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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Hossain, Mohammad Alamgir Amin, Sherif Tawfik Eishah, Hassan Abu Hamdan, Alfadil Ahmed Metwally, Abdelnasser Abdelwakil Limkar, Suresh Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Artificial Intelligence and Data Science AISSMS Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789819765805

Cardiovascular diseases (CVD) are a prominent contributor to illness and death on a global scale, underscoring the need for precise predictive models to facilitate timely intervention. The present study investigates the utilization of deep learning methodologies, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), in the context of predictive modeling of cardiovascular diseases. This study examines the efficacy of three well-known optimization techniques, namely Adam Optimization, RMSprop, and Stochastic Gradient Descent (SGD), within the framework of these neural network architectures. Among the various models based on Convolutional Neural Networks (CNNs), Stochastic Gradient Descent (SGD) has been identified as the optimizer that produces the most favorable outcomes for predicting CVD. The utilization of this optimization technique demonstrated exceptional efficacy in the training of the deep neural network, resulting in superior levels of accuracy, sensitivity, and specificity. On the other hand, it was observed that LSTM-based models exhibited the greatest improvement when utilizing RMSprop optimization. The utilization of RMSprop has been found to have a positive impact on the effectiveness of sequence modeling, resulting in enhanced predictive capabilities for assessing the risk of cardiovascular disease. The efficacy of this technique was demonstrated in its ability to capture temporal dependencies within the dataset, consequently enhancing the predictive capability of the model. The results of this study emphasize the importance of carefully choosing neural network architectures and optimization techniques when constructing predictive models for cardiovascular disease. Customizing the selection of neural network architecture and optimization algorithm according to the unique attributes of the dataset can substantially augment the precision and dependability of CVD risk evaluations. This, in turn, can ultimately lead t

关键词： Risk assessment

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Fine-grained Text-Video Fusion for Referring Video Object Segmentation 4

Fine-grained Text-Video Fusion for Referring Video Object Se...

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4th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Li, Yonglin Wan, Jiaqi Teng, Xiao Lan, Long College of Computer Science and Technology National University of Defense Technology Department of Intelligent Data Science Changsha410073 China

ISBN: (纸本)9798350359589

Referring video object segmentation (RVOS) aims at segmenting an object in a video with its text description. The core of RVOS lies in the modal alignment between the vision and text. To improve the performance, most previous RVOS methods are devoted to exploring more sophisticated visual cues. However, these methods fail to fully exert the inherent structured properties of text and only use coarse text features to tackle vision-text interaction. In this paper, we propose FTVR, an approach that utilizes Fine-grain Text-Video method for Referring video object segmentation to explore fine-grain text information. We introduce a LLM to split the text sentence into different functional phrases and propose three novel modules to enhance the cross-modal alignment. Concretely, we design a dynamic-aware perception module to deal with motion-related phrases and a following global-aware attention module to fuse the outputted motion information. To deal with entity-related phrases, our FTVR also introduces an entity-aware augmentation module to highlight entity information. Extensive experiments show the effectiveness of our method on four popular benchmarks. © 2024 IEEE.

关键词： fine grained fusion large language model Referring video object segmentation

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Prediction of Alzheimer'S Disease from Magnetic Resonance Images (MRI) 5

Prediction of Alzheimer'S Disease from Magnetic Resonance Im...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Manimuthu, A. Peter Jose, P. Gangadevi, E. Kumar, U.Udhaya Anandaraj, M.D. Loyola College Department of Data Science Chennai India Mount Carmel College Department of Computer Science Bangalore India Loyola College Department of Computer Science Chennai India Shanmuga Industries Arts & Science College Department of Mca Tamil Nadu India

ISBN: (纸本)9798350383522

Alzheimer's disease (AD) is an enigmatic, neurodegenerative brain disorder that impairs memory, concentration, and even basic tasks. Every three seconds, a new AD case emerges worldwide, underscoring its widespread impact. Extensive research underscores the pivotal role of early AD detection in curtailing treatment expenses and duration. This study pioneers a dual approach, leveraging both Machine Learning and Deep Learning methodologies to prognosticate Alzheimer's through MRI images. Notably, the VGG16-based model achieves an impressive accuracy of 0.808, surpassing conventional machine learning models (SVM, RF, KNN, DT, NB, LR) and sophisticated deep learning counterparts (CNN, VGG16, ResNet50, MobileNet, DenseNet 121). This outcome presents a pivotal stride toward advancing Alzheimer's diagnosis and optimizing treatment strategies for enhanced patient care. © 2024 IEEE.

关键词： Alzheimer's Disease Deep Learning Machine Learning

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Bilateral Control for Secure Communication against Replay Attack in ORAN-based Vehicular Networks

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 1-14页

作者： Peng, Zongfeng Peng, Changgen Tian, Youliang Ding, Hongfa State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China College of Information Guizhou University of Finance and Economics Guiyang China

Open radio access network (ORAN)-based vehicular networks play a pivotal role in future traffic data sharing. Due to the openness of the ORAN framework, it is necessary to encrypt the sharing data to prevent unauthorized access or misuse by malicious participants. However, current efforts struggle to address some emerging security requirements, such as the bilateral control of sending and access rights in cellular vehicle-to-everything (C-V2X) communications, as well as evolving replay attack on encrypted data. With these challenges, this paper presents a fine-grained access control encryption (FGACE) scheme for secure communication in ORAN-based vehicular networks. Our FGACE enables fine-grained and bilateral control over senders and receivers. We design a novel sanitization algorithm against dishonest sanitizer and replay attack. Furthermore, we deeply explore security models for replay attack on encrypted data to support secure sanitization, i.e., attribute-based replayable CCA security and related definitions. We prove that our FGACE satisfies the proposed security definitions. Feature comparison demonstrates that our scheme does better in security and privacy. Experimental results show that our scheme outperforms compared schemes. When the number of attributes increases to 50, our total computational overhead is lowest, and the total storage overhead is approximately less than 1/3 of other schemes. The impressive performance indicates that our FGACE is practical in ORAN-based vehicular networks. IEEE

关键词： Cryptography

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Information Flows during Constraint Satisfaction Search with Dynamic Heuristics: Information from Preprocessing 1

Information Flows during Constraint Satisfaction Search with...

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1st International Workshop on Artificial Intelligence for Climate Change, 12th Italian Workshop on Planning and Scheduling, 31st RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, and SPIRIT Workshop on Strategies, Prediction, Interaction, and Reasoning in Italy, AI4CC-IPS-RCRA-SPIRIT 2024

作者： Wallace, Richard J. Insight Centre for Data Analytics School of Computer Science and IT University College Cork Ireland

We still do not have an adequate understanding of heuristic methods used for solving constraint satisfaction problems (CSPs). An example of this involves the effects of preprocessing, an essential means of improving CSP search. The traditional explanation for its beneficial effect is that the resulting "problem reduction" leaves fewer possibilities to explore. Recently, however, it was shown that when dynamic variable ordering heuristics are used, other factors related to domain size reduction are much more important. This paper extends this earlier work and clarifies what is involved. The key idea is that the pattern of domain reductions produced by preprocessing constitutes information that by itself enables heuristics to make better decisions. Treating domain reduction as a code and the set of reductions as a message of length n transmitted to the search algorithm, we can distinguish two factors, message discriminability and code quality. We provide evidence that greater problem reduction enhances both. © 2024 Copyright for this paper by its authors.

关键词： Constraint satisfaction problems

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TransTuple: Toward Fast Packet Classification via Adaptive Tuple Replacement 21

TransTuple: Toward Fast Packet Classification via Adaptive T...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Yu, Jiashuo Zhu, Longlong Wu, Rongbang Zheng, Linying Liu, Hongyan Zhang, Dong Wu, Chunming College of Computer and Data Science Fuzhou University China Zhicheng College Fuzhou University China Quan Cheng Laboratory China College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9798331519186

Open vSwitch (OVS) is a widely used software switch in virtualized environments and software-defined networks. OVS uses tuple space search (TSS) for packet classification in the datapath, allowing fast network rule updates, but the increasing number of rules poses a classification performance challenge. To address this, existing methods incorporate decision trees with TSS to form a hybrid structure, enhancing classification speed. However, decision trees tend to overfit the initial ruleset, becoming unbalanced after rule updates and leading to a sharp decline in classification performance. In this paper, we propose TransTuple, a framework to optimize hybrid structures for fast packet classification under rule updates. The core idea of TransTuple is to identify bottleneck branches in decision trees that degrade performance and to replace them with lightweight tuples, providing better throughput under rule updates. These tuples maintain rules using hash tables, enabling fast updating and packet matching on bottleneck branches. We use TransTuple to optimize three state-of-the-art hybrid structured methods, i.e., CutTSS, TabTree, and MBitTree, achieving up to a 3.1x improvement in classification speed during rule updates. © 2024 IEEE.

关键词： Decision trees

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High-Level Synthesis Algorithm for Continuous-Flow Microfiuidic Biochips Under Device Volume Constraints

High-Level Synthesis Algorithm for Continuous-Flow Microfiui...

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2023 International Conference on computer science and Automation Technology, CSAT 2023

作者： Chen, Zhengyang Pan, Youlin Chen, Zhen Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350371437

Continuous-flow microfluidic biochips (CFMBs) can precisely manipulate microfluids for bioassays. In order to enhance the efficiency of bioassays, it is imperative to carefully consider operation scheduling and device binding schemes. The existing work has considered device volume constraints at high-level synthesis to meet the actual fluid transport requirements of the CFMBs. However, this work is based on integer linear programming, which has a high algorithmic complexity and makes it difficult to meet the large-scale integration requirements of CFMBs. Therefore, a fast high-level synthesis algorithm based on a discrete particle swarm algorithm under device volume constraint is proposed. We propose a particle encoding scheme based on the operation priorities and the device binding scheme. In addition, the corresponding particle initialization method and update strategy are proposed to satisfy the device volume constraint. The experimental results show that the proposed algorithm can obtain high-level synthesis results in a short time. © 2023 IEEE.

关键词： Integer programming

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Traffic Flow Prediction Method Based on Similarity Graph and Temporal Convolution Units

Traffic Flow Prediction Method Based on Similarity Graph and...

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2023 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2023

作者： Chen, Hao Chen, Chi-Hua College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350347296

Accurate traffic flow prediction can improve urban commuting efficiency, but how to effectively mine the spatio-temporal characteristics of traffic data is the biggest challenge. Therefore, this paper proposes a spatio-temporal convolution method, which can effectively extract the traffic dynamic correlation of different road nodes, and reconstruct the road network by cosine similarity principle. This principle can reflect the similar situation of traffic change trend of two road nodes in the past period of time. In order to sample the time-dependent features of road nodes, this study uses a time-dependent convolutional network, which has the ability to extract sequence time-dependent features from longer data. © 2023 IEEE.

关键词： Convolution

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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BESP: A novel BCI experiment simulation platform to assist BCI-related algorithm validation 16

BESP: A novel BCI experiment simulation platform to assist B...

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16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

作者： Chen, Qingzhi Ke, Jingshuang Huang, Zhihua Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350330755

In recent decades, many brain-computer interface (BCI) software platforms have emerged. However, there are still some limitations. First, integrating an algorithm on online BCI software platform is difficult and time-consuming. Second, there is no guarantee that an unproven algorithm will work properly. Last, existing platforms do not support automatic recording of result metrics during experiments, and frequent validation of multiple algorithms is inefficient. In this paper, we designed a novel BCI experiment simulation platform (BESP) based on offline analysis to satisfy the needs of algorithm validation. BESP reorganizes the experimental steps and provides some interfaces, which makes algorithm validation simple and efficient. In addition, BESP is able to record the results produced during the experimentation. Then the results of multiple algorithms can be easily analyzed, compared and visualized by the statistical analysis system provided by BESP. With these characteristics, BESP is able to verify the correctness of the algorithm. Finally, we designed a simulated P300 spelling experiment based on BESP, and completed the validation and analysis of Algorithms SPLUCB and TSPLUCB (based on PLUCB and TPLUCB). Furthermore, we used BESP to analyze the differences between the stimulus signals of the 20 subjects. Through the analysis, we found some areas worthy of further study. In summary, BESP is well suited for the process of analysis, optimization and validation among multiple algorithms. © 2023 IEEE.

关键词： Simulation platform

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