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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jiquan Cao, Jian Yang, Junqing Wang, Siqi Chen, Geng Heilongjiang University Department of Computer Science and Technology Harbin China Northwestern Polytechnical University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350386226

White matter microstructure plays a pivotal role in the diagnosis and study of brain disorders. Deep learning-based estimation of white matter microstructural indices from diffusion MRI (dMRI) data has gained increasing research attention in recent years. To conduct effective learning in the heterogeneous space (i.e., x-space and q-space) of dMRI data, hybrid neural networks were proposed and have shown great potential. To this end, we propose a new hybrid neural network, called synergistic dual-path hybrid convolutional neural network (SDH-Net), for more effective microstructure estimation. In our SDH-Net, we propose a dual-path architecture that takes bidirectional asymmetric learning in x-space and q-space to enhance feature representation in heterogeneous domains. Firstly, 3D patches are extracted from each diffusion gradient as vertices, and then a graph is constructed based on the correlation between diffusion angles. In the x-q learning branch, an effective representation is embedded in x-space to enhance learning in q-space, while vice versa in the q-x learning branch. Extensive experiments on data from the human connectome project demonstrate that our SDH-Net outperforms the existing state-of-the-art models. © 2024 IEEE.

关键词： Convolutional neural networks

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Integrity Estimation of Twitter Based Event Recognition Using Scrutiny of Analyzed Data 2

Integrity Estimation of Twitter Based Event Recognition Usin...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Siva Rama Lingham, N. Parameswari, M. Karthick, P. Priya, T. Subha Rathi Rajesh, G. Jagannathan, J. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai India School of Engineering and Technology Jeppiaar University Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore Katpadi632014 India

ISBN: (纸本)9798350328202

Twitter has been observed to be one of the essential data resources for dependable event accreditation. In any case, Twitter-based event affirmation structures can't guarantee assessment concerning their attestation results. Talk statement has been locked in starting late to enable liberal event Recognition. This problem is not yet evident in light of the assumption that most people tend to think that they have a specific limit to the data on Twitter that have frames based on events based on Twitter. Current appraisals see new pieces by seeing and looking at the significant psychological aspects of hitting the details on Twitter. No matter, the scale of the visual cues can be well established, with the intention that it is essential to design an integrated Twitter experiment with another set of extensible data resources to rectify this problem. The problem is the processes by which you should wholeheartedly investigate all converted data because it has different data structures, transfer times, etc. This issue of address, the paper proposes a framework for evaluating the use of Twitter-based events and examines two types of data resources for the reliability of impact testing. Our framework uses the resultant of the request obtaining by various events and relevant to the articles on it. This paper has improved the cleaning test with the proposed process how the integrity estimation of twitter based event can be recognized with the help of group of analyzed data. The evaluation indicates that the proposed system provides visible high-level testing events and a variety of low-level testing events. © 2024 IEEE.

关键词： Impact testing

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CopulaGAN Boosted Random Forest based Network Intrusion Detection System for Hospital Network Infrastructure 14

CopulaGAN Boosted Random Forest based Network Intrusion Dete...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Harshini Sivakami, V. Nivedhidha, M. Ramkumar, M.P. Emil Selvan, G.S.R. Thiagarajar College of Engineering Department of Computer Science and Engineering Madurai India Fatima Michael College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350335095

The growing dependence on technology in healthcare has resulted in the creation of sophisticated hospital networks that are highly linked and vulnerable to cyber threats. A reliable Network Intrusion Detection System (NIDS) is required to identify and prevent such cyberattacks. The network intrusion detection is vital for safeguarding hospital networks and guaranteeing data security. The CICIDS2017 dataset contains a comprehensive set of network traffic characteristics for assessing network intrusion detection systems. Besides that, class imbalance is a prevalent difficulty in intrusion detection and it may have a considerable impact on the effectiveness of classification algorithms. The suggested solution employs a Machine Learning (ML) based NIDS for hospital networks that utilizes CopulaGAN (Generative Adversarial Network) to address the challenges due to imbalanced class ratio. The synthetic samples of minority classes were created to balance the dataset and improve detection accuracy. The Random Forest (RF) algorithm is used to discover the most defining features in the dataset and its hyperparameters are tuned to improve classification performance. Overall, the CopulaGAN boosted Random Forest based NIDS described here is a valuable solution for detecting network intrusions in hospital networks. © 2023 IEEE.

关键词： Classification (of information)

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Resolution Enhancement of Diffusion-Weighted Images via Unified x-q Space Learning

Resolution Enhancement of Diffusion-Weighted Images via Unif...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jiquan Wang, Siqi Zhang, Runlin Cao, Jian Chen, Geng Heilongjiang University Department of Computer Science and Technology Harbin China Northwestern Polytechnical University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350386226

Low resolution is a major issue restricting the application of Diffusion-Weighted Imaging (DWI) in neuroscience research and clinical routine. Super-resolution provides a viable solution to enhance the resolution of DWIs at the post-acquisition stage. Existing methods for DWI super-resolution primarily rely on the information in the x-space (i.e., spatial domain), but fail in exploiting the angular relationships in q-space (i.e., diffusion wavevector domain). In this work, we propose a Unified X-Q space Learning (UXQL) framework that makes full use of x-space and q-space information. Building upon a message-passing scheme, we employ 3D residual convolutional blocks to learn correlations in x-space, while utilizing a spatial attention mechanism to achieve effective q-space learning. Additionally, the T1-w MR image is incorporated into our framework for additional information to assist DWI super-resolution. We conduct experiments on the DWIs from the widely-used Human Connectome Project (HCP). Experimental results demonstrate the effectiveness of UXQL in improving DWI super-resolution, both quantitatively and qualitatively. © 2024 IEEE.

关键词： Graph neural networks

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Gaze Tracking with Multi-Modal Neural Network in Desktop Environments Using Generic Web Camera 6

Gaze Tracking with Multi-Modal Neural Network in Desktop Env...

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6th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Kohel, Karim Osama, Mohamed Mohamed, Nourhan Mokhtar, Mohamed Tamer, Omar Fakhr, Mohamed Waleed University of Northampton Faculty of Engineering Department of Computer Northampton United Kingdom Arab Academy for Science and Technology Faculty of Engineering Department of Computer Cairo Egypt

ISBN: (纸本)9798350382891

Gaze tracking is the process of estimating where a person is looking on the screen using only information from eye movement without additional input from the user, it contributes greatly in understanding and improving human-computer interactions. This paper addresses an improved gaze tracking technique building upon established approaches that are using generic web cameras instead of requiring high-cost specific hardware like infrared cameras and sensors. In the proposed method, a pipeline was constructed of various high performing AI models such as Google's MediaPipe face localization and face landmarks solutions to acquire both Iris metrics and localized eye image data. The data obtained is then passed to the proposed model that uses both image data and location metrics to predict the user's gaze position on the screen using a multi-modal architecture. Experiments done on realistic desktop environments show 8% mean distance error of the screen dimensions with a standard deviation of +/-6% from the actual gaze location. The proposed method is not affected by most logistical limitations affecting other similar approaches. © 2024 IEEE.

关键词： Eye tracking

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An optimal feature selection-based deep learning approach for wheat disease identification

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Multimedia Tools and Applications 2025年第14期84卷 12855-12876页

作者： Raja, S. M. Naveen Nargunam, A. Shajin Department of Information Technology Noorul Islam Centre for Higher Education Thuckalay Kumaracoil India Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Thuckalay Kumaracoil India

Globally, wheat is a staple food crop, which plays a vital role in food security and sustenance for millions of people. However, the diseases in wheat crops affect their growth, productivity, and crop quality. Hence, early and timely disease detection is significant to prevent the loss of productivity and the economy. This article introduces an innovative hybrid model combining Moth Flame Optimization (MFO) and Radial Basis Functional Neural Network (RBFNN) for the prediction of disease wheat leaves from healthy ones. Initially, the wheat leaf image containing the normal and diseased leaves is collected and utilized as input for the system. Then, the dataset was filtered and related leaf attributes are tracked using the Histogram of Oriented Gradients. Subsequently, MFO fitness was employed to select the most informative leaf images from the extracted feature set. By utilizing these selected features, the RBFNN was trained to classify the normal and healthy leaves accurately. This model was evaluated with the wheat leaf dataset and the performances are evaluated. The presented model achieved greater accuracy 99.32%, recall of 99.33%, f-measure of 99.33%, and precision of 99.33%. The implementation outcomes exhibit that the designed framework has great potential for practical application in agriculture. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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An interpolation-based reversible data hiding scheme for internet of things applications

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Discover Internet of Things 2023年第1期3卷 18页

作者： Punia, Riya Malik, Aruna Singh, Samayveer Department of Computer Science & amp Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Punjab India

The exponential growth of the Internet of Things (IoT) has led to an increased demand for secure and efficient data transmission methods. However, there is a tradeoff in the image quality and hiding capacity in the data hiding methods. Therefore, the maximum amount of data that could be stored in the image media is a difficult challenge while maintaining the image quality. Thus, to make the balance between the quality of the images and the embedding capacity, a novel interpolation-based revisable data hiding (RDH) approach is developed for IoT applications. The proposed interpolation technique takes the average of the root value for the product of two neighboring original pixel values and the third original pixel value. And for the central pixel, it takes an average of two interpolated pixels. By doing so, most of the original pixels are considered and the calculated interpolated pixel is much enhanced as its average value. Furthermore, the data hiding is performed in two stages. In the first stage, RSA is performed on the secret message, and then embedding is done based on which intensity range group. The experimental results indicate that the proposed technique enhanced the embedding capacity by 17.58% and produced 7.80% higher PSNR values for the test images as compared to the baseline methods. © The Author(s) 2023.

关键词： Cryptography

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Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

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Dzyaloshinskii-Moriya-type spin-spin interaction from mixed-parity superconductivity

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Physical Review B 2025年第6期111卷 L060504-L060504页

作者： Jabir Ali Ouassou Takehito Yokoyama Jacob Linder Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences NO-5528 Haugesund Norway Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Center for Quantum Spintronics Department of Physics Norwegian University of Science and Technology NO-7491 Trondheim Norway

Interacting impurity spins adsorbed on surfaces have been suggested as basic components for applications in quantum computation and spintronics. Such spins usually prefer a parallel or antiparallel configuration, but weakly noncollinear alignments are possible due to the Dzyaloshinskii-Moriya interaction (DMI) that arises in the presence of relativistic spin-orbit coupling. Here, we show that an effective Dzyaloshinskii-Moriya-type interaction (DMTI) can emerge purely from superconducting correlations without any spin-orbit interaction. We give an analytical proof and provide a numerical study which shows that DMTI arises in mixed-parity superconductors solely from the superconducting pairing. Moreover, we show that the same effect can be realized in Josephson junctions between s-wave and p-wave superconductors, where a phase bias toggles the DMTI entirely on and off. These results enable a way to engineer spin textures using superconducting order.

关键词： Shear waves

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Intelligent IoT-Driven Advanced Predictive Maintenance System for Industrial Applications

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SN computer science 2025年第2期6卷 1-24页

作者： Dhinakaran, D. Edwin Raja, S. Velselvi, R. Purushotham, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of CSE School of Computing Mohan Babu University Tirupati India

Predictive Maintenance (PdM) aims to ensure the continuous operation of high-risk industrial systems. This challenge is especially critical in environments where equipment failure can cause major financial losses and disrupt operations. Traditional PdM approaches, while fairly effective, often fall short in accurately predicting failures due to their reliance on simple statistical methods and predefined rules. The proposed work introduces an Intelligent IoT-Driven Advanced Predictive Maintenance System (APdM) that addresses these limitations. By integrating advanced machine learning techniques, including supervised and reinforcement learning, with IoT technologies to enhance predictive accuracy as well as the operational efficiency. The system leverages Explainable AI (XAI) to ensure transparency in decision-making and employs federated learning for distributed model training, preserving data privacy across multiple edge devices. The proposed APdM system was evaluated using the Kaggle "Air Compressor Predictive Maintenance" Dataset, which provided extensive sensor data from heavy vehicle operations. Comparative analysis against four prevailing PdM approaches—Traditional PdM, Decision Support System-PdM, Genetic Algorithm-based PdM, and AI-based PdM—demonstrated the superior performance of our system. The results show that the APdM system, powered by intelligent IoT integration, diminishes the Mean Error Percentage (MEP) by up to 38.4% compared to traditional methods and decreases the Mean Absolute Error (MAE) by 36.7% relative to DSS-PdM. Additionally, the system achieves a 78% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) over GA-PdM, underscoring its robust performance in real-world industrial scenarios. These outcomes validate the effectiveness of the proposed APdM system, marking a significant improvement in the field of predictive maintenance through the intelligent integration of IoT technologies. © The Author(s), under exclusive licence to S

关键词： Explainable AI IoT Machine learning Predictive maintenance Supervised and reinforcement learning

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