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All Journal Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) JOIV : International Journal on Informatics Visualization
Naveen, Palanichamy
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Computer Science & IT Electrical & Electronics Engineering

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Analysis of multi-criteria recommendation system based on fuzzy algorithm Anaam, Elham Abdulwahab; Haw, Su-Cheng; Ng, Kok-Why; Naveen, Palanichamy; Tong, Gee-Kok
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7801

Abstract

There is a gap in defining the multi-criteria decision-making issues and with recommendation techniques and theories that can help develop the modulation coefficient recommenders. The main objective of this research is to identify an in-depth examination of the category of multiple variables recommendation systems. The methodology that is used in the current study is fuzzy multi-critical decision-making to enhance the precision and appropriateness of the recommendations provided to users, and make recommendations by representing an individual's performance for the product as an ordered collection of rankings in addition to different parameters. The techniques used to make forecasts and produce recommendations using multi-criteria rankings are reviewed. In addition, we propose the multiple-criteria ranking algorithms. Experimental evaluations demonstrated that our proposed algorithms can solve the multi-criteria issues. Furthermore, the research considers unresolved problems and upcoming difficulties for the category of recommendations for multiple variables ratings.
Indonesian-English Textual Similarity Detection Using Universal Sentence Encoder (USE) and Facebook AI Similarity Search (FAISS) Krisnawati, Lucia D.; Mahastama, Aditya W.; Haw, Su-Cheng; Ng, Kok-Why; Naveen, Palanichamy
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.11274

Abstract

The tremendous development in Natural Language Processing (NLP) has enabled the detection of bilingual and multilingual textual similarity. One of the main challenges of the Textual Similarity Detection (TSD) system lies in learning effective text representation. The research focuses on identifying similar texts between Indonesian and English across a broad range of semantic similarity spectrums. The primary challenge is generating English and Indonesian dense vector representation, a.k.a. embeddings that share a single vector space. Through trial and error, the research proposes using the Universal Sentence Encoder (USE) model to construct bilingual embeddings and FAISS to index the bilingual dataset. The comparison between query vectors and index vectors is done using two approaches: the heuristic comparison with Euclidian distance and a clustering algorithm, Approximate Nearest Neighbors (ANN). The system is tested with four different semantic granularities, two text granularities, and evaluation metrics with a cutoff value of k={2,10}. Four semantic granularities used are highly similar or near duplicate, Semantic Entailment (SE), Topically Related (TR), and Out of Topic (OOT), while the text granularities take on the sentence and paragraph levels. The experimental results demonstrate that the proposed system successfully ranks similar texts in different languages within the top ten. It has been proven by the highest F1@2 score of 0.96 for the near duplicate category on the sentence level. Unlike the near-duplicate category, the highest F1 scores of 0.77 and 0.89 are shown by the SE and TR categories, respectively. The experiment results also show a high correlation between text and semantic granularity.
An Automated Face Detection and Recognition for Class Attendance Horn Boe, Chang; Ng, Kok-Why; Haw, Su-Cheng; Naveen, Palanichamy; Abdulwahab Anaam, Elham
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2967

Abstract

Class attendance is a crucial indicator of students' seriousness towards learning. Many institutions continue to use manual methods, which are usually error-prone and unproductive. By leveraging computer vision algorithms, the system accurately captures and verifies the identity of students attending class. This paper aims to investigate and create an automated facial recognition system for classroom attendance to increase the precision and effectiveness of the attendance tracking system. To achieve this, we propose a system using computer vision technologies, namely Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM) for face detection and deep Convolutional Neural Networks (CNN) for face identification. The facial recognition system simplifies attendance recording, requiring participants to only gaze into the camera for the system to record their presence automatically. The system is rigorously tested and evaluated, and its accuracy is compared to our institution's current QR code attendance method. The study results reveal that the recommended approach is more accurate and competent than the existing procedures. The system allows for precise attendance records with real-time face detection and recognition capabilities. This technology ensures accurate and reliable attendance data, empowering organizations to make informed decisions, effectively manage resources, and provide a seamless experience for all students. In addition, a similar attendance system can be deployed for any event in an organization, thereby enhancing overall operational efficiency.
A Comprehensive Review on Cancer Detection and Classification using Medical Images by Machine Learning and Deep Learning Models J, Jayapradha; Su-Cheng, Haw; Naveen, Palanichamy; Anaam, Elham Abdulwahab
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3061

Abstract

In day-to-day life, machine learning and deep learning plays a vital role in healthcare applications to predict various diseases such as cancer, heart attack, mental problem, Parkinson, etc. Among these diseases, cancer is the life-threatening disease that leads a human being to death. The primary aim of this study is to provide a quick overview of various cancers and provides a comprehensive overview of machine learning and deep learning techniques in the detection and classification of several types of cancers. The significance of machine learning and deep learning in detecting various cancers using medical images were concentrated in this study. It also discusses various machine learning and deep learning algorithms that lead to accurate classification of medical images, early diagnosis, and immediate treatment for the patients and explores the methodologies which has been used to predict the cancer with the help of low dose computer tomography to reduce cancer related deaths. As the study narrows down the research into lung cancer, it combats the findings limitations in lung cancer detection models and highlights the need for a deep study of novel cancer detection algorithms. In addition, the review also finds the role of setting up data in lung cancer and the potential of genetic markers in stabilizing the accuracy of machine learning models. Overall, this study gives valuable suggestions to achieve more accuracy in cancer detection and classification using machine learning and deep learning techniques. 
Co-Authors Abdulwahab Anaam, Elham Anaam, Elham Abdulwahab Horn Boe, Chang J, Jayapradha Lucia D. Krisnawati Mahastama, Aditya W. Ng, Kok-Why Su-Cheng Haw Su-Cheng, Haw Tong, Gee-Kok