Thanks for Dr. Dimitrios
to introducing deeplearning convolutional
neural network (CNN) and
its application in theidentification of nekton. Itshows promising future inlots of area and saving
tedious repetition works inscientific researches.
Sheng YE
Thank you for the nicelecture. I could study AI
for expecting age of fish.
Kotaro Yoda

Thank you for yourgreat talk. It is great forme to know about a newway that using AI
technology in fish-study.
YU ZESHU

Thank you for very
interesting presentation.
I hope that thetechnology will improve
to detect daily
resolution of otolith.
Aono Tomoya
I enjoyed your presentation
because I'm interested inmachine-leaning. Also, in
our lab, we research aboutfish aging in many ways,
but no one don't use
approach of machinelearning, so It's very
interesting to me.
Ito Takumi

Thank you for your special lecture,
Prof. Dimitrios Politikos!
Today's talk was very
interesting. I would liketo hear more about
analysis using Machine
Learning as well as Deep
Learning (if you havesuch a topic).
Kazuo ishikawa

The lecture was highlyinteresting and hediscussed lot of advanced
techniques and methods inthe relevant field. I highly
appreciate his effort todisseminate theknowledge! Thank youvery much!
GGN Thushari

Very interesting lecture
and many new concepts
to analyze ocean data.
Integration of many
fields to analyze big data
is interesting. However,accuracy of addressing toa real biological problem
is still debating. I hope weneed to discuss this inlarge scale. Thank you.
Duminda Senevirathna

Thank you for yourinteresting presentation. I
hope that the Deep
Learning system will be a
powerful tool for theidentification of
Rhizalian planktonspecies in the future.
Ryo Ichinohe

Thank you for yourinteresting presentation
and answering my question
during the seminar. It isalso necessary to reduce thetemporal and financial costfor otolith daily age
analyses of fish larvae andjuveniles, which areimportant to know theprocess of the recruitment.
The technique you
introduced will enable toanalyze not only annual
but also daily analyses
from otolith more
efficiently.
Megumi ENOMOTO

I became interested in thetechnology of automaticage assessment from otolith
images, thank you.
Hikaru Jitosho

Thank you for the valuablelecture today!
I was able to learn a lot
about many examples ofapplying AI technology toresearch. I was especiallyinterested in thetechnology to determine
the species of fish fromimages of eggs and larvae.
Then, I have a question,(and maybe I just missed it)
but I thought that we won't
know if the fish species
determination is actuallycorrect until people areable to determine it.
How would you consider
the reliability of the AI's
judgment? I would like tolook into it again myself.
It was really interesting tolearn about so many newtechnologies. Thank youvery much !!
Mana Mura

Dear Dr Dimitrios, thankyou for your detailed
explanation about themachine learning forfisheries science. It has
motivated us young
researchers to explore thepossible new tools for ourtarget organisms.
Lum Wai Mun

Thank you for a great
lecture. Although thelecture is out of my field(microbial ecology ), thecontent is very interestingand inspiring. I knew DL
and its robustness in otherfields but didn't know theapplication to the aquaticfield. This lecture does not
only expand my knowledge
but also motivates me toapply this powerful tool to
my research. Thank youvery much again and wish
your safe.
Sato Takuya

Thank you for yourinteresting lecture, it
really opened my eyes tocombine the AI withfisheries biology! It would
be more technology can beapplied into massive datacollective process, so that
more accurate
management measures canbe made for ocean andfishery! Hope I can also
make use of these AI
methods into my future
research! Thank you forthe inspirations! Good luck
and all the best!
Yumeng Pang

Thank you for your interestingpresentation. I thought yourresearch was very significant in thestudy of fish ecology and ecosystemmodel.
So, I have three questions about yourpresentation.
1) Which part of the process did you
have the most difficulty with, when
automating fish age prediction ?
2) I believe that some otoliths are so
irregular in shape that they cannot
be assessed for age.
How many of these otoliths are there
in relation to the total number ofsamples?
Also, if the percentage of suchotoliths is large, I think it is highly
likely that only biased results will beobtained even if deep learning isused for age assessment. In otherwords, I think it may not be possible
to determine the growth and age ofthe population. What are you
thinking about this?
3) How do you think this otolith
analysis can be applied to your own
ecosystem modeling research?
I think that by using otoliths toassess age, we can learn about thedistribution and growth of samples
at different ages. Therefore, I think
it will be useful in reproducing theecology of fishes at different ages ingrowth-and-migration models.
In my research, I have developed agrowth-migration model for a small
pelagic fish, the saury, and haveused the daily growth rate estimatedfrom otoliths to compare the growthof saury from model. I am very
interested in applying otolith
analysis to the model and would liketo hear your thought about thisquestion.
Thank you again for the nice lecture.
Hitomi Oyaizu