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Syllable and Morpheme Segmentation of Macedonian Language 46

Syllable and Morpheme Segmentation of Macedonian Language

引用

46th ICT and Electronics Convention, MIPRO 2023

作者： Mitreska, M. Zdravkova, K. Faculty of Computer Science and Engineering Skopje Macedonia

ISBN: (纸本)9789532331042

Communication is the key to human development. Approximately 5% of the world's population experience some form of hearing disability. Modern assistive devices and technologies can improve the communication skills of hearing impaired people by transcribing the speech into text. The creation of such an application depends on the language specific morphosyntactic properties. It usually starts with the syllabification. The research presented in this paper focuses on the development of an automatic system for rule-based and sonority-based syllable and morpheme segmentation of Macedonian language, which can be easily incorporated into an efficient speech recognition system. The segmentation rules for breaking the words down into syllables and into morphemes were created according to the new orthography of the Macedonian language. For the sonority-based approach, a novel phonological distance measure was introduced capable of efficient syllable clustering. The implementation of the framework is developed in Python using several data structures for optimized performance and CPU usage. Both segmentation strategies were evaluated using the electronic lexicon consisting of more than one million words. A linguistic expert was consulted during the entire process. The consistency of the obtained results promises their sustainability for further speech processing applications. © 2023 MIPRO Croatian Society.

关键词： Speech recognition

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An Empirical Study of Unanswered Python-Related Questions on Stack Overflow

An Empirical Study of Unanswered Python-Related Questions on...

引用

2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Nugroho, Yusuf Sulistyo Halim, Salsabil Aisyah Abdul Islam, Syful Kurniawan, Yogiek Indra Erlina, Tati Universitas Muhammadiyah Surakarta Informatics Engineering Faculty of Communication and Informatics Surakarta Indonesia BSMRSTU Computer Science and Engineering Faculty of Engineering Gopalganj Bangladesh Universitas Jenderal Soedirman Informatics Engineering Faculty of Engineering Purwokerto Indonesia Universitas Andalas Computer Engineering Faculty of Information Technology Padang Indonesia

ISBN: (纸本)9798350376210

As a high-level programming language, Python supports user-friendly coding and system integration. It is not only used for data analytics but is also applied in software development. Although it has several benefits, users have various issues while running Python code. When programmers face issues, they often use Stack Overflow (SO) to get assistance for their problems. However, many Pythonrelated questions on SO are not responded to and remain unanswered. Thus, in this paper, we analyzed these unanswered questions to identify potential weaknesses of user and discussion characteristics. We applied a mixed-methods approach, including quantitative and qualitative analysis of user reputation, question types, and discussion topics. Our analysis shows that unanswered questions are mostly from low-reputation users (72.52%), followed by mid-reputation users (17.93%), and high-reputation users (9.5 5%). The distribution of the unanswered question types shows that low-reputation users ask more 'how' type of question (38.90%) followed by mid and high-reputation users. In contrast, the 'what' and 'why' questions are mostly posted by high-reputation users, followed by mid and low-reputation users. Our word-cloud analysis suggests a correlation between user reputation and the question topics. We acknowledge that there are limitations in our study, include potential misclassifications due to the time of posting and biases in manual labeling, which may affect the accuracy of our findings. This study provides insights into unanswered questions posted by the community, open up potential areas for future work such as improving support mechanisms and community engagement. © 2024 IEEE.

关键词： Python

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Sentimental Analysis for Trump Tweets Using R 8th

Sentimental Analysis for Trump Tweets Using R

引用

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Nishok, V.G. Bharathi, N. Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9789819786657

Twitter is the best source for sentiment analysis, product reviews, current issues, etc. Sentiment analysis extracts positive and negative opinions from the Twitter data set, and R studio provides the best environment for this Twitter sentiment analysis. Find if Trump’s phone Android is fake or not by comparing his iPhone and Android tweets using R with datasets. Detailed analysis is not there. The Twitter data collected might not be representative of the entire population. By using filtering of tweets as we need Android and iPhone tweets here and usage of tweets per day, no of tweets with and without quotes and pictures and links, taking common words from Android and iPhone and trying with log ratio that is Android /iPhone and with log ratio and common word find sentiment. We got good results by the usage of Twitter of each device, no of tweets quoted and non-quoted, no picture/link and picture/link, most common words, words compare with Android and iPhone and log ratio, finding sentiment of it which type of word it is. My work helps his account is a trump or not for Android as it contains negative tweets by common words, Android and iPhone words and log odd ratio with that it detects sentiment as it is a positive, negative or neutral by showing anger, sad, happy, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Potato Leaf Disease Classification using Pre Trained Deep Learning Techniques -A Comparative Analysis 5

Potato Leaf Disease Classification using Pre Trained Deep Le...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Sangar, G. Rajasekar, V. Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9798350361155

Potatoes are essential for food production and consumption, but pests and illnesses can cause major economic losses. To quickly identify potato leaf diseases, image processing, computer vision, and deep learning can be used. We propose the "Potato Leaf Disease" dataset with 4072 photos of varied circumstances to address current dataset limitations. Our comparison investigation used DenseNet121, ResNet50, VGG19, EfficientNetB0, InceptionV3, and MobileNetV2 for feature extraction and SVM, KNN, and Random Forest for classification. The most accurate model was EfficientNetB0 with SVM Linear Kernel at 94.81%. Model efficiency in varied environmental situations shows dataset realism. Training took 38.0030 seconds and testing 1.3503 seconds. This study highlights the need of realistic datasets and presents a reliable potato leaf disease classification method. The dataset and model offer new opportunities for agricultural sustainability and crop security, making them useful for the potato business. © 2024 IEEE.

关键词： Deep learning

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Gaze Tracking with Multi-Modal Neural Network in Desktop Environments Using Generic Web Camera 6

Gaze Tracking with Multi-Modal Neural Network in Desktop Env...

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6th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Kohel, Karim Osama, Mohamed Mohamed, Nourhan Mokhtar, Mohamed Tamer, Omar Fakhr, Mohamed Waleed University of Northampton Faculty of Engineering Department of Computer Northampton United Kingdom Arab Academy for Science and Technology Faculty of Engineering Department of Computer Cairo Egypt

ISBN: (纸本)9798350382891

Gaze tracking is the process of estimating where a person is looking on the screen using only information from eye movement without additional input from the user, it contributes greatly in understanding and improving human-computer interactions. This paper addresses an improved gaze tracking technique building upon established approaches that are using generic web cameras instead of requiring high-cost specific hardware like infrared cameras and sensors. In the proposed method, a pipeline was constructed of various high performing AI models such as Google's MediaPipe face localization and face landmarks solutions to acquire both Iris metrics and localized eye image data. The data obtained is then passed to the proposed model that uses both image data and location metrics to predict the user's gaze position on the screen using a multi-modal architecture. Experiments done on realistic desktop environments show 8% mean distance error of the screen dimensions with a standard deviation of +/-6% from the actual gaze location. The proposed method is not affected by most logistical limitations affecting other similar approaches. © 2024 IEEE.

关键词： Eye tracking

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Self-Learning Robot Manipulator Controller Using Reinforcement Learning

Self-Learning Robot Manipulator Controller Using Reinforceme...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Xhin, Ong Yu Jo, Hudyjaya Siswoyo Swinburne University of Technology Faculty of Engineering Computer & Science Sarawak Malaysia

ISBN: (纸本)9798350350821

Traditionally, robot control has relied on mathematical solutions, particularly inverse kinematics (IK), to determine the joint angles needed for a desired end-effector pose. However, these solutions face significant challenges as robots increase in complexity with higher degrees-of-freedom (DoF) and adaptability requirements. This work introduces an approach for controlling robot manipulators by addressing the IK problem using the artificial intelligence (AI) technique. In this paper, a deep reinforcement learning approach is proposed to solve the IK problem for a 6-DoF industrial robot manipulator. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed to train an agent capable of learning the robot's motion, allowing for random initial and target positions and orientations. Several reward functions are implemented to enhance the accuracy of the robot's movement towards the desired position and orientation. This proposed approach demonstrates a high success rate and low mean error, outperforming other state-of-the-art methods. Furthermore, the approach is validated on a physical robot, confirming its practical applicability and effectiveness. © 2024 IEEE.

关键词： Deep reinforcement learning

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Intelligent Real-Time Hypoglycemia Prediction for Type 1 Diabetes 2

Intelligent Real-Time Hypoglycemia Prediction for Type 1 Dia...

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Mahdy, Nevien A. Elnokrashy, Ahmed F. Hammad, D.A. Mohamed, Wael A. Benha University Benha Faculty of Engineering Electrical Engineering Department Benha Egypt Nile University Faculty of Information Technology and Computer Science Computer Science Department Giza Egypt Benha University Benha Faculty of Engineering Basic Engineering Sciences Department Benha Egypt

ISBN: (纸本)9798350362633

Hypoglycemia in Type 1 Diabetes (T1D) refers to a condition where blood glucose (BG) levels drop to abnormally low levels, typically below 70 mg/dL. This can occur when there is an excessive amount of insulin relative to the blood glucose level, leading to an imbalance that can be dangerous and potentially life-threatening if not promptly treated. The availability of large amounts of data from continuous glucose monitoring (CGM), insulin doses, carbohydrate intake, and additional vital signs, together with deep learning (DL) techniques, has revolutionized algorithmic approaches for BG prediction in T1D, achieving superior performance. In our study, we employed a Long Short-Term Memory (LSTM) neural network architecture to predict hypoglycemia events in patients with T1D. For the training and testing, we utilized the OhioT1DM (2018) dataset. In addition, real-time data collected from an individual patient for the evaluation. This patient utilized the CGM FreeStyle Libre (FSL) system, along with a smartwatch to monitor step count. The LSTM model exhibited performance demonstrating exceptional levels of sensitivity, specificity, and accuracy scores of 97.09%, 94.17%, and 95.63%, respectively, when assessed using the Ohio test dataset. Our research provides strong evidence supporting the system's efficacy in managing hypoglycemia events in individuals diagnosed with T1D. © 2024 IEEE.

关键词： Classification Deep learning Glucose prediction Hypoglycemia Type 1 diabetes

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Enhancing Decisions of Goalkeeper and Kicker Players in the RoboCup 2D Simulation League through Behavioral Cloning 20

Enhancing Decisions of Goalkeeper and Kicker Players in the ...

引用

20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Nasiri, Mohammad Hesam Zonouzi, Seyed Hassan Majid Salimi-Badr, Armin Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9798350383942

RoboCup is recognized as a landmark project in the robotics and artificial intelligence industry, providing a crucial platform for the advancement of research and innovations in autonomous and multi-agent systems. This study delves into the use of behavioral cloning in the RoboCup 2D Simulation League to imitate the best decisions and improve the performance of the goalkeeper along with the kicker players. To improve the performance of our soccer team in the league, we have developed a new approach that combines behavioral cloning and game log parsing. To achieve this, we played extensively against the champion team Helios and analyzed the actions and decisions of their goalkeeper and kicker players. This data was used to create a large dataset that our team could use to improve their imitation skills. Our innovative log generator tool and game logs parser made the labeling process easier and will also be useful for future research in the league. Novel features are extracted to represent the state of the game considering the limitations of each player including their head direction along with their field of view. Next, a neural structure is trained to imitate the behavior of kicker players and the goalkeeper. Moreover, the time limit for decision-making in each turn is considered. This study showed that using behavioral cloning for imitation learning noticeably improved our team's actions and decisions due to the enhancement of the number of accurate passes, on-target shoots, and saves by the goalkeeper. © 2024 IEEE.

关键词： Decision making

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COVID-Pneumonia Classification using Chest X-Ray 14

COVID-Pneumonia Classification using Chest X-Ray

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Manjunath, Apeksh S. Monis, Derryl Kevin Gurukiran, D. Shastry, Sushil K. Ravikumar, V. Vidyavardhaka College of Engineering Computer Science and Engineering Mysore India Vidyavardhaka College of Engineering Dean Faculty Computer Science and Engineering Mysore India

The traditional methods of examining the reports physically is time consuming and chances of missing out smaller details are more. In the present world, Image Processing in the medical field has a significant impact in making the job of medicos easy. To make this job easy it is expected to have an automated system for medical image analysis related Covid-19. In this paper we are analyzing various approaches available for medical image analysis and to propose an efficient approach to increase the current system's performance. © Grenze Scientific Society, 2023.

关键词： Image classification

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Research on API Sequence Recommendation Based on Code Property Graph 5

Research on API Sequence Recommendation Based on Code Proper...

引用

5th International Conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Luo, Xianchao Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331533991

The utilization of APIs not only enhances software reusability but also improves development efficiency and reduces costs. However, with the continuous increase in the number of APIs, developers face difficulties in quickly recalling and comprehending the appropriate API call sequences and their constraints. Thus, there is a need for an efficient API sequence recommendation tool. The key task in achieving efficient API sequence recommendation is to understand the semantic structure of the code context. However, most researchers currently lack an analysis of program structure in API sequence recommendation studies, leading to a decrease in the accuracy of these tools. To achieve a deeper understanding of the semantic context of code, this paper employs semantic syntax information and incorporates control structure information to improve the accuracy of API sequence recommendations. Specifically, this study first employs early fusion techniques based on the abstract syntax tree to incorporate control flow edges and data dependency edges - two types of heterogeneous edges - to construct a new representation of code known as a code property graph. This graph is then used to analyze the semantic syntax information and control structures of the code. Subsequently, a relational graph attention network model is employed to analyze the code property graph. Experimental results indicate that the accuracy of API sequence recommendations significantly improves with the incorporation of code control structures. © 2024 IEEE.

关键词： computer software reusability

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