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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Barveen, A. Mohamed Faizal, M.K. Geetha, S. M.I.E.T. Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Trichy India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350375190

Memes are a visual representation of images with text embedded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. It is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LSTM to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLSTM and CNN have been used to extract contextual features. This study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. The proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. The proposed system uses both text and image data. To improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training. © 2024 IEEE.

关键词： Image enhancement

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Enhancing digital security using Signa-Deep for online signature verification and identity authentication

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第2期8卷 58-69页

作者： Ch, Ravikumar Sridevi, Mulagundla Ramchander, M. Ramesh, Vankudoth Kumar, Vadapally Praveen Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Applications Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Emerging Technologies CVR College of Engineering Hyderabad 500039 India

In the contemporary digital realm, the utilization of online services has surged, facilitated by the seamless integration of deep learning technology, which is paramount in applications demanding precision and efficiency. A pivotal use case in this context is online handwritten signature verification, where the need for exceptional accuracy is indisputable. This paper introduces ‘Signa-Deep, ’ an innovative approach designed to address the challenge of online signature verification and the determination of an individual’s authorization status. The study explores a range of methodologies, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), GoogleNet, and MobileNet, to discern the authenticity of signatures and affirm the identity of the signatory. The results of our proposed method are promising, showcasing its potential to significantly enhance the security of digital transactions and identity verification processes. In summary, ‘Signa-Deep’ harnesses deep learning technology to bolster the accuracy and reliability of online signature verification, thereby contributing to the overall robustness of digital interactions and identity validation processes. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Authorization Status Deep Learning Digital Transactions Security Identity Authentication Online Signature Verification

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A Quantitative Study on the FaceNet System 4th

A Quantitative Study on the FaceNet System

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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Gopakumar, Rajesh Kotegar, Karunakar A. Vishal Anand, M. Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819942831

Face recognition is the science of identifying and recognizing human faces in various situations, keeping the constraints like pose variation and occlusion in mind. Due to its impactful applications in safety and security systems, face recognition is becoming extremely popular and is researched extensively even today. The FaceNet method is among the most tested approaches in Deep learning face identification. This method uses a deep convolution neural network for training the data. The face embedding generated can be used to train a face identification system. This study aims to comprehend the FaceNet system, evaluate its performance, and test its accuracy on seven standard datasets. The study also tries to compare how well the FaceNet method works compared to other popular holistic and hybrid methods. From the outcomes of this study, we can conclude that FaceNet showed outstanding results and was better than the other methods. The FaceNet system reached a minimum of 90% accuracy on all standard datasets used on both the pre-trained models, which is a significant number for any face recognition method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Face recognition

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data Lake Development Status and Outlook 3

Data Lake Development Status and Outlook

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3rd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2023

作者： Li, Guonong Hu, Wenyu You, Tongzhen School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China

ISBN: (纸本)9781510668867

To adapt to the increasing demand for big data storage and analysis, and to solve the difficulties and pain points of data Warehouse technology, it is necessary to build an efficient and high-quality data storage architecture. In this thesis, the evolution, technical background, architecture, and the current status of domestic and international research of the data Lake concept were presented, data Lake and data Warehouse were compared and their advantages and disadvantages were summarized. The feasibility of data Lakehouse and ‘data Lake + data Middle-end’ was discussed, and the problems that should be solved in the construction of ‘data Lake + data Middle-end’ were prospected. Finally, the challenges faced by the further development of data Lake technology were analyzed, and some ideas to solve these problems were put forward. © 2023 SPIE.

关键词： Lakes

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Robust Sensor Networks: Applying AI for Fault Identification in Challenging Operational Contexts 5

Robust Sensor Networks: Applying AI for Fault Identification...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Manjunatha, A.S. Venkatramana Bhat, P. Ramakrishna, M. Prabhu, Matti Nidhi Dept. of Computer and Communication Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Dept. of Computer Science and Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal576104 India

ISBN: (纸本)9798331518097

Wireless Sensor Networks (WSNs) are highly implemented in harsh and dynamic environments. In such configurations, faults and anomalies easily occur, causing unwanted disruptions in data accuracy and network performance. In the context of reliability and stability in WSN operations, effective fault detection techniques are vital. This research work discusses how several machine learning models may be applied, including Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors in order to detect fault in WSNs. The performance evaluation was performed based on major criteria, which are accuracy, precision, recall, and Fl score. This, therefore, makes these approaches to machine learning effective in identifying faults efficiently, hence offering a well-balanced solution that is scalable and robust for integrity and operational effectiveness of WSN s. © 2024 IEEE.

关键词： Random forests

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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Compact Multitasking Multichromosome Genetic Algorithm for Heuristic Selection in Ontology Matching

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6752-6766页

作者： Xue, Xingsi Lin, Jerry Chun-Wei Su, Tong Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Silesian University of Technology Department of Distributed Systems and IT Devices Akademicka Gliwice44-100 Poland Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China

Ontology matching (OM) is critical for knowledge integration and system interoperability on the semantic web, tasked with identifying semantically related entities across different ontologies. Despite its importance, the complexity of terminology semantics and the large number of potential matches present significant challenges. Existing methods often struggle to balance between accurately capturing the multifaceted nature of semantic relationships and computational efficiency. This work introduces a novel approach, a compact multitasking multichromosome genetic algorithm for Heuristic selection (HS) in OM, designed to navigate the nuanced hierarchical structure of ontologies and diverse entity mapping preferences. Our method combines compact genetic algorithms with multichromosome optimization for entity sequencing and assigning HS, alongside an adaptive knowledge transfer mechanism to finely balance exploration and exploitation efforts. Evaluated on the ontology alignment evaluation initiative's benchmark, our algorithm demonstrates superior ability to produce high-quality ontology alignments efficiently, surpassing comparative methods in both effectiveness and efficiency. These findings underscore the potential of advanced genetic algorithms in enhancing OM processes, offering significant contributions to the broader AI field by improving the interoperability and knowledge integration capabilities of semantic web technologies. © 2024 IEEE.

关键词： Genetic algorithms

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Uncertain graph generating approach based on differential privacy for preserving link relationship of social networks

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International Journal of Security and Networks 2022年第1期17卷 28-38页

作者： Yan, Jun Tian, YuPan Liu, Hai Wu, ZhenQiang School of Computer Science Shaanxi Normal University Xi’an710119 China School of Mathematics and Computer Applications Shangluo College Shangluo726000 China Guizhou Provincial Key Laboratory of Public Big Data Guiyang550025 China

With the widespread use of social networks in our daily life, the personal privacy in social networks has become a growing concern. To prevent the link relationship of social networks from disclosing users’ sensitive information when the social networks data is released, an uncertain graph approach based on differential privacy is introduced, which can resist attacks based on background knowledge and possesses better data utility. In this approach, we propose modification of edges based on random response (MERR) algorithm and injection of uncertainty based on k-edges-differential privacy (IUDP) algorithm. The MERR algorithm can modify the edge of original graph according to random response mechanism, while the IUDP algorithm injects uncertainty to generate an uncertain graph. For evaluating our approach, the expectation of editing distance between two graphs is adapted to measure the level of privacy preserving. In addition, our approach is conducted in different datasets and compared with other approaches. The experimental results indicate that this approach achieves differential privacy and has better data utility. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Privacy-preserving techniques

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An Optimal Wind Turbine Control Based on Improved Chaotic Sparrow Search Algorithm with Normal Cloud Model

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 108-125页

作者： Yang, Qi-Peng Wang, Chia-Hung Dao, Thi-Kien Nguyen, Trong-The Zhao, Qi-Gen Chen, Shu-Meng College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam

This study suggests a Chaotic Sparrow Search Algorithm (CSSA) based on the Sparrow Search Algorithm (SSA) for a wind turbine generator unit’s pitch controller optimal parameters. The optimal parameters set of a wind turbine pitch controller is a complex problem that needs to be tuned to archive a smooth quality performance of the outcome power operations. The CSSA is carried out with Tent chaotic mapping updating equations and a standard cloud model to avoid the original SSA algorithm drawbacks and increase quality performance to solve the optimal controller parameters. The experiments compare the CSSA against other algorithms on testing function, and the optimal pitch controller parameters demonstrate that the CSSA can solve smoothing the output power of the wind turbine unit, reducing the effect of wind speed fluctuations on the power grid. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Wind Turbine Generators

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Kernel Fence GAN: Unsupervised Anomaly Detection Model Based on Kernel Function 1

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14th International Symposium on Intelligence Computation and applications, ISICA 2023

作者： Niu, Lu Li, Shaobo Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610041 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

ISBN: (数字)9789819743933

ISBN: (纸本)9789819743926

data imbalance and difficult construction of high-dimensional data cause the low accuracy of anomaly detection. This paper proposes an unsupervised anomaly detection model Kernel Fence GAN to increase the accuracy. In this adversarial generative network, the generator's loss function consists of three items: the encirclement loss limits the generated samples to lie at the decision boundary of normal samples, the central dispersion loss maximizes the range of the decision boundary, and the enhanced dispersion loss uses kernel function to nonlinearly map the generated samples to RKHS space, which can maximize sample differences and avoid outliers. The purpose of training the discriminator is to accurately identify whether the input sample is a normal sample in the original dataset or an abnormal sample generated by the generator. In this paper, validation experiments are performed on two datasets: on two-dimensional synthetic dataset, the experiment result shows that the model has faster convergence rate than Fence GAN. On the KDD99 dataset, compared with traditional and advanced deep learning methods, both precision and recall have been correspondingly improved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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