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检索条件"任意字段=IEEE International Conference on Engineering of Complex Computer Systems"

共 202630 条记录，以下是71-80 订阅

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Collision Prevention System - Specializing in Animals Equipped with Distance Estimation

Collision Prevention System - Specializing in Animals Equipp...

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2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Savithri, C.N. Nandhini, R. Harini, R. Lekha, C. Nithya Vishnu, V. Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529833

The rising incidence of vehicle-animal collisions poses a serious threat to both human safety and wildlife. This paper presents a comprehensive approach to collision avoidance and animal detection using computer vision techniques, with a specific focus on generic animals. Our system employs a trained convolutional neural network (CNN) model to accurately detect animals in real-time from input video streams. The methodology integrates image processing techniques to identify animals within complex backgrounds and estimate their distance from the vehicle. A distance threshold calculation is incorporated to dynamically evaluate collision risk. Upon detecting an animal within a defined proximity, the system initiates a breaking and alert mechanism to mitigate collision risks. This automated solution not only enhances road safety but also contributes to wildlife conservation efforts. Experimental results demonstrate the system's high detection accuracy and robustness in diverse environments, affirming the potential for scalable implementation in rural and wildlife-prone areas. This study contributes to advancing collision avoidance technology through the integration of AI- driven visual processing and real-time alert systems. © 2025 ieee.

关键词： Invertebrates

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RAFM: On designing a robustness assessment framework to evaluate the hardness of malware detectors against adversarial attack

RAFM: On designing a robustness assessment framework to eval...

引用

2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Gaur, Avantika Chamoli, Snigdh Negi, Priyanka Mishra, Preeti Doon University Department of Computer Science Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331513894

Artificial intelligence (AI)-driven intrusion detection systems (IDS) are crucial in modern technological environments such as virtual domains for identifying complex attack patterns. Although these machine learning (ML) based security models are highly effective, they remain vulnerable to adversarial attacks that target the ML model and exploit its sensitivity by carrying out small perturbations in the input sample. The proposed framework, RAFM, is designed to evaluate the robustness of deep learning-based and gradient-boosting decision tree-based IDS models. It generates adversarial samples through functionality-preserving byte-level manipulations, such as DOS header modifications and shifting techniques, designed to evade detection while maintaining malware functionality. The framework highlights vulnerabilities in these systems by testing against diverse malware families. It offers valuable insights for developing a more resilient AI-based intrusion detection solution for securing virtual machines (VMs) from adversarial attacks. © 2025 ieee.

关键词： Adversarial machine learning

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Compact and Secure Image Encryption for IoT systems Employing ECC and AES Hybrid Cryptography

Compact and Secure Image Encryption for IoT Systems Employin...

引用

2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Wandile, Piyush S Singh Kirar, Bhupendra Jain, Saurabh Sahu, Yatendra Computer Science and Engineering Indian Institute of Information Technology Madhya Pradesh Bhopal India Electronics and Communications Engineering Indian Institute of Information Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9798331529833

This study focuses on development of a secure and efficient image cryptosystem optimized for IoT devices. The proposed technique addresses the and resource constraints of IoT devices, it introduces a hybrid encryption approach where an amalgam of algorithms such as Arnold's Cat Map, ECC, Diffie-Hellman (ECDH), AES is used for optimized encryption for the image data. This combination ensures compact, secure, and resource-efficient encryption suitable for IoT systems. The system is evaluated through various performance metrics demonstrating its effectiveness in lightweight cryptography applications while ensuring high levels of security against common cryptanalytic attacks. © 2025 ieee.

关键词： Encryption algorithms

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Explainable AI for Leukemia Diagnosis: Modified ResNet with Hard Attention Mechanisms 1

Explainable AI for Leukemia Diagnosis: Modified ResNet with ...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Ramanujam, E. Sekar, K. Bavashree, G.S. Prabakar, Shreya Sai National Institute of Technology Department of Computer Science and Engineering Silchar India Rajalakshmi Engineering College Department of Aiml Chennai India

ISBN: (纸本)9798331529246

Leukemia is a highly complex and aggressive form of cancer, necessitating accurate and timely diagnosis for effective treatment. This study investigates the use of Explainable Artificial Intelligence (XAI) techniques to enhance computer-aided diagnosis (CAD) systems for leukemia. A modified ResNet architecture, integrated with a hard attention mechanism, is proposed to improve model performance and interpretability. The approach emphasizes robust segmentation of white blood cell (WBC) nuclei, utilizing advanced image processing techniques and U-Net models for precise feature extraction. Model evaluation focuses on both accuracy and interpretability, employing Grad-CAM and clustering space analysis to assess network focus and transparency in decision-making. The results underscore the strengths and limitations of the proposed architecture, offering insights into further advancements in XAI to improve the reliability and transparency of CAD systems. This work ultimately aims to advance clinical outcomes in leukemia diagnosis by providing more reliable and interpretable diagnostic tools. The proposed model produces 99.7% of accuracy for detection and classification of leukemia. © 2025 ieee.

关键词： Diseases

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Nanostructured Boron-Doped Diamond Electrodes for Enhanced Heavy Metal Sensing 38

Nanostructured Boron-Doped Diamond Electrodes for Enhanced H...

引用

38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Ul Banna, G. M. Hasan Siegenthaler, James R. Kabir, Ahmed Azwad Murillo Martinez, Raul Li, Wen Michigan State University Department of Mechanical Engineering East LansingMI United States Michigan State University Department of Electrical and Computer Engineering East LansingMI United States Michigan State University Institute for Quantitative Health Science & Engineering East LansingMI United States Fraunhofer Usa Center Midwest East LansingMI United States

ISBN: (纸本)9798331508890

This paper presents a novel fabrication technique that enables the precise formation of nanostructures on targeted regions of boron-doped diamond (BDD) electrodes. The process utilizes a custom-grown porous anodic aluminum oxide (AAO) template created by anodization, followed by reactive ion etching (RIE) to transfer the nanostructures from the AAO template to the BDD surface. This method eliminates the need for complex two-step diamond synthesis approaches and is more time- and cost-efficient. The fabricated nanostructured BDD (NSBDD) surface increases the electrochemical active surface area by 556% with nanostructures on the surface that helps to lower the limit of detection (LOD) significantly in heavy metal sensing. © 2025 ieee.

关键词： Reactive ion etching

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Classifying Human Emotions through EEG data with Machine Learning 1

Classifying Human Emotions through EEG data with Machine Lea...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Kaur, Gaganjot Gupta, Meenu Kumar, Rakesh Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798331529246

The Psychological and neurobiological phenomenon is called an emotion. The capability of human beings to present different expressions clashes with the emotional state of mind. Therefore, it is challenging to identify a human's real emotional state by observing physical appearance. Nowadays, using emotional engineering to predict human emotions is one of the major attempts of Machine Learning (ML). This study aims to investigate the usage of techniques of machine learning for efficient emotion prediction that relies on electroencephalography (EEG) signals. EEG dataset is complex and sometimes noisy, making it difficult to interpret using traditional methods. So, in this research the Kaggle dataset collected through a Muse headband, five distinct ML methods are applied to classify emotions. Performance metrics such as precision, recall, F1 score, support, and accuracy are employed to differentiate between different classification techniques. Random Forest turned out to be the best method due to its high accuracy 99%. Three emotions are predicted based on the results of performance metrics. This study benefits mental health monitoring, neurofeedback therapy, stress management, human-computer interaction, etc. This study lays the groundwork for exploring this field and helps develop more sophisticated control systems in the future. © 2025 ieee.

关键词： Emotion Recognition

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Micromachined Silica Resonators for Biosensing Applications 38

Micromachined Silica Resonators for Biosensing Applications

引用

38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Sumaria, Vedant Min, Hwall Farazi, Soheil Tadigadapa, Srinivas Iota Bioscience AlamedaCA United States Illumina San Diego United States Northeastern University Department of Electrical and Computer Engineering Boston United States

ISBN: (纸本)9798331508890

This paper explores micromachined chip-scale, silica resonator-based biosensors. Innovative fabrication processes and robust sensor operation configurations for realizing calorimetric, optical, and gravimetric biosensors based on bulk acoustic wave and chip-scale microspherical glass shell optical resonators are presented. © 2025 ieee.

关键词： Whispering gallery modes

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Diamagnetically Levitated and Trapped Graphite Mechanical Resonators 38

Diamagnetically Levitated and Trapped Graphite Mechanical Re...

引用

38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Wang, Yunong Gage, Alexander Lee, Jaesung Feng, Philip X.-L. University of Florida Herbert Wertheim College of Engineering Department of Electrical and Computer Engineering GainesvilleFL32611 United States University of Central Florida College of Engineering & Computer Science Department of Electrical and Computer Engineering OrlandoFL32816 United States

ISBN: (纸本)9798331508890

We report on studying diamagnetic levitation and rigid body resonances of millimeter- to centimeter-scale trapped graphite mechanical resonators, by combining theoretical analysis with experimental demonstrations. Harnessing the strong diamagnetic susceptibility of pyrolytic graphite, we demonstrate stable levitation of square graphite thin plates with varying dimensions and masses above permanent magnets, without the need of active control. The resonance motions are excited by dielectric gradient forces and detected via an ultrasensitive optical interferometry system at room temperature. We observe rigid body resonance modes with frequencies between 25 Hz and 50 Hz and with their quality (Q) factors from 30 to 70. The Q factors are primarily constrained by air damping and eddy current damping. This work represents an initial step toward developing high-stability, anchor-less levitating resonant systems with large masses, paving the way for high-performance resonant sensors with minimal energy dissipation and exceptional thermal and mechanical isolation with very small power consumption. © 2025 ieee.

关键词： Damping

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Quantum Long Sort-Term Memory-based Identification of Distributed Denial of Service Attacks 4

Quantum Long Sort-Term Memory-based Identification of Distri...

引用

4th ieee international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Tripathi, Sarvapriya Upadhyay, Himanshu Soni, Jayesh Florida International University Electrical and Computer Engineering Miami United States

ISBN: (纸本)9798331518882

Distributed Denial of Service (DDoS) is defined as an attack targeting a server, where the server is overwhelmed by an overflow of traffic, leading to a computer system being compromised. To prevent these attacks from happening, thus allowing the computer systems to maintain their normal behavior, a long short-term memory (LSTM) Time-Series model can recognize sequential characteristics in the data in order to predict whether a DDoS attack is occurring. Our approach leverages various aspects of network traffic such as Flow ID, Source IP, Destination IP, and Destination Port, and analyzes the accuracy and reliability using LSTMs to improve DDoS attack prediction accuracy. In this study we analyzed the relative efficiency of Quantum LSTM (QLSTM) over that of Classical LSTM, and found that both Quantum and Classical LSTM models train quickly and within a few epochs the accuracy increases to 0.99 and above. The validation of the models proved that QLSTM achieved better Recall, Accuracy, Precision, and F1 scores as compared to classical LSTM, making it an ideal candidate for DDoS attack predictions. This study proves the potential of applying deep learning techniques to strengthen network security, which has become increasingly important with the rise of complex cybersecurity threats. © 2025 ieee.

关键词： Denial-of-service attack

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Performance Analysis of Fractional Wireless Power Transfer systems via Scattering Parameters

Performance Analysis of Fractional Wireless Power Transfer S...

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2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Ali, Arshaque Mehta, Utkal The University of the South Pacific School of Information Technology Engineering Mathematics and Physics Suva Fiji

ISBN: (纸本)9798331529833

Analyzing fractional-order wireless power transfer (WPT) systems through scattering parameters reveals several advantages over their classical counterparts. Compared to the fixed value in classical systems, the most notable improvement is the variable nature of the critical coupling coefficient in fractional systems. This contrasts with the classical system, which shows optimal performance only under near-perfect coupling conditions and within a narrow frequency band around the resonant frequency. The efficiency analysis reveals that the fractional system maintains a higher transfer efficiency at lower coupling coefficients by leveraging higher frequency operations, a capability not present in classical WPT systems. The ability to optimise performance through frequency adjustment rather than physical coupling modifications represents a significant advancement in wireless power transfer technology. © 2025 ieee.

关键词： Inductive power transmission

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