Papers - THI THI ZIN
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A Study on Automatic Individual Identification of Wild Horses Reviewed
K. Shiiya, R. Yamada, Thi Thi Zin, I. Kobayashi
2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech) 492 - 493 2022.3
Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
Wild horses called "Misaki-uma"are inhabited Cape Toi in the southern part of Miyazaki Prefecture in Japan. Although wild horse, it needs health care. It is a difficult task for aging management association members to monitor the vast habitat range of horses and to identify individual during routine management. In addition, the current methods of individual identification because of contact with horses or to requiring specialized knowledge, ordinary people cannot perform it. In this paper we propose a method for automatic individual identification of wild horses without contact using an RGB camera.
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A Study on Worker Tracking Using Camera to Improve Work Efficiency in Factories Reviewed
I. Hidaka, S. Inoue, Thi Thi Zin
2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech) 568 - 569 2022.3
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
The population of Japan is declining every year. As the population declines, the number of employees in enterprises also decreases, and one of the issues for small and medium-sized enterprises is the decline in productivity due to a shortage of employees. It is necessary to improve work efficiency to compensate for the shortage of employees and the resulting decrease in productivity. If changes in the work process and unnecessary movements in the work process can be eliminated, it will lead to shortening of work time and improvement of work efficiency. In this paper, to improve work efficiency for factories, we aim to track the workers in the factory using a camera and show the trajectory of works.
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Dairy Cattle Detection in Loose Housing Calving Pen by Using Semantic Segmentation Networks Reviewed International journal
Swe Zar Maw, Thi Thi Zin, Pyke Tin
ICIC Express Letters, Part B: Applications 13 ( 3 ) 279 - 286 2022.3
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
When it comes to controlling a cattle farm, being able to accurately forecast when calving will happen can be quite beneficial because it allows employees to assess whether or not assistance is required. If such help is not provided when it is required, the calving process may be prolonged, severely impacting both the mother cow and the calf ’s health. Multiple diseases may result from such a delay. During the production cycle, one of the most crucial events for cows is calving. An accurate video-monitoring technique for cows can spot abnormalities or health issues early, allowing for prompt and effective human interference. To make this surveillance automated, a crucial task is to detect the dairy cattle. For this purpose, in this research, we have proposed an effective semantic segmentation network for segmenting the cow from the 360-degree surveillance camera. The proposed network is a modified version of the U-Net architecture. An additional mod-ule is added in the U-Net architecture which is named as Convolutional Long Short-Term Memory (ConvLSTM) block. The ConvLSTM block allows for effective feature sharing between the less dense layers and denser layers. Experiments with our suggested method were carried out at a big dairy farm in Japan’s Oita Prefecture. The suggested method’s experimental findings demonstrate that it holds promise in real-world applications.
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Automatic Detection and Tracking of Mounting Behavior in Cattle Using a Deep Learning-Based Instance Segmentation Model Reviewed International coauthorship International journal
Su Myat Noe, Thi Thi Zin, Pyke tin, I. Kobayashi
International Journal of Innovative Computing, Information and Control 18 ( 1 ) 211 - 220 2022.2
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：International Journal of Innovative Computing, Information and Control
In precision livestock farming, estrus detection in cattle is particularly im-portant for cattle breeding management. With accurate estrus detection, artificial in-semination can be administered, which proportionally affects the productivity of livestock farms. Most estrus behaviors can be successfully detected by recognizing the mating postures of cattle. Therefore, in this paper, we propose an estrus detection approach that tracks and identifies cattle mating postures individually based on video inputs. To achieve precise identification and to obtain individual cattle information, segmenting each cattle from its background is a vital step. To solve pixel-level segmentation masks for the cattle in an outer ranch environment, an instance segmentation approach based on a Mask R-CNN deep learning framework is also proposed. In this paper, individual cattle segmentation for detecting the mounting behaviors is carried out first. This is followed by a lightweight tracking algorithm as a post-processing step which is our study innovation. The training data were collected by installing surveillance cameras at a livestock farm, and for the testing data, various datasets from different camera placements were used. The proposed approach achieved 95.5% detection accuracy in identifying the estrus be-haviors of cattle.
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A Stochastic Modeling Procedure for Predicting the Time of Calving in Cattle Reviewed International journal
Thi Thi Zin, K. Sumi, Pann Thinzar Seint, Pyke Tin, I. Kobayashi, Y. Horii
ICIC Express Letters, Part B: Applications 13 ( 1 ) 49 - 56 2022.1
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
In this paper we introduce a stochastic modeling technique for predicting time to the occurrence of calving events in cattle. Specifically we establish an application procedure of Wald’s fundamental identity in sequential analysis to predict the time to dairy cow calving as accurately as possible. We have well recognized that Wald’s identity is a fairly handy tool for studying the properties of random walks arising in queueing and dam theories and many other stochastics processes. The identity enables us to obtain ab-sorption probabilities of random walks with one or more barriers which can be interpreted as the occurrence of a calving event in cattle. In order to investigate the proposed problem more insight, we consider the activities of a pregnant cow around the calving event as a sequence of random variables forming a random walk. We then derive results for pre-dicted calving times at which an individual cow calving event occurs in a video-monitored maternity barn. For experimentations, two special probability distributions parameterized by using some real-life data are utilized. The outcome results show the proposed method is promising with high accuracy.
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Image technology based detection of infected shrimp in adverse environments Reviewed International coauthorship International journal
Thi Thi Zin, T. Morimoto, Naraid Suanyuk, T. Itami and Chutima Tantikitti
Songklanakarin Journal of Science and Technology 44 ( 1 ) 112 - 118 2022.1
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Songklanakarin Journal of Science and Technology
In recent years, countries around Japan and especially in Southeast Asia, white spot disease (WSD) is highly infectious and severely damages shrimp aquaculture. At the same time, the various diseases are occurring in shrimp farms. In the early stages of infection, shrimp shows three abnormal behaviors: (1) they appear in the shallow waters of the farm, (2) they do not move and do not eat even when feeding, and (3) they suddenly stop moving. Currently, infected shrimps are found by visual inspection, which places a burden on the farmers and delays the discovery. Therefore, in this paper, we proposed a system for detecting infected shrimp by using image processing technology in order to eliminate the delay of discovery and reduce the burden of farmers. According to our experimental results, the proposed system has 95% precision, 100% recall rate and an accuracy of 96.4% by using hold-out evaluation method.
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An absorbing markov chain model to predict dairy cow calving time Reviewed International journal
Swe Zar Maw, Thi Thi Zin, Pyke Tin, I. Kobayashi, Y. Horii
Sensors 21 ( 19 ) 2021.10
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Sensors
Abnormal behavioral changes in the regular daily mobility routine of a pregnant dairy cow can be an indicator or early sign to recognize when a calving event is imminent. Image processing technology and statistical approaches can be effectively used to achieve a more accurate result in predicting the time of calving. We hypothesize that data collected using a 360-degree camera to monitor cows before and during calving can be used to establish the daily activities of individual pregnant cows and to detect changes in their routine. In this study, we develop an augmented Markov chain model to predict calving time and better understand associated behavior. The objective of this study is to determine the feasibility of this calving time prediction system by adapting a simple Markov model for use on a typical dairy cow dataset. This augmented absorbing Markov chain model is based on a behavior embedded transient Markov chain model for characterizing cow behavior patterns during the 48 h before calving and to predict the expected time of calving. In developing the model, we started with an embedded four-state Markov chain model, and then augmented that model by adding calving as both a transient state, and an absorbing state. Then, using this model, we derive (1) the probability of calving at 2 h intervals after a reference point, and (2) the expected time of calving, using their motions between the different transient states. Finally, we present some experimental results for the performance of this model on the dairy farm compared with other machine learning techniques, showing that the proposed method is promising.
DOI： 10.3390/s21196490
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A Study on Diagnosis of Parkinson's Disease by Walking Video Reviewed International journal
T. Haruyama, Thi Thi Zin, K. Sakai, H. Mochizuki
2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021 758 - 759 2021.10
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
Parkinson's disease (PD) is a progressive nervous system disorder that accompanied with resting tremor, bradykinesia, muscle rigidity and impaired posture. The diagnosis for gait disturbance in Parkinson's disease is subjective mostly depends on the experience and skills of experts due to lack of quantitative criterion. As a consequence, nonspecialist doctors could easily make wrong assessment for gait disturbance. Therefore, in this paper, we propose a diagnostic method for PD by analyzing the state of walking with the aids of image processing technology. An experiment was conducted using walking videos recording to confirm the effectiveness of the proposed method.
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Predicting Calving Time of Dairy Cows by Time Series Model
Tunn Cho Lwin, Thi Thi Zin, Yokota Mitsuhiro
宮崎大学工学部紀要 50 87 - 94 2021.9
Language：English Publishing type：Research paper (scientific journal) Publisher：宮崎大学工学部
Calving time prediction is an important factor in dairy farming. The careful monitoring of cows can help to decrease the loss of calf rates during the calving time; moreover, to know the exact time of birth is crucial to make sure timely assistance. However, direct visual observation is time-wasting, and the continuous presence of observers during calving time may disturb cows. Therefore, in this study, the recording from video cameras and counting the number of standing to lying and lying to standing transitions of 25 cows before 72 hours of calving time are used. The time series approaches namely the exponential distribution probability and autoregressive integrated moving average (ARIMA) model are applied to predict the calving time and the root mean square error (RMSE) is used to check the accuracy and error value of the experiment. By these methods, the calving time is predicted with exact time interval by using
exponential probability. Moreover, the ARIMA model is better accuracies in predicting calving time than autoregressive (AR) and moving average (MA) models. -
Real-time action recognition system for elderly people using stereo depth camera Reviewed International journal
Thi Thi Zin, Ye Htet, Akagi Y., Tamura H., Kondo K., Araki S., Chosa E.
Sensors 21 ( 17 ) 2021.9
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Sensors
Smart technologies are necessary for ambient assisted living (AAL) to help family mem-bers, caregivers, and health-care professionals in providing care for elderly people independently. Among these technologies, the current work is proposed as a computer vision-based solution that can monitor the elderly by recognizing actions using a stereo depth camera. In this work, we intro-duce a system that fuses together feature extraction methods from previous works in a novel combination of action recognition. Using depth frame sequences provided by the depth camera, the system localizes people by extracting different regions of interest (ROI) from UV-disparity maps. As for feature vectors, the spatial-temporal features of two action representation maps (depth motion appearance (DMA) and depth motion history (DMH) with a histogram of oriented gradients (HOG) descriptor) are used in combination with the distance-based features, and fused together with the automatic rounding method for action recognition of continuous long frame sequences. The experimental results are tested using random frame sequences from a dataset that was collected at an elder care center, demonstrating that the proposed system can detect various actions in real-time with reasonable recognition rates, regardless of the length of the image sequences.
DOI： 10.3390/s21175895
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Framework of cow calving monitoring system using video images Reviewed International journal
K. Sumi, Thi Thi Zin, I. Kobayashi, Y. Horii
Journal of Advances in Information Technology 12 ( 3 ) 240 - 245 2021.8
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Journal of Advances in Information Technology
In modern dairy farms, calving is a very critical point in the life cycle of productive cows and has played a major role in making farm profits and welfare of cows. In this time, a tremendous number of researchers have been studied the problem of calving mostly to predict the time about to calve and to investigate calving process by using wearable sensors. Like human beings, cows also have environmental pressures by wearing sensors on their bodies sometimes may cause calving difficulties. Thus in this paper, an automatic video based cow monitoring system is proposed to reduce losses of dairy farms caused from calving problems. Specifically, this paper investigates some behaviors of cows to predict time for calving process including cow movements, tail up, stretching the legs, repeating standing and sitting. In doing so, we focus on increasing movement and tail up. Here, the inter-frame difference is used for analyzing the movement and count in every frame. In addition, by extracting the head and tail position the activity of tail up or not will be recognized so that time for calving can be estimated. Finally, the proposed method for calving is confirmed by using self-collected video sequences.
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Systematic inclusion study on some rare gemstones of the mogok area, mandalay region, myanmar Reviewed International coauthorship International journal
Htin Lynn Aung, Thaire Phyu Win, Thi Thi Zin
ICIC Express Letters, Part B: Applications 12 ( 8 ) 751 - 756 2021.8
Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
The Mogok area is situated in Mogok Township, Mandalay Region. It is bounded by Latitude 22◦ 52′-23◦ 00′ N and Longitude 96◦ 10′-96◦ 33′ E. The rock sequence of the study area consists of medium to high grade metamorphic rocks; marble, gneiss, and intrusive igneous rocks; Kabaing granite, leucogranite and syenite. It is famous for pres-ence of ruby and sapphire. Exceptionally some rare gemstones also are discovered. The present work is especially intended to explain systematically the inclusions of some rare gemstones from the Mogok area. Liquid feather inclusions present in jeremejevite. Two-phase inclusions occur in morganite and petalite. In petalite, tube-like inclusions also present. Opaque inclusion and solid inclusion occur in rutile and treacle granular inclusion and finger print inclusion observe in sinhalite.
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Feature Detection and Analysis of Cow Motion Classification for Predicting Calving Time Reviewed International journal
Thi Thi Zin, Saw Zay Maung Maung, Pyke Tin and Y. Horii
International Journal of Biomedical Soft Computing and Human Sciences (IJBSCHS) 26 ( 1 ) 11 - 20 2021.7
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal)
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Handwritten character recognition on android for basic education using convolutional neural network Reviewed International journal
Thi Thi Zin, Shin Thant, Moe Zet Pwint, T. Ogino
Electronics (Switzerland) 10 ( 8 ) 2021.4
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Electronics (Switzerland)
An international initiative called Education for All (EFA) aims to create an environment in which everyone in the world can get an education. Especially in developing countries, many children lack access to a quality education. Therefore, we propose an offline self-learning application to learn written English and basic calculation for primary level students. It can also be used as a supplement for teachers to make the learning environment more interactive and interesting. In our proposed system, handwritten characters or words written on tablets were saved as input images. Then, we performed character segmentation by using our proposed character segmentation methods. For the character recognition, the Convolutional Neural Network (CNN) was used for recognizing segmented characters. For building our own dataset, handwritten data were collected from primary level students in developing countries. The network model was trained on a high-end machine to reduce the workload on the Android tablet. Various types of classifiers (digit and special characters, uppercase letters, lowercase letters, etc.) were created in order to reduce the incorrect classification. According to our experimental results, the proposed system achieved 95.6% on the 1000 randomly selected words and 98.7% for each character.
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Smart irrigation: An intelligent system for growing strawberry plants in different seasons of the year Reviewed International coauthorship International journal
Ye Htet, Htin Kyaw Oo, Thi Thi Zin
ICIC Express Letters, Part B: Applications 12 ( 4 ) 359 - 367 2021.4
Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
Agriculture productivity is very important for a country’s economy. There-fore, varying the way of cultivating plants could provide more foods than before and thus smart irrigation would be one of the best solutions. Therefore, the proposed system main-ly focused on strawberry plants to produce fruits in all seasons using intelligent systems within the small-scale farm. The system emphasized automatic drip irrigation and environment adjustment system integrated with sensors to control temperature, water, and fertilizers supply. Moreover, leaf analysis using image processing controlled by the Raspberry Pi is implemented for the detection of plant nutrient deficiency symptoms. As for the communication unit to inform the users via sensors, Internet of Things technology is adopted. The experimental results show that the plants bear fruits efficiently throughout the year by using the proposed irrigation system and also the symptoms can be detected in early stages as soon as they appeared on the leaves.
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Automatic detection of mounting behavior in cattle using semantic segmentation and classification Reviewed
Su Myat Noe, Thi Thi Zin, Pyke Tin, Ikuo Kobayashi
LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies 227 - 228 2021.3
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
In cattle farming sector, the accurate detection of estrus plays a vital role because incorrect timing for artificial insemination affects the cattle business. The noticeable sign of estrus is the standing heat, where the cattle standing to be mounted by other cows for a couple of seconds. In this paper, we proposed cattle region detection using deep learning semantic segmentation model and automatic detection of mounting behavior with machine learning classification methods. Based on the conducted experiment, the results show that a mean Intersection of Union (IoU) of 98% on the validation set. The pixel-wise accuracy for two classes (cattle and background) was found to be both 98%, respectively. For the classification, the proposed method compares the four supervised machine learning methods which can detect with the accuracy rate of Support Vector Machine, Naïve Bayes, Logistic Regression and Linear Regression are 87%, 96%, 90%, and 80% respectively. Among them, Naïve Bayes algorithm perform the best. The novelty of this work noticeably implies that deep learning semantic segmentation could be effectively employed as a pre-processing step in segmenting the cattle and background prior to using various classification models.
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Petrochemical characteristics of the granitoid rocks of Northern Myanmar Reviewed
Htin Lynn Aung, Thaire Phyu Win, Thi Thi Zin
LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies 229 - 230 2021.3
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
The research area is located on the Mogaung - Kamaing-Hpakant road in Hpakant Township, Kachin State, northern Myanmar. The dominant lithologic units comprise igneous and metamorphic rocks. The present work is mainly intended to establish the petrogenesis of the igneous rocks based on the petrochemical analysis results. The igneous rocks are mainly microgranite and serpentinite. Major element analysis of some rocks was determined by XRF spectrometer and interpreted the genesis of these rock units. On the basis of the petrochemical characteristics, the microgranite of the study area is I-type peraluminous granitoid formed by partial melting of mantle and / or lower crust in the extensional tectonics.
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Markov chain monte carlo method for the modeling of posture changes prior to calving Reviewed
Thi Thi Zin, Pyke Tin, Pann Thinzar Seint, Yoichiro Horii
LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies 291 - 292 2021.3
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
An accurate and careful analysis of posture changes for a dairy cow prior to calving plays an important role in making calving time prediction. The patterns of activities such as frequent changes in postures of a pregnant cows during the time closer to calving are utilized as indicators to predict the time of calving. In this paper, we introduce Markov Chain Monte Carlo (MCMC) method to generate the patterns of four states activities such lying, transitions from lying to standing, standing itself and transitions from standing to lying based on the monitored cow activity changes data three days prior to calving. The validity of the generated cow activities in posture changes data is compared with the actual collected data in terms of Euclidean and Cosine distance measures. The experimental results show that the method in this paper can be used as a generalized method to generate synthetic data series of dairy cow activities prior to calving.
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Activity-integrated hidden markov model to predict calving time Reviewed International journal
K. Sumi, Swe Zar Maw, Thi Thi Zin, Pyke Tin, I. Kobayashi, Y. Horii
Animals 11 ( 2 ) 1 - 12 2021.2
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Animals
Accurately predicting when calving will occur can provide great value in managing a dairy farm since it provides personnel with the ability to determine whether assistance is necessary. Not providing such assistance when necessary could prolong the calving process, negatively affecting the health of both mother cow and calf. Such prolongation could lead to multiple illnesses. Calving is one of the most critical situations for cows during the production cycle. A precise video-monitoring system for cows can provide early detection of difficulties or health problems, and facilitates timely and appropriate human intervention. In this paper, we propose an integrated approach for predicting when calving will occur by combining behavioral activities extracted from recorded video sequences with a Hidden Markov Model. Specifically, two sub-systems comprise our proposed system: (i) Behaviors extraction such as lying, standing, number of changing positions between lying down and standing up, and other significant activities, such as holding up the tail, and turning the head to the side; and, (ii) using an integrated Hidden Markov Model to predict when calving will occur. The experiments using our proposed system were conducted at a large dairy farm in Oita Prefecture in Japan. Experimental results show that the proposed method has promise in practical applications. In particular, we found that the high frequency of posture changes has played a central role in accurately predicting the time of calving.
DOI： 10.3390/ani11020385
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Consumer behavior analyzer in internet of things (Iot) environments Reviewed
Swe Nwe Nwe Htun, Thi Thi Zin, Pyke Tin
International Journal of Innovative Computing, Information and Control 17 ( 1 ) 345 - 353 2021.2
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：International Journal of Innovative Computing, Information and Control
This paper proposes an analyzer of consumer behavior in Internet of Things (IoT) environments. This analyzer is most useful in predicting the intentions of users during searches, and especially during image searches. Since most technologies are connected on the Internet, search results can be characterized using image-similarity measures. In this paper, information on image similarities is extracted using a Convolutional Neural Network (CNN) in IoT environments. In this proposed consumer behavior analyzer, the similarity measures characterizing the relationships between images are transformed into Markov Chain transition probabilities, and their stationary probabilities are then analyzed to describe the priority order for search results conforming with consumer intentions. In order to confirm the validity of the proposed method, the Yelp public dataset was used. The outcomes using this analyzer are promising, and this analyzer might be instrumental in making further improvements in practical applications of consumer technologies.