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2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Arnia, Fitri Saddami, Khairun Muharar, Rusdha Dwi Pratiwi, Dea Ananda Nurdin, Yudha Universitas Syiah Kuala Department of Electrical and Computer Engineering Indonesia Universitas Syiah Kuala Telematics Research Center Indonesia FernUniversität in Hagen Faculty of Mathematics and Informatics Germany

ISBN: (纸本)9798350382822

Diabetes mellitus, causing substantial global mortality with millions affected in 2019, necessitates early detection methods to aid Diabetic Foot Ulcer (DFU) patients. We proposed a mobile application for early DFU identification using a Convolutional Neural Network (CNN) classifier. The application embedded a fine-tuned MobileNetV2 model specifically adapted for DFUs, trained on a plantar thermogram dataset through transfer learning. We evaluate the model's classification performance pre- and post-embedded into the application. Results reveal that the pre-embedded model achieved 90% accuracy, 100% specificity, and 80% sensitivity. After embedding, the model retained 85% accuracy, 80% specificity, and 90% sensitivity. Notably, a slight accuracy decrease post-embedded is likely attributed to the normalization of the input image. This study emphasizes that the interplay between model performance and mobile embedded might influence the model's performance. It underscores the meticulous consideration of mobile platform-specific processes crucial for accurate and effective early DFU detection. © 2023 IEEE.

关键词： Infrared imaging

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Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency - A review and examples of quantitative measurement methods

Evaluating the Inclusiveness of Artificial Intelligence Soft...

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2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Alevizos, Vasileios Georgousis, Ilias Simasiku, Akebu Messinis, Antonis Karypidou, Sotiria Malliarou, Dimitra Karolinska Institute Department of Learning Informatics Managament and Ethics Stockholm Sweden International Hellenic University Mlv Research Group Department of Computer Science Kavala Greece Zambia University Department of Computer Software Engineering Ndola Zambia Greece International Hellenic University Mlv Research Group Department of Computer Science Greece IntelliSolutions Athens Greece

ISBN: (纸本)9798350394528

The escalating integration of Artificial Intelligence (AI) in various domains, especially Project Management (PM), has brought to light the imperative need for inclusivity in AI systems. This paper investigates the role of AI software in augmenting both the inclusiveness and efficiency within the realm of PM. The research pivots around specific criteria that define and measure the inclusiveness of AI in PM, highlighting how AI, when developed with inclusiveness in mind, can significantly enhance project outcomes. However, there are inherent challenges in achieving this inclusiveness, primarily due to biases embedded in AI learning databases and the design and development processes of AI systems. The study offers a comprehensive examination of AI's potential to revolutionize PM by enabling managers to concentrate more on people-centric aspects of their work. This is achieved through AI's ability to perform tasks such as data collection, reporting, and predictive analysis more consistently and efficiently than human counterparts. However, the incorporation of AI in PM extends beyond mere efficiency;it represents a paradigm shift in the epistemology of PM, calling for a deeper understanding of AI's role and impact on society. Despite these advantages, the adoption of AI comes with significant challenges, particularly in terms of bias and inclusiveness. Biased AI learning databases, which use shared and reusable datasets, often perpetuate initially discriminatory algorithms. Moreover, unconscious biases and stereotypes of AI designers, developers, and trainers can inadvertently influence the behavior of the AI systems they create. This necessitates a paradigmatic shift in how AI systems are developed and governed to ensure they do not replicate or exacerbate existing social inequalities. The research proposes a methodological approach involving the development of criteria for inclusion and exclusion, alongside data extraction, to evaluate the inclusiveness and efficienc

关键词： Project management

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Spatial, Spatiotemporal, and Feature Analysis of Electroencephalogram During Imagined Speech with Visual vs Audio Cues 3

Spatial, Spatiotemporal, and Feature Analysis of Electroence...

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3rd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2023

作者： Fitriah, Nilam Budikayanti, Astri Zakaria, Hasballah Latifah Erawati Rajab, Tati Universitas Indonesia Faculty of Computer Science Depok Indonesia Universitas Indonesia Department of Neurology Jakarta Indonesia Institut Teknologi Bandung Department of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350327762

People with speech disorders have difficulties in social life and welfare. To overcome this barrier, researchers have conducted studies to recognize imagined speech from biosignals. The essential biosignals in producing speech are brain signals. This study aimed to determine activated brain regions during the imagined speech by spatial and spatiotemporal analysis to assist feature selection in imagined speech recognition. We recorded the normal subjects' brain signals using electroencephalography (EEG) when they imagined saying a word without vocalization. The protocol ran in two different-time sessions to cover the EEG nonstationary. We utilized visual cues in the first sub-session and audio cues in the second one. One word per sub-session had seven trials, each consisting of a rest state and cue presentation, followed by four repetitions of the imagined speech state, each preceded by a preparation state. We calculated power spectral entropy (PSE) values from the acquired dataset and used them as a feature since they captured signals' activity. The normalized PSE above 0.5 (middle value) was considered as high activity. The results showed that repeated-trial protocol induced brain activity during imagined speech. For visual or audio-cued imagined speech, the selected channels were in the central and left frontal regions, with additional right temporal, left parietal, and left occipital regions for visual-cued imagined speech. The highest PSE was found in the central region. Meanwhile, the lowest PSE was shown in the preparation state, confirming that cue presentation was not affecting imagined speech states. © 2023 IEEE.

关键词： Electroencephalography

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Enhancing Voxel-Based Human Pose Classification Using CNN with Modified VGG16 Method 8

Enhancing Voxel-Based Human Pose Classification Using CNN wi...

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Rahmanti, Farah Zakiyah Pradnyana, Gede Aditra Priyadi, Ardyono Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya60111 Indonesia Universitas Pendidikan Ganesha Department of Informatics Bali81116 Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9798350344691

Human pose classification based on 3D point cloud is a challenging problem in pattern recognition and computer vision. The human pose prediction based on a 3D point cloud is a first step in human monitoring because the advantages are robustness to light and having an accurate location in 3D space. This research proposed a novel method for human pose classification based on a 3D point cloud to overcome that condition. However, with the proposed method of voxel-based feature extraction and Convolutional Neural Network (CNN) modified VGG16 achieved great success in classifying human poses with 3D point cloud inputs. This research proposes a CNN with modified VGG16 network to classify 3D point cloud human poses by present voxel-based feature extraction. This work uses our primary 3D point cloud data from LiDAR 32-channel. Before 3D point cloud learning, the first step is pre-processing data with normalization and extracting features with voxelization. Our experiment uses two types of classification cases, namely the classification for binary-class and multi-class 3D point cloud human poses. Experimental results show that our proposed method performs well and excellently, obtaining an accuracy value of 90% for the binary-class case and outperforming other existing methods. With our proposed method, it will be possible to recognize human poses better. © 2023 IEEE.

关键词： Feature extraction

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Deep Learning Based License Plate Number Recognition for Smart Cities

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computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

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A Robust Preprocessing Method for Measuring Image Visual Quality Using Log-Polar FFT Features 8th

A Robust Preprocessing Method for Measuring Image Visual Qua...

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8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023

作者： Chen, Guang Yi Krzyzak, Adam Valev, Ventzeslav Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia1113 Bulgaria

ISBN: (纸本)9783031495519

To register two images, existing methods normally estimate the parameters of an affine transform, and then perform an inverse affine transform to the test image by using the estimated parameters. Any metric can then be applied to the reference image and the normalized image. In this paper we propose a new method for measuring visual image quality which circumvents the need to estimate the parameters of the affine transform. Instead, we use the log-polar transform and the fast Fourier transform (FFT) to extract features that are invariant to translation, rotation, and scaling. We apply the existing structural similarity index measure (SSIM) to the two invariant feature images, where no inverse transform is needed. Experimental results show that our proposed method outperforms the standard metric, the mean SSIM (MSSIM), significantly in terms of visual quality scores. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image visual quality quality metrics rotation invariance scale invariance translation invariance

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Predicting Protein-Protein Interactions Using Self-Attention-Based Deep Neural Networks and FastText Embeddings 15

Predicting Protein-Protein Interactions Using Self-Attention...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Oviya, I.R. Shanmukha Sravya, N. Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350370249

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model's performance is assessed through 5-fold cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of 92.28% and 94.08%, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Constructivist Learning: The Role of Generative AI in Personalised Learning Experiences 26

Enhancing Constructivist Learning: The Role of Generative AI...

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26th International Conference on Enterprise Information Systems, ICEIS 2024

作者： Guo, Hua Yi, Weiqian Liu, Kecheng School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Harrow School Druries House London United Kingdom Informatics Research Centre University of Reading Reading United Kingdom

ISBN: (纸本)9789897586927

This paper explores the transformative role of generative AI in enhancing constructivist learning, where students actively construct knowledge through meaningful experiences. By investigating the synergies between generative AI and constructivist learning, the study uncovers how AI fosters personalized educational experiences. The research underscores the profound influence of generative AI on constructivist learning, empowering students to become active, motivated, and lifelong learners by tailoring their education, fostering creativity and collaboration, and upholding ethical principles. The study advocates for the responsible and purposeful integration of generative AI, which would revolutionize education and prepare students for future challenges. Copyright © 2024 by Paper published.

关键词： Students

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Experimental Learning in Electrical engineering with the Erasmus+ DIG-SENSING Blended Intensive Program 32

Experimental Learning in Electrical Engineering with the Era...

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32nd Annual Conference of the European Association for Education in Electrical and Information engineering, EAEEIE 2023

作者： Laura, Grindei Lorente, Vicent Posadas-Yague, Juan-Luis Grout, Ian Fischer, Thomas Technical University of Cluj-Napoca Department of Electrotechnics and Measurements Cluj-Napoca Romania Universitat Politècnica de València School of Informatics Department of Computer Engineering Valencia Spain University of Limerick Department of Electronic and Computer Engineering Limerick Ireland University of Applied Sciences Campus Vienna Department Engineering Vienna Austria

ISBN: (纸本)9789038656908

This paper presents the Erasmus+ DIG-SENSING project which is a Blended Intensive Program (BIP) that has the main objective to increase the motivation of students in achieving knowledge and practical experience in the field of electronic circuits. This is to be achieved through projects that integrate the design of electronic circuits, and the programming of microcontrollers through an initial online series of Webinars and local activities within the partner institutions from March to May 2023 that are then followed by a physical Summer School in July 2023. The structure and implementation of DIG-SENSING, with the benefits for students, academic staff, and partner universities will be presented. In this project, the participating students must develop and tackle a challenge that brings together Information and Communication Technologies in the framework of solving socio-environmental *** paper also presents and evaluation of students perception after the first webinar. © 2023 Fontys University of Applied Science.

关键词： Students

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Prediction of Parkinson’s disease based on artificial neural networks using speech datasets

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13571-13584页

作者： Liu, Wei Liu, Jierong Peng, Tao Wang, Guojun Balas, Valentina Emilia Geman, Oana Chiu, Hung-Wen School of Information and Communication Engineering Hainan University Hainan Haikou570228 China School of Computer Science and Cyber Engineering Guangzhou University Guangdong Guangzhou510006 China College of Biomedical Information and Engineering Hainan Medical University Street Hainan Haikou571199 China Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad310130 Romania Department of Health and Human Development Stefan cel Mare University of Suceava Suceava720229 Romania Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan

Parkinson’s disease (PD) is a progressive disorder of the nervous system that affects movement. Early prediction of PD can increase the chances of earlier intervention and delay the onset of the disease. Vocal impairment is one of the most important signs in the early stages of PD. Therefore, PD detection based on speech analysis and vocal patterns has attracted significant attention recently. In this paper, we propose a vowel-based artificial neural network (ANN) model for PD prediction based on single vowel phonation. Firstly, we propose a novel multi-layer neural network based on speech features to predict PD. The speech samples from 48 PD patients and 20 healthy individuals are processed into four types: vowel, number, word, and short sentence. Secondly, we establish ANN models with single-type speech samples versus combinations of multi-type speech samples, respectively. Comparative experiments demonstrate that the single-type vowel model is superior to other single-type models as well as multi-type models. Finally, we build a vowel-based ANN model for PD prediction and evaluate its performance. Extensive experiments demonstrate that the proposed model has a prediction accuracy of 91%, sensitivity of 99%, specificity of 82%, and area under the receiver operating characteristic curve (AUC) of 91%, which is superior to the performance of previous methods. Overall, this study demonstrates that the proposed model can provide good classification accuracy for predicting PD and can improve the rate of early diagnosis. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Network layers

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