2024年度の研究に関わる業績（研究発表・論文・外部資金・受賞等）です。
The list shows published papers, grants, and awards in 2024-2025 season.

2024年度 / Apr. 2024 〜 Mar. 2025

  • 博士論文 / Doctral Thesis
  • 修士論文 / Master Thesis
  • 卒業論文 / Graduate Thesis
  • 雑誌論文 (Journal): 7 papers
    • Ahmad Hakimi Ahmad Sa’ahiry, Abdul Halim Ismail, Masahiro Toyoura, Latifah Munirah Kamaruddin, Mohd Sani Mohamad Hashim, Muhamad Safwan Muhamad Azmi, Hiromitsu Nishizaki, Xiaoyang Mao, “Device diversity in crowdsourced WiFi fingerprinting database for autonomous mobile robot”, International Journal of Advanced Robotic Systems, Vol.21, No.6, pp.1-16, Nov./2024, DOI: 10.1177/17298806241297, IF: 2.3
      Abstract: Positioning and navigation of mobile robot is the main feature for the trajectory or motion of the mobile robot. Conventional mobile robot positioning and navigation system relies heavily on fusion of multiple costly sensors, which does not promote mass production. This paper aim is to use readily and available technologies which is WiFi due to its reliability as it is pre-deployed, and it exist in most of the building. The system used are based on indoor positioning system (IPS) by using a crowdsourced fingerprinting method. This seeks to improve crowdsourced fingerprinting database performance by solving the issue of the device diversity or heterogeneity of difference devices. To cope with the crowdsourced fingerprinting database as the location estimation method for the robot application, deep neural network (DNN) is employed. The proposed method namely ratio and ranged-based (RRB) shows an improvement of 60% increments by implementing the pre-processing technique of the raw data before feeding it to the DNN. The comparison between other method shows that RRB is better in term of accuracy in three validation techniques, which are root mean square error (RMSE), distance error and accuracy between true and estimate position. This improvement effectively could facilitate the actual positioning system utilizing the WiFi infrastructure for the mobile robot in very near future.
    • Noraini Azmi, Latifah Munirah Kamarudin, Ahmad Shakaff Ali Yeon, Ammar Zakaria, Syed Muhammad Mamduh Syed Zakaria, Hiromitsu Nishizaki, Latifah Mohamed, Xiaoyang Mao, Mohd Hafiz Fazalul Rahiman, “Integration of dual band radio waves and ensemble-based approach for rice moisture content determination and localisation”, Journal of Stored Products Research, Vol.108, Pages 102399, Sep/2024, DOI: 10.1016/j.jspr.2024.102399, IF: 2.7
      Abstract: Maintaining optimal moisture content in grain storage is critical to ensuring adequate supply throughout the year, but it presents a significant challenge. Current moisture measurement methods often necessitate sophisticated and costly equipment. This paper introduces an approach employing real-time rice moisture content determination and detection of spoilage (specifically wet spots) within a storage facility achieved through the utilisation of radio waves operating at 2.4 GHz and 868 MHz, along with an ensemble-based machine learning algorithm. Experimental samples spanning from 12% to 30% moisture levels were collected, then subjected to pre-processing, and subsequently employed to train the Ensemble-based Rice Moisture Content and Localisation (eRMCL) algorithm. The eRMCL produced an effective prediction of both rice moisture content and the localisation of wet spots within the grain storage unit. The results show that compared to support vector machine, random forest, and machine learning methods, the eRMCL algorithm had the best performance metrics, with an accuracy of 94.8% in predicting the moisture content and location of spoilage in storage. The measurement of moisture content and the identification of wet spots in rice storage using the dual frequency wave approach were found to be more accurate than with a single frequency band. Thus, the dual frequency band is a novel method for the determination of the moisture content of stored rice and the localisation of the spoilage area.
    • Chuo Hiang Heng, Masahiro Toyoura, Chee Siang Leow, Hiromitsu Nishizaki, “Analysis of Classroom Processes Based on Deep Learning With Video and Audio Features,” IEEE Access, vol. 12, pp. 110705-110712, 2024, DOI: 10.1109/ACCESS.2024.3434742, IF:3.9
      Abstract: An active learning-type class is a class in which students take the initiative. In order to improve active learning-type classes, attempts have been made to review the content after the class using video taken by the lecturer, but this is burdensome because it takes a long time to review the video. Although methods have been proposed for estimating the classroom process at each time, there is still room for improvement. In this paper, we propose a method for estimating the classroom process at each time with higher accuracy than conventional methods. The proposed method uses deep learning to improve the accuracy of the conventional method, which only uses classical SVM. We also used an ablation study to find an appropriate combination of input modalities. Furthermore, we introduced ensemble LSTM to handle data with different modalities. The proposed method achieved the highest accuracy of 98.8% and the lowest accuracy of 64.4% in estimating classroom activities, with an average classification accuracy of 80.1%.
    • Hui Fern Soon, Amiza Amir, Hiromitsu Nishizaki, Nik Adilah Hanin Zahri, Latifah Munirah Kamarudin, “An FPA-Optimized XGBoost Stacking for Multi-Class Imbalanced Network Attack Detection”, International Journal of Advanced Computer Science and Applications, Vol.15, No.7, pp. 1380-1390, 2024. DOI:10.14569/IJACSA.2024.01507134, IF: 2.1
      Abstract: Network anomaly detection systems face challenges with imbalanced datasets, particularly in classifying underrepresented attack types. This study proposes a novel framework for improving F1-scores in multi-class imbalanced network attack detection using the UNSW-NB15 dataset, without resorting to resampling techniques. Our approach integrates Flower Pollination Algorithm-based hyperparameter tuning with an ensemble of XGBoost classifiers in a stacking configuration. Experimental results show that our FPA-XGBoost-Stacking model significantly outperforms individual XGBoost classifiers and existing ensemble models. The model achieved a higher overall weighted F1-score compare to the individual XGBoost classifier and Thockchom et al.’s heterogeneous stacking ensemble. Our approach demonstrated remarkable effectiveness across various levels of class imbalance, for example Analysis and Backdoor which is highly underrepresented classes, and DoS which is moderately underrepresented class. This research contributes to more effective network security systems by offering a solution for imbalanced classification without resampling techniques’ drawbacks. It demonstrates that homogeneous stacking with XGBoost can outperform heterogeneous approaches for skewed class distributions. Future work will extend this approach to other cybersecurity datasets and explore its applicability in real-time network environments.
    • Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Mohd Yusoff Mashor, Latifah Munirah Kamarudin, Zeti-Azura Mohamed-Hussein, Zeehaida Mohamed, Wei Chern Ang, “Optimizing hybrid neural networks for precise COVID-19 mRNA vaccine degradation prediction”, International Journal of Advanced and Applied Sciences, Vol. 11, No. 7, pp.87-100, July/2024, DOI: 10.21833/ijaas.2024.07.011, IF: 0.4
      Abstract: Conventional hybrid models often miss an essential factor that can lead to less effective performance: intrinsic sequence dependence when combining various neural network (NN) architectures. This study addresses this issue by highlighting the importance of sequence hybridization in NN architecture integration, aiming to improve model effectiveness. It combines NN layers—dense, long short-term memory (LSTM), and gated recurrent unit (GRU)—using the Keras Sequential API for defining the architecture. To provide better context, bidirectional LSTM (BiLSTM) and bidirectional GRU (BiGRU) replace their unidirectional counterparts, enhancing the models through bidirectional structures. Out of 25 NN models tested, 18 four-layer hybrid NN models consist of one-quarter dense layer and the rest BiLSTM and BiGRU layers. These hybrid NN models undergo supervised learning regression analysis, with mean column-wise root mean square error (MCRMSE) as the performance metric. The results show that each hybrid NN model produces unique outcomes based on its specific hybrid sequence. The Hybrid_LGSS model performs better than existing three-layer BiLSTM networks in predictive accuracy and shows lower overfitting (MCRMSEs of 0.0749 and 0.0767 for training and validation, respectively). This indicates that the optimal hybridization sequence is crucial for achieving a balance between performance and simplicity. In summary, this research could help vaccinologists develop better mRNA vaccines and provide data analysts with new insights for improvement.
    • Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Mohd Yusoff Mashor, Latifah Munirah Kamarudin, Zeti-Azura Mohamed-Hussein, Zeehaida Mohamed, Wei Chern Ang “COVID-19 mRNA vaccine degradation rate prediction using artificial intelligence techniques: A narrative review”, International Journal of Advanced and Applied Sciences, Vol. 11, No. 6, pp.215-228, June/2024, DOI: 10.21833/ijaas.2024.06.023, IF: 0.4
      Abstract: As diseases become more common, the use of mRNA (messenger ribonucleic acid) vaccines is becoming more important. These vaccines can be developed quickly and have a low risk of side effects. However, they are sensitive to environmental conditions, which means they need careful storage and transport, creating challenges in distributing them. Testing the stability of an mRNA vaccine requires a lot of work and time, as it needs many lab tests. Artificial Intelligence (AI) offers a new solution by using the genetic information in RNA sequences to predict how quickly these vaccines might break down. This approach helps address potential shortages of vaccines by avoiding some of the challenges with vaccine distribution. The COVID-19 pandemic has greatly sped up the use of AI in this area. This change is significant because using AI to predict and improve the stability of mRNA vaccines was not well explored before the pandemic. This paper reviews recent studies that use AI to study mRNA vaccines during the COVID-19 pandemic. It points out that the main issue with these vaccines is how long they can be stored before they are no longer effective due to their sensitivity to environmental conditions. By looking at these studies, the paper not only shows how AI and vaccine research are coming together but also points out opportunities for more research. The goal of this review is to outline effective methods to improve the use of mRNA vaccines and encourage more scientific research and development in this field. This is an important step in improving how we deal with pandemics.
    • Tasneem Sofri, Allan Melvin Andrew, Hasliza A Rahim, Hiromitsu Nishizaki, Latifah Munirah Kamarudin, Peng Wen Wong, Ping Jack Soh, “Enhancing Predictive Models for Assessing 5G Exposure Effects on Human Health and Cognition through Supervised Machine Learning: A Multi-Stage Feature Selection Approach”, Przeglad Elektrotechniczny, Vol.2024, No.6, pp.122-128, June/2024. DOI: 10.15199/48.2024.06.23
      No prior reviews have focused on any comprehensively examine the effects of 5G exposure (700 MHz to 30 GHz) on human health and cognition using supervised Machine Learning (ML). This novel research combined the Multi-Stage Feature Selection (MSFS) and hybrid features for classification machine learning model. The approach which includes the use of MSFS, yielded better results in terms of accuracy, precision, F1- score, sensitivity, and specificity when contrasted with the approach that did not incorporate MSFS with accuracy more than 0.95 for both datasets.
  • 国際会議論文 (Reviewed conference papers): 10 papers
    • So Watanabe, Chee Siang Leow, Junichi Hoshino, Takehito Utsuro, Hiromitsu Nishizaki, “”, Proceedings of the 2024 APSIPA Annual Summit and Conference, pp.-, Macau, Dec 3 – 6, Dec./2024, Presented on 5th/Dec/2024 in Macau
    • Leow Chee Siang, Tsutomu Tanzawa, Bong Tze Yaw, Koji Makino, Kazuyoshi Ishida, Hiromitsu Nishizaki, “Development of a Fruit Theft Reporting System Using a Compact Microcontroller with Deep Learning Based on Suspicious Sounds”, Proceedings of the 50th Annual Conference of the IEEE Industrial Electronics Society (IECON 2024), pp.1-6, Nov./2024. Presented on 4th/Nov/2024 in Cicago, USA
    • Ryosuke Shimazu, Chee Siang Leow, Prawit Buayai, Koji Makino, Xiayang Mao, Hiromitsu Nishizaki, “High Quality Color Estimation of Shine Muscat Grape Using Vision Transformer”, Proceedings of the 23rd International Conference on Cyberworlds (Cyberworlds 2024), pp.195-202, Oct/2024. Presented on 31st/October in Kofu, Yamanashi, Japan
    • Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin, “Prediction Model with Penalized Hyperparameter Optimization for mRNA Vaccine Degradation Based on Tetra-nitrogenous-base Analysis”, Proceedings of the 23rd International Conference on Cyberworlds (Cyberworlds 2024), pp.249-256, Oct/2024. Presented on 31st/October in Kofu, Yamanashi, Japan
    • Muhammad Rasydan Mazlan, Abdul Syafiq Abdull Sukor, Abdul Hamid Adom, Latifah Munirah Kamarudin, Hiromitsu Nishizaki, Norasmadi Abdul Rahim, “Analysis of Acute Stress Reaction with EEG using MI-mRMR EEG Channel Selection and Ensemble Learning with Majority Voting”, Proceedings of the 23rd International Conference on Cyberworlds (Cyberworlds 2024), pp.272-279, Oct/2024. Presented on 30st/October in Kofu, Yamanashi, Japan
    • Jae Hyun Lee, Ye-Seul Jang, Hiromitsu Nishizaki, Won-Du Chang, “The dynamic load-induced bioelectric potentials of knee joint”, Proceedings of the 23rd International Conference on Cyberworlds (Cyberworlds 2024), pp.358-359, Oct/2024. Presented on 30st/October in Kofu, Yamanashi, Japan
    • Makoto Hotta, Chee Siang Leow, Norihide Kitaoka, Hiromitsu Nishizaki, “Evaluation of Speech Translation Subtitles Generated by ASR With Unnecessary Word Detection”, Proceedings of the 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE), pp.835-839, Oct/2024, Presented on 31st/Oct in Kitakyusyu, Fukuoka, DOI: 10.1109/GCCE62371.2024.10760522
    • Hideaki Yajima, Chee Siang Leow, Hiromitsu Nishizaki, “Text Detection and Style Classification From Images Using Vision Transformer and Transformer Decoder”, Proceedings of the 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE), pp.632-636, Oct/2024, Presented on 30th/Oct in Kitakyusyu, Fukuoka, DOI: 10.1109/GCCE62371.2024.10761042
    • Kazuyoshi Ishida, Chee Siang Leow, Tsutomu Tanzawa, Tze Yaw Bong, Hiromitsu Nishizaki, Koji Makino, “Evaluation of LoRa-Based Long-Range Communication in a Fruit Theft Prevention Device”, Proceedings of the 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE), pp.291-294, Oct/2024, Presented on 29th/Oct in Kitakyusyu, Fukuoka, DOI: 10.1109/GCCE62371.2024.10760408
    • Shuto Nakagomi, Yutaka Suzuki, Masayuki Morisawa, Hiromitsu Nishizaki, Muramatsu Hideaki, Motoki Arakawa, “Development of GUI Application for Multimodal Analysis for Evaluation of Swallowing Function”, Proceedings of the 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE), pp.974-978, Oct/2024, Presented on 30th/Oct in Kitakyusyu, Fukuoka, DOI: 10.1109/GCCE62371.2024.10760652
  • 口頭発表 (Domestic conference, not reviewed)
    • 渡辺蒼，レオ チーシャン，西崎博光，星野准一，宇津呂 武仁，”複数の大規模言語モデルを用いた円卓会議による接客評価”，人工知能学会第38回全国大会論文集，No. 4Xin2-100，pp.1-4，2024.5（2024/5/31発表，浜松市）
    • 長谷川遼，銭本友樹，宇津呂武仁，西崎博光，吉岡真治，神門典子，”大規模言語モデルによる自由記述アンケート自動集約のための疑似訓練事例生成”，人工知能学会第38回全国大会論文集，No. 1J4-OS-10b-03，pp.1-4，2024.5（2024/5/28発表，浜松市）
  • 外部資金 (Grant)
  • 書籍・雑誌記事 (Books, Magazines)
  • 学外授業・セミナー
  • 表彰・報道等