigating the effects of applying text preprocessing tasks in the sentiment analysis for Malay language corpus, despite having enough data to work on it. Thus, this study investigates the impacts of text preprocessing to sentiment analysis on the Malay language using different approaches. The focus is more on handling the emojis and emoticons, typographical errors, repeated characters in the word, and the stemming process. The dataset, which is related to product reviews, was collected manually using a web scraping technique. The Support Vector Machine (SVC) classifier was applied to assess the effects of different text preprocessing approaches on the quality of sentiment classification based on accuracy, precision, recall, and F1-score. The results showed that the text preprocessing tasks have significant impacts on the performance accuracy of the Malay sentiment analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Malay document corpus Sentiment analysis Social media Texts pre-processing

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Elucidating Theory of Malaysian Data Crisis in Demystifying Dark Data Catalyst 6

Elucidating Theory of Malaysian Data Crisis in Demystifying ...

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6th IEEE Symposium on Computers and informatics, ISCI 2024

作者： Ajis, Ahmad Fuzi Md Ibrahim, Ag. Asri Ag Verma, Manoj Kumar UiTM Cawangan Johor Center of Information Science College of Computing Informatics and Mathematics Segamat Malaysia University Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Mizoram University Department of Library and Information Science Aizawl India

ISBN: (纸本)9798350353853

This article presents the findings of a research on the dark data phenomenon faced by Small and Medium Enterprises (SME) in Malaysia in connection to the features of dark data from SME perspectives. The dark data was found to be influenced by the data shadow in the form of grey and black data which each differentiated by the intensity of data darkness, a condition that influenced data from being found and utilized. In this study, there were 18 samples produced from Inductive Grounded & Emergent Theory Sampling, which was used to discover acceptable samples for the study, were subjected to qualitative research. Using semi-structured interviews that were recorded, transcribed, and analyzed using the Grounded Theory Methodology, data was obtained. The outcomes of the study emphasized on the emergence of Data Crisis which found to be the major factors contributing to the dark data often found in SME archives. The research demonstrates that there is a dearth of literature on dealing with dark data, indicating that dark data epistemology is still emerging. The findings indicated that the Data Crisis is a situation in which a business proprietor is confronted with the repercussions of inadequate data management. On the basis of the experiences of Malaysian SMEs, a model was proposed to elucidate the data crisis. © 2024 IEEE.

关键词： Data assimilation

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Exploration and Showcase of FSMs and HFSMs Traffic in Low-Cost Educational Driving Simulator 9th

Exploration and Showcase of FSMs and HFSMs Traffic in Low-...

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9th International Conference on Computational Science and Technology, ICCST 2022

作者： Cheng, Yun-Quan Mansor, Sarina Chin, Ji-Jian Karim, Hezerul Abdul Kar-Weng, Ban Faculty of Engineering Multimedia University Selangor Cyberjaya Malaysia Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia

ISBN: (纸本)9789811984051

Finite state machine (FSM) is a model of computation that executes an exact finite number of states at any given time where Hierarchical Finite State Machines (HFSM) can group multiple FSMs and execute as one state. Given these techniques can be used to exclusively execute certain states, it is widely used in games. In this paper we will explore and showcase the implementation of FSMs and HFSMs to create traffic non-playing characters (NPC) in our low-cost educational driving simulators. We will also discuss possible techniques that can be use. It is also in the interest to push for more understanding in any gap for traffic NPCs in similar low-cost educational driving sims in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Costs

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Direct Current Motor Malfunction Detection Through Metastatic Optimized Audio Analysis and Classification 7

Direct Current Motor Malfunction Detection Through Metastati...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Bozovic, Aleksandra Jovanovic, Luka Desnica, Eleonora Radovanovic, Ljiljana Zivkovic, Miodrag Bacanin, Nebojsa University of Novi Sad 'Mihajlo Pupin' Technical Faculty Zrenjanin Serbia Singidunum University Technical Faculty Belgrade Serbia Singidunum University Faculty of Informatics And Computing Belgrade Serbia

ISBN: (纸本)9798350359299

This study investigates the integration of advanced signal processing techniques with the AdaBoost classification algorithm for the purpose of identifying malfunctions in brushless DC motors through audio analysis. Given the increasing significance of brushless DC motors in modern manufacturing and transportation, the timely detection of anomalies becomes crucial for maintaining consistent operational efficiency and preventing potential injuries. Despite regular maintenance practices, the occurrence of unexpected mechanical and electrical failures often eludes operators, leading to the potential escalation of minor malfunctions into catastrophic failures. Consequently, the implementation of a reliable and consistent monitoring system is imperative in practical applications. The proposed solution leverages audio analysis as an effective approach. Through the amalgamation of machine learning and advanced signal processing techniques, this research employs audio signal classification to discern malfunctions in brushless DC motors from audio recordings of their operations. The extraction of dominant frequencies using the Fast Fourier Transform (FFT) enhances the analysis, and the AdaBoost classifier is subsequently employed to assess the proper functioning of the motors. Recognizing the substantial impact of hyperparameter selections on classifier performance, a modified version of the Red Fox Optimization (RFO) algorithm is introduced to meet the specific demands of this study. Results demonstrate that the optimizers, when appropriately constructed, achieve perfect accuracy when applied to a real-world dataset, highlighting the potential practical applicability of the proposed methodology. © 2024 IEEE.

关键词： Brushless DC motors

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