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检索条件"任意字段=International Multi-Conference in Computer Science and Computer Engineering"

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Mix Transformer depth-wise separable convolution UNet for breast mass segmentation in mammographic 5

Mix Transformer depth-wise separable convolution UNet for br...

引用

5th international conference on Artificial Intelligence and computer science, AICS 2023

作者： Yutong, Zhong Yan, Piao School of Electronic Information Engineering Changchun University of Science and Technology Jining Changchun130022 China

ISBN: (纸本)9781510668621

Mammography is main tests used for breast cancer risk assessment. However, the mass segmentation and classification of mammograms are extremely challenging. To reduce computational costs and the workloads of radiologists, deep neural networks have been widely used in various medical image segmentation and classification tasks. Since there are some common features between these two tasks, a multi-task learning approach to solve both tasks are a promising direction. We propose a mix Transformer depth-wise separable convolution U-Network (MTDUNet) for mass segmentation of mammograms. We introduce depth-wise separable convolutions to replace traditional convolutions and improve the network’s perception of multi-scale features within the receptive field. Additionally, due to the inherent limitations of convolutional networks, we introduce mix transformer to model remote contextual information. We conducted evaluations the proposed GATNet on two publicly available breast mass segmentation datasets. The average Dice similarity coefficients between the MTDUNet results and INBreast and CBIS-DDSM data were 89.90% and 83.63%, respectively. The experimental results indicate that MTDUNet can significantly reduce the spatial complexity of medical image segmentation networks and effectively save computational resources. © 2023 SPIE.

关键词： Convolution

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A Blockchain-Based Key Management Scheme in FANETs 4

A Blockchain-Based Key Management Scheme in FANETs

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4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Yin, Datong Han, Lei Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9798331531409

Flying Ad-Hoc Networks (FANETs) enable the deployment of Unmanned aerial vehicles (UAVs) in a clustered manner to execute complex missions, yet their inherent openness poses significant challenges to communication security. However, as a crucial part of ensuring communication security, most existing FANET key management schemes rely on base stations or other infrastructures. This reliance not only complicates communication between UAVs and base stations but also makes the system vulnerable to single point of failure. To address these issues, we introduce a key management scheme based on Blockchain. This scheme in-tegrates the key tree with the secret sharing scheme, which achieves the autonomous generation and updating of group keys, the dynamic joining or exiting of UAVs, and the withdraw of malfunctioning UAVs. Security and performance analyses demonstrate that our scheme effectively resists a variety of security threats, exhibits lower computational overhead and energy consumption compared to similar schemes, making it well-suited for heterogeneous FANET environments. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Enhanced Classification of Intel Images Using Refined EfficientNet and MobileNetV2 Frameworks 4

Enhanced Classification of Intel Images Using Refined Effici...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Jain, Raghav Sharma, Rishabh Tiwari, Devesh Joshi, Kireet Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttrakhand Dehradun248002 India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering U.P Greater Noida India

ISBN: (纸本)9798350349900

The proposed study describes the functioning of two modern deep learning models, namely EfficientNet and MobileNetV2, within the scope of image classification from a wide range of datasets. This study is designed to categorize images into six groups, namely: buildings, mountains, forests, sea/ocean, glaciers, and streets. The study utilizes a dataset from the Kaggle repository to assess the effectiveness of these models. The research methodology will include data preprocessing, modeling, and model evaluation. This paper will find out the balance between accuracy and efficiency which is provided by each model. EfficientNet is compared with MobileNetV2 because of its scalable structure and compromise between model depth, width, and resolution versus the latter which is known for its low weight and efficiency, especially in constrained circumstances. The research utilizes a framework of accuracy, precision, recall, F1-score metrics, and model efficiency analyses for comprehensive result evaluation and scrutiny. The analysis of this study indicates the unique advantages of separate models, for instance, EfficientNet is more accurate in most cases and MobileNetV2 is a good solution when you have to save computer resources. Comparing models highlights the importance of the selection that is made based on the specific application instead of performance metrics or computational demands. © 2024 IEEE.

关键词： Deep Learning EfficientNet Intel MobileNetV2

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A New Prediction-Based Algorithm for Dynamic multi-objective Optimization Problems 26th

A New Prediction-Based Algorithm for Dynamic Multi-objectiv...

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26th international conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Karkazan, Kalthoum Topcuoglu, Haluk Rahmi Sahmoud, Shaaban Computer Engineering Department Faculty of Engineering Marmara University Istanbul Turkey Computer Engineering Department Fatih Sultan Mehmet Vakif University Istanbul Turkey

ISBN: (纸本)9783031302282

The mechanism for reacting to the changes in an environment when detected is the key issue that distinguishes various algorithms proposed for dynamic multi-objective optimization problems (DMOPs). The severity of change is a significant approach to identify the dynamic characteristics of DMOPs. In this paper, a prediction-based strategy based on utilizing the degree of the changes is presented to address environmental changes. In case of a change detection in the given DMOP, the severity of change is evaluated and an appropriate reaction mechanism is followed based on the degree of the observed change. To accelerate the convergence process, the algorithm may respond multiple times for the same change. The performance of our algorithm is evaluated by comparing it with dynamic multi-objective evolutionary algorithms using six benchmarks. The effectiveness of our algorithm is demonstrated in the experimental study where it outperforms other compared algorithms in most of the tested instances considered. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Evolutionary algorithms

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Detection of dim small targets in strong backgrounds based on Local Feature Maximum Difference 4

Detection of dim small targets in strong backgrounds based o...

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4th international conference on computer Vision and Data Mining, ICCVDM 2023

作者： Ma, Tao Xu, Jinhao Qi, Xiaoping Zhang, Jianlin Chengdu610209 China University of Chinese Academy of Sciences Beijing100049 China Beijing China National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Chengdu China

ISBN: (纸本)9781510674448

关键词： Feature extraction

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multi-Branch Information Network for Advanced Image Captioning 3

Multi-Branch Information Network for Advanced Image Captioni...

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3rd international conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2024

作者： Song, Caixia School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9798331530280

Understanding images from different perspectives and performing advanced semantic consistency transformation across modalities are key elements in generating natural language descriptions that are relevant and coherent with the current image captions. Insufficient understanding or errors in interpreting visual features of images can lead to inconsistent descriptions or generate hallucination problems. Moreover, current techniques often employ attention mechanisms to achieve semantic alignment in different representation spaces while overlooking the underlying heterogeneity of multimodal data. In this paper, we propose a multi-Branch Information Network for Advanced Image Captioning that integrates comprehensive visual semantic understanding and advanced semantic consistency transformation into a single image captioning model architecture. Technically, we first utilize a cross-modal retrieval model to obtain fine-grained contextual descriptions for each image region, aiming to compensate for and alleviate hallucination. Next, we introduce a novel mapping network that reconstructs visual information in the textual space and achieves consistency transformation between visual and textual modalities within the textual domain. Empirical evidences show that, ours outperforms the latest approaches on the COCO dataset and achieves the best CIDEr score of 138.4% in the Karpathy test set. © 2024 IEEE.

关键词： computer architecture cross-modal gap Encoding Feature extraction Image captioning Transformers

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Optimizing Tabular Vector-Borne Disease Surveillance with Machine Learning Classification Techniques 2

Optimizing Tabular Vector-Borne Disease Surveillance with Ma...

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2nd international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Abinaya, N. Harikrishnan, V.S. Santhiya, S. Jayadharshini, P. Rathika, P. Nishanandhini, A. Priyadharshini, C. Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Department of ECE Karpagam Academy of Higher Education Coimbatore India Department of Artificial Intelligence Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798331541217

Vector-borne diseases (VBDs) represent a critical global health challenge due to the rising incidence and potential for serious outcomes. This study aims to enhance VBD surveillance by leveraging machine learning classification techniques to identify and predict vector-borne diseases based on symptomatology. Using a comprehensive dataset sourced from Kaggle, multiple classification algorithms, including Support Vector Machines, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, and multi-Layer Perceptron, were applied and evaluated. The results demonstrate that the multi-Layer Perceptron model achieved the highest accuracy, underscoring its effectiveness in disease classification. The findings of this study support the advancement of public health initiatives, enabling proactive disease management through optimized VBD prediction and classification. © 2024 IEEE.

关键词： Support vector regression

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A Novel Cellular Automaton Model for Optimized multi-Exit Crowd Evacuation in High-Density Environments 25

A Novel Cellular Automaton Model for Optimized Multi-Exit Cr...

引用

25th international Arab conference on Information Technology, ACIT 2024

作者： Alidmat, Omar Khair Alla Abuowaida, Suhaila Alhenawi, Esraa Awwad, Hebatullah Khattab Al-Momani, Adai Yusof, Umi Kalsom Elrashidi, Ali Zarqa University Computer Science Department Zarqa Jordan Al-Albayt University Computer Science Department Mafraq Jordan Universiti Sains Malaysia School of Computer Sciences Pulau Pinang Malaysia University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Evacuation systems play a critical role in mitigating casualties and property damage during emergency situations. Comprehending evacuee behavior in overcrowded scenarios is vital for developing effective evacuation strategies. Nonetheless, evacuating large crowds from buildings with multiple exits presents a significant challenge, particularly when exits are asymmetrical and crowds are dense. This paper proposes an innovative two-dimensional cellular automaton model for multi-exit evacuation, designed to streamline evacuee decision-making in an asymmetrical exit layout within dense crowds. The proposed model addresses limitations in traditional evacuation models by incorporating dynamic counting areas and enhanced parameters, including congestion, distance, and empty parameters. These improvements enable the model to more accurately represent evacuee behavior during emergency situations, particularly in environments with multiple exits. The model effectively captures complex crowd dynamics, such as arching and shockwaves, and adjusts exit selection based on real-time congestion levels. Simulations demonstrate that the proposed model significantly reduces evacuation time, unit evacuation time, and travel distance compared to existing models. Key findings highlight the model's ability to mitigate bottlenecks at congested exits, optimize exit utilization, and enhance overall evacuation efficiency. Future research aims to refine the model with real-world data for broader applications in diverse settings like stadiums and airports, thereby improving safety protocols during emergencies. © 2024 IEEE.

关键词： Decision making

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Fraud Identification in Financial Transactions: Machine Learning-Based Anomaly Detection Method

Fraud Identification in Financial Transactions: Machine Lear...

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2024 international conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sharma, Sweta Kavitha, Netala Dhaliwal, Navdeep Rajalakshmi, B. Sumalatha, Ippa Kavitha, R. Gauhati Commerce College Department Of Accountancy Guwahati21 India Department Of Computer Science And Engineering Visakhapatnam Duvvada India Lovely Professional University Phagwara India New Horizon College Of Engineering Department Of Computer Science Bangalore India Institute Of Aeronautical Engineering Dundigal Hyderabad India Bannari Amman Institute Of Technology Department Of Computer Science And Engineering Erode Sathyamangalam638401 India

ISBN: (纸本)9798350366846

Two major financial crimes that have reached epidemic proportions worldwide are credit card holder fraud and financial statement fraud. The efficacy of corporate governance, the calibre of financial reports, and the legitimacy of audit functions have all suffered from the collapse of well-known corporations. Credit card theft and financial statement fraud have grown to be serious problems for organizations worldwide. The predicted accuracy scores of Support Vector Classifiers (SVM) and Logistic Regression were found to be greater than those of the other classifiers employed in the unbalanced training dataset. An experimental model that first applies the class balancing technique to transform the unbalanced dataset into a balanced data set was developed in alignment with the research purpose and to design a model to improve results and reduce False Negative Rate. Using a nonlinear support vector machine model, a classifier was trained on the dataset. The AUC score of the suggested model was higher. By creating a new, selected hybrid class balancing technique and using the Random Forester algorithm to create an optimum classifier and analyse the outcomes, the experimental model of class balancing was further refined. Based on the results of this experiment, it was found that the suggested approach outperforms the prior model, generates a good AUC score, and maximizes false negatives. The approach can be used to address other comparable categorization issues for better results. It is not just restricted to online financial transactions;it also aids in lowering false negatives in credit card fraud transactions. © 2024 IEEE.

关键词： Support vector machines

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Advancements in Object Detection for Accurate and Efficient Visual Recognition Using Machine Learning 2

Advancements in Object Detection for Accurate and Efficient ...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Fernandez, F. Mary Harin Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Chennai India Computer Engineering Department University of Technology and Applied Sciences Nizwa Oman

ISBN: (纸本)9798350383867

Object detection in images and videos is a vital task in computer vision where its application goes beyond the automobile driving and surveillance, to healthcare. The paper introduces an object detection model which is specific and powerful, that is able to recognize visually with machine learning. The framework is constructed on the basis of the recent innovations in deep learning and attentive mechanisms, which not only lead to the best performance in object detection today but also guarantee the widest applicability. Using an extensive experiment which reveals the described model superiority over the current state-of-the-art algorithms by evaluating mAP indices, whereas the latter has a normal complexity. The ablation study uncovers the impact of core parameters including attention mechanisms and semi-supervised techniques, underlining their importance in the growth of detection precision. Moreover, the illustrative comparison of outcomes from detecting objects on the framework calls for attention to the capacity of the networks to objectify in different categories. Our outcome also suggest that although the proposed framework has a human-level performance in task of object detection there is still a scope to enhance their performance above the human level. Summarizing, this work concludes the conversation and can be considered in the progress of research developments of the object detection systems aimed at practical object meeting situations. © 2024 IEEE.

关键词： computer Vision Deep Learning Machine Learning Object Detection Visual Recognition

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