論文 - 相川 勝
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Mg W.H.E., Zin T.T., Tin P., Aikawa M., Honkawa K., Horii Y.
Scientific Reports 15 ( 1 ) 2378 2025年12月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：Scientific Reports
Accurately predicting the calving time in cattle is essential for optimizing livestock management and ensuring animal welfare. Our research focuses on developing a robust system for calving cattle classification and calving time prediction, utilizing 12-h trajectory data for 20 cattle. Our system classifies cattle as abnormal (requiring human assistance) or normal (not requiring assistance) and predicts calving times based on their individual behaviors. We employed a tailored YOLOv8 model for efficient and precise cattle detection, effectively filtering out noise such as people and trucks. Our Customized Tracking Algorithm (CTA) maintains continuous identity tracking for each cow, enabling accurate re-identification even during occlusions. To minimize some ID switching errors over extended tracking periods, we integrated IDs optimization in the CTA utilizing Global IDs identification. We extracted and compared three total movement features for classifying cattle as abnormal or normal. For predicting calving times for each cow, we utilized and compared three cumulative movement features. Our system is fully automated, detecting and tracking all 20 cattle continuously for 12 h without manual assistance, and achieving an overall accuracy of 99%. By comparing three features derived from the trajectory tracking data for each point in a frame, we achieve 100%, 95%, 85% accuracy in classifying cattle as abnormal or normal and predict their calving times with a precision of within the next 6 h, within the next 9 h, within the next 8 h, respectively. Our system enables farmers to provide timely assistance, ensuring the health and safety of both the cow and the calf. Furthermore, it aids in optimizing resource allocation and enhancing overall farm efficiency, emphasizing the critical importance of calving time prediction in sustainable livestock farming.
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A Study on Health Management by Behavior Analysis of Calves 査読あり
Nishiyama T., Kazuhisa S., Aikawa M., Kobayashi I., Zin T.T.
Lecture Notes in Electrical Engineering 1322 LNEE 144 - 151 2025年
掲載種別：研究論文（学術雑誌） 出版者・発行元：Lecture Notes in Electrical Engineering
It is important to always monitor the health of cattle, especially calves, and the frequency of observation increases with environmental changes in addition to once a day. In addition, calves tend to be more susceptible to infectious diseases because of their immature immune systems. Therefore, rearing management is extremely important. And the number of dairy cattle-keeping households and the total number of cattle are decreasing, while the number of cattle per household is increasing, indicating that management is becoming larger in scale. In this study, we proposed the development of a health management system by analyzing calf behavior using a 3D camera. Experiments were conducted at the Sumiyoshi Field of Miyazaki University to confirm the effectiveness of the proposed method.
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Cow’s Back Surface Segmentation of Point-Cloud Image Using PointNet++ for Individual Identification 査読あり
Kyaw P.P., Tin P., Aikawa M., Kobayashi I., Zin T.T.
Lecture Notes in Electrical Engineering 1321 LNEE 199 - 209 2025年
掲載種別：研究論文（学術雑誌） 出版者・発行元：Lecture Notes in Electrical Engineering
An automatic cow health monitoring and management system cannot function effectively without an identification system in livestock farming. While 2D image-based computer vision currently achieves high accuracy in cow identification, its effectiveness can be significantly decreased by changes in lighting, environmental factors, and other limitations. To address these limitations, an identification system based on point-cloud images will be developed by using a combination of 3D TOF camera and 2D RGB camera. This system includes detection and segmentation stage, feature extraction stage, and identification stage. In this study, I focus on detecting and segmenting of cow back surface region from a point-cloud image using the PointNet+ + algorithm. Two segmentation models are trained and compared based on single-scale grouping (SSG) and multi-scale grouping (MSG) features. The extracted cow back surface region offers a rich set of features valuable for several applications, including individual cow identification, lameness detection, and body condition scoring.
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Cattle Lameness Detection Using Leg Region Keypoints from a Single RGB Camera 査読あり
Myint B.B., Zin T.T., Aikawa M., Kobayashi I., Tin P.
Lecture Notes in Electrical Engineering 1321 LNEE 180 - 189 2025年
掲載種別：研究論文（学術雑誌） 出版者・発行元：Lecture Notes in Electrical Engineering
The recent rise of machine learning and deep learning has significantly impacted the field of computer vision, particularly in tasks like object detection, object tracking, and classification. This surge in interest has underscored the critical role of feature extraction as a foundational step in these machine learning pipelines. Our research focuses on applying feature extraction techniques to a cattle lameness dataset. We specifically extract features related to the movement of key points on cattle legs across a sequence of video frames. By analyzing the variations in these points, we aim to identify features that can efficiently differentiate between lame and no lame cattle using popular machine learning algorithms. All four classifiers achieved strong testing accuracy above 75%, with SVM excelling at over 84%.
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Cattle Lameness Classification Using Cattle Back Depth Information 査読あり
Tun S.C., Tin P., Aikawa M., Kobayashi I., Zin T.T.
Lecture Notes in Electrical Engineering 1321 LNEE 160 - 170 2025年
掲載種別：研究論文（学術雑誌） 出版者・発行元：Lecture Notes in Electrical Engineering
The livestock industry plays a crucial role in sustaining agricultural production and rural economies. Monitoring cattle health, however, presents significant challenges on large farms where traditional methods require diagnosing each animal individually. Lameness is a major issue affecting cattle health, leading to decreased production performance on many farms. Timely detection of lameness is essential for providing effective early treatment. In this study, we propose a system using specialized depth cameras to monitor and analyze cattle back information for classifying lameness scores. We employ Detectron2 for cattle detection and segmentation, and the Intersection over Union (IOU) method for tracking, focusing solely on the cattle’s depth region. We extract various features from the cattle’s back depth data and utilize three different machine learning algorithms: K-Nearest Neighbor (KNN), Gradient Boosting, and Extra Trees for lameness score classification. The models KNN, Gradient Boosting, and Extra Trees showed strong training and validation results. Testing showed Extra Trees performing well with 88.2% morning and 89.0% evening accuracy. Our approach demonstrates the potential of depth camera in effectively classifying lameness scores, offering significant implications for livestock health management. This method not only improves the efficiency and accuracy of health monitoring in large-scale farming but also provides a practical solution for early detection and treatment of lameness, thereby enhancing overall farm productivity.
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Automatic Body Temperature Detection in Calves and Alarm System Using Thermographic Camera 査読あり
Moe A.S.T., Zin T.T., Aikawa M., Kobayashi I.
Lecture Notes in Electrical Engineering 1321 LNEE 190 - 198 2025年
掲載種別：研究論文（学術雑誌） 出版者・発行元：Lecture Notes in Electrical Engineering
The health monitoring of cows is crucial in livestock farming, particularly for calves, which are more susceptible to infectious diseases than adult cattle. This vulnerability is significantly influenced by the maturation of the calf’s immune system, rearing environment, and stress management. Traditional methods of health monitoring require substantial manpower, which can be impractical and inefficient. This paper proposes an automatic body temperature detection system for calves using thermal imaging. Leveraging the capabilities of thermal images, which are widely used in security, medical, and industrial applications, this system aims to identify and monitor the body temperature of calves. By detecting the head and eyes of the calf and extracting temperature data through the Detectron2 object detection method, the system can provide timely notifications to farmers or veterinarians. The overall average detection rate of head and eye regions was 94.5%. This approach enhances the efficiency of livestock health management, reducing the reliance on manual labor and enabling early detection of health issues.
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Mg W.H.E., Zin T.T., Tin P., Aikawa M., Honkawa K., Horii Y.
IEEE Open Journal of the Industrial Electronics Society 6 216 - 234 2025年
掲載種別：研究論文（学術雑誌） 出版者・発行元：IEEE Open Journal of the Industrial Electronics Society
This research introduces an automated system for cattle monitoring and calving time prediction, utilizing trajectory data embedded with time-series analysis. Designed for large-scale farms, our system offers continuous 12-h monitoring, ensuring precise capture of cattle movements. By utilizing time series analysis on the trajectory data, our system predicts calving events in advance, effectively distinguishing between abnormal (requiring human assistance) and normal (not requiring assistance) for each cow. We utilized 360° surveillance cameras to provide comprehensive coverage without disturbing the cattle's natural behavior. We employed tailored versions of the Detectron2 and YOLOv8 models to achieve efficient and precise cattle detection, comparing their performance in terms of missed detections and false detections. For tracking, we used our customized tracking algorithm, which minimizes ID switching and ensures continuous identification even in challenging conditions such as occlusions. While some ID switching errors still occur over extended tracking periods, we integrated tracking and identification to further optimize the handling of track IDs and global IDs. Our system incorporates a 4-h forecasting of cattle movement using Euclidean fluctuating summation (EFS) feature combined with our custom long short-term memory model. Experimental results demonstrate a detection accuracy of 98.70%, tracking and identification accuracy of 99.18%, and forecasting with an average error rate of 14.07%. Furthermore, the system accurately classifies cattle as either normal or abnormal and predicts calving events a 4-h in advance using the EFS feature, comparing its performance with various machine learning algorithms. The system's seamless integration significantly enhances farm management and animal welfare.
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Automated Cattle Identification via Image-Based Ear Tag Recognition 査読あり
Shimizu Y., Zin T.T., Aikawa M., Kobayashi I.
Lecture Notes in Electrical Engineering 1321 LNEE 138 - 147 2025年
掲載種別：研究論文（学術雑誌） 出版者・発行元：Lecture Notes in Electrical Engineering
A cattle management system using image processing technology has been proposed to reduce the labor burden of livestock farmers and improve management efficiency. To make cattle management using image processing more efficient, individual identification is necessary. Therefore, we focused on the 10-digit individual identification number given to each cow. In this study, face detection is first performed using YOLOv8, and ear regions are set from the detected face regions. The ear tag region is extracted from the ear region using color information, and the individual identification number on the ear tag is read. The identification result of the ear tag number may not be correctly identified due to the fact that a part of the ear tag is not captured due to the movement of the face or ear. Therefore, we proposed a mechanism to give a confidence score to the character identification results, update the ear tag number identification results, and obtain the ear tag number correctly, and confirmed its effectiveness through a demonstration experiment.
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Development of a real-time cattle lameness detection system using a single side-view camera 査読あり
Myint B.B., Onizuka T., Tin P., Aikawa M., Kobayashi I., Zin T.T.
Scientific Reports 14 ( 1 ) 13734 2024年12月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：Scientific Reports
Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracking, and classification. This research investigates the application of deep learning for cattle lameness detection in dairy farming. Our study employs image processing techniques and deep learning methods for cattle detection, tracking, and lameness classification. We utilize two powerful object detection algorithms: Mask-RCNN from Detectron2 and the popular YOLOv8. Their performance is compared to identify the most effective approach for this application. Bounding boxes are drawn around detected cattle to assign unique local IDs, enabling individual tracking and isolation throughout the video sequence. Additionally, mask regions generated by the chosen detection algorithm provide valuable data for feature extraction, which is crucial for subsequent lameness classification. The extracted cattle mask region values serve as the basis for feature extraction, capturing relevant information indicative of lameness. These features, combined with the local IDs assigned during tracking, are used to compute a lameness score for each cattle. We explore the efficacy of various established machine learning algorithms, such as Support Vector Machines (SVM), AdaBoost and so on, in analyzing the extracted lameness features. Evaluation of the proposed system was conducted across three key domains: detection, tracking, and lameness classification. Notably, the detection module employing Detectron2 achieved an impressive accuracy of 98.98%. Similarly, the tracking module attained a high accuracy of 99.50%. In lameness classification, AdaBoost emerged as the most effective algorithm, yielding the highest overall average accuracy (77.9%). Other established machine learning algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Random Forests, also demonstrated promising performance (DT: 75.32%, SVM: 75.20%, Random Forest: 74.9%). The presented approach demonstrates the successful implementation for cattle lameness detection. The proposed system has the potential to revolutionize dairy farm management by enabling early lameness detection and facilitating effective monitoring of cattle health. Our findings contribute valuable insights into the application of advanced computer vision methods for livestock health management.
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AI-enhanced real-time cattle identification system through tracking across various environments 査読あり
Mon S.L., Onizuka T., Tin P., Aikawa M., Kobayashi I., Zin T.T.
Scientific Reports 14 ( 1 ) 17779 2024年12月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：Scientific Reports
Video-based monitoring is essential nowadays in cattle farm management systems for automated evaluation of cow health, encompassing body condition scores, lameness detection, calving events, and other factors. In order to efficiently monitor the well-being of each individual animal, it is vital to automatically identify them in real time. Although there are various techniques available for cattle identification, a significant number of them depend on radio frequency or visible ear tags, which are prone to being lost or damaged. This can result in financial difficulties for farmers. Therefore, this paper presents a novel method for tracking and identifying the cattle with an RGB image-based camera. As a first step, to detect the cattle in the video, we employ the YOLOv8 (You Only Look Once) model. The sample data contains the raw video that was recorded with the cameras that were installed at above from the designated lane used by cattle after the milk production process and above from the rotating milking parlor. As a second step, the detected cattle are continuously tracked and assigned unique local IDs. The tracked images of each individual cattle are then stored in individual folders according to their respective IDs, facilitating the identification process. The images of each folder will be the features which are extracted using a feature extractor called VGG (Visual Geometry Group). After feature extraction task, as a final step, the SVM (Support Vector Machine) identifier for cattle identification will be used to get the identified ID of the cattle. The final ID of a cattle is determined based on the maximum identified output ID from the tracked images of that particular animal. The outcomes of this paper will act as proof of the concept for the use of combining VGG features with SVM is an effective and promising approach for an automatic cattle identification system
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AUTOMATED CATTLE DETECTION USING MASK R-CNN AND IOU-BASED TRACKING WITH A SINGLE SIDE-VIEW CAMERA 査読あり
Myint B.B., Onizuka T., Tin P., Aikawa M., Kobayashi I., Zin T.T.
International Journal of Innovative Computing, Information and Control 20 ( 5 ) 1439 - 1447 2024年10月
掲載種別：研究論文（学術雑誌） 出版者・発行元：International Journal of Innovative Computing, Information and Control
In precision livestock farming, the early detection of lameness in cattle is an extremely important aspect of effective breeding management. Timely identification of lameness not only facilitates prompt and cost-efficient treatment but also plays a crucial role in avoiding possible future diseases. This study emphasizes the significance of intelligent visual perception systems for lameness detection in dairy cattle, particularly in the lane between from Milking Parlor to Cattle Barn. To address the cattle lameness issue, we employ an advanced deep learning, and image processing technique, i.e., Mask R-CNN from Detectron2 to detect and identify cattle regions for feature extraction of lameness detection. On the other hand, cattle tracking using IoU is also an important part of data accumulation for lameness classification. The results of this study contribute to ongoing efforts in precision animal husbandry and demonstrate the potential of intelligent visual recognition systems for early lameness detection.
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Revolutionizing Cow Welfare Monitoring: A Novel Top-View Perspective with Depth Camera-Based Lameness Classification 査読あり
San Chain Tun, T. Onizuka, Pyke Tin, M. Aikawa, I. Kobayashi, and Thi Thi Zin
Journal of Imaging 10 ( 3 ) 2024年3月
掲載種別：研究論文（学術雑誌）
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Customized Tracking Algorithm for Robust Cattle Detection and Tracking in Occlusion Environments 査読あり
Wai Hnin Eaindrar Mg, Pyke Tin, M. Aikawa, I. Kobayashi, Y. Horii, K. Honkawa, Thi Thi Zin
Sensors 2024年2月
掲載種別：研究論文（学術雑誌）
DOI： 10.3390/s24041181
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AI Driven Movement Rate Variability Analysis Around the Time of Calving Events in Cattle 査読あり
Wai Hnin Eaindrar Mg, Pyke Tin, M. Aikawa, I. Kobayashi, Y. Horii, K. Honkawa, Thi Thi Zin
Lecture Notes in Electrical Engineering 227 - 237 2024年
掲載種別：研究論文（学術雑誌）
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A STUDY OF THE EARLY DETECTION OF OTITIS MEDIA IN CALVES WITH TWO TYPES OF CAMERAS 査読あり
Nishiyama T., Shiiya K., Aikawa M., Kobayashi I., Zin T.T.
革新的コンピューティング・情報・制御に関する速報 － B：応用 15 ( 11 ) 1183 - 1191 2024年
掲載種別：研究論文（学術雑誌） 出版者・発行元：ICIC International 学会
Calves tend to be more susceptible to infections than adult cattle. This may be due to a less mature immune system, stress from the rearing environment, and other factors. Early detection of disease can help prevent deterioration and the spread of infection. Therefore, in this study, we proposed to investigate the early detection of mycoplasma otitis media in a non-contact manner using RGB and thermal imaging cameras. We then conducted experiments at the Sumiyoshi Field of the University of Miyazaki to confirm the effectiveness of the proposed method.
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A 3D CAMERA APPROACH TO EVALUATING BODY CONDITION SCORE IN WALKING DAIRY COWS 査読あり
Chikunami M., Zin T.T., Aikawa M., Kobayashi I.
革新的コンピューティング・情報・制御に関する速報 － B：応用 15 ( 10 ) 1089 - 1097 2024年
掲載種別：研究論文（学術雑誌） 出版者・発行元：ICIC International 学会
Body Condition Score (BCS) is an important index for assessing body fat accumulation in cattle and plays a crucial role in managing cattle productivity, feeding efficiency, and overall health. Currently, BCS evaluations predominantly rely on visual assessment and palpation by specialized personnel, which is time-consuming and laborintensive. Consequently, many farms refrain from utilizing BCS for cattle management. Previous studies have focused on BCS evaluation of stationary dairy cows in rotary parlors, but this approach is not feasible for small and medium-sized livestock producers lacking such facilities. To enable BCS management for dairy cows on any farm, we propose a system utilizing image processing technology for evaluating cows while walking. In this system, 3D cameras are employed to capture images, and an evaluation model is constructed using feature extraction and multiple regression analysis. This model allowed the evaluation of cows with large BCS within an error margin of 0.25.
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Robustness comparison of machine learning algorithms for NIDS under the same environment 査読あり
Masaki Tagawa, Kunihito Yamamori, Masaru Aikawa
Proceedings of the Joint Symposium of The Twenty-Eighth International Symposium on Artificial Life and Robotics (AROB 28th 2023) 931 - 935 2023年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Performance of Machine Learning base NIDS on Re-organized Kyoto 2016 Dataset 査読あり
Ryo Saito, Masaru Aikawa, Kunihito Yamamori
Proceedings of the Joint Symposium of The Twenty-Eighth International Symposium on Artificial Life and Robotics (AROB 28th 2023) 926 - 930 2023年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Heuristic base music arrangement suppressing on discord progression 査読あり
Kosuke Yoshida, Masaru Aikawa, Kunihito Yamamori
Proceedings of the Twenty-Seventh International Symposium on Artificial Life and Robotics 1052 - 1056 2022年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Driving trajectory optimization by reinforcement learning for motorsports 査読あり
Akinobu Iwai, Masaru Aikawa, Kunihito Yamamori
Proceedings of the Twenty-Seventh International Symposium on Artificial Life and Robotics 1047 - 1051 2022年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Simulation of Thermal Environment Estimation inside Barn for Improvement in Livestock Productivity 査読あり
Masato TODA, Dai ISHIBASHI, Masaru AIKAWA, Hideaki MURAKOSO, Hidenori OGIWARA, Ryuusuke KAWAMURA
Proceedings of the 7th Asian Conference on Mechanics of Functional Materials and Structure 2021年3月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Tuning Support Tool for WAF Mod Security by Log Analysis with Machine Learning 査読あり
Chihiro Kudo, Kunihito Yamamori, Masaru Aikawa, Kentaro Inoue, Ryo Saito
PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS 539 - 543 2021年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Reinforcement Learning Approach for Imperfect Information Games 査読あり
Akinobu Iwai, Kunihito Yamamori, Masaru Aikawa, Kentaro Inoue
PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS 533 - 538 2021年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Computer aided arrangements using cadences and melodic regularity 査読あり
Kosuke Yoshida, Kunihito Yamamori, Masaru Aikawa, Kentaro Inoue
PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS 529 - 532 2021年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Delivery Route Suboptimization Combined by Map Application and Genetic Algorithm 査読あり
Yoshitaka Matsushita, Masaru Aikawa, Kentaro Inoue, Kunihito Yamamori
PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS 524 - 528 2021年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Network Design for Session Type NIDS 査読あり
Ryo Saito, Kunihito Yamamori, Masaru Aikawa, Kentaro Inoue
PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS 518 - 523 2021年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Affect of data unbalance in "Kyoto 2016 Dataset" for NIDS with machine learning 査読あり
Ryo Saito, Masaru Aikawa, Kentaro Inoue, Kunihito Yamamori
Proceedings of The Twenty-fifth International Symposium on Artificial Life and Robotics 612 - 616 2020年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Microwave analysis based on parallel finite element method 査読あり
A. Takei, I. Higashi, M. Aikawa and T. Yamada
Journal of Advanced Simulation in Science and Engineering ( 6 ) 215 - 233 2019年4月
記述言語：英語 掲載種別：研究論文（学術雑誌）
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Hierarchical policy gradient method with the combination of reinforcement and auxiliary learning 査読あり
Masaya Yoshida, Kentaro Inoue, Masaru Aikawa, Kunihito Yamamori
Proceedings of the 24th International Symposium on Artificial Life and Robotics 706 - 710 2019年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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公開脆弱性データベースを利用した対応順位決定支援ツール
山森一人， 齊藤燎， 相川勝， 井上健太郎
宮崎大学工学部紀要 ( 47 ) 305 - 309 2018年8月
記述言語：日本語 掲載種別：研究論文（大学，研究機関等紀要）
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A novel audio fingerprinting method based on music elements for similar music retrieval 査読あり
T.Aoshima, K.Yamamori, M.Aikawa, K.Inoue
Proc. 23rd International Symposium on Artificial Life and Robotics 452 - 456 2018年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Lightweight virtual node protocol for large scale IoT device networks 査読あり
Y.Shinooka, K.Yamamori, M.Aikawa, K.Inoue
Proc. 23rd International Symposium on Artificial Life and Robotics 478 - 483 2018年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Reinforcement learning approach for smart air traffic control support system 査読あり
M.Watanabe, K.Yamamori, M.Aikawa, K.Inoue
Proc. 23rd International Symposium on Artificial Life and Robotics 457 - 461 2018年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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視野制限環境下での追跡問題における強化学習の学習効率向上法
山森一人， 吉田雅也， 相川勝
宮崎大学工学部紀要 ( 46 ) 313 - 316 2017年7月
記述言語：日本語 掲載種別：研究論文（大学，研究機関等紀要）
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Development of Vulnerability Detection tool for Web Application based on MVC Framework 査読あり
Shoichiro Saruwatari, Kunihito Yamamori, Masaru Aikawa
Proceedings of the 22nd International Symposium on Artificial Life and Robotics 525 - 529 2017年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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三次元頂点情報を用いた訓練画像生成による画像分類精度の向上
十楚航, 山森一人, 相川勝
火の国情報シンポジウム2016論文集 ( 1B-4 ) 1 - 8 2016年3月
記述言語：日本語 掲載種別：研究論文（研究会，シンポジウム資料等）
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Performance evaluation of partitioning crossover for national TSPs 査読あり
Kento SEKI, Kunihito YAMAMORI, Masaru AIKAWA, Ikuo YOSHIHARA
Proceedings of the 21st International Symposium on Artificial Life and Robotics 331 - 336 2016年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Classification accuracy improvement by synthesized training images from 3D object model 査読あり
Wataru JUSO, Kunihito YAMAMORI, Masaru AIKAWA
Proceedings of the 21st International Symposium on Artificial Life and Robotics 332 - 335 2016年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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An automatic music composition by concatenating pieces based on fitness evaluation 査読あり
Takafumi YAMADA, Masaru AIKAWA, Kunihito YAMAMORI
Proceedings of the 21st International Symposium on Artificial Life and Robotics 336 - 341 2016年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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多数決に基づく公開鍵決定プロトコルによる中間者攻撃への対応とその評価
猿渡翔一郎,山森一人, 相川勝
情報処理学会研究報告（インターネットと運用技術） ( 6 ) 1 - 6 2015年9月
記述言語：日本語 掲載種別：研究論文（研究会，シンポジウム資料等）
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A Heuristic and Stochastic Approach for Automatic Music Composition 査読あり
T.Yamada, K.Yamamori, M.Aikawa
Proc. 20th International Symposium on Artificial Life and Robotics 525 - 530 2015年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Natural English-Japanese Translation by Complementing of Proper Particles 査読あり
H.Haraguchi, K.Yamamori, M.Aikawa
Proc. 20th International Symposium on Artificial Life and Robotics 514 - 518 2015年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Pipelined multi-threading approach for high-speed global alignment on GPGPU 査読あり
T.Yamamoto, M.Aikawa, K.Yamamori
Proc. 20th International Symposium on Artificial Life and Robotics 519 - 524 2005年1月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）