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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Muh Adnan Esa Prakasa Yuyun Najirah Umar Nasrullah Adi Sadli A. Edeth Fuari Anatasya Hazriani Mashur Razak dept. of informatics engineering Handayani University of Makassar Makassar Indonesian National Research and Innovation Agency research center for data and information science Bandung Indonesian dept. of computer system Handayani University Makassar Makassar Indonesian

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This research compares the performance of two pre-trained models based on the transformer, namely text-to-text transfer transformer (T5) and bidirectional and auto-regressive transformer (BART), through a fine-tuned for correcting grammar in Indonesian sports news texts. We used vennify/t5-base-grammar-correction and onionLad/grammar-correction-bart-base, was trained using a dataset of 10,075 sentences from Indonesian language sports news sites. We tested the model performance using bilingual evaluation understudy (BLEU), google's language evaluation understanding (GLEU), training loss, and validation loss. The results show that BART was more efficient in learning data patterns and has better generalization ability, supported by BLEU and GLEU scores of 0.8560 and 0.9655. Meanwhile, T5 maintains simpler sentence structures, supported by BLEU and GLEU scores of 0.8120 and 0.9564. BART excels at handling more complex contexts, making it more effective at accurately correcting errors in dynamic sports news. Both models have their respective advantages, with BART being better at correcting detailed grammatical errors, while T5 offers consistent correction of more simple sentence structures.

关键词： Training Measurement Analytical models Accuracy computational modeling Computer architecture Transformers Grammar Sports Context modeling

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Gradual Further Pre-training Architecture for Economics/Finance Domain Adaptation of Language Model

Gradual Further Pre-training Architecture for Economics/Fina...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Sakaji, Hiroki Suzuki, Masahiro Izumi, Kiyoshi Mitsugi, Hiroyuki The University of Tokyo School of Engineering Department of Systems Innovation Tokyo Japan Daiwa Institute of Research Ltd. Frontier Technologies Research Center. Data-driven Science Dept. Tokyo Japan

ISBN: (纸本)9781665480451

We propose a new pre-trained architecture for economics/finance domain adaptation of language models in this paper. Pre-trained language models have become commonplace in a wide range of language processing applications. Because they learn from generic documents such as Wikipedia, many pre-trained language models are not fully adapted to the domain. As a result, there are two approaches: one is to develop a domain-specific pre-trained language model, and the other is to adapt the model learned on general documents to the domain through further pre-training with domain texts. However, no definitive better method has been discovered, and each project is working on it in different ways. As a result, this study focuses on the Japanese language's economics/financial domains and investigates how pre-trained language models can be better adapted to domain-specific tasks. © 2022 IEEE.

关键词： computational linguistics

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Leveraging YOLOv8 for Real-Time Parking Space Detection

Leveraging YOLOv8 for Real-Time Parking Space Detection

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Mahabintang Pallawabonang Muh. Adnan Sitti Zuhriyah Amirul Mukmin Esa Prakasa Yuyun dept. of informatics engineering Handayani University of Makassar Makassar Indonesia dept. of Computers System Handayani University of Makassar Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesian

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This research develops a smart parking system based on computer vision integrated with the Firebase platform, utilizing the YOLOv8 algorithm and the OpenCV library. This system is designed to detect vehicles and determine the availability status of parking slots in real time through video recording analysis. The dataset was obtained from recorded parking activity videos, then processed using Roboflow for data labeling, and fine-tuned on Google Colab. The resulting model shows a vehicle detection accuracy of up to 99.1% with high stability, as indicated by the training loss graph. The process of mapping the parking area is carried out through an OpenCV-based application for the identification and configuration of parking slots. The status of each parking slot is automatically updated to the Firebase Realtime database, enabling efficient historical data recording and information management. Testing shows the system's reliable performance under various lighting conditions, with high capabilities in vehicle detection and real-time data management. This study makes a significant contribution to the development of parking management solutions through the integration of technology that supports operational efficiency and high accuracy.

关键词： Training Accuracy Web design Image processing Machine learning Automated parking Streaming media Real-time systems Video recording Testing

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Synchronous Parallel Heuristics for Solving the Joint Order Batching and Picker Routing Problem

Synchronous Parallel Heuristics for Solving the Joint Order ...

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Congress on Evolutionary Computation

作者： Son Thai Tran Rui Jorge Almeida Christof Defryn Inneke Van Nieuwenhuyse Dept. of Quantitative Economics School of Business and Economics Maastricth University Maastricht The Netherlands Dept. of Data Analytics and Digitization School of Business and Economics Maastricth University Maastricht The Netherlands Dept. of Engineering Management Faculty of Business and Economics University of Antwerp Antwerp Belgium Faculty of Sciences FlandersMake@UHasselt and Data Science Institute Hasselt University Hasselt Belgium

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The joint order batching and picker routing problem is an important problem for improving warehouse efficiency. The goal is to minimize the total distance travel of picking customer orders. It is NP-hard, indicating that exact solutions are intractable for large instances. Many solution methods provided in the current literature use local search to solve the problem sequentially, often including a complex searching procedure for the order batching problem (OBP) followed by a simple heuristic for the picker routing problem (PRP). In this paper, we propose three heuristics that jointly solves the OBP and the PRP: two of these are combinations of variable neighborhood search, simulated annealing, and tabu search, while the third one uses guided local search. We discuss how the search procedure of the proposed algorithms can be parallelized, and benchmark them against state-of-the-art heuristics using well-studied instances. The results show a reduction of 3.06% to 7.63% in the geometric mean of the total travel distance across 64 instances. Increasing the number of threads in parallelization leads to diminishing returns in reducing running time and has no statistical effect on travel distance. In contrast, increasing the chunk size results in a linear increase in running time for all proposed algorithms, and improves the travel distance obtained when the batch size is 75 items.

关键词： Heuristic algorithms Simulated annealing Evolutionary computation Benchmark testing Routing Search problems

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Air Traffic and Usage Predictions in Avionic Communications using Attention Based VAEGAN Model

Air Traffic and Usage Predictions in Avionic Communications ...

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Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Hind Mukhtar Raymond Schaub Melike Erol-Kantarci Satcom Direct Inc. Melbourne Florida University of Ottawa Ottawa Ontario Dept. of Data Science Satcom Direct Inc. Melbourne Florida Dept. of Electrical & Computer Engineering University of Ottawa Ottawa Ontario

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

The need for uninterrupted connection is an enabler for enhanced connectivity in aircrafts. Satellite based communication in aircrafts exhibits high latency and can have limited data rates. Furthermore, the increasing demand for air travel can strain the capacity of satellite communication systems, necessitating the development of more robust traffic prediction methods. This necessity is particularly pronounced in the realm of business aviation, given the irregular traffic patterns compared to scheduled commercial flights. In this paper, we present an attention-based VAEGAN model designed to forecast the number of active tails within satellite beams. We extend the capabilities of our proposed model to predict the volume of upstream and downstream usage within these satellite beams. To validate our model, we employ real avionics data collected from the two most heavily traversed flight routes. Finally, we perform a comparative analysis, benchmarking the performance of existing machine learning-based techniques with our proposed model. The findings indicate that the proposed VAEGAN model exhibits superior performance in forecasting irregularities in the timeseries pattern, specifically in forecasting unusual highs or lows in the number of aircrafts within the satellite beam, outperforming alternative models.

关键词： Adaptation models Solid modeling Satellites Atmospheric modeling Tail Predictive models Aerospace electronics

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Comparative Analysis of Machine Learning Techniques for Arabic Text Classification Using the SANAD dataset

Comparative Analysis of Machine Learning Techniques for Arab...

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Mechatronics and Robotics engineering (ICMRE), International Conference on

作者： Hani Attar Yousef Sharrab Saja Smadi Safa Jbaily Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Data Science and Artificial Intelligence Isra University Amman Jordan dept. Computer Science Yarmouk University Irbid Jordan

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Applying Machine Learning (ML) models for Arabic text classification is a growing research field that has recently raised a large number of research. Generally, classifying text involves selecting a category based on its content from a list of pre-established categories. data classification is among the most popular Natural Language Processing (NLP) activities. Moreover, automatic article classifications, such as the Single-label Arabic News Articles dataset (SANAD), are desperately needed to improve the retrieval of the requested data, which this paper investigates. The ML leverages large datasets to improve efficiency; indeed, by locating focal spots within the text, the learning mechanisms facilitate attention tools better suited for handling lengthy texts. The proposed work employs a dataset of 7,000 articles of separately classified Arab news items and then breaks them into seven categories of 1,000 news stories each: politics, education, sports, religion, culture, finance, and medicine. The investigated SANAD consists of three primary datasets from three news portals: Akhbarana, AlArabiva, and AlCtrl. Finally, to obtain trustable information, this study implements a variety of Conventional ML techniques, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression model to classify the investigated dataset. Our results show that the suggested solution outperformed the analytical search in the Arabic language in terms of accuracy, whereas the Logistic Regression model shows the best results among the seven utilized publicly available dataset classifications.

关键词： Support vector machines Logistic regression Adaptation models Accuracy Text categorization Artificial neural networks Nearest neighbor methods Natural language processing Robots Sports

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Automated Machine Learning for Test Case Prioritization

SSRN

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SSRN 2023年

作者： Romero, José Raúl Ramírez, Aurora García-Martínez, Carlos Dept. of Computer Science and Numerical Analysis University of Córdoba AN Córdoba14071 Spain Andalusian Research Institute in Data Science and Computational Intelligence University of Córdoba AN Córdoba14071 Spain

Test case prioritization (TCP) involves ordering and selecting the most relevant test cases to verify that the current functionality of a software system remains unaffected by code changes. Recently, TCP has been solved by machine learning (ML), predicting the failure probability of each test case. However, software engineers may struggle to identify and implement the most suitable predictive models for TCP. As new builds adapt the test suite being tested, the model performance may decline as these new builds are incorporated. In this study, we address these challenges by applying automated workflow composition and hyperparameter optimization. Both are considered tasks within automatic machine learning (AutoML). With this aim, our proposal employs grammar-guide genetic programming as the underlying mechanism for implementing the AutoML algorithm. Our experimental results demonstrate that our approach can adapt to the particularities of the system under test, selecting the most appropriate workflow and hyperparameters for each build. More importantly, our approach eliminates the need for testers to possess extensive knowledge of ML, while allowing them to inspect workflows to understand the evolution of the prioritization process. This research showcases the potential of AutoML in software engineering, specifically for the TCP problem. © 2023, The Authors. All rights reserved.

关键词： Genetic programming

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Leveraging Reinforcement Learning and AlphaGo Algorithms for Enhanced Cancer Detection: A Novel Approach in Medical AI

Leveraging Reinforcement Learning and AlphaGo Algorithms for...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Pawan Whig Iti Batra Seema Nath Jain Pronaya Bhattacharya Shweta Redkar Satyabrata Roy VIPS-TC New Delhi India Vivekananda College Delhi University Delhi India School of Engineering and Technology Amity University Kolkata India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India Dept. of CSE Manipal University Jaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Advancements in artificial intelligence (AI) have opened new avenues for enhancing healthcare outcomes, particularly in the field of cancer detection. This research paper investigates the application of reinforcement learning (RL) and AlphaGo algorithms to improve cancer detection accuracy and efficiency. By integrating the strategic decision-making capabilities of AlphaGo with the adaptive learning approach of RL, we propose a novel AI model designed to analyze medical imaging data for the early and precise identification of malignant cells. Our experiments, conducted on a large dataset of 10,000 labeled medical images, demonstrate significant improvements in key performance metrics, including accuracy, sensitivity, specificity, precision, and the F1-score, compared to traditional machine learning models. The proposed model achieved an accuracy of 94.7 % , a sensitivity of 92.3%, and a specificity of 96.8%. These findings highlight the potential of RL and AlphaGo algorithms to revolutionize cancer diagnostics, offering more reliable and timely detection methods. The study also addresses the challenges and ethical considerations of implementing AI in clinical settings, emphasizing the need for transparent and interpretable systems. This research suggests a promising future for AI-driven cancer detection, ultimately aiming to improve patient outcomes and healthcare efficiency.

关键词： Adaptation models Ethics Accuracy Sensitivity Decision making Reinforcement learning Cancer detection Reliability Medical diagnostic imaging Cancer

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Design and Implementation of a Mobile Application for Intelligent Attendance Tracking

Design and Implementation of a Mobile Application for Intell...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Priyanka Kaushik Nadhiya S Deepak Jain Anupama Verma Saurabh Kumar S. Bhavadharani Dept. of Computer Science Engineering Chandigarh University Chandigarh India Dept. of AI & Data Science Dhanalakshmi Srinivasan College Coimbatore India Dept. of Management IIM Indore Indore India Dept. of Business Management & Comm Usha Martin University Jharkhand India Dept. of Business Management & Commerce Mangalayatan University Aligarh India Dept. of Commerce Meenakshi A.H. Edu. & Research Chennai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Taking attendance is an important task in schools, universities, and other institutions. However, traditional attendance taking methods such as paper-based attendance sheets or manual entry into an electronic system are time-consuming and prone to errors. In recent years, the emergence of facial recognition technology has opened up new possibilities for attendance management systems. This research paper discusses the development and implementation of a Smart Attendance System using Facial Recognition technology using OpenCV in educational institutions. The study explores the benefits, limitations, and ethical considerations associated with this technology and its implications for the future of attendance management systems. This model takes use of LBPH and gja, exfilaface recognition to increase accuracy over conventional models. After this, the connection of recognized faces is compared with the kdatabase containing student’s faces. This model will be an effective method for controlling student attendance.

关键词： Privacy Accuracy Face recognition Scalability Refining Organizations Real-time systems Mobile applications Security Faces

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Smart City Solutions Leveraging Internet of Things (IoT) Technology

Smart City Solutions Leveraging Internet of Things (IoT) Tec...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Bhawesh Kumawat E. Shalini Vinay Tomar Rajesh Kumar Upadhyay Divyanshu Gupta Geetha P V Dept. of CSE Madhav University Pindwara India Dept. of Computer Science Meenakshi A.H. Edu. & Research Chennai India Dept. of Chemistry Jiwaji University Gwalior India Dept. of EEE Usha Martin University Jharkhand India Dept. Electronics & Comm. Engineering J.S. University Shikohabad India Dept. of AI & Data Science Dhanalakshmi Srinivasan College Coimbatore India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The widespread adoption of the Internet of Things (IoT) is facilitating the development of Smart City initiatives globally. Everyday objects are increasingly equipped with electronic gadgets and connected through various protocol suites, becoming part of a vast network. According to a recent Gartner study, by 2020, smart cities are projected to deploy 50 billion connected devices. This advancement is expected to make urban environments more adaptive and resilient to future changes. This paper explores current and future trends in smart cities and IoT, highlighting the technologies and applications driving their growth. We examine how the expansion of IoT directly impacts the evolution of smart cities and discuss the interplay between the two, as well as the factors motivating their development. Smart cities have the potential to reduce manual labor, mitigate disaster risks, and streamline daily life. Lastly, we address some of the challenges associated with IoT in the context of smart cities and propose potential solutions to these issues.

关键词： Technological innovation Protocols Smart cities Scalability Strategic planning Public security Internet of Things Resource management Waste disposal Sustainable development

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