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共 89044 条记录，以下是251-260 订阅

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Behavior-based Machine Learning Approaches for Malware Detection 2

Behavior-Based Machine Learning Approaches for Malware Detec...

引用

2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Srivastava, Vivek Khare, Manya Kansal, Manasvi Harsh Department of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

The present research investigates the implementation of various methods of machine learning to analyze fluid information from executable files for the purpose identify contamination. We extracted feature vectors from a dataset that contained both malicious and benign samples. Standard metrics like Fl-score, recall, and precision were used to train and evaluate a range of classifiers, including Decision Trees, Random Forests, Support Vector Machines, and Logistic Regression. Our test findings show that the models successfully distinguish between harmful and benign files, with Random Forest providing the highest level of consistency in all evaluation measures. These Results demonstrate the promise of machine learning methods to improve systems for detecting malware. © 2025 ieee.

关键词： Adversarial machine learning

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ResuMatcher: An Intelligent Resume Ranking System 3

ResuMatcher: An Intelligent Resume Ranking System

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dhobale, Prasad Bhoir, Vinayak Vyavhare, Abhijeet Yelkar, Pratik Dharmadhikari, Pramod A. School of Computer Engineering MIT Academy of Engineering Alandi Pune India

ISBN: (纸本)9798331527549

The growing complexity of recruitment processes necessitates advanced solutions for resume screening and ranking. In this paper, we present ResuMatcher, an AI-powered resume ranking system that leverages the power of Large Language Models (LLMs) for semantic understanding. Unlike traditional keyword-based systems, ResuMatcher evaluates the context and meaning behind terms in resumes and job descriptions, providing a more accurate and efficient matching process. Using models such as BERT and transformer-based architectures, ResuMatcher can assess candidate qualifications by considering the underlying semantics of job requirements and candidate skills. The system was evaluated on a diverse dataset across industries such as IT, healthcare, and finance, and showed a significant improvement in both accuracy and efficiency compared to conventional Applicant Tracking systems (ATS). Results from user testing demonstrated high satisfaction rates among both job seekers and recruiters, highlighting ease of use, relevance of matches, and efficiency gains in the hiring process. © 2025 ieee.

关键词： Semantics

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A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 ieee.

关键词： Contrastive Learning

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Predicting Stock Prices with K-Nearest Neighbors using Moving Average Techniques 1

Predicting Stock Prices with K-Nearest Neighbors using Movin...

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

作者： Yuvaraj, S. Chenni Kumaran, J. Kajendran, K. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This research analyzes the effectiveness of the K-Nearest Neighbors algorithm combined with moving average techniques, Five-day and Ten-day Exponential Moving Averages, specifically EMA-5 and EMA-10, to predict stock prices. The study will focus on the adjusted closing price to deal with the autocorrelation issue that is common in time series data. The performance of the models was evaluated based on four key metrics: mean absolute error, mean squared error, root mean square error, coefficient of determination, overfitting risk test and generalization capability test. It was shown from the results that KNN models integrated with EMA-5 and EMA-10 offered high accuracy, one of which, with an R2 coefficient indicating the capacity to capture stock price trends with an accuracy of up to 99%. The research pointed out how short-term EMAs, combined with machine-learning algorithms, can substantially improve stock price prediction accuracy. It gives useful insights into stock price behaviour, therefore providing a decisive precedent for future works in creating a viable method for selecting trading strategies using machine-learning tools and moving averages. © 2025 ieee.

关键词： Time series

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Early Stage Pepper Leaf Disease Detection using Deep Learning based Capsule Network with Convolutional Neural Network 1

Early Stage Pepper Leaf Disease Detection using Deep Learnin...

引用

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

作者： Jeevitha, S. Venkadesh, R. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Agriculture plays a vital part in the economy of some countries, providing benefits such as food, national income, and employment. This results in substantial growth in agricultural produce, financial losses, and a decline in quality and quantity. Farmers have traditionally detected diseases through eye tests, but this method has drawbacks. However, previous research suggests that binary classification needs improvement because some types are more challenging to determine. To address these issues, various pepper diseases are classified based on DL techniques to address these issues. First, image pre-processing uses a Median Filter (MF) to reduce noise and increase brightness. Furthermore, the Active Contour Segmentation (ACS) method detects affected and unaffected areas in the image. Moreover, the Texture Color Intensity Feature Selection (TCIFS) method is employed to select the optimal features of pepper disease. Finally, the proposed Capsule Network with Convolutional Neural Network (CapsNet-CNN) technique detects pepper leaf disease. The proposed method attains an accuracy of 98.32%, recall of 97.43%, and precision of 98.04% compared to other methods. © 2025 ieee.

关键词： Median filters

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A Review on Bioinformatics using DNA Cryptography 8

A Review on Bioinformatics using DNA Cryptography

引用

8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kairi, Animesh Bhadra, Tapas Karmakar, Samit Verma, Aman Saha, Sonia Gupta, Soumili Chowdhury, Souraja Aliah University Computer Science Kolkata India University of Engineering & Management Dept. of Electronics & Communication Engineering Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798331520762

DNA-based encryption with bioinformatics has emerged as a promising field for secure data storage and transfer. This study analyzes the bioinformatics aspects of DNA cryptography, focusing on important advancements and challenges. We look at techniques like DNA origami, enzymatic reactions, and DNA strand displacement that use DNA sequences to encode and decode data. Additionally discussed are the security implications of DNA-based cryptography, including privacy issues, data integrity issues, and the potential for DNA-based attacks. Finally, we outline some possible directions for further review, such as developing more robust error-correction systems, looking at cutting-edge DNA-based encryption methods using bioinformatics, and addressing the ethical and societal implications of this emerging technology. © 2025 ieee.

关键词： Bioinformatics

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Integrating DDP to Enhance Cryptographic Potential of Latin Squares Through Increased Permutation Diversity 8

Integrating DDP to Enhance Cryptographic Potential of Latin ...

引用

8th international conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Akhlaq, Muhammad Mahfooz, Syed Zeeshan College of Computer Science and Engineering University of Hafr Al-Batin Hafr Al-Batin Saudi Arabia

ISBN: (纸本)9798331539696

One application of Latin square lies in their utilization within cryptographic systems. By adding complexity through permutation-based replacement, Latin squares strengthen security against assaults such as frequency analysis, hence aiding cryptographic systems. This paper proposes utilizing Diamond Dispersed Pattern (DDP) in Latin squares instead of a simple pattern to enhance their cryptographic potential through increased permutation diversity and resistance against attacks. The attributes and efficacy of DDP is discussed, underscoring their relevance in cryptography for achieving outcomes with a more managed use of Latin squares. © 2025 ieee.

关键词： Ciphertext

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Enhancing AR/VR Experiences with a Pinch-Gesture Multi-Layer Virtual Keyboard System

Enhancing AR/VR Experiences with a Pinch-Gesture Multi-Layer...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Donggyu Munasinghe, Viduranga Lee, Tae-Ho Jin, Hyun-Jun Kim, Tae Sung Kim, Jin-Sung Lee, Hyuk-Jae Seoul National University Department of Electrical and Computer Engineering Korea Republic of Kangwon National University Department of Electronic Engineering Korea Republic of Sun Moon University Department of Electronic Engineering Korea Republic of

ISBN: (纸本)9798331510756

Advancements in augmented reality (AR) and virtual reality (VR) technologies suggest that AR/VR devices will soon become widely accessible. Despite this growth, there remains a lack of specialized virtual keyboard systems optimized for AR/VR interactions. To address this, we propose a multi-layer virtual keyboard system utilizing pinch-based gesture recognition, specifically designed for AR/VR applications. Our simulation model demonstrates that the proposed system achieves significantly faster typing speeds due to its layered structure and gesture-based interface, resulting in substantial gains in user input efficiency and experience compared to traditional keyboard solutions. © 2025 ieee.

关键词： Virtual environments

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Securing Wireless and Optical Networks: Advanced Strategies for Network and Information Security in Modern Communication systems

Securing Wireless and Optical Networks: Advanced Strategies ...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Dhasmana, Gulshan Sharma, Vishal Boob, Nandini Shirish Aravind, K. Reddy, R. Akhilesh Yadav, Kanchan Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India

ISBN: (纸本)9798331512965

In the era of rapidly changing communication systems, securing wireless and optical networks has become of essence to protect sensitive data and ensure operation. This study addresses network and information security in current communication systems by studying cutting edge techniques to enhance network and information security in wireless and optical networks. The wireless networks are convenient and may be moved around, but they are also prone to hacking, illegal access or some other cyberattacks. As optical networks possess both a large capacity and relatively low latency, it is not without special security issues such as fibre tapping and signal jamming. This study presents detailed analyses of state of the art authentication techniques, intrusion detection systems (IDS), and encryption algorithms specifically designed for these networks. Then there are, such as Quantum Key Distribution (QKD) which almost assuarly unbreakable techniques are examined alongside conventional cryptographic methods. Additionally, anomaly detection based on machine learning is explored for real time threat identification in optical and wireless channels. It stresses the need of the cross domain solutions and multi layered security frameworks covering the network layer and the physical security measures. Through case studies and recent developments in cyber threats and network resilience, this article provides a thorough understanding of how these tactics could be used to make networks such as wireless network and optical network more resilient against increasing cyber risks. © 2025 ieee.

关键词： Network intrusion

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Improved Gated Recurrent Unit based Network Traffic Anomaly Detection 3

Improved Gated Recurrent Unit Based Network Traffic Anomaly ...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Jasim, Layth Hussein Nagarathna, P. Sunitha, Yada Lekha, V. Padmavathy, R. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science Bengaluru India Cvr College of Engineering Department of Computer Science and Engineering Hyderabad India Dhanalakshmi Srinivasan College of Engineering Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331508456

The detection of unpredicted events or rare patterns in traffic data plays an important role in intelligence traffic management, that offers alerts of incidents. To address the problem of less accuracy in industrial network traffic anomaly detection, in this manuscript developed the Improved Gated Recurrent Unit (GRU) for multi-type anomaly detection. The pre-processing included label encoding and non-string numerical process. The Synthetic Minority Over-Sampling Technique with Edited Nearest Neighbors (SMOTE-ENN) technique is utilized for balancing the data by oversampling and under sampling. The improved GRU is assigned for extracting linear, non-linear and temporal features from network traffic data. These features are given to classifier developed based on Softmax for obtaining high performance detection on traffic attacks. The efficiency is evaluated based on UNSW-B15 dataset with the detection accuracy of 0.97 for DoS attacks, 0.96 for Fuzzers attacks and 0.94 for Worms attacks while compared to existing algorithms like conventional GRU for network traffic anomaly detection. © 2025 ieee.

关键词： Telecommunication traffic

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