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Automating Exam Assessment Through Digital Transformation and image processing 3rd

Automating Exam Assessment Through Digital Transformation an...

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3rd International conference on Electronic Governance with Emerging Technologies, EGETC 2024

作者： Dogan, Onur Department of Management Information Systems Izmir Bakircay University Izmir35665 Turkey Smart University and Digital Transformation Coordinatorship Izmir Bakircay University Izmir35665 Turkey

ISBN: (纸本)9783031770289

The paper presents a study aimed at optimizing the reading and evaluating process of multiple-choice exam sheets (optical forms) as a smart campus application at Izmir Bakircay University, significantly reducing the time and cost involved. Traditional manual evaluation methods are time-consuming and expensive. The proposed solution leverages digital transformation and image processing technologies. The algorithm developed processes scanned images of optical forms, generates scores, and provides statistical reports without requiring specialized knowledge. The methodology is divided into three main steps: detecting sections of the optical form, cropping sections, reading marked circles based on coordinates, reporting exam results, and statistical analysis. Testing with previously used optical forms showed 100% accuracy at a resolution of 600×600 dpi. Errors caused by user handling were anticipated and the system was designed to be robust against such errors. The results of implementing this automated system include a 98.15% reduction in processing time and a 99.95% reduction in cost. The new evaluation time is around 5 min per exam, costing 347 TL. The process now requires only one person instead of four. This study exemplifies the potential of digital transformation and image processing to enhance operational efficiency and cost-effectiveness in academic settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical data processing

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EnigmaArt: Dual image Encryption and Compression via Autoencoding and Pixel Conversion 4th

EnigmaArt: Dual Image Encryption and Compression via Autoe...

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4th International conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Tiwari, Naveen Kumar Rajput, Shyam Singh Yadav, Aditya National Institute of Technology Patna Bihar Patna India Rajkiya Engineering College Kannauj UP Kannauj India

ISBN: (纸本)9783031837890

Securing sensitive visual data is essential in the digital age. Our research presents a sophisticated model of image encryption that combines RNA pixel conversion with an autoencoder. A convolutional neural network (CNN) is used for efficient feature extraction. Convolutional encoding and RNA pixel conversion, which map pixels to RNA sequences (Adenine, Cytosine, Guanine, Uracil) enabling stronger encryption, are the model’s primary innovations. By guaranteeing that every encryption instance is distinct, this dynamic technique strengthens security. Our approach ensures security and picture integrity by demonstrating robust encryption capabilities with minimum reconstruction loss (0.0617), as tested on the MNIST dataset. Furthermore, the encryption procedure is optimized by a fractional order optimizer, FGL_Adam, which incorporates weight decay, learning rate modifications, and a security improvement factor. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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12th Ecuadorian conference on Information and Communication Technologies, TICEC 2024

12th Ecuadorian Conference on Information and Communication ...

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12th Ecuadorian conference on Information and Communication Technologies, TICEC 2024

ISBN: (纸本)9783031754302

The proceedings contain 24 papers. The special focus in this conference is on Information and Communication Technologies. The topics include: Pancreas Segmentation Using SRGAN Combined with U-Net Neural Network;Using Artificial Intelligence and X-ray images to Train and Predict COVID-19 and Pneumonia: Tool for Diagnosis and Treatment;Region of Interest Features and Classification of MRI Brain Lesions;deep Learning-Based Leukemia Diagnosis from Bone Marrow images;Breast Thermographic image Augmentation Using Generative Adversarial Networks (GANs);Evaluating Histopathological Cancer Detection: A Comparative Analysis of CNN Architectures for Tumor Detection in Lymph Node Pathology;ViTSigat: Early Black Sigatoka Detection in Banana Plants Using Vision Transformer;automatic Parking Space Segmentation Using K-Means Clustering and image processing Techniques;neural Agents with Continual Learning Capacities;centinela: An Intelligent System Based on an Integrated Architecture for Supporting Scholars;Optimizing Predictive Models in Healthcare Using Artificial Intelligence: A Comprehensive Approach with a COVID-19 Case Study;predictive Model Proposal in Telemetry Using Machine Learning Techniques to Anticipate Water Degradation in Aquaculture;Evaluation of the Use of Artificial Intelligence Techniques in the Mitigation of the Broadcast Storm Problem in FANET Networks;adaptation Dynamics of Galápagos Finches: Evolutionary Responses to Climate Variation Explored through Machine Learning;incident Alert Priority Levels Classification in Command and Control Centre Using Word Embedding Techniques;physics Informed Neural Networks and Gaussian Processes-Hamiltonian Monte Carlo to Solve Ordinary Differential Equations;Pre-processing of the Text of ECU 911 Emergency Calls;Secure BLE Communication Between Android Devices and Embedded Systems for IoT Applications;modeling and Control of a Peltier Thermoelectric System Applying a Multi-objective Optimization Approach;wearable Device for Ac

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Photovoltaic Cell Defect Classification Using Attention U-Net image Segmentation 4th

Photovoltaic Cell Defect Classification Using Attention U-Ne...

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4th International conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Alzu’bi, Ahmad Saleh, Zaid Department of Computer Science Jordan University of Science and Technology Irbid22110 Jordan Department of Electrical Engineering Jordan University of Science and Technology Irbid22110 Jordan

ISBN: (纸本)9783031821523

The need to automate solar cell quality monitoring techniques is growing as solar power utilization gets significant momentum. However, the detection of internal defects in photovoltaic (PV) cells is still quite a challenge, highlighting the demand for a reliable method to reduce the requirement for manual inspections. This paper presents a deep learning model to automatically detect and classify defective PV cells in Electroluminescence images using attention-based U-Net image segmentation. The proposed deep learning architecture uses ResNet50 and attention U-Net networks to formulate a set of discriminating image features, providing an effective procedure of deep transfer learning. The extracted image descriptors are encoded and learned from image masks and annotations that were reprocessed to improve the generalization capability of the photovoltaic cell classifier. The semantic deep learning model with image augmentation and mask processing provides a baseline to detect and classify any possible faults in photovoltaic cells. The experimental results demonstrate the effectiveness of semantic attention U-Net-Resnet50 deep architecture in classifying PV cells, achieving a precision of 95% and an F1-score of 94%. It also achieved an IoU of 90% for PV image segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Brain Tumor Diagnosis using Deep Learning: A Systematic Review and Meta-analysis of MRI image-Based Studies

Brain Tumor Diagnosis using Deep Learning: A Systematic Revi...

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3rd International conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Dineshbhai, Patel Kruti Maniar, Himanshu Bhagwan Mahavir University Gujarat Surat India

ISBN: (纸本)9783031863042

Traditional methods for the categorization of brain tumors identification and mostly depend on radiologists' time-consuming manual interpretation and subjective. Detecting a brain tumor in its early stages can be quite very difficult for doctors. Traditional methods for the categorization of brain tumors identification and mostly depend on radiologists' time-consuming manual interpretation and subjective. This process can be subjective, complex and laborious. Recent developments in deep learning have greatly improved the automation and accuracy of medical image processing, including the categorization and identification of brain tumors from MRI scans. The study emphasizes how crucial it is to solve MRI image dataset: A variety of datasets, including medical datasets, are hosted by Kaggle, a platform for data science competitions (Kaggle offers datasets related to brain tumors at (https://***/datasets) in MRI image constraints, optimize segmentation algorithms, and incorporate cutting-edge methods like data. MRI pictures may be more susceptible to noise and other environmental disruptions. Doctors find it more challenging to diagnose the tumor and its sources as a result. In order to overcome this, we created a method that uses grayscale image conversion to identify brain tumors from images. To remove noise and other distracting elements from the image, we employ filters. The system's processing will include a preprocessing step for the selected image. To find brain cancers in the MRI pictures, multiple algorithms are applied at the same time. However, the edges of the image may not be crisp and distinct in the early stages of a brain tumor. As a result, to identify the edges in the pictures, we are using image segmentation. We have developed a number of filtering algorithms and segmentation procedures to extract information from photos. Precision can be increased overall with this strategy. © The Author(s), under exclusive license to Springer Nature Switz

关键词： Brain Tumor Classification Magnetic Resonance image (MRI) Segmentation

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Early Detection of Lameness in Dairy Cattle Using Activity Data, image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact 28th

Early Detection of Lameness in Dairy Cattle Using Activity D...

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Vuppalapati, Chandrasekar Ilapakurti, Anitha Vissapragada, Sandhya Vuppalapati, Sriya Kedari, Sharat Kedari, Santosh Vuppalapati, Jaya San Jose State University San Jose United States Hanumayamma Innovations and Technologies Inc Fremont United States

ISBN: (纸本)9783031859328

The economic burden of lameness in dairy cattle is a significant challenge facing the worldwide dairy industry. Studies suggest that it can cost the industry billions of dollars annually, with the exact cost varying by region. In the United States, the cost of lameness is estimated to be approximately $2 billion per year, while in the United Kingdom, it is around £200 million per year. These costs include lost milk production, decreased fertility, and treatment expenses. The impact of lameness on dairy cattle is far-reaching, posing a significant economic burden on farmers and countries and affecting animal welfare, milk production, and farm profitability. It is particularly concerning for small and marginal farmers, who are vulnerable to economic and financial sustainability concerns. Early detection and prevention of lameness are crucial to reducing these costs and improving the overall health and productivity of dairy herds. Prevalence rates vary globally, ranging from 17% to 35% in dairy herds, underscoring the urgent need for innovative solutions. Our proposed paper offers a novel approach to addressing this challenge. By combining activity data captured by Cow Necklace IoT Sensor and image analysis with Computer Vision, alongside expert veterinarian input, we provide a comprehensive and data-driven solution for early lameness detection in dairy cattle. This approach has the potential to significantly improve animal welfare, farm management practices, and overall herd productivity, leading to substantial economic benefits for farmers and the agricultural sector as a whole. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Farms

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Enhancing Satellite image Resolution Using Super Resolution Techniques 2nd

Enhancing Satellite Image Resolution Using Super Resolution ...

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2nd International conference on Applied Intelligence, ICAI 2024

作者： Berezovsky, Vladimir Kamenev, Alexander Sharshov, Ivan Shoshina, Kseniya Vasendina, Irina Aleshko, Roman Desyatova, Tatyana Federal University Arkhangelsk163002 Russia

ISBN: (纸本)9789819619030

This study investigates various super-resolution (SR) methods for enhancing the spatial resolution of satellite imagery. Traditional interpolation techniques, such as nearest-neighbor, bilinear, bicubic, and Lanczos, are compared with modern deep learning-based models, including EDSR, ESPCN, FSRCNN, and LapSRN. The research demonstrates that deep learning approaches outperform traditional methods in terms of image quality, as indicated by higher PSNR and SSIM values. Additionally, the integration of geographic information systems (GIS) data into the SRGAN framework is explored, further improving image reconstruction by processing collateral data with residual scaling in RRDB blocks. The results show that combining advanced SR methods with optimized computational resources provides significant improvements in satellite image resolution, making it highly relevant for environmental monitoring, urban planning, and disaster management. Future research should focus on optimizing these models for specific applications and exploring emerging technologies for further enhancements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Satellite imagery

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BM3D-UGanNet: A Hybrid Deep Learning Network for Low-Light Thangka image Enhancement 19th

BM3D-UGanNet: A Hybrid Deep Learning Network for Low-Light ...

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19th Chinese conference on image and Graphics Technologies and Applications, IGTA 2024

作者： Peng, Quanhong Zhang, Dan Zhou, Mingquan Zhang, Jianpeng Song, Meihua Wang, Ning Xu, Chenhao Computer College of Qinghai Normal University Xining81017 China Xining81017 China

ISBN: (纸本)9789819799183

Thangka is a Tibetan traditional painting art known for its intricate textures and profound cultural significance. Typically displayed in low-light environments in museums to prevent light from affecting its quality, this also makes it difficult for visitors to capture details and is not conducive to digital preservation and transmission. Traditional image enhancement techniques, such as GAN networks that require paired images for training, are limited in their application to Thangka datasets. To address this issue, we propose an image enhancement method called BM3D-UGanNet, which can be trained without paired data. This method combines the BM3D algorithm with the UGanNet network structure, effectively enhancing the visual quality of Thangka images under low-light conditions while preserving their details and texture. This technology not only improves the viewing experience but also provides high-quality image resources for art research and education, holding significant academic and practical value. In addition, the low-light Thangka dataset we have collected provides valuable resources for future research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gallium nitride

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Recognition of the Hornbill Eye’s Iris Using Bit Plane processing, Un-sharp Masking and High Boost Filtering 2nd

Recognition of the Hornbill Eye’s Iris Using Bit Plane Proc...

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2nd Analytics Global conference, AGC 2024

作者： De, Parthasarathi Chakrabarti, Samiddha Department of Computer Science and Engineering Sister Nivedita University Kolkata India Department of Computer Science and Engineering Narula Institute of Technology Kolkata India

ISBN: (纸本)9783031751561

This research focuses on advancing the recognition capabilities of the Hornbill Eye’s Iris through the integration of sophisticated image processing methodologies. Similar to humans, iris patterns of any animals are unique and stable in nature which makes a reliable source for the identity of any individual, this can be particularly valuable in wildlife conservation and management. Animal iris recognition can be employed in the development of monitoring systems for wildlife. This can be crucial for tracking endangered species, monitoring population dynamics, and studying migration patterns. A novel method for the recognition of Iris of the Hornbill is proposed in the paper where collected iris images are processed through Canny Edge Detector for precise edge extraction, followed by Circular Hough Transform for the Localization of the Region of Interest (ROI) that is eye’s iris, and then for the image Enhancement Un-Sharp Masking is applied to accentuate subtle details and edges;and High Boost Filtering to refines the image by amplifying high-frequency components. The boosted grey Scale image corresponding to the four LSB’s are used for matching purpose by weighted arithmetic mean of the intensity values. The results obtained are found to give higher accuracy in terms of Recognition rate where Equal error rate, False Acceptance Ratio and False rejection ratio are compared with Human iris Recognition. The experimental results demonstrate the efficiency of the proposed method in achieving robust and reliable recognition outcomes, making it a valuable contribution to the field of biometric animal identification systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hough transforms

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5th International conference on Emerging Information Security and Applications, EISA 2024

5th International Conference on Emerging Information Securit...

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5th International conference on Emerging Information Security and Applications, EISA 2024

ISBN: (纸本)9783031804182

The proceedings contain 18 papers. The special focus in this conference is on Emerging Information Security and Applications. The topics include: A Privacy-Preserving and Fault-Tolerant Data Aggregation Scheme in Smart Grids;local Differential Privacy for Key-Value Data Collection and Analysis Based on Privacy Preference and Adaptive Sampling;comparative Study of Artificial Intelligent Approaches for Phishing Website Detection;exploring Interpretability in Backdoor Attacks on image Classification;attribute-Based Secret Key Signature Scheme;digital Token Transaction Tracing Method;GPT-Based Wasm Instruction Analysis for Program Language processing;research on Key Technologies of Fair Deep Learning;adaptive Differential Privacy Based Optimization Scheme for Federated Learning;cascading Failures Model with Noise Interference in Supply Chain Networks;DefMPA: Defending Model Poisoning Attacks in Federated Learning via Model Update Prediction;SDDRM: An Optimization Algorithm for Localized Differential Privacy Based on Data Sensitivity Differences;blockchain-Based Key Management Scheme in Internet of Things;privacy Optimization of Deep Recommendation Algorithm in Federated Framework;delegated Proof of Stake Consensus Mechanism Based on the Overall Perspective of Voting;a Distributed Privacy-Preserving Data Aggregation Scheme for MaaS Data Sharing.

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