ICIPRoB2024 Conference Program
The Program is Scheduled as Sri Lanka Local Time(GMT+5:30)
Day 01 : March 09, 2024
Opening Ceremony(08.40 - 09.00)

Welcome speech by ICIPRoB2024 General Chair
ICIPRoB2024 opening speech by Mr. Dhammika Marasinghe / Chair IEEE Sri Lanka
sectionKeynote speak 01 (Chair: Prof. Lasith Gunawardana )(09.00 -10.00)
Prof. Emi Yuda
Associate professor, Graduate School of Information Sciences (GSIS) Tohoku University

Title: Method for objectively evaluation of human-robot interactions using
emotion measurement

Abstract:
Emotion measurement has become increasingly important in robotics-human interactions inrecent years. Robots are increasingly used in many areas of everyday life, such as caregiving,education, entertainment, counseling, etc. In these applications, emotional interactions between
humans and robots are key to success. In this talk, I will discuss the application of objective
evaluation methods for human emotion measurement. Actual projects and research results onemotion measurement techniques using biometric signal processing will be presented. Throughthis talk, I will provide insights into the development of robotics technologies that incorporate
emotion. It could lead to revolutionary advances in future robot applications.

Tea Break : 10.00~ 10.15

Keynote speak 02(Chair: Prof. Susantha Wijesinghe )(10.15 - 11.15)
Prof. Tetsuya Ogata
Professor, Department of Intermedia Art and Science, Faculty of Science andEngineering, Waseda University, Japan.
Joint Appointed Fellow, Artificial Intelligence Research Center, National Institute ofAdvanced Industrial Science and Technology

Title: Enhancing Robot Performance: Deep Predictive Learning for Adaptive
Perception and Action

Abstract:
Our research tackles challenges in artificial intelligence known as 'Moravec's Paradox.' We aim
to enhance robots' performance in various tasks, including those requiring multiple arms. We
introduce a novel method, 'deep predictive learning,' which combines ideas from neuroscience's'predictive coding' with robotics. This keynote presents our findings in robotics research utilizing
deep predictive learning, along with our collaborations with prominent companies. We also
provide an overview of our remarkable smart robot, 'AIREC,' backed by Japan's Moonshot
project led by the Cabinet Office.

Keynote speak 03 (Chair: Dr. Waruna Premachandra )(11.15 - 12.15)
Dr Patrick Holthaus
Senior Research Fellow and Robot House Manager
Robotics Research Group, School of Physics, Engineering and Computer Science,
University of Hertfordshire, United Kingdom

Title: Socially credible robot companions
Abstract:
Assistive robots are designated to support people in their daily activities. In this keynote, I will
discuss how such robots can deliver various types of assistance when designed as robot
companions and what scientific questions are important to address in this context. My
presentation will also introduce the UH Robot House, a unique facility for researching humanrobot interaction, along with its robot residents. Robot House provides a realistic homeenvironment off-campus where our research team can investigate the aforementioned questions
looking towards a future where robotic companions play a greater role in caring for older people.
The third part of the keynote will address one of the fundamental research questions of social
robots, that is, how nonverbal communication can facilitate interactions between humans androbots. For that, I will present a few studies with human participants that look at what effects
robot social behaviors have on human trust and acceptability towards them.

Lunch Break : 12.15~ 13.15

Day 01 : March 09, 2024 (13.15~14.30)
Session 01: Computer vision & Image processing 1
Chairs: Prof. Naiwala Chandrasiri, Prof. Yuji Iwahori
Paper

Title, Authors and AbstractID

3

Title: Synthesis of super-resolution images by GAN using Channel Attention
Authors: Jingan Liu, Naiwala P. Chandrasiri
Abstract
With the development of deep learning, many studies have been conducted on techniques to generate
high-resolution images from low-resolution images containing different types of degradation and noise.
In this paper, we proposed two super-resolution image synthesis approaches based on a mainstreamalgorithm ESRGAN. The first approach is to apply channel attention to the generator of ESRGAN. The
second one is to add an image quality evaluation metrics Learned Perceptual Image Patch Similarity(LPIPS) to the discriminator. Channel attention has been used to obtain positive results in image
classification. We expect that the combination of channel attention and convolutional neural networks
could be used to generate high-quality super-resolution images. Most of the recent studies use the peaksignal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), etc. as image evaluation
metrics and aim to improve their scores. However, these metrics evaluate images on a pixel-by-pixel
basis, which raises a gap with human evaluation results. In our experiment, we evaluated our proposed
methods on two image quality indices, Naturalness Image Quality Evaluator (NIQE) and LPIPS, using abenchmark dataset. The evaluation by NIQE and LPIPS showed that our proposed channel attentionbased models significantly improved the naturalness and perceptual evaluation value of images
compared to previous studies. LPIPS and NIQE scores of the synthesized images were closer to thevalues of the original high-resolution images generated by the model proposed in this study.

5

Title: Data Augmentation Method for Classification of Lymph Node Metastasis using Diffusion
Model
Authors: Kosuke Suzuki, Yuji Iwahori, Kenji Funahashi, M. K. Bhuyan, Akira Ouchi, Yasuhiro
Shimizu
Abstract
Automatic judgment of the presence or absence of cancer metastasis to lymph nodes is an important
topic in cancer staging to reduce the burden of medical doctors to check a large number of CT images.
Therefore, the machine learning approach becomes helpful and desired. However, using CT images formachine learning requires annotation by a special radiologist, and it is difficult to prepare a large dataset.
In recent years, many approaches to the generation of images by machine learning have been proposed
and applied to medical images. In this paper, we propose a method to generate images around a lymph
node in an arbitrary metastatic state using a diffusion model, which is a generative model that is gaining
attention on behalf of GAN. Experiments were also conducted to learn the classification of cancer
metastasis using the generated images for data augmentation, showing that the generated images are
effective in improving classification performance.

6

Title: Image Processing based Real-Time Online Attendance Monitoring System using Facial
Recognition
Authors: Ravishka Fernando, Hashini Athauda
Abstract
With the advancement of technology and due to the recent pandemic situation, the education sector
has turned to the online teaching method. But the main problem here is the inconvenience andirregularities in the student’s attendance. To ensure traditional attendance and reduce time wastage,
this research aims to explore and implement an automated attendance marking system using facialrecognition technology. This will enable students to be marked present or absent in real-time and will
also help teachers to identify students who are present or absent for the online session. As a novelty ofthis research gives a more efficient and accurate method for attendance marking and eliminating manualand false attendance marking in online sessions. The proposed system employs the latest
advancements in image processing and machine learning techniques such as the Haar Cascade featureand LBPH algorithm to accurately detect and recognize the face of a student. The performance of thesystem is evaluated on its own dataset which was the images of students captured through a video
stream from a web camera and the results demonstrate through a confusion matrix its effectiveness inaccurately recognizing faces and marking attendance in real-time. The results showed that theattendance system achieved 99.22% accuracy and can accurately mark the attendance of students in
an Excel sheet. This real-time GUI-based system is unique in that it revolutionizes the traditional
attendance marking process by automating it and providing real-time attendance data.

18

Title: Image Classification Method to Identify Mature Coconut
Authors: K.K.L.B. Adikaram, A.M.N.N. Senevirathna, W.P.P.M.
Premachandra

Wijesinghe,

Chinthaka

Abstract
This study focuses on developing an automated image classification method to identify mature coconuts,
a critical phase in coconut harvesting. By emulating the technique of expert coconut pickers who assesscolor and texture, our goal is to revolutionize the industry, enhancing efficiency, reducing labor costs,
and mitigating environmental impact. The preliminary model developed using Teachable Machine anestablished image classification software tool showed highly promising results in the identification of
mature coconuts.

25

Title: Enhancing Sign Language Classification through Reservoir Computing and Depth Camera
Technology
Authors: Witchuda Thongking, Ardi Wiranata, Shingo Maeda, Chinthaka Premachandra
Abstract
The depth camera has emerged as a efficacious tool for facilitating interactions between individuals andmachines. Its advantageous features, including a lightweight design, high durability, completeness,superior image quality, and versatility, have prompted researchers to explore its integration into
advanced applications. Currently, the prevailing methods for classifying sign language exhibit certain
limitations, such as suboptimal accuracy and inefficiency in gesture recognition technologies. In thisstudy, we conduct a comprehensive examination of hand language characteristics rooted in American
Sign Language (ASL), utilizing gesture-based interactions and augmenting sensing performancethrough preprocessing and the application of reservoir computing in the training model. We undertakean evaluation of gesture characteristics captured by the depth camera, delineating a method forpreprocessing, machine learning, and the technical prediction of pertinent gesture features derived fromstandard depth camera video footage, thereby establishing a robust signal model. Our machine-learningmodels adeptly predict American Sign Language signs. Correlation values derived from the depth
camera data demonstrate a pronounced alignment with naturally occurring variations in metrics
observed within specific gestures. Our methodologies for quantifying gestures with depth cameras
contribute to heightened accessibility in quantitative motion analysis. The outcomes of the reservoir
computing implementation exhibit the successful classification of three hand language signs, thereby
manifesting high precision and recall. The robust performance of the classification is pivotal for practicalapplications and underscores the efficacy of our approach.

Day 01 : March 09, 2024 (14.30~15.30)
Session 02: - AI and applicationsChairs: Dr. Sagara Sumathipala, Dr. Naohisa Hashimoto
Paper

Title, Authors and Abstract

ID

4

Title: Mobility Environment Evaluation Platform Using Location-Based Big Data
Authors: Takumi Soma, Tetsuya Manabe
Abstract
This paper constructs a mobility environment evaluation platform to realize comfortable mobility
environments for pedestrians by using location-based big data. This platform can directly evaluate thewalkability of mobility environment considering crowded ratio, walking routes, and time change, by using
location-based big data. First, the data estimated to pedestrians in walking is extracted from location
data of Global Navigation Satellite System (GNSS). Next, the inflow/outflow data of the based region is
extracted. And the walking routes is visualized every hour of the target area including based region.
Finally, the walkability discussed by using various graphical representations. Consequently, this paper
shows availability of this platform by the walkability analysis.

7

Title: Centralized and Labeled Academic Journal Library Using Machine Learning & Deep
Learning Approaches
Authors: Ranul Dayarathne, Naveen Hedalla Arachchi, Nipuna Dilshan, Wanuja Ranasinghe,
Gamage Upeksha Ganegoda
Abstract
An academic journal library which is centralized and labeled by subject areas gives all learners,
researchers, and scholars an opportunity to easily get the information they want in diverse fields. The
study uses a high-level architecture design based on a data set from a sci-hub database which consistsof components such as layout extraction, error detection, summarization, multi-label classification, andcontextual search query optimization. These methodologies consist of a process for extracting thecontent, checking grammar or spelling errors, checking errors in structure and a two-step summarization
process. In multi-label classification, NLP and algorithms are used to tackle the problem of classimbalance. This study also presents a contextual analysis and search query refinement approach via
NER, synonym expansion, and intent classification. Machine learning models along with relevant feature
extraction enhance search result optimization. Some future works include extending error detection
beyond IEEE and Springer format, studying summarization via ROUGE score and relevance measure,and enhancements in the evaluator metrics for user query analysis and search result ranking. This study
intends to transform how academic papers are discovered to provide an enjoyable and informativeknowledge-searching process.

13

Title: Smart Agro Soil Analyzer for Sustainable Farming
Authors: Ashan Pathiraja, Susantha Wijesinghe, Anil Pradeep Kumara
Abstract
Chemical fertilizers are crucial for increasing crop productivity and soil fertility, but their continuous use
leads to a decline in soil organic matter and a reduction in agricultural soil quality. To address this issue
and protect soil health, soil micronutrient content, and overall soil health must be measured andanalyzed. This paper presents the development of a portable, low-power consumption soil analyzing
system that supports both online and offline modes for small- and large-scale farmers, combining
cellular-based low-power wide area network technologies with commercially available integrated soil
sensors. Extensive field testing, encompassing small and large-scale farming operations, validates thesystem's accuracy and usability. The user-friendly interface and cost effectiveness make it an accessible
tool for diverse agricultural landscapes.

56

Title: Design and Development of a Voice Interactive Children Educational Robot ‘TINY’ with
Natural Language Processing
Authors: Nohori Jobaida, Mohammad Shidujaman, Ahsan Mahbub, Hooman Samani, ChinthakaPremachandra, Mohammad Faisal Uddin, Saadia Binte Alam
Abstract
Educational robotics is to improve children's and teenagers' skill and competency development via theuse of electronic components and robots in an interdisciplinary learning environment. It focuses mostly
on the STEAM fields, while it can also touch on subjects like geography, history, and languages. STEAM
education relates these 5 subjects those are-Science, Technology, Engineering, Arts and Mathematics.
The capacity to which children could interact with a social robot in a novel learning environment is being
studied. A method for measuring how actively children with learning difficulties participate in adequately
prepared activities is described, utilizing multimodal machine learning. The goal is to find the child’s
sustained attention throughout the conversation. To achieve this goal, visual and aural data are recorded
between the kid and the robot. Education robots for kids are emerging as tools for learning which canenhance young children's early reading and language skills considering recent technical developments.
Social robots are those that can interact with humans on a mental and social level. The most recent
studies on social robots and how typically developing kids, aged 0 to 8, pick up early language andliteracy abilities have been examined. For the entirety of the study, five main themes were identified. Our
designed robot TINY, a theoretical foundation for social robotic education, which stands for Teach,
Inspire, Nurture, and Yield. Research has shown that social robots can facilitate early language
acquisition, a thorough study has been done on the formation of early reading abilities. Although it has
been demonstrated that educational robots for young children can develop language development,
further research is required to ascertain the impact of social robots on early literacy development inyoungsters.

Tea Break : 15.30~ 15.40

Day 01: March 09, 2024 (15.40~17.00)
Session 03 -Robot, IOT1Chairs: Dr. Upeksha Ganegoda, Prof. Tomotaka Kimura
Paper
ID

Title, Authors and Abstract

2

Title: TechGrow: IoT Application for Implementation of Smart Farming System
Authors: Mohamed Buhary Fathima Sanjeetha, Pradeep Abeygunawardhana
Abstract
Over the last several years, there has been growing interest in building smart agricultural systems. Smartfarming practices may increase crop production while using less input. Most farmers don't know about
new technology and methods. In this study, a unique IoT-based wireless assistant is conceived and will
deploy for field activities. This suggested wireless assistant has environmental sensors suchtemperature, soil moisture and humidity. It also incorporates model rsp 32. This innovative wirelessassistant can sense wetness, temperature, move forward or backward, and light on a particular work.
Also, we use repeats to spread the Wi-Fi to the surroundings farm. The suggested wireless assistant will
be tested in the fields, with excellent results. This technology is helpful for smart agricultural systems.

10

Title: Pear Blossom Counting System with Drones Using YOLO and Deep SORT
Authors: Sota Okubo, Kohei Tsunewaki, Tomotaka Kimura, Hiroyuki Shimizu, Takefumi Hiraguri,
Akane Shibasaki, Tomohito Shimada, Yoshihiro Takemura
Abstract
In this paper, we propose a pear blossom counting system with drones using YOLO (You Only Look
Once) and Deep SORT (Simple Online and Realtime Tracking) for pollination system. To establish thepollination system, it is necessary to understand the blossoming status of pear blossoms. In our proposedsystem, a drone is flown to capture movies among pear trees, and the number of pear blossoms andbuds are counted from the images using a object detection method, i.e, YOLO. If the number of pear
blossoms and buds can be counted, it is possible to estimate the best time for pollination. Moreover, toestimate the number of blossoms, we use a tracking algorithm, i.e., Deep SORT. Through experiments
using movies taken during the blossoming period, we show that the proposed method can count the
number of blossoms and buds.

11

Title:
Path
Finding
Robot
for
Authors: U Premarathne, P Wijesinghe

EnergyEfficient

Automated

Grass

Cutter

Abstract
Coverage path planning is a well identified problem in robotics. For lawn mover applications this is aformal problem modeled using cell based coverage problem. In this paper, we describe an energy
efficient design and development of a robot that can cut grass over a flat terrain using offline coveragepath planning. Cell sizes differ in geometric shapes. Simulations of the coverage and the field tests wereconducted. As evidenced by the results, energy efficient design is suitable for small and medium lawns.
Modular design can be used to extend the coverage over much larger areas with expected energy
efficiency.

12

Title: IoT-Based Smart Medicine Dispenser
Authors: Ashan Pathiraja, Susantha Wijesinghe
Abstract
This research introduces an innovative solution to address medication adherence challenges,
particularly prevalent among the elderly and individuals facing health limitations. The proposed IoTbased Smart Medicine Dispenser combines a physical device, mobile application, and cloud server tocreate a complete system. The device accurately dispenses medications according to a schedule, withreal-time monitoring facilitated by a Human Machine Interface (HMI) display. The mobile app allows easyschedule configuration, while the cloud server stores and syncs data. Various testing demonstrated thesystem's reliability and potential to enhance patient outcomes. Identified capabilities, such as userfriendly interfaces and cloud storage, highlight the system's effectiveness. Despite room for
improvement, including canister detecting mechanisms and audible instructions, this research lays thegroundwork for advanced smart medication dispensing systems, offering a promising avenue to combat
medication non-adherence and improve overall healthcare outcomes.

38

Title: Fall detection in automated buses using in-vehicle camera -basic study using support
vector machine
Authors: Kousei Ido, Amey Dakare, Yen Tran, Naohisa Hashimoto, Toru Kumagai, Takahiro Miura,
Yanbin Wu, Makoto Itami
Abstract
To enhance in-vehicle safety in automated buses, a system that can detect whether falls have occurredin the bus can support the remote human controllers in quickly responding to the running bus. In thisstudy, we designed a fall detection system that utilizes Wong Kin Yiu's yolov7 for extracting featuresfrom the video data. Then support vector machine was used for the discriminant task of whether a fall
has occurred or not given an image as the input data. We tested our system with various settings,
including full features versus reduced features, and raw images from the camera versus images whichcontain full views of the human inside the bus, all with multiple camera views. And finally, we confirmedthat the 360-degree camera could detect human falls. Using several evaluation criteria, such as
accuracy, precision, recall, and F1 scores, we showed how the detection ability of the system varied
according to different settings of the test. The highest F1 score of 0.977 was achieved in the experiment.
In addition, we found that if a full-body view of a human being could be achieved, the accuracy of the falldetections would be greatly improved. Finally, we discuss several potential directions for further researchof this topic.

End of Day 01

Day 02 : March 10, 2024
Workshop 01 (Chair: Prof. Susantha Wijesinghe)(08.30 - 09.00)
Dr. Sagara Sumathipala
Senior Lecturer, University of Moratuwa, Sri Lanka

Title: Enhancing Information Retrieval in the Age of Gen AI
This workshop explores the evolution of AI applications in information extraction. With theavailability of large datasets and high computational power, the industry has witnessed aremarkable rise in AI advancements. The next wave of AI, known as Gen AI, is now ubiquitous,
reshaping today's dynamic business landscape. The demand for chatbots capable of delivering
personalized information extraction has surged. Leveraging large language models (LLM) thatgather insights from vast document repositories has greatly facilitated addressing this demand.
In this workshop, we aim to uncover the transformative potential of Retrieval Augmented
Generation (RAG) technology within the space of Conversational AI. RAG revolutionizes
traditional chatbots by enhancing their capabilities and intelligence. Participants will dig into thefundamentals of RAG architecture and understand how it empowers chatbots to become
exceptionally intelligent. Through interactive sessions, attendees will obtain practicalexperience implementing RAG models, enabling them to develop chatbots proficient inunderstanding queries and providing insightful responses.

Workshop 02 (Chair: Dr. K.K.L.B. Adikaram)(09.00 - 09.30)
Dr. Asanka Perera
Mechatronics Lecturer, University of Southern Queensland, Australia

Title: Human life sign detection from drone video
In search and rescue operations, it is crucial to rapidly distinguish between those who are alive
and those who are not. With this information, emergency teams can prioritize their operations
to save more lives. However, in some natural disasters, people may be lying on the ground,covered with dust, debris, or ashes, making them difficult to detect through video analysis tuned
to human shapes. We have introduced a technology that estimates the locations of people fromaerial videos using image and signal processing designed to detect breathing movements. Our
study demonstrates the effectiveness of this method in detecting both clearly visible people andthose who are fully occluded by debris.

Workshop 03 (Chair: Dr. Sagara Sumathipala)(09.30 - 10.00)
Mr. Chamika Janith Perera
PhD Researcher, Graduate School of Engineering, Mie University, Japan

Title: Use of Drone based Spectral imaging in agricultural decision making- an
introduction
Modernizing the agricultural decision-making process is a long-overdue step to meet theincreasing demand with limited available resources. Sensing plays a crucial role in prediction,
estimation, and detection tasks within agriculture. Among the various sensing methods, spectral
sensing (Hyperspectral and Multispectral) has emerged as a key component in the decisionmaking process. This workshop session will focus on introducing hyperspectral andmultispectral imaging in the context of agricultural decision-making. We will discuss theimportance of sensing, what is spectral imaging, key highlights of spectral imaging, the current
state of the art, entry barriers, and will place specific emphasis on drone-based sensing
methodologies.

Tea Break : 10.00~ 10.15

Day 02 : March 10, 2024 (10.15~11.15)
Session 04: Data Analytics, AI, Data MiningChairs: Dr. Mohammad Shidujaman, Prof. Emi Yuda
Paper

Title, Authors and Abstract

ID

17

Title: Classification of Football Player Actions Using Sensing Data
Authors: Yukito Hirasawa, Yuichiro Kinoshita, Latifah Munirah Kamarudin, Ammar Zakaria
Abstract
This study classifies the actions of football players using sensing data acquired from wearable sensors
attached to players and the ball. More than 800 sensing data with the labels of five types of player actions
were created as a dataset. The neural networks were trained using 19 input items created by consideringtime-series variations in player and ball locations. The trained neural network model demonstrated aclassification accuracy of 82.08 %. The model successfully obtained sufficient accuracy for all types ofactions. These results demonstrate that the sensing data and created input items can be effectively
utilized for classifying the actions of football player.

22
Title: Proximity-Based Reward Systems
Authors:Marc-André Blais, Moulay Akhloufi

forMulti-Agent

Reinforcement

Learning

Abstract
Unmanned vehicles, such as drones, have surged in popularity in recent years. Swarms of these vehicles
offer new opportunities in applications such as agriculture, weather monitoring and natural events
management. However, efficiently controlling a large swarm of unmanned vehicles poses a significant
challenge. Intelligent solutions, particularly reinforcement learning, have been proposed to address thischallenge. We introduce a proximity-based reward system for multi-agent reinforcement learning tohandle the issue of reward sparsity. Our goal is to develop an approach for controlling a swarm towards
a common objective while maintaining robust swarm cohesion. In this paper, we compare various
distance-based functions to build a comprehensive reward system. Specifically, we explore theEuclidean, Manhattan, Chebyshev and Minkowski distances in our experiments. We evaluate the impactof these proximity-based reward systems on four RL algorithms. We conduct a comparison of our reward
systems using various metrics during validation and test episodes. Our goal is to highlight the importanceof comparing different algorithms and distance functions in the development of multi-agent reinforcement
learning systems.We conduct a comparison of our reward systems using various metrics during
validation and test episodes. Our goal is to highlight the importance of comparing different algorithms
and distance functions in the development of multi-agent reinforcement learning systems.

28

Title: Ontology based Machine Learning
Recommendation
Authors: Maduri Hansanie, Thushari Silva

ApproachforFacialSkincareProducts

Abstract
The need to preserve facial skin health and improve attractiveness has become more widespread in themodern world. There has been competition among skincare companies to research and create novel
products. The dynamic skincare market creates a wide range of skincare products. Therefore, selecting
the best skincare products suitable for a consumer’s skin type and condition can be quite challenging.
Skin impairments could worsen if products contain ingredients inappropriate for the user’s skin type. This
study proposes a unique system architecture for recommending facial skincare products that combine
ontological and machine-learning benefits. The ML engine is developed for facial skin condition
identification, which uses a CNN model to identify acne severity based on the user's facial image. An
ontology was constructed using the Protégé ontology editor, which included hierarchical relationships
between user profiles, skincare information, and skincare product information. The semantic similarity
between these concepts was mapped by the Protégé tool for the skincare product recommendationengine. The system UI takes inputs for customization and recommendation of facial care products basedon key factors such as the user’s skin type, concerns, acne severity level, and allergy ingredients. Users
can provide feedback and ratings for the products recommended. The developed system had an
accuracy of 87.5% based on a survey conducted with 24 participants who tested the system. Therefore,
the proposed ontology-based ML approach is effective and accurate for facial skincare productrecommendations.

57

Title: Sentiment Analysis with Deep Learning Methods for Performance Assessment andComparison
Authors: Achhiya Sultana, Mahady Hasan, Mohammad Shidujaman, Chinthaka Premachandra
Abstract
The activity of obtaining and evaluating perspectives of individuals, feelings, mindsets of others, views,
and so on, toward various things including subjects, goods, and ideas is known as sentiment analysis(SA), often called sentiment mining (SM). People are producing massive quantities of thoughts andfeedback regarding goods, offerings, and daily operations as a result of the quick expansion of using
online applications like blogs, social media platforms, and web pages. Companies, government
agencies, and institutions can collect and evaluate general population attitudes along with opinions using
sentiment analysis to acquire business insight and improve decision-making. The paper represents acomplete research on sentiment analysis based on DL (deep learning) approaches to give researchers
an idea of the evaluation of feelings and associated disciplines. This research represents the previous
studies of emotional analysis and illustrates the methodology of our work. The methodology explainsdata extraction, data preprocessing, text preprocessing, feature extraction, feature selection, and so on.
The dataset applied in the study is an IMDb movie reviews dataset containing equal amounts of samples
for training and testing. Then, we discussed sentiment analysis techniques which are Simple Neural
Networks (SNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Using
the methods, the outcome states that the simple neural network model generates an accuracy of 74.99%
and a Convolutional Neural Network of 85.79%. Besides, the Recurrent Neural Network shows 86.46%
which is the highest one. Furthermore, based on the results of the confusion matrix, we investigated the
optimum model to attain the highest precision, recall, and F1 score.

Day 02 : March 10, 2024 (11.15~12.30)
Session 05: - Computer vision & Image processing 2Chairs: Dr. Kohei Shimasaki, Dr. Surangi Bandara
Paper

Title, Authors and AbstractID

26

Title: Analyze the Efficacy of Parkinson's Disease Detection using features from Fundus
ImagesAuthors: Justin Paul Kolengadan, Dr. Pitchumani Angayarkanni S
Abstract
To address the challenge of bridging the gap between virtual and real-world environments in robotics,
this study proposes an innovative approach that leverages Luma AI's Neural Radiance Fields (NeRF)
and Unreal Engine 5 (UE5). The study uses standard smartphone cameras to capture video footage andefficiently create photorealistic 3D virtual environments. These environments are then integrated with
UE5 to simulate realistic robotic navigation and manipulation tasks. The method's effectiveness isvalidated through objective evaluations using Navigation2 for operational efficacy and YOLO for
accurate object recognition in both virtual and real settings. The study demonstrates a significant
reduction in the time and resources required for environment creation compared to existing methods like
NeRF2real and Matterport3D. This advancement facilitates simulation-driven development and testingin robotics and establishes a new standard for virtual environment generation.

29

Title: Near Infrared Therapy: A Non-invasive Device for Arteriovenous Fistula Patency in Dialysis
Treatment
Authors: Jaichandar Kulandaidaasan Sheba, Sun Tianyu, Cai Zhi Qiang
Abstract
A novel, non-invasive device that provides simple method for arteriovenous fistula (AVF) maintenance.
This device performs near infrared (NIR) therapy on dialysis patients with AVF created on them. The
objective of this device is to improve the AVF patency by implementing light with combination of different
NIR wavelengths. According to studies, this would be able to deliver energy to AVF, stimulate andactivate cell activities that leads to higher AVF patency and maturation rate. Here we have designed adevice that can be used in the experimental study to increase the flow rate across AVF.

32

Title:
Disease
Detection
inTomato
Plant
using
Deep
Residual
Authors: Aakanksha Sharaff, Aman Dhale, Druva Kumar Gunda, Nisheeth Agrawal

Learning

Abstract
With the accelerated rise of human population, food is bound to be quintessential of our worries in the
future, thus mitigating plant diseases, and errors in their detection would be extremely helpful. The
traditional method of examining plant diseases involved an erudite of the field to be present, but various
developments in neural networks have made this process trivial by requiring only the image of the cropfor its disease detection. There has been a myriad of attempts to improve the scope of plant disease
detection to try and make it a lot more computer-dependent and mitigate the involvement of the enduser by using deep learning. This is achieved by using convolutional neural networks which excel in thefield of image detection. This paper is based on the fine-tuning aspect of deep learning, i.e., this paper
tries to improve the performance of a pre-trained model and compare the accuracies of pre-existingmodels which are popular in this aspect of plant disease detection. This model has been trained
specifically for tomato plant leaf dataset, which was sourced from Kaggle, which includes 10 distinct
classes including both diseased and healthy leaves.

35

Title: Object substitution-based contextual domain randomization for generating neural network
training
data
Authors: Wen Kang Teoh, Ikuo Mizuuchi
Abstract
Domain randomization is a data synthesis approach for training neural network models on simulated
images for the purpose of generalization to real-world images through randomizing parameters of therendering process such as lighting, backgrounds, object poses, and object textures, leading to improved
performance in image-based neural network applications. Domain randomization increases the amountof variability in simulated data to handle the amount of variability that exists in real-world data. In thispaper, we present a novel substitution-based approach, object substitution-based contextual domain
randomization (OSCDR), which preserves the contextual and structural integrity of real-world data, and
it is expected to enable the synthesis of cheap, high quality, and highly variable datasets for a wide
variety of image-based neural network applications, including object detection/localization, autonomous
driving, and robot navigation.

48

Title: Pixel-level Vibration Imaging for Flying Objects with Translational Slide Image Sequences
Authors: Kohei Shimasaki, Tomoaki Okamura, Idaku Ishii
Abstract
We propose a pixel-level vibration imaging method for high frame rate (HFR)-video-based localizationof flying objects with large movement. When the ratio of the translation speed of a target to its vibration
frequency is large, obtaining its frequency response in image intensities becomes difficult because one
or no waves are observable at the same pixel. Our method can precisely localize flying and moving
objects with fllapping vibration frequency by virtually translating multiple image sequences for pixel-level
short-time Fourier transform to observe multiple waves at the same pixel to estimate moving direction.
The effectiveness of the proposed method is demonstrated by analyzing several HFR videos of flying
insects in real scenarios.

12.30-13.30 Lunch BreakDay 02 : March 10, 2022 (13.30~14.45)
Session 06: - Security, UAV and automationChairs: Dr. K.K.L.B. Adikaram, Mr. B. H. Sudantha
Paper

Title, Authors and Abstract

ID

36

Title: Maritime Security Enhanced: A Fuzzy Logic Approach to Bilge and Flooding Risk
Assessment in Warships
Authors: Mihiraj Karunanayake, Savindu Nanayakkara, Thilina Kumara, Thathsara Nanayakkara,
Pradeep Gunathilaka
Abstract
To maintain a ship’s stability, early detection of bilges and flooding is crucial. Loss of the stability will leadto risk of life and property onboard ships. In order to identify bilge/flooding sites and ring alarm with an
audible and visual warning that specifies the bilge location/area on a monitoring panel, ships areequipped with traditional bilge alarm systems. In the aspect of a war ship, flooding risk is higher than thatof a cargo/merchant ship. Due to that, conventional monitoring systems have become inefficient toassure safety of life out at sea. Hence, the proposed system is designed to logically reason many aspectsof bilge and flooding to give accurate bilge alarms and support decision making. Fuzzy logic has beenfound as an emerging technology in the research field to comply this research gap. In the proposedsystem, sensors have been utilized to acquire input variables in real time. Output is forwarded to the end
user after applying Mamdani fuzzy inference to decide the bilge/flooding condition and risk severity ofthe ship. Center of Gravity (COG) de-fuzzification method has been applied to extract the crisp value ofthe risk severity as a percentage. The test results have been evaluated after installing onboard awarship.The input and output fuzzy membership functions have been optimized by evaluating the resultswith the aid of expertise personnel. Thus, the determined risk factor is proved to be sound enough totake efficient and effective action against flooding or high bilges.

27

Title: RapidSim: Enhancing Robotic Simulation with Photorealistic 3D Environments via
Smartphone-Captured NeRF and UE5 Integration
Authors: Yuminosuke Sato, Yaguchi Yuichi
Abstract
To address the challenge of bridging the gap between virtual and real-world environments in robotics,
this study proposes an innovative approach that leverages Luma AI's Neural Radiance Fields (NeRF)
and Unreal Engine 5 (UE5). The study uses standard smartphone cameras to capture video footage andefficiently create photorealistic 3D virtual environments. These environments are then integrated with
UE5 to simulate realistic robotic navigation and manipulation tasks. The method's effectiveness isvalidated through objective evaluations using Navigation2 for operational efficacy and YOLO for accurate
object recognition in both virtual and real settings. The study demonstrates a significant reduction in thetime and resources required for environment creation compared to existing methods like NeRF2real andMatterport3D. This advancement facilitates simulation-driven development and testing in robotics andestablishes a new standard for virtual environment generation.

49

Title: Comparative Analysis of Two Pico Satellites: Performance, Sensor Integration, andCommunication
Technologies
Authors: Md Hasibur Rahman, Dr. Saadia Binte Alam, Dr Mohammad Faisal Uddin
Abstract
In this research, we evaluate the effectiveness of two Pico satellite designs – a can-shaped model anda cube-shaped model – focusing on their performance during controlled parachute descents from a
specific altitude. The study centers on embedded sensors in these satellites, which collect environmental
and kinematic data throughout the descent. These sensors track temperature, humidity, barometricpressure, gyroscope readings, acceleration, and magnetometer data, offering detailed insights into each
design's descent dynamics. The primary goal is to assess how the can and cube-shaped Pico satellites
withstand descent pressures and their data gathering efficacy during parachute-assisted landings. Thisevaluation is crucial for understanding how structural designs impact the performance and adaptabilityof pico-satellites in freefall conditions. Additionally, the research includes a comparative analysis of two
communication devices: the cube satellite equipped with an NRF24L01+LNA, and the can satellite withan ESP32S wireless development board. This comparison aims to identify which communication
technology is more suitable for the challenging environment of parachute-assisted descent, with a focus
on the reliability and quality of data transmission.

9

Title:
UAV
Wireless
Power
SupplyPlanning
Authors: Shouta Sogawa, Tomotaka Kimura, Jun Cheng

Based

on

Sensing

Intervals

Abstract
In this paper, we propose a power supply planning method for unmanned aerial vehicles based on thesensing interval of sensors. The sensors do not require a constant supply of power, and can determine
whether sufficient power is available to complete their next sensing operation. Therefore, there is noneed to supply power to sensors with long sensing intervals, with power instead being supplied when
the sensing time approaches. In this way, unmanned aerial vehicles can prioritize other sensors that areapproaching their sensing time, thus improving the system performance. Through simulation
experiments, it is shown that the proposed method improves the rate at which sensing is performed at
the desired time.

30

Title: A Review: Attacks, Detection,
Author: Kolitha Warnakulasooriya

andPrevention

on

the

Commercial

DronesAbstract
In recent years, the rising popularity of commercial drones across various industries and applications
has been notable. These modern drone systems boast an array of advanced features such as
Autopiloting, Video Streaming, and Swarming. However, the pressing need to address the cybersecurity
vulnerabilities inherent in commercially available drones cannot be overstated. The prevalence of open
technologies, coupled with constraints on costs for robust security implementations, and theovershadowing focus on other functionalities, underscores the urgency of this discussion. This paper
aims to engage in a comprehensive discussion of current threats and propose easily implementable
prevention mechanisms for manufacturers to enhance the cybersecurity of commercial drones at a broad
level.

Day 02 : March 10, 2024 (14.45~16.00)
Session 07: Robot, IOT 2 Chairs: Dr. Patrick Holthaus, Prof. Lasith Gunawardane

Paper

Title, Authors and Abstract

ID

42

Title: Optimization of Monocoque Drone Frame Using Generative Design
Authors: Arockia Selvakumar Arockiadoss, R A Nikki Novah, K S Sajal, S Sayee Pratap, S
Sivarajan, S Sivarajan, Daniel Schilberg
Abstract
Current issues in the drone fabrication are to increase the structural rigidity by having an optimum or by
reducing weight of the drone. Similarly, to have enhanced structural integrity of the frame while lifting
heavy loads during the flight, a solution to this problem will lead to reduce the flight time completion oftasks. To address these problems a generative design approach is implemented to design a monocoquedrone frame. The designed frame has been analyzed to withstand the various failure loads using
Autodesk Fusion 360. Based on the generative design approach 20 possible design outcomes are
obtained and out of 20 one best design is selected based on the factor of safety and the stress values.
Further, four different load cases are considered for the static structural analysis of the selected drone
frame to withstand the payload of 1.5 kg. The results obtained from the simulations and finite element
analyses were found to be satisfactory. Finally, the drone was fabricated using Nylon PA12 material andthe drone frame weight is found to be 150 grams lighter than the conventional drone.

44

Title: Fabrication of a Revolutionary Pick-and-Place Robot with Omnidirectional Mobility
Authors: Md Hasibur Rahman, Dr. Saadia Binte Alamm, Md Fayed Al Monir, Dr Mohammad Faisal
Uddin
Abstract
The paper outlines the creation of an innovative robot capable of omnidirectional movement, enhancing
its ability to navigate confined industrial spaces. This robot, uniquely designed for picking and placing
tasks, underwent a comprehensive development process, including planning, design, fabrication, andprogramming. It represents a notable advancement in the field of industrial robotics, with potential
applications in environments where traditional robots may be less effective. The discussion also covers
possible future improvements and broader applications of this technology.

50

Title: A Companion Robot for Reducing Stress and Increasing Workability
Authors: Nihara Mayurawasala, Udaka Manawadu, Dilmi Kulugammana, Ravindra Silva
Abstract
This research explores the efficacy of a companionrobot as a stress intervention for professionals
working from home. The robot, designed to observe and engage in friendly conversations, offers stressreducing suggestions based on continuous monitoring of user behavior. Our findings demonstrate thatthe stress reduction companion robot effectively reduced stress and improved workability by accurately
detecting facial emotions, stress levels, and speech. The system’s ability to minimize distractions andinterpret facial expressions highlights its success in enhancing user well-being and overall experience.
Further research is needed to assess long-term effectiveness and address ethical considerations forwidespread implementation. This study presents a novel approach to address stress-related issues andenhance workability in various settings.

16

Title: Implementation of human-following robot system applying virtual delay to posture of aleaderAuthor: Fumiaki Takemori
Abstract
This paper develops an implementation of a measurement and control system in which a vehicle follows
its predecessor while maintaining a certain distance. First, we construct a model that virtually delays thereference coordinates of the leading target backwards, and propose a control law that follows the steering
angle of the following vehicle to that virtual delay point. Next, a range sensor is used to detect the positionand orientation of the leader, and the position and orientation is estimated using a template matching
method based on the person's body contour point group measured by the sensor. Finally, theeffectiveness (including robustness) of the proposed system is demonstrated in some simulations andhuman tracking experiments.

55

Title: Roopkotha: A Companion Robot for Enhancing Interactive Storytelling with Natural
Interaction
Authors: Kazi Mayesha Mehzabin, Md. Zahidul Islam, Md. Ashaduzzaman Nur, Mohammad
Shidujaman, Hooman Saman, Haipeng Mi
Abstract
Roopkotha is a storytelling robot that seamlessly combines traditional story-telling methods andtechnology, creating a captivating robot storyteller. We are creating a special prototype in the world of
robots that tells stories in a way that is easy to understand and enjoy. In this era of technologicaladvancement, Roopkotha combines voice recognition with Bangla Language processing, emotion
recognition, human behavior detection along environmental monitoring of humidity and temperature.
Roopkotha aims to revolutionize the way stories are told and engage with users on a deep emotional
level. Furthermore, Roopkotha is equipped with advanced facial expressions and emotion recognition
technology. The emotion recognition feature helps the robot to have a profound connection with theusers.

58

Title: Visual Light Communication Based User Verification System for Drone Delivery
Authors: Nilupul Senevirathna, Chinthaka Premachandra
Abstract
The realm of goods transportation has witnessed a paradigm shift with the advent of modern
technological advancements, notably the utilization of drones for efficient and innovative movement ofgoods. This paper explores the challenges and advancements in drone delivery systems, emphasizing
the critical need for secure and accurate parcel handover methods. We introduce a novel drone delivery
LED array-based light communication system that addresses the drawbacks associated with traditionalverification processes. The system employs pattern recognition technology, facilitated by a Raspberry Pi
single-board computing unit and integrated with a smartphone application for seamless interaction.
Through comprehensive testing and analysis, our research showcases the effectiveness and potentialof the proposed system in revolutionizing parcel delivery services.

16.15-16.30 Tea BreakDay 02 : March 10, 2024 (16.30~16.45)
Award Ceremony : 16.30~16.45
By Award Chairs
Vote of Thanks:
End of Day 02