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5th International Conference on Advancements in computing, ICAC 2023

作者： Saranga, R.G.V. Jayasinghe, A.H.N. Divyantha, U.A.S. De Silva, S.W.L.P. Swarnakantha, N.H.P. Ravi Supunya Kumari, P. K. Suriya Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children. © 2023 IEEE.

关键词： Image enhancement

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Real-Time Segmentation of IHC Images From Microscope Using Deep Learning Architecture 2

Real-Time Segmentation of IHC Images From Microscope Using D...

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2nd IEEE National Biomedical engineering Conference, NBEC 2023

作者： Hasan, Md Jahid Siti Halimatul Munirah Wan Ahmad, Wan Ahmad Fauzi, Mohammad Faizal Tung Hiong Lee, Jenny Khor, See Yee Meng Looi, Lai Abas, Fazly Salleh Multimedia University Faculty of Engineering Cyberjaya Malaysia Multimedia University Center for Visual Computing Faculty of Computing and Informatics Faculty of Engineering Cyberjaya Malaysia Sarawak General Hospital Department of Pathology Kuching Malaysia University Malaya Medical Center Department of Pathology Kuala Lumpur Malaysia Queen Elizabeth Hospital Department of Pathology Kota Kinabalu Malaysia Multimedia University Faculty of Engineering and Technology Cyberjaya Malaysia

ISBN: (纸本)9798350338546

Segmentation of nuclei in digital histopathology image analysis plays a critical role in the early assessment of breast cancer and may enable patients to get appropriate treatment. In this paper, we create a real-time application that thoroughly examines the effectiveness of various deep learning models, including U-Net, SegNet, ResNet50-Unet, and ResNet50-SegNet, in the domain of real-time segmentation. For real-time implementation, we use an industrial machine vision camera mounted to the microscope, stream the image from the microscope glass slide and segment it using the model. This experiment aims to identify the best deep-learning model for real-time segmentation of nuclei for immunohistochemistry (IHC)-stained glass slides. The models are evaluated in offline mode using test images from estrogen receptor IHC stains, taken from whole-slide images. The effectiveness of the model for real-time work is based on its segmentation computational time. For offline evaluation, the highest F1-score and Jaccard index is achieved by ResNet50-SegNet (85.21%) and ResNet50-Unet (0.725) accordingly. These findings support the proof of concept that deep learning models can effectively segment nuclei in real-time from IHC-stained glass slides. This research serves as a foundation for the future construction of fast and efficient deep learning models for real-time histopathological analysis directly from the microscope. © 2023 IEEE.

关键词： Image segmentation

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High-Precision Fractional-Order Integrator for Generating Pink Noise from White Noise 45

High-Precision Fractional-Order Integrator for Generating Pi...

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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Tokic, Duje Jurisic, Drazen University of Zagreb Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

ISBN: (纸本)9789532331035

For audio testing, a pink noise generator is a very useful and widely used tool for equalization of loudspeaker systems, room acoustics and crossovers. It can be built as a digital, analog or mixed circuit. The pink noise generator consists of a white noise source followed by a fractional order integrator (FOI) with a roll-off of 10 dB/decade. In this way, the obtained output power is constant in each decade, which is essential for testing audio devices within the entire audio frequency band. In this paper, an active realization of an FOI with an order 1/2 is presented, using an active-RC circuit that uses an operational amplifier as the active element. To build an FOI, we can use different approximation methods. We use our recently published Minimax method (MMX), with unequally (optimally) spaced singularities and compare it with a method proposed by other authors. The latter intuitively considers optimal singularities equally spaced on a logarithmic scale. We demonstrate the superiority of our proposed MMX approach. © 2022 Croatian Society MIPRO.

关键词： Timing circuits

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Objective Quality Assessment of Stereoscopic Video Using Inflated 3D Features

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SN Computer Science 2024年第6期5卷 1-14页

作者： Imani, Hassan Islam, Md Baharul Faculty of Engineering and Natural Sciences Bahcesehir University Yildiz Ciragan Cd Istanbul Besiktas 34349 Turkey Department of Computing and Software Engineering Florida Gulf Coast University Fort Myers 33965 FL United States

Convolutional Neural Networks (CNNs) have been receiving research attention for Stereoscopic Video Quality Assessment (SVQA) in recent years. Recently, researchers have used 3D CNNs for extracting useful spatial and temporal features from stereo videos and have used them for detecting the reduction in the quality of the stereoscopic videos. To our best knowledge, the concept of transfer learning (TL) has not been well-examined in SVQA. Pretraining and fine-tuning are approaches used in deep neural networks to transform the knowledge learned from other general fields. The previous methods that utilized TL used very heavy 3D ResNet architectures with several layers;therefore, they are very time-consuming. In this paper, we develop a new model for SVQA and use the Inflated 3-Dimensional ConvNet (I3D) network as the backbone feature extractor for our model. We first apply left and right videos to I3D models to extract their features. Then, we apply 3D CNNs to learn quality-aware features from stereo videos. We evaluate our proposed method using LFOVIAS3DPh2 and NAMA3DS1- COSPAD1 SVQA datasets. Extensive experimental studies on two datasets prove that the proposed method correlates with the subjective results. The Root-Mean-Square Error (RMSE) for the NAMA3DS1-COSPAD1 dataset is 0.2454, and the high amount of Linear Correlation Coefficient (LCC) and Spearmen Rank Order Correlation Coefficient (SROCC) values (0.895 and 0.901 respectively) for LFOVIAS3DPh2 dataset show the compatibility of the results with human visual system (HVS). Despite having lighter architecture than the best performing method, the proposed method outperforms most of the methods and overall it is the second best performing method available. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 3 Dimensional convolutional neural networks Disparity Human visual system Motion Objective quality assessment Stereoscopic video

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Nutrient Deficiency Classification using AlexNet Architecture 14

Nutrient Deficiency Classification using AlexNet Architectur...

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14th IEEE Control and System Graduate Research Colloquium, ICSGRC 2023

作者： Sabri, Nurbaity Zulkifli, Zuhri Arafah Shari, Anis Amilah Halim, Faiqah Hafidzah Zaini, Hazrati Ismail, Khairul Nurmazianna Universiti Teknologi Malaysia School of Computing Faculty of Engineering Johor Skudai81310 Malaysia Universiti Teknologi Mara College of Computing Informatics and Mathematics Campus Jasin Melaka Malaysia

ISBN: (纸本)9798350346237

Oil palm significantly contributes to Malaysia's Gross Domestic Product (GDP). To maintain palm oil productivity, keeping the oil palm crop in good health, and free from nutrient deficiencies is necessary. Therefore, a method to analyze nutrient deficiencies is required. Nutrient deficiencies can be detected in the oil palm leaves. The study proposes an image-processing method that classifies three crucial nutrient deficiencies in a palm oil leaf: Nitrogen, Potassium, and Magnesium. To improve the processing rate, a convolution neural network (CNN) based on the AlexNet architecture has been implemented, which has been successful in image classification tasks and produced remarkable results in automated classification. The AlexNet architecture consists of five (5) convolved layers and three (3) fully connected layers, achieving an accuracy of 90.85%. This shows that AlexNet is an efficient algorithm for classifying nutrient deficiencies in palm oil leaves. However, a lower classification rate was observed for Magnesium compared to Nitrogen and Potassium. Due to the similarity in texture and color which may cause misinterpretation by the classifier. Further exploration of other CNN architectures will be conducted in the future to improve classification accuracy. © 2023 IEEE.

关键词： Palm oil

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An EfficientNet to Classify Monkeypox-Comparable Skin Lesions Using Transfer Learning

An EfficientNet to Classify Monkeypox-Comparable Skin Lesion...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Hossen, Md. Rifat Alfaz, Nazia Sami, Adnan Tanim, Sharia Arfin Bin Sarwar, Talha Islam, Md. Kamrul Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science Dhaka Bangladesh College of Computing and Applied Science Universiti Malaysia Pahang Faculty of Computing Pekan Malaysia

ISBN: (纸本)9798350346473

Monkeypox is an infectious illness caused by the DNA-based monkeypox virus, which has raised public health concerns due to its rapid transmission to over 50 countries. Direct physical interaction with infected humans or infected animals is the main reason behind the spread of this virus. The appearance of skin problems such as smallpox and rashes are the most frequently reported symptoms of this virus. Since cases of monkeypox are increasing rapidly around the world, stopping the spread of this zoonosis by providing early diagnosis and treatment is crucial before the emergence of a pandemic similar to COVID-19. In this study, we aim to propose a transfer learning-based approach using the EfficientNet-B0 architecture to identify monkeypox subjects by using skin lesion image samples. However, distinguishing monkeypox from other comparable infectious skin illnesses like chickenpox and measles is challenging. Therefore, additionally, this study identifies other diseases that also cause blisters and rashes on the skin, like chickenpox, and measles. During the data distribution phase, 5-fold cross-validation is used to validate the work's reliability by assuring that every sample is utilized for training and validation. For the evaluation of the model's classification performance, accuracy and loss are recorded for each training epoch. Moreover, precision, recall, F1-score, and confusion matrix are generated upon completion of the model training. This proposed approach is experimented on a public dataset and has shown remarkable performance by providing an overall 96.53% classification accuracy, 96.57% precision, 96.53% recall, and 96.52% F1-score. © 2023 IEEE.

关键词： Viruses

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Machine Learning Prediction Based Integrated Smart Energy Management System to Improve Home Energy Efficiency 21

Machine Learning Prediction Based Integrated Smart Energy Ma...

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21st International Symposium on Distributed computing and Applications for Business engineering and Science, DCABES 2022

作者： Al-Adaileh, Ahmed Khaddaj, Souheil Kingston University Science Engineering & Computing Faculty London United Kingdom

ISBN: (纸本)9781665454629

This paper proposes an integrated smart energy management system that applies different machine learning regression techniques to gather, enhance, and prepare various relevant data taken from the surrounding environment to predict and schedule the running periods of one of the schedulable appliances in the household. The system was applied to a case study with encouraging results showing energy consumption reduction rates up to 36%. © 2022 IEEE.

关键词： Energy management

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Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex engineering Problems: Principles and Applications

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2023-2027页

作者： Danial Jahed Armaghani Ahmed SalihMohammed Ramesh Murlidhar Bhatawdekar Pouyan Fakharian Ashutosh Kainthola Wael Imad Mahmood School of Civil and Environmental Engineering University of Technology SydneyUltimoNSW 2007Australia College of Engineering University of SulaimaniKurdistan46001Iraq Engineering Department American University of IraqSulaimaniKurdistan46001Iraq Centre of Tropical Geoengineering(GEOTROPIK) Institute of Smart Infrastructure and Innovative Engineering(ISIIC)Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Civil Engineering Semnan UniversitySemnanIran Geo-Engineering and Computing Laboratory Banaras Hindu UniversityVaranasi221005India Scientific Research Centre Soran UniversitySoranErbilKurdistan-Region44008Iraq

Computational Intelligent(CI)systems represent a pivotal intersection of cutting-edge technologies and complex engineering challenges aimed at solving real-world *** comprehensive body of work delves into the realm of CI,which is designed to tackle intricate and multifaceted engineering problems through advanced computational *** history of CI systems is a fascinating journey that spans several decades and has its roots in the development of artificial intelligence and machine learning *** a wide array of practical examples and case studies,this special issue bridges the gap between theoretical concepts and practical implementation,shedding light on how CI systems can optimize processes,design solutions,and inform decisions in complex engineering *** compilation stands as an essential resource for both novice learners and seasoned practitioners,offering a holistic perspective on the potential of CI in reshaping the future of engineering problem-solving.

关键词： offering bridges landscape

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Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

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Computer Systems Science & engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman Shaker El-Sappagh Tamer Abuhmed Mohammed Elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

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Position Paper: Enhancing the Learning and Mastery of Academic Writing in the Serbian Language Through an AI Tool with Adaptive Scaffolding 17

Position Paper: Enhancing the Learning and Mastery of Academ...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Francišković, Teodor Sakal Gajić, Dušan Luburić, Nikola Slivka, Jelena Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad Serbia

ISBN: (纸本)9789897587467

Academic writing is a significant challenge for many learners striving for proficiency. Adaptive scaffolding techniques and AI tools in education have proven effective in addressing this challenge and supporting learners in improving their academic writing skills when used correctly. This position paper proposes combining adaptive scaffolding techniques with AI tools in a public university's final year academic writing course to enhance the learning experience and mastery of academic writing skills in Serbian. The proposed plan outlines the course structure and details how the AI-driven adaptive scaffolding techniques will be integrated to support the learning experience, focusing on summative and formative feedback from the AI tool. The proposed plan is a work in progress. It will be implemented in the next iteration of the course for evaluation, taking into account potential counter-arguments and their impact on the tool's development and the student's learning experience and outcomes. This study will analyse our plan's effectiveness in enhancing the learning experience and outcomes. The expected outcome is to assist students in their learning while contributing to the development of AI in education and the Serbian language. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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