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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Sumegi, Milan Quan, Wei Brooks, Hadley L. Shark, Lik-Kwan Research Centre in Engineering School of Engineering and Computing University of Central Lancashire United Kingdom

ISBN: (纸本)9798400716720

This research introduces an innovative method specifically designed for comprehensive error detection in the 3D printing process. The Between-layer Structural Similarity (BLSS) technique gauges the similarity between displacement maps, which are generated using the Structural Similarity Index Measure (SSIM). This allows for the differentiation between consecutive layers in both simulated and actual prints. Utilising the Fast Fourier Transform (FFT), the displacement maps can be converted to the frequency domain and assessed for similarity. The proposed approach involves three key processing stages: print simulation and processing, print capture and processing, and BLSS-based structural analysis and evaluation. This methodology not only reduces material wastage but also lays the foundation for automated systems capable of halting or terminating live printing processes upon error detection. Experimental results reveal that printing defects occur below 97.5 % of the similarity value, establishing this threshold as appropriate. This underscores the effectiveness of the system in error detection and its potential for real-time quality control in 3D printing. © 2024 Owner/Author.

关键词： 3D printing

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Hydrogen Energy Storage System:Review on Recent Progress

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Energy engineering 2025年第1期122卷 1-39页

作者： MilleniumWong Hadi Nabipour Afrouzi Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang TigaKuchingSarawak93350Malaysia College of Engineering Faculty of ComputingEngineering and the Built EnvironmentBirmingham City UniversityBirminghamWest MidlandsEnglandB47XGUK

A hydrogen energy storage system(HESS)is one of the many risingmodern green innovations,using excess energy to generate hydrogen and storing it for various *** that,there have been many discussions about commercializing HESS and improving it ***,the design and sizing process can be overwhelming to comprehend with various sources to examine,and understanding optimal design methodologies is crucial to optimize a HESS *** that,this review aims to collect and analyse a wide range of HESS studies to summarise recent *** different collections of studies are studied,one was sourced by the main author for preliminary readings,and another was obtained via *** findings from the Web of Science platform were also examined for amore comprehensive *** findings include the People’sRepublic of China has been active in HESS research,as most works and active organizations originate from this *** has been mainly researched to support power generation and balance load demands,with financial analysis being the common scope of *** is a common tool used for HESS design,modelling,and optimization as it can handle complex *** neural network(ANN)has the potential to be used to model the HESS,but additional review is required as a formof future *** a commercialization perspective,pressurized hydrogen tanks are ideal for hydrogen storage in a HESS,but other methods can be considered after additional research and *** this review,it can be implied that modelling works will be the way forward for HESS research,but extensive collaborations and additional review are ***,this review summarized various takeaways that future research works on HESS can use.

关键词： Hydrogen energy storage system VOSViewer design review sizing

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The impact of backbone selection in YOLOv8 models on brain tumor localization

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Iran Journal of Computer Science 2025年 1-23页

作者： Ranjbarzadeh, Ramin Crane, Martin Bendechache, Malika School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland ADAPT Research Centre School of Computer Science University of Galway Galway Ireland

This study investigates the utilization of the You Only Look Once (YOLOv8) deep learning framework for accurately identifying the location of brain tumors in medical imaging. We investigate the effects of model size and pretraining on the accuracy and computational efficiency of tumor detection by utilizing different setups of the YOLOv8 model. These setups include various configurations, ranging from very small to large, and can be pretrained on the COCO dataset or not. The experimental results, carried out on Google Colaboratory using NVIDIA Tesla T4 GPUs, show that pretrained models often achieve better performance by utilizing the extensive feature representations learned from the COCO dataset, resulting in increased precision in tumor location. For instance, the YOLOv8-XS model pretrained on COCO achieves an IoU of 0.278 and a Dice coefficient of 0.435, whereas its non-pretrained counterpart attains only 0.241 IoU and 0.388 Dice, indicating a 15% improvement in tumor localization accuracy. Similarly, pretrained YOLOv8-L achieves 0.269 IoU, outperforming standard object detection models such as Mask R-CNN (IoU: 0.212) and Faster R-CNN (IoU: 0.228). These results highlight the impact of pretraining on model performance, particularly for lightweight architectures, while also revealing diminishing returns for larger models. The research uncovers a subtle connection between the complexity of the model, pretraining, and the time required for training. It emphasizes the possible advantages and constraints of pretraining for various sizes of models. © The Author(s) 2025.

关键词： Brain tumor localization COCO dataset Deep learning Object detection Transfer learning

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Literature Survey: Decentralized Health Record Management System 1

Literature Survey: Decentralized Health Record Management Sy...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Pandita, Anshika Morwadkar, Suchitra Joshi, Padmaja Girase, Janvi Gosavi, Nikita More, Isha Cummins College of Engineering for Women Pune India Centre for Development of Advanced Computing Mumbai India

ISBN: (纸本)9798350355611

In today's fast-paced world of technology, we rely on digital interactions more than ever. But with this convenience comes a growing need for security and trust. As we navigate our daily lives online, issues such as data breaches, unauthorized access, and fraud are real concerns that can affect us all. It is vital to prioritize the safety of our digital experiences to build the trust we need to thrive in this digital age. These challenges are particularly acute in the management of personal health records (PHRs), where existing centralized systems do not offer reliable, easily traceable, and securely managed control over patient's medical data. This paper explores the revolutionary capabilities of blockchain technology in addressing the critical shortcomings of traditional PHR management systems. The unique characteristics of the blockchain are decentralization, security, immutability, transparency, and anonymity, which presents a groundbreaking solution to the issues plaguing current systems. By eliminating the risks associated with centralized data storage, blockchain provides a secure and transparent platform for managing health records, ensuring that they remain tamper-proof. The literature survey conducted in this paper examines various research studies that have proposed and explored Blockchain-based solutions for health record management. These studies highlight the significant advantages of blockchain, including enhanced data integrity, privacy, and patient control over health information. Comparative analysis underscores the need for a paradigm shift from centralized to decentralized systems that enhance both the security and efficiency of managing health records. © 2025 IEEE.

关键词： Electronic health record

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Evaluation of Shallow Convolutional Neural Network in Open-World Chart Image Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第6期48卷 185-198页

作者： Bajić, Filip Habijan, Marija Nenadić, Krešimir University Computing Centre University of Zagreb Zagreb10000 Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek31000 Croatia

Data’s role is pivotal in the era of internet technologies, but unstructured data poses comprehension challenges. Data visualizations like charts have emerged as crucial tools for condensing complex information. Classifying charts and applying various processing techniques are vital to interpreting visual data. Traditional chart image classification methods rely on predefined rules and have limited accuracy. The advent of support vector machines (SVMs) and convolutional neural networks (CNNs) significantly improved the accuracy of these methods. This research evaluates our previously introduced Shallow convolutional neural network (SCNN) architecture for chart image classification, comprising four convolutional layers, two max-pooling layers, and one fully-connected layer. The network achieves state-of-the-art results, requiring smaller datasets and reduced computational resources. When two networks are combined into Siamese SCNN (SSCNN), emphasizing generalization, it achieves high accuracy with small datasets and excels in open-set classification. The evaluation process encompasses the utilization of six publicly available datasets. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Data visualization

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Life and death of a data set: a forensic investigation

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Spectroscopy Europe 2023年第2期35卷 32-35页

作者： Davies, Antony N. SERC Sustainable Environment Research Centre Faculty of Computing Engineering and Science University of South Wales United Kingdom

Davies talks about spectroscopic data set. As originally delivered the spectrometer was capable of generating some of the best data sets people had ever been able to measure. Excellent signal-to-noise ratios and extremely stable calibrations. The associated computer hardware was somewhat behind the state-of-the-art, but this was quite normal due to the long development times for the instrument hardware. And so, in the hands of many expert, and some less expert, scientists, this wonderful spectrometer gave birth to many spectroscopic data sets--reinforcing theories and dispelling some myths. Over its lifespan serving as an excellent measurement platform allowing many modifications to the original basic equipment. So, while our data was young there were no problems. All the measurement parameters were stored with the data set so we could check the instrument had been set up correctly.

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Design of an Optical Wireless Communication Link Using Mid Wave Infrared Laser Source & Comparison with Short Wave Infrared Laser Sources in Different Atmospheric Anomalies: An Analytical Study

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Nonlinear Optics Quantum Optics 2024年第1-2期60卷 85-111页

作者： Mukherjee, Shibabrata Paul, Sujoy Mazumdar, Saswati Electrical Engineering Department Jadavpur University West Bengal Kolkata700032 India Centre for Distributed Computing Computer Science Engineering Department Jadavpur University West Bengal Kolkata700032 India

Optical Wireless Communication (OWC), sometimes referred to as Free Space Optics (FSO), is the backbone of leading-edge communication systems that offer high performance and fast speed. The main pitfall of this system is that the optical signal is hindered by the different atmospheric anomalies. As a result, the system performance has been distorted and degraded. Among the different atmospheric anomalies like fog, rain, snow, etc., fog is the main enemy of this system. Using the properly chosen optics at the transmitter and receiver sides and wavelength, the system performance may be increased in diverse weather conditions. In this article, four wavelengths like two Mid Wave Infrared (MWIR) (3400 nm, 4100 nm) and two Short Wave Infrared (SWIR) (1550 nm & 2000 nm), 27 dBm optical peak power, the OOK modulation technique, 10 MHz modulation bandwidth, 10-7 BER, 2 km optical wireless link distance have been considered. From the theoretical simulation results, it has been initiated that MWIR optical sources (like 4100 nm and 3400 nm) contribute 72.22 dB, and 71.27 dB link margin at adverse atmospheric anomalies respectively, whereas SWIR Optical sources like 1550 nm & 2000 nm furnish 67.70 dB, 68.50 dB link margin respectively for 2 km link distance. The 4100 nm wavelength Laser source performs better in different atmospheric anomalies and provides a better pointing error angle in clear, as well as adverse weather conditions, compared to other wavelengths. © 2024 Old City Publishing, Inc.

关键词： Space optics

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Hyperspectral imagery quality assessment and band reconstruction using the prophet model

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 47-61页

作者： Ping Ma Jinchang Ren Zhi Gao Yinhe Li Rongjun Chen National Subsea Centre School of ComputingEngineering and TechnologyRobert Gordon UniversityAberdeenUK School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhouChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina

In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land *** study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet *** introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global *** metric effectively captures the intricate noise and distortions inherent in the HSI ***,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality *** validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are *** a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is *** authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed *** addition,the authors assess the classification accuracy utilising these reconstructed *** various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band ***,the authors’approach obviates the need for manually prefiltering of noisy *** comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.

关键词： band reconstruction band quality hyperspectral image(HSI) prophet model

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Classification of Fungi Images Using Different Convolutional Neural Networks 8

Classification of Fungi Images Using Different Convolutional...

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8th International Conference on Information Technology Research, ICITR 2023

作者： Nawarathne, U.M.M.P.K. Kumari, H.M.N.S. University of Peradeniya Computing Centre Faculty of Engineering Peradeniya Sri Lanka

ISBN: (纸本)9798350359503

Fungi offer vital solutions to humanity through roles in medicine, agriculture, and ecological balance while presenting potential threats. They have yielded antibiotics, food fermentation, and nutrient recycling however, fungal infections, crop diseases, and spoilage highlight their dark side. Therefore, it is important to identify fungi to harness their potential benefits and mitigate threats. Offering quick and accurate identification through image classification improves the aforementioned features. Therefore, this study classified images of five types of fungi using convolutional neural networks (CNN). Initially, dataset distribution was observed, and it was identified that there was a class imbalance in the dataset. To address this issue, data augmentation technique was used. Several preprocessing techniques were also applied to understand the model training behavior with their application. Then the images were rescaled into six different resolution combinations such as original images, low-resolution images, high-resolution images, a mix of original and low-resolution images, a mix of original and high-resolution images, and a mix of low and high-resolution images. Then these data were trained using 13 pre-trained CNN models such as Xception, VGG16, VGG19, InceptionResNetV2, ResNet152, EfficientNetB6, EfficientNetB7, ConvNeXtTiny, ConvNeXtSmall, ConvNeXtBase, ConvNeXtLarge, ConvNeXtXLarge, BigTransfer (BiT). To evaluate these models, accuracy, macro average precision, macro average recall, macro average f1score, and loss learning curve assessment were used. According to the results, the BiT model preprocessed with normalization, which used a mix of original and high-resolution images, performed the best, producing a model accuracy of 87.32% with optimal precision, recall, and f1-score. The loss learning curve of the BiT model also depicted a low overfitting aspect proving the model's optimal behavior. Therefore, it was concluded that the BiT model with the mix

关键词： Image classification

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A Sentiment Analysis of COVID-19 Tweets Data Using Different Word Embedding Techniques

A Sentiment Analysis of COVID-19 Tweets Data Using Different...

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2023 International Research Conference on Smart computing and Systems engineering, SCSE 2023

作者： Nawarathne, U.M.M.P.K. Kumari, H.M.N.S. University of Peradeniya Computing Centre Faculty of Engineering Peradeniya Sri Lanka

ISBN: (纸本)9798350341454

The COVID-19 virus that invaded the world in 2019 caused many casualties while creating enormous mental turmoil among humans. During this pandemic period, humans were confined to prevent the virus from spreading. Due to the isolation, people used social media platforms like Twitter to express their ideas. Therefore, this study analyzed tweets related to COVID-19. Initially, text data processing techniques were employed, and sentiment labels were assigned. Then the data were trained using different machine learning (ML) models such as Multinomial Naïve Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbours (KNN), Logistic Regression (LR), Extreme Gradient Boosting (XGB), and CatBoost (CB). During the training phase, word embedding techniques such as Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Global Vectors for Word Representation (Glove), Bidirectional Encoder Representations from Transformers (BERT), and Robustly Optimized BERT-Pretraining Approach (RoBERTa) were used, and evaluation metrics such as accuracy, macro average precision, macro average recall, and macro average f1-score were calculated to evaluate these models. According to the results, the CB model, which used the RoBERTa technique, achieved an accuracy of 97%. Therefore, it can be concluded that CB with RoBERTa provides better results when classifying tweet data. © 2023 IEEE.

关键词： Embeddings

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