Call for Participation - 11^th International Conference on Natural Language Processing (NLP 2022)

September 17~18, 2022, Copenhagen, Denmark

Call for Participation

We invite you to join us on 11^th International Conference on Natural Language Processing (NLP 2022)will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing and its applications. The Conference looks for significant contributions to all major fields of the Natural Language processing in theoretical and practical aspects.

Program Schedule

Day 1

8:00 to 9 AM --Delicious light breakfast served at the start of the meeting

GRASS: A Syntactic Text Simplification System based on Semantic Representations

Rita Hijazi ^{1, 2}, Bernard Espinasse¹ and Núria Gala², ¹Laboratoire Informatique et Systèmes, Aix-Marseille University, Marseille, France, ²Laboratoire Parole et Langage, Aix-Marseille University, Aix-en-Provence, France

ABSTRACT

Automatic Text Simplification (ATS) is the process of reducing the linguistic complexity of a text to improve its understandability and readability, while still maintaining its original information, content and meaning. Several text transformation operations can be performed such as splitting a sentence into several shorter sentences, substitution of complex elements, and reorganization. It has been shown that the implementation of these operations essentially at a syntactic level causes several problems that could be solved by using semantic representations. In this paper, we present GRASS (GRAph-based Semantic representation for syntactic Simplification), a rule-based automatic syntactic simplification system that uses semantic representations. The system allows the syntactic simplification of complex constructions, such as subordination clauses, appositive clauses, coordination clauses, and passive forms. It is based on transformations of graph-based meaning representation of the text expressed in DMRS (Dependency Minimal Recursion Semantics) notation using rewriting rules. The experimental results obtained on a reference corpus and according to specific metrics outperform the results obtained by other state of the art systems on the same reference corpus.

KEYWORDS

Syntactic Text Simplification, Graph-Based Meaning Representation, DMRS, Graph-Rewriting.

Chess is a Natural Language Game

Michael DeLeo and Erhan Guven, Whiting School of Engineering, Johns Hopkins University, Baltimore, USA

ABSTRACT

Representing a board game and its positions by text-based notation enables the possibility of NLP applications. Language models, can help gain insight into a variety of interesting problems such as unsupervised learning rules of a game, detecting player behavior patterns, player attribution, and ultimately learning the game to beat state of the art. In this study, we applied BERT models, first to the simple Nim game to analyze its performance in the presence of noise in a setup of a few-shot learning architecture. We analyzed the model performance via three virtual players, namely Nim Guru, Random player, and Q-learner. In the second part, we applied the game learning language model to the chess game, and a large set of grandmaster games with exhaustive encyclopaedia openings. Finally, we have shown that model practically learns the rules of the chess game and can survive games against Stockfish at a category-A rating level.

KEYWORDS

Natural Language Processing, Chess, BERT, Sequence Learning.

A transformer based multi-task learning approach leveraging translated and transliterated data to hate speech detection in Hindi

Prashant Kapil and Asif Ekbal, Department of Computer Science and Engineering, IIT Patna, India

ABSTRACT

The increase in usage of the internet has also led to an increase in unsocial activities, hate speech is one of them. The increase in Hate speech over a few years has been one of the biggest problems and automated techniques need to be developed to detect it. This paper aims to use the eight publicly available Hindi datasets and explore different deep neural network techniques to detect aggression, hate, abuse, etc. We experimented on multilingual-bidirectional encoder representations from the transformer (M-BERT) and multilingual representations for Indian languages (MuRIL) in four settings (i) Single task learning (STL) framework. (ii) Transfering the encoder knowledge to the recurrent neural network (RNN). (iii) Multi-task learning (MTL) where eight Hindi datasets were jointly trained and (iv) pre-training the encoder with translated English tweets to Devanagari script and the same Devanagari scripts transliterated to romanized Hindi tweets and then fine-tuning it in MTL fashion. Experimental evaluation shows that cross-lingual information in MTL helps in improving the performance of all the datasets by a significant margin, hence outperforming the state-of-the-art approaches in terms of weighted-F1 score. Qualitative and quantitative error analysis is also done to show the effects of the proposed approach.

KEYWORDS

M-BERT, MuRIL, Weighted-F1, RNN, cross-lingual.

Mining Online Drug Reviews Database for the Treatment of Rheumatoid Arthritis by using Deep Learning

Pinar Yildirim, Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul, Turkey

ABSTRACT

In this paper, a research study to extract knowledge in the online patient reviews for rheumatoid arthritis is introduced. Rheumatoid arthritis is a long-term and disabling autoimmune disease. Today, a huge amount of people have rheumatoid arthritis in the world. Considering the importance of the medication of rheumatoid arthritis, we aimed to investigate patient reviews in WebMD database and get some useful information for this disease. Our results revealed that etanercept treatment has the highest number of reviews. Data analysis was applied to discover knowledge on this drug. Deep learning approach was used to predict the effectiveness of etanercept and classification results were compared with other traditional classifiers. According to the comparison of classifiers, deep neural network has better accuracy metrics than others. Therefore, the results highlight that deep learning can be encouraging for medical data analyses. We hope that our study can make contributions to intelligent data analysis in medical domain.

KEYWORDS

Classification, Deep Learning, Etanercept, Online Drug Reviews.

A Simple Neural Network for Detection of Various Image Steganography Methods

Mikołaj Płachta and Artur Janicki, Warsaw University of Technology, Warsaw, Poland

ABSTRACT

This paper addresses the problem of detecting image steganography based in JPEG files. We analyze the detection of the most popular steganographic algorithms: J-Uniward, UERD and nsF5, using DCTR, GFR and PHARM features. Our goal was to find a single neural network model that can best perform detection of different algorithms at different data hiding densities. We proposed a three-layer neural network in Dense BatchNormalization architecture using ADAM optimizer. The research was conducted on the publicly available BOSS dataset. The best configuration achieved an average detection accuracy of 72 percent.

KEYWORDS

Steganography, deep machine learning, detection malware, BOSS database, image processing.

Comparing Spectroscopy Measurements in the Prediction of in vitro Dissolution Profile using Artificial Neural Networks

Mohamed Azouz Mrad, Kristóf Csorba, Dorián László Galata, Zsombor Kristóf Nagy and Brigitta Nagy, Budapest University of Technology and Economics, Budapest, Hungary

ABSTRACT

Dissolution testing is part of the target product quality that is essential in approving new products in the pharmaceutical industry. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. Raman and near-infrared (NIR) spectroscopies are two fast and complementary methods that provide information on the tablets physical and chemical properties and can help predict their dissolution profiles. This work aims to compare the information collected by these spectroscopy methods to support the decision of which measurements should be used so that the accuracy requirement of the industry is met. Artificial neural network models were created, in which the spectroscopy data and the measured compression curves were used as an input individually and in different combinations in order to estimate the dissolution profiles. Results showed that using only the NIR transmission method along with the compression force data or the Raman and NIR reflection methods, the dissolution profile was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy measurements increased the prediction accuracy.

KEYWORDS

Artificial Neural Networks, Dissolution prediction, Comparing spectroscopy measurement, Raman spectroscopy, NIR spectroscopy & Principal Component Analysis.

An Intelligent Community-Driven Mobile Application to Automate the Classification of plants using Artificial Intelligence and Computer Vision

Yifei Tong¹ and Yu Sun², ¹Trinity Grammar School, 119 Prospect Rd, Summer Hill NSW2130, ²California State Polytechnic University, Pomona, CA, 91768, Irvine, CA92620

ABSTRACT

How can the ef iciency of volunteers be improved in performing bushcare in the limited amount of time able tobespent caring for each location every month [1]? Bushcare is a volunteer activity with a high dif iculty curve for volunteers just starting out as the crucial skill of distinguishing the native plants from the harmful invasive species only comes with experience and memorization[2]. The lack of ability to distinguish targeted plants will greatly reduce the ef iciency of the volunteers as they workthrough the limited amount of time they have at each location each month while also discouraging newly joinedvolunteers from continuing this activity. To assist newly joined volunteers, the majority of each would likely be from a younger demographic with a digital app that could help the user distinguish the species of plant, making it easier for them to start familiarizingthemselves with both the native and invasive species in their area [3]. The user could simply have to take a pictureof the plant they wish to identify and the software would use its image recognition algorithm trained with a databaseof dif erent species of plants to identify the type of plant and whether it needs to be removed. At the same time, moreexperienced volunteers could continue to use this app, identifying errors in the app’s identification to make it morereliable.

KEYWORDS

Flutter, Machine learning, Firebase, Image recognition.

A Data-Driven Analytical System to Optimize Swimming Training and Competition Performance using Machine Learning and Big Data Analysis

Tony Zheng¹ and Yu Sun², ¹Troy High School, 2200 Dorothy Ln, Fullerton, CA 92831, ²California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

ABSTRACT

Many swimmers are constantly incorporating new and dif erent training regimes that would let them improve quickly [2]. However, it is dif icult for a swimmer to see their progress instantly. This paper develops a tool for swimmers, specifically swimmers, to predict their future results. We applied machine learning and conducted a qualitative evaluation of the approach [3]. The results show that it is possible to determine their future performance with decent accuracy. This application considers the swimmers performance history, age, weight, and height to predict the most accurate results.

KEYWORDS

Machine Learning, Mobile APP, database.

Lunch Break 1 to 2 PM

Session 2 - 2 PM to 6 PM

Fast Rank Optimization Scheme by the Estimation of Vehicular Speed and Phase Difference in Mu-MIMO

Shin-Hwan Kim¹, Kyung-Yup Kim², Sang-Wook Kim³ and Jae-Hyung Koo⁴, ¹Access Network Technology Team, Korea Telecom, Seoul, Korea, ²Access Network Technology Team, Korea Telecom, Seoul, Korea, ³Access Network Technology Department, Korea Telecom, Seoul, Korea, ⁴Network Research Technology Unit, Korea Telecom, Seoul, Korea

ABSTRACT

Resent MU-MIMO(Multi User-Multi Input Multi Output) scheme is one of the important and advanced technologies. In particular, it is a suitable technique to increase the capacity from the point of view of solving cell load, which is one of the big issues in the contents of 5G commercial field optimization. While this MU-MIMO technology has an important advantage of cell capacity expansion, there is a disadvantage like an interference problem due to each multi-user beams. It is important to use the advanced beamforming technology for MU-MIMO to overcome these disadvantages. Therefore, by applying the interference cancelling technology among inter UE(User Equipment) beams to improve each UE’s performance, it will contribute to improving the cell throughput. This paper introduces the various techniques of eliminating interference in MU-MIMO system. Also, it is important that UE reports rank indicator reflected the interference of multi-user beams. This paper analyses the problem of the conventional method of the rank decision in MU-MIMO system, estimates the vehicular speed quickly with the proposed rank optimization technique, and shows the DL(Downlink) UE’s performance is improved by applying a proposed rank value suitable for vehicular speed. This technique will be effectively applied to increase the overall cell capacity by improving the DL UE’s throughput in the MU-MIMO system.

KEYWORDS

MU-MIMO, 5G, multi-user, interference, UE, DL, rank indicator, cell capacity.

Cyberbullying Detection using Ensemble Method

Saranyanath K P¹, Wei Shi² and Jean-Pierre Corriveau¹, ¹School of Computer Science, Carleton University, Ottawa, Canada, ²School of Information Technology, Carleton University, Ottawa, Canada

ABSTRACT

Cyberbullying is a form of bullying that occurs across social media platforms using electronic messages. In this paper we propose three different approaches, and five models to identify cyberbullying on a generated social media dataset, derived from multiple online platforms. Our initial approach consists in enhancing a Support Vector Machines. Our second approach is based on DistilBERT, which is a lighter and faster Transformer model than BERT. Staking the first three models we obtain two more ensemble models. Contrasting the ensemble models with the three others, we observe that the ensemble models outperform the base model concerning all evaluation metrics except precision. While the highest accuracy, of 89.6%, was obtained using an ensemble model, we achieved the lowest accuracy, at 85.53% on the SVM model. The DistilBERT model exhibited the highest precision, at 91.17%. The model developed using the different granularity of features outperformed the simple TF-IDF.

KEYWORDS

Machine Learning, Natural Language Processing, Support Vector Machine, DistilBERT, Cyberbullying.

Classification of Depression using Temporal Text Analysis in Social Network Messages

Gabriel Melo, Kayke Bonafé and Guilherme Wachs-Lopes, Department of Computer Science, University Center of FEI, São Paulo, Brazil

ABSTRACT

Depression is a topic that has gained prominence in recent years. According to the WHO [1] , depression affects more than 294 million people around the world. Works such as [2] [3] indicate that early diagnosis is an important field of research since, in more severe cases, depression can lead to suicide. Therefore, this work proposes, implements and evaluates a computational model based on natural language processing to classify depressive tendencies of Twitter users through their posts over time. As a result, an F-Measure of 83.58% was obtained using not only textual content, but also the sentiment analysis of the documents. With this data, it is possible to perform a comparison to check whether the detection of depression is more related to the constant variation of emotions or the message conveyed by the text.

KEYWORDS

Depression, Natural Language Processing, Machine Learning.

3DHero: An Interactive Puzzle Game Platform for 3D Spatial and Reasoning Training using Game Engine and Machine Learning

David Tang¹ and Yu Sun², ¹Irvine High School, 4321 Walnut Avenue, Irvine, CA 92604, ²California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

ABSTRACT

The well-known puzzle game Tetris, where arrangements of 4 squares (tetrominoes) fall onto the field like meteors, has been found to increase the brain’s ef iciency [1]. Many variations came into existence ever since its invention. Sometimes, the leveling can become a double-edged sword, so this game is essentially a Zen mode without a leveling system. This game is built for people who want to play a 3D version of Tetris at a speed they themselves have set. This paper designs a game to exercise spatial visualization. This study uses a Unity/C++-based game [2]. This game will be tested by kids on the autism spectrum, and we will conduct a qualitative evaluation of the approach. No results have been shown yet, and that is due to the fact that this study is still a work in progress. I am trying to make the game comply with the latest Tetris design guidelines that I can find online (that is, the 2009 guideline).

KEYWORDS

Tetris, Spatial visualization, 3-Dimensional Perception.

WassBERT: High Performance BERT-based Persian Sentiment Analyzer and Comparison to Other State-of-the-art Approaches

Masoumeh Mohammadi and Shadi Tavakoli, DepartMent of DataScience & Machine Learning, Telewebion, Tehran, Iran

ABSTRACT

Applications require the ability to perceive others opinions as one of the most outstanding parts of knowledge. Finding the positive or negative feelings in sentences is called sentiment analysis (SA). Businesses use it to understand customer sentiment in comments on websites or social media. An optimized loss function and novel data augmentation methods are proposed for this study, based on Bidirectional Encoder Representations from Transformers (BERT). First, a crawled dataset from Persian movie comments on various sites has been prepared. Then, balancing and augmentation techniques are accomplished on the dataset. Next, some deep models and the proposed BERT are applied to the dataset. We focus on customizing the loss function, which achieves an overall accuracy of 94.06 for multi-label (positive, negative, neutral) sentences. And the comparative experiments are conducted on the dataset, where the results reveal the performance of the proposed model is significantly superior compared with other models.

KEYWORDS

Bidirectional encoder representations from transformers (BERT), Bidirectional long short-term memory (Bi-LSTM), Comment classification, Convolutional neural network (CNN), Deep learning, Opinion mining(OM), Natural language processing (NLP), Persian language sentiment classification, Persian Sentiment analysis, Text mining.

Performance Evaluation for the use of ELMO Word Embedding in Cyberbullying Detection

Tina Yazdizadeh and Wei Shi, School of Information Technology, Carleton University, Ottawa, Ontario, Canada

ABSTRACT

Communication using modern internet technologies has revolutionizedthe ways humans exchange information.Despite all the advantages made available by information and communication technology, its applicability is still limited due to problems caused by personal attacks or pseudo-attacks.Thesetoxic contents may be in the form of texts (e.g., online chats, emails), speech, or even images or movie clips on social media platforms.Because cyberbullyingof an individual via the use of such toxic digital contentmay have severe consequences, it is essential to design and implement, among others, various techniques to automatically detect cyberbullying from the social media content using machine learning approaches.During a cyberbullying detection process, word embedding techniques are used to represent words for text analysis, typically in the form of a real-valued vector that encodes the meaning of words such that the words that are closer in the vector space are expected to be similar in meaning.The extracted embeddings are then used todecide if a digital input contains cyberbullying content.Supplying strong word representations to classification methods is an important issue.In this paper, we evaluate the ELMo word embedding against three other word embeddings, namely, TF-IDF, Word2Vec, and BERT, usingthree basic machine learning models and four deep learning models.The results show that the ELMo word embeddings have the best results when combined with neural network-based machine learning models.

KEYWORDS

Cyberbullying, Natural Language Processing, Word Embeddings, ELMo, Machine Learning

An Intelligent Food Inventory Monitoring System using Machine Learning and Computer Vision

Tianyu Li¹, Yu Sun², ¹St. George’s School, 4175 W 29th Ave, Vancouver, BC V6S 1V1, Canada, ²California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

ABSTRACT

Due to technological advancements, humans are able to produce more food than ever before. In fact, the foodproduction level is so high that all population could be supported if the food resource is distributed correctly. Yet, it is more than common to see items left expiring on the supermarket shelves, wasting the food resource that couldotherwise be useful. Neither are the adverse impacts on the climate due to food disposal in anyone’s favor orinterest. This paper proposes an application to identify the stock status of supermarket items, specifically food items, so that supermarket managers can react to the selling status and prevent oversupply. The key tool implementedinthe application is computer vision, specifically YOLOv5, which uses convolutional neural networks [13]. The model automatically recognizes and counts the items in a taken picture. We applied our computer vision model tonumerous supermarket shelf photos and conducted an evaluation of the model’s precision and speed. The resultsshow that the application is a useful tool for users to log supermarket stock information since the computer visionmodel, despite lacking slightly in object detection precision, can return a reliable count for well-taken photos. As aplatform where such information is shared, the application is therefore a viable tool for store managers to import amounts of food accordingly and for the public to be informed and make smart buying choices.

KEYWORDS

Flutter, Roboflow, Computer Vision, Inventory Management.

Day 2

Session 3 - 9 AM to 12. 30 PM

Comparison of Various Forms of Serious Games: Exploring the Potential use of Serious Game Walkthrough in Education Outside the Classroom

Xiaohan Feng¹ and Makoto Murakami², ¹Graduate School of Information Sciences and Arts, Toyo University, Kawagoe, Saitama, Japan, ²Dept. of Information Sciences and Arts, Toyo University, Kawagoe, Saitama, Japan

ABSTRACT

The advantages of using serious games for education have already been proven in many studies, especially narrative VR games, which allow players to remember more information. On the other hand, game walkthrough can compensate for the disadvantages of gaming, such as pervasiveness and convenience. This study investigates whether game walkthrough of serious games can have the same learning effect as serious games. Use game creation (samples) and questionnaires, this study will compare the information that viewers remember from game walkthrough and actual game play, analyze their strengths and weaknesses, and examine the impact of the VR format on the results. The results proved that while game walkthrough allows subjects to follow the experiences of actual game players with a certain degree of empathy, they have limitations when it comes to compare with actual gameplay, especially when it comes to topics that require subjects to think for themselves. Meanwhile game walkthrough of VR game is not a medium suitable for making the receiver memorize information. For prevalence and convenience, however, serious games walkthrough is a viable educational option outside the classroom.

KEYWORDS

Serious game, multimedia, educational game, virtual reality, narratology, Education Outside the Classroom(EOTC).

Frame Size Optimization using a Machine Learning Approach in WLAN Downlink MU-MIMO Channel

Lemlem Kassa¹, Jianhua Deng¹, Mark Davis² and Jingye Cai¹, ¹School of Information and Software Engineering, University of Electronic Science and Technology China (UESTC), Chengdu 610054, China, ²Communication Network Research Institute (CNRI), Technological University, D08 NF82 Dublin, Ireland

ABSTRACT

A Machine Learning (ML) is an innovative solution that can autonomously extract patterns and predict trends based on environmental measurements and performance indicators as input to provide a self-driven intelligent network systems that can configure and optimize themselves. Under the effects of heterogeneous traffic demand among users and varying channel conditions in WLAN downlink MU-MIMO channels, it is challenging to achieve the maximum system throughput performance. In addressing these issues, the existing studies have proposed different approaches. However, most of these approaches did not consider a machine-learning based optimization solution. The main contribution of this paper is to propose a machine-learning based adaptive approach that can optimize system frame size that would maximize the system throughput of WLAN in the downlink MU-MIMO channel. In this approach, the Access Point (AP) performs the maximum system throughput measurement and collects the “frame size-system throughput patterns” which contains knowledge about the effects of traffic condition, channel condition, and number of stations (STAs). Based on these patterns, our approach uses neural networks to correctly model the system throughput as a function of the system frame size. After training the neural network, we obtain the gradient information to adjust the system frame size. The performance of the proposed ML approach is evaluated over the FIFO aggregation algorithm under the effects of heterogenous traffic patterns for VoIP and Video traffic applications, channel conditions, and number of STAs.

KEYWORDS

Frame Size Optimization, Downlink MU-MIMO, WLAN, Network Traffic, Machine Learning, Neural Network, Throughput Optimization.

FindMyPet: An Intelligent System for Indoor Pet Trackingand Analysis using Artificial Intelligence and Big Data

Qinqin Guo¹, Yu Sun², ¹Portola High School, 1001 Cadence, Irvine, CA 92618, ²California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

ABSTRACT

Pet tracking has been an important service in the pet supply industry, as it is constantly needed by countless pet owners [1]. As of 2021, about 90 million families in the U.S. alone have a pet, that is about 70% of all American households. However, for most owners of smaller pets such as cats, hamsters, and more, not being able to find the pet within the house has been a problem bothering them. This paper proposes a tool to use Raspberry Pi for gathering signal strength data of the blue tooth devices and using Artificial Intelligence to interpret the gathered data in order to get the precise location of the indoor moving object [2]. The system is applied to arrive with the location of pets within the house to an accurate level where the room that the pet is located in is correctly predicted. A qualitative evaluation of the approach has been conducted. The results show that the intelligent system is effective at correctly locating indoor pets that are constantly moving.

KEYWORDS

Raspberry Pi, Firebase, machine learning, Artificial Intelligence(AI).

Review on Deep Learning Techniques for Underwater Object Detection

Radhwan Adnan Dakhil and Ali Retha Hasoon Khayeat, Department of Computer Science, University of Kerbala, Karbala, Iraq

ABSTRACT

Repair and maintenance of underwater structures as well as marine science rely heavily on the results of underwater object detection, which is a crucial part of the image processing workflow. Although many computer vision-based approaches have been presented, no one has yet developed a system that reliably and accurately detects and categorizes objects and animals found in the deep sea. This is largely due to obstacles which scatter and absorb light in an underwater setting. With the introduction of deep learning, scientists have been able to address a wide range of issues, including safeguarding the marine ecosystem, saving lives in an emergency, preventing underwater disasters, and detecting, spooring, and identifying underwater targets. However, the benefits and drawbacks of these deep learning systems remain unknown. Therefore, the purpose of this article is to provide an overview of the dataset that has been utilized in underwater object detection and to present a discussion of the advantages and disadvantages of the algorithms employed for this purpose.

KEYWORDS

Underwater Object Detection, Deep Learning, Convolutional Neural Network (CNN), Underwater Imaging.

Brand Name (To do): An Interactive and Collaborative Drawing Platform to Engage the Autism Spectrum in Art and Language Learning using Artificial Intelligence

Xuanxi Kuang¹ and Yu Sun², ¹University High school, 4771 Campus Drive, Irvine, CA 92612, ²California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

ABSTRACT

Special communities specific to Autism Spectrum disorder face difficulties both socially and communicably [1]. Autism spectrum disorder will affect their expression and response to society, and theyll have a hard time learning and following complex directions [2]. This paper proposes software to promote ones collaborative skills and drawing skills with interaction with the AI system. At the same time, it also tries to raise awareness of the special group in our society. As an open platform, each individual will have opportunities to work with other users to cooperate, and theyll have a chance to learn drawing step by step from drawing that is contributed by more than 15 million players around the world. They can decorate the object with a color adjective to enhance their sense of beauty. In order to test the usability of the software, we did two experiments to test the accuracy of the graph and color combination. The result shows this software achieves a high accuracy on color input and obtains a correct graph from the input.

KEYWORDS

Interactive, Artificial intelligent, Self learning process.

Early Detection of Parkinson’s Disease using Machine Learning and Convolutional Neural Networks from Drawing Movements

Sarah Fan¹ and Yu Sun², ¹Sage Hill School, 20402 Newport Coast Dr, Newport Beach, CA 92657, ²California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

ABSTRACT

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes uncontrollable movements and dif iculty with balance and coordination. It is highly important for early detection of Parkinson’s disease in order for patients to receive proper treatment. This paper aims to aid in the early detection of Parkinson’s disease by using a convolutional neural network for PD detection from drawing movements. This CNN consists of 2 convolutional layers, 2 max-pooling layers, 2 dropout layers, 2 dense layers, and a flattened layer. Additionally, our approach explores multiple types of drawings, specifically spiral, meander, and wave datasets hand-drawn by patients and healthy controls to find the most ef ective one in the discrimination process. The models can be continuously trained in which the test data can be inputted to dif erentiate between healthy controls and PD patients. By analyzing the training and validation accuracy and loss, we were able to find the most appropriate model and dataset combination which was the spiral drawing with an accuracy of 80%. With a proper model and a larger dataset for increased accuracy, this approach has the potential to be implemented in a clinical setting.

KEYWORDS

Machine Learning, Deep Learning, Parkinson Disease.

Generative Approach to the Automation of Artificial Intelligence Applications

Calvin Huang¹ and Yu Sun², ¹University High School, 4771 Campus Dr, Irvine, CA 92612, ²California State Polytechnic University, Pomona, CA, 91768, Irvine

ABSTRACT

In order to use the full power of artificial intelligence, many are required to navigate through a complex processthat involves reading and understanding code. Understanding this process can be especially intimidating to domainexperts who wish to use A.I to develop a project, but have no former experience with programming. This paperdevelops an application to allow for any domain expert (or normal person) to gather data, assign labels, andtrainmodels automatically without the use of software to do so. Our application, through a server, allows the user tosendHTTP API requests to train models, upload images to the database, add models/labels, and access models/labels.

KEYWORDS

Tensorflow Lite, Flask, Flutter, Google Colab.

Highlights of NLP 2022 include:

• 9^th International Conference on Artificial Intelligence & Applications (ARIA 2022)
• 9^th International Conference on Computer Science and Engineering (CSEN 2022)
• 8^thInternational Conference on Software Engineering and Applications (SOFEA 2022)
• 8^th International Conference on Signal Processing and Pattern Recognition (SIPR 2022)
• 8^th International Conference of Networks, Communications, Wireless and Mobile Computing (NCWC 2022)
• 3^rd International Conference on Data Science and Machine Learning (DSML 2022)
• 3^rdInternational Conference on Education and Integrating Technology (EDTECH)

Registration Participants

Non-Author / Co-Author/ Simple Participants (no paper)

250 EURO (With proceedings)

For registration and details mail: nlp@nlp2022.org or nlp.conf@yahoo.com