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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Sandra Johnson Lourdu Jennifer J R G. Karthikeyan Vengadapathiraj M D Sasireka Department of Artificial Intelligence & Data Science R.M.K. Engineering College Chennai Department of Electronics and Communication Engineering S.A Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Electronics and Communication Engineering Rajalakshmi Instittue of Technology Chennai Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai

ISBN: (纸本)9781665482721

Detection of diseases, including diabetic retinopathy, may be greatly improved by taking a fundus picture of the back of the eye (DR). Complications in diabetics are the most common cause of vision problems, notably in younger and much more financially secure age groups. The risk of blindness in patients with DR may be reduced if they are diagnosed early enough. An ophthalmologist examined the fundus picture and used DR screening to look for lesions. However, the increase in incidence of DR is not correlated with the number of ophthalmologists who are able to interpret fundus pictures. Delay in prevention and treatment of DR may result as a result of this. Consequently, an automated diagnosis system is required to assist ophthalmologists in increasing the diagnostic process efficiency. The concatenate model is used in this study to differ fundus images into three categories: those without diabetic retinopathy, those with non-proliferative diabetic retinopathy, and those with proliferative diabetic retinopathy. We're using DenseNet121 and Inception-ResNetV2 for our models. Two models' feature extraction findings are integrated using the multilayer perceptron (MLP) classification approach. Compared to a single model, our strategy provides an increase in accuracy, precision, and recall of 91 percent and 90 percent for the F1-score. Deep-learning-based DR categorization utilizing fundus picture data was successfully shown in this experiment.

关键词： Deep learning Retinopathy Blindness Multilayer perceptrons Aerospace electronics Feature extraction Diabetes

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Enhancing IoT Security with Self-Adaptive Blockchain-Based Intrusion Detection System

Enhancing IoT Security with Self-Adaptive Blockchain-Based I...

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Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： Pramod Kumar Naik Khaja Mannanuddin Sundaresan K Swati Sah Harshal Patil Firos A Department CSE Dayananda Sagar University Bangalore India School of computer science & AI SR University Warangal Telangana India Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore Anna University Chennai School of SET Sharda university Greater Noida India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of computer Science and Engineering Rajiv Gandhi University (A Central University)Rono-Hills Doimukh Arunachal Pradesh India

The extensive adoption of Internet of Things (IoT) devices has resulted in a revolution across a variety of business sectors by presenting previously unimaginable prospects for connection and automation. Despite this, there has been a significant increase in the usage of IoT, which has led to important security issues. This research presents a ground-breaking security solution known as the Self-Adaptive Blockchain-Based Intrusion Detection System (SAB-IDSI), which was developed particularly for Internet of Things (IoT) settings. In order to determine how well SAB-IDSI performs in contrast to traditional Intrusion Detection Systems (IDS), a series of experiments and assessments were carried out. The findings indicate that there was a significant increase in the accuracy of the intrusion detection. The outstanding capability of SAB-IDSI to reliably detect genuine incursions while limiting false alarms is demonstrated by its accuracy, which is 96.2%. False alerts are kept to a minimum. In addition to this, it has a very high recall rate of 94.8%, which ensures that it has a very high capture rate of genuine incursions. The fact that SAB-IDSI has a balanced F1-Score of 95.5% further validates its overall efficacy in the detection of intrusions. The average reaction time of 12.5 milliseconds for SAB-IDSI is clear evidence of its effectiveness in rapidly responding to threats, greatly surpassing the performance of traditional IDS. This highlights its potential for timely threat mitigation, which is essential in situations where time is of the essence. In addition to this, SAB-IDSI has a significantly lower energy consumption rate, which demonstrates its potential to increase the amount of time that Internet of Things devices may remain operational while also lowering total energy expenses.

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Enhancing The Felsenstein Method To Reinvigorate Phylogenetic Starting In The Big data

Enhancing The Felsenstein Method To Reinvigorate Phylogeneti...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ashendra Kumar Saxena Priyanka Vishwkarma Sandeep Kumar N. Thangarasu K. S. Bhuvaneshwari Bhandari Mahesh Ashok College of Computing Science and Information Technology Teerthankar Magaveer University Moradabad Uttar Pradesh India Department of Computer Science & Engineering Invertis University Bareilly Uttar Pradesh India School of Commerce and Management IIMT University Meerut Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data science Karpagam College of Engineering Coimbatore Tamilnadu India Vishwakarma Institute of Information Technology Pune Maharashtra India

The present research examines how the Felsenstein approach might be improved in light of huge data and evolutionary inference. The Felsenstein approach, phylogeny starting points, huge data, statistical techniques, and testing hypotheses are among the important ideas and terms covered in the overview. It emphasizes the importance of repeatability in evolutionary inferences as well as the difficulties brought on by rogue genera and bootstrapping proportions. The article suggests a brand-new strategy to increase the accuracy of phylogenetic investigations that makes use of transfer separation and bipartition distance. The abstract's conclusion emphasizes the need of better methods for managing huge datasets and for recognizing instability taxonomic.

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Deterministic relation between optical polarization and lattice symmetry revealed in ion-doped single microcrystals

arXiv

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arXiv 2022年

作者： Li, Peng Guo, Yaxin Liu, Ao Yue, Xin Yuan, Taoli Zhu, Jingping Zhang, Yanpeng Li, Feng Key Laboratory for Physical Electronics and Devices The Ministry of Education School of Electronic Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'An710049 China School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi'An710021 China

Rare-earth ions doped crystals are of great significance for micro-sensing and quantum information, whilst the ions in the crystals emit light with spontaneous partial polarization, which is, though believed to be originated from the crystal lattice structure, still lacking a deterministic explanation that can be tested with quantitative accuracy. We report the experimental evidence showing the profound physical relation between the polarization degree of light emitted by the doped ion and the lattice symmetry, by demonstrating, with unprecedented precision, that the lattice constant ratio c/a directly quantifies the macroscopic effective polar angle of the electric and magnetic dipoles, which essentially determines the linear polarization degree of the emission. Based on this discovery, we further propose a pure optical technology to identify the three-dimensional orientation of a rod-shaped single microcrystal using the polarization-resolved micro-spectroscopy. Our results, revealing the physical origin of light polarization in ion-doped crystals, open the way towards on-demand polarization control with crystallography, and provide a versatile platform for polarization-based microscale sensing in dynamical systems. © 2022, CC BY.

关键词： Metal ions

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Undamped soliton-like domain wall motion in sliding ferroelectrics

arXiv

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arXiv 2025年

作者： Shi, Yubai Gao, Yuxiang He, Ri Wang, Hua Zhang, Binwen Zhong, Zhicheng CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Center for Quantum Matter School of Physics Zhejiang University Hangzhou310058 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Fujian Key Laboratory of Functional Marine Sensing Materials Center for Advanced Marine Materials and Smart Sensors College of Material and Chemical Engineering Minjiang University Fuzhou350108 China

Sliding ferroelectricity, which is a unique polarity recently discovered in bilayer van der Waals materials, achieves polarization switching through in-plane interlayer sliding. The unique mechanism, combining intralayer stiffness and interlayer slipperiness, leads to wider domain walls (DWs) and faster DW motion compared to conventional ferroelectrics. Herein, using machine-learning-assisted molecular dynamics simulations and field theory analysis, we find the DW in classical sliding ferroelectric bilayer 3R-MoS2 system exhibits uniformly accelerated motion under an external field, like the Newtonian particle with undamped motion. Remarkably, DW velocity remains constant even after the external field removal, completely deviating from the velocity breakdown observed in conventional ferroelectrics. We further propose an experimental approach to validate this undamped soliton-like DW behavior in sliding ferroelectric systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm

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Academic Radiology 2025年

作者： Dahou, Abdelghani Elaziz, Mohamed Abd Khattap, Mohamed G. Hassan, Hend Galal Eldeen Mohamed Ali School of Computer Science and Technology Zhejiang Normal University Jinhua 321004 China Mathematics and Computer Science department University of Ahmed DRAIA Adrar 01000 Algeria Department of Mathematics Faculty of Science Zagazig University Zagazig 44519 Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Diagnostic Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo 11591 Egypt Technology of Radiology and Medical Imaging Program Faculty of Applied Health Sciences Technology Galala University Suez 435611 Egypt

Rationale and Objectives: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other neuropathies. Magnetic Resonance Neurography (MRN) provides advanced imaging but requires specialized interpretation. This study proposes an AI-based framework that combines deep learning (DL) with the modified Hiking Optimization Algorithm (MHOA) enhanced by a Comprehensive Learning (CL) technique to improve the classification of nerve injuries (neuropraxia, axonotmesis, neurotmesis) using MRN data. Materials and Methods: The framework utilizes MobileNetV4 for feature extraction and MHOA for optimized feature selection across different MRI sequences (STIR, T2, T1, and DWI). A dataset of 39 patients diagnosed with BP was used. The framework classifies injuries based on Seddon's criteria, distinguishing between normal and abnormal conditions as well as injury severity. Results: The model achieved excellent performance, with 1.0000 accuracy in distinguishing normal from abnormal conditions using STIR and T2 sequences. For injury severity classification, accuracy was 0.9820 in STIR, outperforming the original HOA and other metaheuristic algorithms. Additionally, high classification accuracy (0.9667) was observed in DWI. The proposed framework outperformed traditional methods and demonstrated high sensitivity and specificity. Conclusion: The proposed AI-based framework significantly improves the diagnosis of BP by accurately classifying nerve injury types. By integrating DL and optimization techniques, it reduces diagnostic variability, making it a valuable tool for clinical settings with limited specialized neuroimaging expertise. This framework has the potential to enhance clinical decision-making and optimize patient outcomes through precise and timely diagnoses. © 2025 The Association of University Radiologists

关键词： Brachial Plexopathy (BP) Comprehensive Learning (CL) Deep Learning (DL) Feature Selection (FS) Hiking Optimization Algorithm (HOA) Magnetic Resonance Neurography (MRN) MobileNetV4

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An Effective Sensor Filtering Model for Mobile Devices Based on Long Short-Term Memory Technique in Sensor Archives

An Effective Sensor Filtering Model for Mobile Devices Based...

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artificial intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： A. Jasmine Gilda Suresh G S. Sivamrugan Suresh Kumar. R Sushmitha. K R. Thandaiah Prabu Department of Computer Science and Engineering R.M.K. Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai Department of Electronics and Communication Engineering Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Chennai

It is expected that the Internet of Things (IoT) would be capable to deliver smart applications by aggregating data through many detectors spread across the natural world. A sensor registration system (SRS) is used by a mobile device in order to provide information obtained from ambient sensors. After this, the mobile device connects directly to get useful data. The Sensor Reporting System (SRS) has to offer information for sensors that have a chance of successfully connecting. This procedure is also known as sensor filtering, and it is dependent on the location of the user. In practice, the GPS may sometimes display an incorrect location, which results in anunable to connect. To improve the completion rate of interactions among mobile devices and environmental detectors, we provide a double collaboration approach that simultaneously collects GPS data and predictions from prior trajectories. Furthermore, we update the procedure for assessing detector filtering in SRS by integrating a Monte Carlo-based modeling stream within the procedure of gauging the resource supply rate. Realistic research shows that LSTM-based path forecasting can efficiently remove position irregularities and make up for the lack of position information.

关键词： Monte Carlo methods Filtering Detectors Predictive models Knowledge discovery Mobile handsets Trajectory

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Revolutionizing IoT Network Security with Deep Learning-Anomaly Detection Model

Revolutionizing IoT Network Security with Deep Learning-Anom...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Manish Vyas R Vijayaganth Jigyasha Chandhok Aviral Srivastava S. Arumugam Mohit Tiwari Computer Science Department College Name & Address: Vikrant Institute of Technology and Management Indore Artificial Intelligence and Data Science M.Kumarasamy College of Engineering Thalavapalayam Karur Computer Science & Engineering Tula’s Institute of Engineering and Management NAAC Grade A+ Accredited institute Dhoolkot Dehradun Uttarakhand Amity University Rajasthan Amity University Rajasthan NH 11 C kant kalwar Jaipur Computer Science and Applications Periyar Maniammai Institute of Science & Technology Periyar Nagar Vallam Thanjavur Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering A-4 Rohtak Road Paschim Vihar Delhi

The term “Internet of Things” (IoT) is used to describe the collection of data and the connectivity of items to the web that requires little to no human interaction. The IoT is a network of interconnected devices that can collect and disseminate information. Increased security and privacy worries accompany the launch of new devices due to the proliferation of Internet connections and the development of cutting-edge technology like the IoT. These days, the IoT is used everywhere, but especially in logistics, manufacturing, and healthcare. While these emerging IoT applications greatly enhance the usefulness of smart objects, they also present new security risks. Because of this, adapting existing intrusion detection systems (IDS) for use with IoT networks is a topic of intense study. Many IDS experts have found success with machine learning (ML) and deep learning (DL) techniques. By combining deep extraction through the Convolutional autoencoder with deep learning to identify the best features, this work delivers an improved IDS that can be used for anomaly detection. Improves to the deep learning approach include an evaluation of hyperparameter effectiveness, a stage of feature pruning using an autoencoder neural network, and an examination of the sturdiness of the most effective deep neural networks for circumstances exaggerated by Gaussian noise over some of the features in question. Despite the noise, the results show that the formed IoT dataset is useful for anomaly detection with deep learning methods.

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Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal

arXiv

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arXiv 2023年

作者： Leng, Yicheng Fang, Chaowei Li, Gen Fang, Yixiang Li, Guanbin School of Artificial Intelligence Xidian University Xi’an China School of Data Science The Chinese University of Hong Kong Shenzhen China Afirstsoft Shenzhen China School of Computer Science and Engineering Research Institute of Sun Yat-sen University in Shenzhen Sun Yat-sen University Guangzhou China GuangDong Province Key Laboratory of Information Security Technology China

Visible watermarks, while instrumental in protecting image copyrights, frequently distort the underlying content, complicating tasks like scene interpretation and image editing. Visible watermark removal aims to eliminate the interference of watermarks and restore the background content. However, existing methods often implement watermark component removal and background restoration tasks within a singular branch, leading to residual watermarks in the predictions and ignoring cases where watermarks heavily obscure the background. To address these limitations, this study introduces the Removing Interference and Recovering Content Imaginatively (RIRCI) framework. RIRCI embodies a two-stage approach: the initial phase centers on discerning and segregating the watermark component, while the subsequent phase focuses on background content restoration. To achieve meticulous background restoration, our proposed model employs a dual-path network capable of fully exploring the intrinsic background information beneath semi-transparent watermarks and peripheral contextual information from unaffected regions. Moreover, a Global and Local Context Interaction module is built upon multi-layer perceptrons and bidirectional feature transformation for comprehensive representation modeling in the background restoration phase. The efficacy of our approach is empirically validated across two large-scale datasets, and our findings reveal a marked enhancement over existing watermark removal techniques. Copyright © 2023, The Authors. All rights reserved.

关键词： Restoration

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