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International Conference on Green Energy, Computing, and Intelligent Technology, GEn-CITy 2023

作者： Razaq, Fareeha Bilal, Muhammad Ramzan, Muhammad Naveed, Muhammad Razzaq, Samreen Department of Computer Science University of Sargodha Sargodha Pakistan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (纸本)9789819998326

Potato is the most widely grown and consumed food throughout the world. There are a number of potato crop diseases that affect production, and these diseases differ in symptoms, circumstances, and controls. Early detection and recognition of disease information can aid in disease prevention and production. This paper presents deep learning models using proposed CNN and pre-trained models for potato disease detection and classification. The proposed model is more efficient and accurate at detection and classification. To perform classification, we used two datasets: PLD and PlantVillage. The Xception CNN model serves as the foundation for our proposed model. It achieved 1.00 accuracy, 0.99 precision, 1.00 recall, and 0.99 F1-score on the PLD dataset. On the PlantVillage dataset, it achieved 1.00 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score. We also compared the results of Inception-ResNet-V2, MobileNet-V2, VGG-19, Inception-V3, and Xception models with the performance of our proposed model. For three classes of potato leaves, the proposed CNN model produced more accurate results than other pre-trained models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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High-Accuracy Hand Segmentation for Palmprint Biometric systems Improving Auto-Encoder Techniques 8

High-Accuracy Hand Segmentation for Palmprint Biometric Syst...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Yalcin, Kadir Dogan, Ramazan Ozgur Dogan, Hulya Bingol, Ozkan Graduate Education Institute Gumushane University Department of Artificial Intelligence and Intelligent Systems Gumushane Turkey Trabzon University Faculty of Computer and Information Sciences Department of Artificial Intelligence Engineering Trabzon Turkey Karadeniz Technical University Faculty of Engineering Department of Software Engineering Trabzon Turkey Gumushane University Faculty of Eng. and Natural Sciences Department of Software Engineering Gumushane Turkey

ISBN: (纸本)9798331540104

Biometric technologies have been widely adopted in various commercial products, ranging from security systems to personal devices, due to their exceptional reliability and user-friendliness. Among these, palmprint biometrics has gained increasing attention for its advantages, such as ease of use, a larger surface area, and the ability to capture more features compared to other biometric methods. A typical palmprint biometric system involves five key stages: (a) Image Acquisition, (b) Hand Segmentation, (c) Pattern Generation, (d) Feature Extraction, and (e) Verification/Identification. The overall success of the system depends on the effectiveness of each stage, with hand segmentation being crucial for identifying the most relevant features for accurate verification. This study aims to achieve high-accuracy biometric recognition by segmenting hands from images captured in uncontrolled environments with complex backgrounds. Accuracy (ACC), Intersection over Union (IoU), F1-Score, and Dice Coefficient are the four critical metrics used to assess the performance of segmentation techniques. The findings demonstrate that the U-Net + ViT and MA-Net + ResNet34 techniques outperform traditional auto-encoder methods in both quantitative performance metrics and visual analysis, leading to significantly improved hand segmentation for biometric systems. © 2024 IEEE.

关键词： Image acquisition

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Automated Surface Classification System Using Vibration Patterns - A Case Study With Wheelchairs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 884-895页

作者： Yu, Haoxiang Raychoudhury, Vaskar Saha, Snehanshu Edinger, Janick Smith, Roger O. Gani, Md Osman The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States Miami University Department of Computer Science & Software Engineering OxfordOH45056 United States Bits Pilani K.K. Birla Goa Campus Department of Computer Science & Information Systems Appcair Zuarinagar403726 India University of Hamburg Department of Informatics & Distributed Operating Systems Hamburg20146 Germany University of Wisconsin-Milwaukee Department of Rehabilitation Sciences & Technology MilwaukeeWI53706 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Finding a barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility are yet to be investigated. In order to address this problem, we present the WheelShare system, which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowdsourcing. Based on this surface-specific vibration, we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this article include the following. 1) We have developed a machine-learning-enabled surface recognition system that uses sensor data to capture surface-induced vibration patterns. 2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. 3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing and successfully recognized the surface characteristics. © 2020 IEEE.

关键词： Accelerometers

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End-to-end entity extraction from OCRed texts using summarization models

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Neural Computing and Applications 2024年第35期36卷 22347-22363页

作者： Villa-García, Pedro A. Alonso-Calvo, Raúl García-Remesal, Miguel Biomedical Informatics Group Department of Artificial Intelligence Universidad Politécnica de Madrid Campus Montegancedo s/n Boadilla del Monte Madrid28660 Spain Biomedical Informatics Group Department of Languages Informatic Systems and Software Engineering Universidad Politécnica de Madrid Campus Montegancedo s/n Boadilla del Monte Madrid28660 Spain

A novel methodology is introduced for extracting entities from noisy scanned documents by using end-to-end data and reformulating the entity extraction task as a text summarization problem. This approach offers two significant advantages over traditional entity extraction methods while maintaining comparable performance. First, it utilizes preexisting data to construct datasets, thereby eliminating the need for labor-intensive annotation procedures. Second, it employs multitask learning, enabling the training of a model via a single dataset. To evaluate our approach against state-of-the-art methods, we adapted three commonly used datasets, namely, Conference on Natural Language Learning (CoNLL++), few-shot named entity recognition (Few-NERD), and WikiNEuRal domain adaptation (WikiNEuRal + DA), to the format required by our methodology. We subsequently fine-tuned four sequence-to-sequence models: text-to-text transfer transformer (T5), fine-tuned language net T5 (FLAN-T5), bidirectional autoregressive transformer (BART), and pretraining with extracted gap sentences for abstractive summarization sequence-to-sequence models (PEGASUS). The results indicate that, in the absence of optical character recognition (OCR) noise, the BART model performs comparably to state-of-the-art methods. Furthermore, the performance degradation was limited to 3.49–5.23% when 39–62% of the sentences contained OCR noise. This performance is significantly superior to that of previous studies, which reported a 10–20% decrease in the F1 score with texts that had a 20% OCR error rate. Our experimental results demonstrate that a single model trained via our methodology can reliably extract entities from noisy OCRed texts, unlike existing state-of-the-art approaches, which require separate models for correcting OCR errors and extracting entities. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optical character recognition

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Uvlhub: A Feature Model Data Repository Using Uvl and Open Science Principles

SSRN

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SSRN 2023年

作者： Romero-Organvidez, David Galindo, José A. Sundermann, Chico Horcas, Jose-Miguel Benavides, David Department of Computer Languages and Systems University of Seville Spain Institute of Software Engineering and Programming Languages Ulm University Germany CAOSD ITIS Software University of Málaga Spain

Feature models are the de facto standard for modelling variabilities and commonalities. Concrete models in different domains exist;however, many are in private or sparse repositories or belong to discontinued projects. The dispersion of knowledge of feature models hinders the study and reuse of these artefacts in different studies. The Universal Variability Language (UVL) is a community effort textual feature model language that promotes a common way of serialising feature models independently of concrete tools. Open science principles promote transparency, accessibility, and collaboration in scientific research. Although some attempts exist to promote feature model sharing, the existing solutions lack open science principles by design. In addition, existing and public feature models are described using formats not always supported by current tools. This paper presents UVLHUB, a repository of feature models in UVL format. UVLHUB provides a front end that facilitates the search, upload, storage, and management of feature model datasets, improving the capabilities of discontinued proposals. Furthermore, the tool communicates with Zenodo providing a permanent save of datasets and following open science principles. UVLHUB includes existing datasets and is readily available to include new data and functionalities in the future. It is maintained by three active universities in variability modelling. © 2023, The Authors. All rights reserved.

关键词： Concretes

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Development of Day-Ahead Peer-to-Peer Energy Trading with Time-Series Clustering

Development of Day-Ahead Peer-to-Peer Energy Trading with Ti...

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2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023

作者： Noorfatima, Nadya Jung, Jaesung Onen, Ahmet Yoldas, Yeliz Ajou University Department of Energy Systems Research Suwon Korea Republic of Sultan Qaboos University Department of Electrical and Computer Engineering Muscat Oman Kayseri University Department of Software Engineering Kayseri Turkey

ISBN: (纸本)9798350301571

Day-ahead trading of electricity has been applied to ensure the balance between the amount of electricity sold and bought. Even so, due to the intermittent distributed energy resources (DERs), the actual condition can be varied significantly, and forecasting can be costly in order to provide high accuracy to minimize losses. Hence, this paper proposes a novel model-based day-ahead peer-to-peer (P2P) energy trading with regionalized trading prices, which are determined through time-series clustering. To improve the determination of price regions, the data parameter is derived from the day-ahead condition, which is forecasted from network condition, trading capacity, and trading price of the P2P energy trading. The performance of the proposed model of day-ahead P2P energy trading is evaluated with respect to the market operation stability and optimality. © 2023 IEEE.

关键词： day-ahead trading distributed optimization Peer-to-peer energy trading time-series clustering

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Learning and Motivational Impact of Game-Based Learning: Comparing Face-To-Face and Online Formats on computer Science Education

arXiv

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arXiv 2024年

作者： López-Fernández, Daniel Gordillo, Aldo Pérez, Jennifer Tovar, Edmundo Computer Science Department Universidad Politécnica de Madrid Madrid Spain Computer Languages and Systems and Software Engineering Department Universidad Politécnica de Madrid Madrid Spain

Contribution: This article analyzes learning and motivational impact of teacher-authored educational video games on computer science education and compares its effectiveness in both face-to-face and online (remote) formats. This work presents comparative data and findings obtained from 217 students who played the game in a face-to-face format (control group) and 104 students who played the game in an online format (experimental group). Background: Serious video games have been proven effective at computer science education, however it is still unknown whether the effectiveness of these games is the same regardless of their format, face-to-face or online. Moreover, the usage of games created through authoring tools has barely been explored. Research questions: Are teacher-authored educational video games effective in terms of learning and motivation for computer science students? Does the effectiveness of teacher-authored educational video games depend on whether they are used in a face-to-face or online format? Methodology: A quasi-experiment has been conducted by using three instruments (pre-test, post-test and questionnaire) with the purpose of comparing the effectiveness of Game-Based Learning in face-to-face and online formats. A total of 321 computer science students played a teacher-authored educational video game aimed to learn about software design. Findings: The results reveal that teacher-authored educational video games are highly effective in terms of knowledge acquisition and motivation both in face-to-face and online formats. The results also show that some students’ perceptions were more positive when a face-to-face format was used. Copyright © 2024, The Authors. All rights reserved.

关键词： Motivation

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A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

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Materials Today: Proceedings 2023年 80卷 2701-2705页

作者： Qahatan Alsudani, Mustafa Abbdal Reflish, Salah H. Moorthy, Kohbalan Mundher Adnan, Myasar Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

Currently, effective Intrusion-detection systems (IDS) still represent one of the important security tools. However, hybrid models based on the IDS achieve better results compared with intrusion detection based on a single algorithm. But even so, the hybrid models based on traditional algorithms still face different limitations. This work is focused on providing two main goals;firstly, analysis based on the main methods and limitations of the most-recent hybrid model-based on intrusion detection, secondly, to propose a novel hybrid IDS model called TLBO-ELM based on the Firefly algorithm and Fast Learning Network. © 2021

关键词： Optimization

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MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Disease Prediction Through Machine Learning Classifiers 4th

MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Dise...

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4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023

作者： Loba, Jannatul Mia, Md. Rajib Mahmud, Imran Mahi, Md. Julkar Nayeen Whaiduzzaman, Md. Ahmed, Kawsar Department of Software Engineering Daffodil International University Dhaka Bangladesh School of Information Systems Queensland University of Technology Brisbane Australia Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9783031423161

Alzheimer’s disease (AD) is a neurological illness that worsens with time. The aged population has expanded in recent years, as has the prevalence of geriatric illnesses. There is no cure, but early detection and proper treatment allow sufferers to live normal lives. Furthermore, people with this disease’s immune systems steadily degenerate, resulting in a wide range of severe disorders. Neuroimaging Data from magnetic resonance imaging (MRI) is utilized to identify and detect the disease as early as possible. The data is derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) collection of 266 people with 177 structural brain MRI imaging, DTI, and PET data for intermediate disease diagnosis. When neuropsychological and cognitive data are integrated, the study found that ML can aid in the identification of preclinical Alzheimer’s disease. Our primary objective is to develop a model that is reliable, simple, and rapid for diagnosing preclinical Alzheimer’s disease. According to our findings (MRIAD), the Logistic Regression (LR) model has the best accuracy and classification prediction of about 98%. The ML model is also developed in the paper. This article profoundly, describes the possibility to getting into Alzheimer’s disease (AD) information from the pre-clinical or non-preclinical trial datasets using Machine Learning Classifier (ML) approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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W-restrained Bidirectional Bounded-Suboptimal Heuristic Search 33

W-restrained Bidirectional Bounded-Suboptimal Heuristic Sear...

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33rd International Conference on Automated Planning and Scheduling, ICAPS 2023

作者： Atzmon, Dor Shperberg, Shahaf S. Sabah, Netanel Felner, Ariel Sturtevant, Nathan R. Department of Computer Science Royal Holloway University of London United Kingdom Department of Software and Information Systems Engineering Ben-Gurion University Israel University of Alberta Canada

ISBN: (纸本)1577358813

In this paper, we develop theoretical foundations for bidirectional bounded-suboptimal search (BiBSS) based on recent advancements in optimal bidirectional search. In addition, we introduce a BiBSS variant of the prominent meet-in-the-middle (MM) algorithm, called Weighted MM (WMM). We show that WMM has an interesting property of being Wrestrained, and study it empirically. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Artificial intelligence

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