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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Hong, Fanghua Wang, Jinhu Lu, Andong Wang, Qunjing School of Electronic and Information Engineering Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

RGBT tracking aims to take full advantage of the complementary advantages of RGB and thermal infrared (TIR) modalities to achieve robust tracking in complex scenes. However, current approaches face limitations when dealing with the quality-imbalanced problem. In this paper, we introduce a novel augmentative fusion learning framework that aims to maximize the potential of existing fusion modules in modality quality imbalanced scenarios. In particular, we design a modality stochastic degradation strategy to improve the robustness of the fusion module in modality quality imbalance scenarios. Meanwhile, to further enhance the fusion performance with the modality quality imbalance inputs, a self-supervised constraint is introduced to reconstruct the modality features before degradation by combining high-quality modality and degraded modality information. Finally, the effectiveness of the proposed method is verified by evaluating it on two standard RGBT datasets and two state-of-the-art algorithms. And the results indicate that our method achieved superior performance without adding any parameters or computational complexity. © 2025 SPIE.

关键词： RGB color model

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Detection of Melanoma Insitu Using Trained CNN Model

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SN computer Science 2024年第8期5卷 1-13页

作者： SethuMadhavi, R. Premkumar, Anitha Satheesha, T.Y. Bhasker, B. DharmaTheja, M. Asha, P.N. School of Computer Science Engineering REVA University Bengaluru 560064 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Karnataka Bangalore 560064 India School of Science and Technology Department of Engineering University of London London United Kingdom School of Computing and Information Technology REVA University Bengaluru 560064 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & amp D Institute of Science and Technology Chennai India

Being the most deadly kind of skin cancer, melanoma presents a serious risk to life and is increasingly prevalent, particularly among men. It is characterized by aggressive cell multiplication that can spread within the body if left untreated, making early detection crucial for successful treatment. While machine learning techniques have been used for melanoma diagnosis, they often have limitations in performance. To address this, we propose an advanced deep learning framework incorporating a novel hybrid architecture that combines Convolutional Neural Networks (CNNs) with attention mechanisms. This framework also incorporates advanced techniques for model interpretability, such as visualizing the areas of the image that the model focuses on for its decision-making, to enhance transparency and trustworthiness for medical professionals and leverages transfer learning with pre-trained networks to boost performance. We utilized a range of optimization techniques, including AdaBelief and dynamic learning rates, to enhance model training. Our evaluation metrics included accuracy, sensitivity, specificity, and precision. Comparative results demonstrated that our model outperformed traditional techniques, with the ResNet50 architecture combined with the Adam optimizer achieving an accuracy of 92%, sensitivity of 90%, and specificity of 94%. These findings demonstrate the efficacy of our deep learning strategy by showing a notable improvement in melanoma detection performance. It is imperative to underscore that actual clinical trials or pilot studies are required to validate the model's effectiveness and reliability in real-world scenarios. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cancer detection Convolutional Neural Network (CNN) Deep learning Dermatology Melanoma Skin cancer detection Transfer learning

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Nested U-Net-Based Polyp Segmentation for Accurate Diagnosis of Colorectal Cancer

Nested U-Net-Based Polyp Segmentation for Accurate Diagnosis...

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2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Bhatt, Bhavini R. Jani, Kuntesh Maltare, Nilesh Vahora, Safvan Gujarat Technological University Department of Computer/ It Engineering Ahmedabad India L. D. College of Engineering Department of Information Technology Ahmedabad India Government Engineering College Department of Information Technology Modasa India

ISBN: (纸本)9798350341072

Colorectal cancer, a prominent global malignancy characterized by abnormal polyp growth in the colon or rectum, presents significant challenges in accurate diagnosis and treatment. Manual polyp segmentation during colonoscopy is hampered by obscured visibility, varying polyp morphology, and potential human error. To address these limitations, automated segmentation methodologies have emerged as crucial tools for improving diagnostic precision and efficiency. While conventional machine learning techniques have been applied to alleviate these complexities, the emergence of deep learning has opened new avenues for intricate pattern recognition and feature extraction. This research proposes an innovative approach to polyp segmentation, harnessing the Nested U-Net architecture complemented by a combination loss function. The proposed methodology strategically integrates augmentation techniques to counteract overfitting, a common concern in deep learning applications. Evaluation metrics such as dice coefficient, Intersection over Union (IoU), precision, and recall are employed to measure the performance of the proposed approach. Compared to U-Net-based polyp segmentation, the proposed approach exhibits a substantial increase of 0.60% in training dice coefficient, 1.15% in training IoU and 0.01% in precision. The results of extensive experimentation demonstrate the superiority of the proposed Nested U-Net-based polyp segmentation over U-Net-based approaches. © 2023 IEEE.

关键词： Diseases

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Efficient Deep CNN Model for COVID-19 Classification

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computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

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A Context-based Image Retrieval Method for Persian Databases 9

A Context-based Image Retrieval Method for Persian Databases

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9th International Conference on Web Research, ICWR 2023

作者： Fakhrzadeh, Azadeh Rabiei, Mohammad Rahnama, Mohadese Information Technology Research Department Tehran Iran Alzahra University Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350399691

Descriptive summarization of documents in databases results in better indexing and management of information. Images in documents usually contain valuable information and retrieving them provides tools for document summarization. In context-based image retrieval systems, descriptive tags for the images are extracted from auxiliary information sources available to them. The search engine uses these tags to retrieve images. Here we suggest an automated image tagging method that exclusively relies on information mined from the document's text associated with the image. Because of complications in the Persian language, lack of resources, and studies on this language, it has received little attention in the literature. The suggested method is built based on Persian documents. Two groups of tags are suggested. Specific tags are extracted from the caption of the image and the nearby text. General tags are obtained from the keywords of the document. Suggested methods are evaluated on the test data from the Iran scientific information database (GANJ), the largest database of Persian scientific documents. The F-measure of our suggested method is 43% for the specific tags. As for general tags, 89% are descriptive and the false positive rate is 0.002. Although suggested method has been tested on scientific documents it can be generalized for any type of Persian. © 2023 IEEE.

关键词： information management

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A Paradigm Shift in Ethereum Network Analysis Through Google BigQuery,Portation, and Visualization 6

A Paradigm Shift in Ethereum Network Analysis Through Google...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Suresh Kumar, N. Mirdula, S. Singh, Pushpa Gayathri, T. Jayanthi, S. Anandraj, M. School of Computer Science and Engineering Jain Deemed-to-be University Ramanagara India SRM TRP Engineering College Department of Electronic and Communication Engineering Trichy India IILM University Department of Computer Science and Engineering Greater Noida India MVJ College of Engineering Department of Computer Science and Engineering Bangalore India Guru Nanak Institute of Technology Department of Information Technology Ibrahimpatnam India Mahendra College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350306613

The primary objective of this research is to comprehensively explore and analyze the dynamics of the Ethereum network using innovative methodologies and system architectures. The study aims to extract meaningful statistics from the Ethereum blockchain, focusing on account activity, popularity trends, and the distribution of transactions. Through rigorous data collection, robust query construction, and advanced analytical techniques, the research seeks to provide valuable insights into how the Ethereum network has evolved, particularly in the aftermath of the 'crypto bubble explosion.' The overarching goal is to contribute to the understanding of Ethereum's structural patterns, user behaviors, and the impact of external factors on the network. The research has yielded significant results, unveiling key insights into Ethereum network dynamics. The data collection phase, facilitated by Google BigQuery, successfully captured and filtered relevant information from a specific block range post the 'crypto bubble explosion.' The SQL queries, strategically designed for active account identification and popularity assessment, demonstrated efficiency and accuracy in handling the vast Ethereum dataset. The proposed system, introducing the novel methodology of 'portation,' showcased its efficiency in extracting and interpreting Ethereum blockchain data using Google BigQuery. The system architecture, as illustrated in the diagram, proved to be a well-coordinated and dynamic framework, emphasizing the seamless flow of data and processes. © 2024 IEEE.

关键词： Crypto Bubble Explosion Decentralized Data Transactions Ethereum Blockchain Google BigQuery Power-law Analysis

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Generative Adversarial Networks for Cybersecurity Threat

Generative Adversarial Networks for Cybersecurity Threat

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Ojha, Anatha Charan Agarwal, Ankita Swetha, G. Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

We provide a unique method to enhance cybersecurity threat prediction using generative adversarial networks (GANs). Through competitive training, the generator and discriminator parts of GANs are customized for application in the cybersecurity industry. Three key algorithms underlie this method. First, to create believable threats, GANs are employed in the GAN Architecture for Threat Generation. The discriminator distinguishes between actual and phony threat data, while the generator generates false threat data. These simulated assaults are a significant part of cybersecurity threat predictions. Second, Long Short-Term Memory (LSTM) neural networks are used in the Threat Scenario Prediction Algorithm's Time Series Forecasting Algorithm. To predict possible outcomes, long short-term memory (LSTM) models the temporal dependency of historical threat data. This approach offers perceptions into prospective threats, enabling the adoption of preventative actions. Finally, Markov chains are used by the Threat Evolution Algorithm to depict the dynamic evolution of cybersecurity threats. In order to shed light on how dangers vary over time, it forecasts the chance of a transition from one state of hazard to another. We get a deeper understanding of threat trajectories and how they influence an organization's cybersecurity by adopting a more dynamic approach. This comprehensive approach combines state-of-the-art techniques in threat scenario generation, forecasting, and evolution modeling, providing organizations with a robust toolset to proactively address cybersecurity challenges. © 2023 IEEE.

关键词： Long short-term memory

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Hybrid CNN-LSTM Transfer Learning for Dengue Diagnosis from Raman Spectroscopy Images 5

Hybrid CNN-LSTM Transfer Learning for Dengue Diagnosis from ...

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5th IEEE International Conference on Telecommunications and Photonics, ICTP 2023

作者： Ahmmed, Syed Das, Sanchita Rani Leon, Mazharul Islam Rubaiyat Hossain Mondal, M. Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh United International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350393477

The diagnosis of dengue fever can be made quickly and effectively with Raman spectroscopy. However, the diagnostic procedure presents difficulties;for example, the distinction between healthy and infected spectra is extremely subtle. Deep learning (DL)-based diagnosis of dengue from Raman spectroscopy is gaining research interest. This paper introduces an innovative hybrid model that combines the architecture of a long-short-term memory recurrent neural network (LSTM RNN) with the capabilities of a pre-trained convolutional neural network (CNN). We present the CNN-LSTM Transfer Learning (HCL-TL) method, which uses Raman spectroscopy images to transform the field of data-driven dengue disease diagnosis. The proposed approach utilizes hyperparameters to refine the DL models. Both the initial training and subsequent fine-tuning stages incorporate model checkpoints and callbacks to monitor loss and prevent overfitting. A dataset consisting of 635 spectra-460 of which were infected and 175 of which were healthy-was used to evaluate the suggested model. Both holdout and 10-fold cross-validation methods are used for splitting the data samples. When applied to testing data for dengue infection diagnosis, the proposed HCL- TL method achieves a classification accuracy of 97.73%. The HCL-TL method outperforms the state-of-the-art DL and machine learning models in terms of accurately classifying dengue fever from Raman spectroscopy images, according to the results. © 2023 IEEE.

关键词： Raman spectroscopy

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Control the Interval Estimation to Enhance the Performance of Optimized Link State Routing 4

Control the Interval Estimation to Enhance the Performance o...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Ishqeem, Omar Ammar Sarreb, Rebah Daw Hatif Libya Company Libyan Post Telecommunications & Information Technology Company Libya Sabratha university Faculty of Engineering Rigdalin Dept. of Computer Engineering Libya

ISBN: (纸本)9798350372632

This paper investigates the creation of self-organizing Mobile Ad hoc Networks (MANETs) employing Optimized Link State Routing Protocol (OLSR) with Multipoint Relay (MPR). The routing protocol in MANET controls the management of node mobility by using metrics to evaluate the quality of link states. The primary concept of OLSR is to choose the MPR that identifies the route from the source to the destination node. The MPR relays Hello and Topology Control (TC) messages between nodes used to discover and distribute link state information. The Hello and TC messages interval plays a critical role in the link state in the network. Accordingly, link failure for a few seconds may lead to a significant performance deterioration. This work enhances the performance of OLSR by estimating the efficient Hello and TC message interval for faster route recovery. Many experiments are simulated and analyzed by a set of parameters such as MPR count, TC traffic sent, throughput, delay, and data dropped. All our results revealed that the proposed Control of the Interval Estimation-Optimized Link State Routing (CIE-OLSR) has better performance than the traditional OLSR. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Do-It-Yourself (DIY) Hand Sanitizer

Do-It-Yourself (DIY) Hand Sanitizer

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2022 International Conference on Interdisciplinary Research in technology and Management, IRTM 2022

作者： Khatoon, Munshi Saba Chakrabarty, Shreemanti Pal, Sudipta Basu University Of Engineering & Management Computer Science Information Technology Department Kolkata India University Of Engineering & Management Computer Science & Engineering Department Kolkata India University Of Engineering & Management Computer Science Technology & Computer Science Information Technology Department Kolkata India

ISBN: (纸本)9781665478861

The demand and consumption of hand sanitizers have increased throughout the world with the beginning of the pandemic of COVID-19. It had led to a reduction in these products at all levels. Big companies, firms, and malls have installed automatic touchless hand sanitizer machines. These are used to prevent the spread of the virus among the masses. But in our homes, we tend to use the same pump hand sanitizer bottle after coming from outside. This increases the chance of spreading the virus among our dear ones. So, we thought of making an Automated (Do-it-yourself) hand sanitizer for our homes. © 2022 IEEE.

关键词： Microcontrollers

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