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共 35495 条记录，以下是701-710 订阅

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An Innovative Ensemble Approach to Detecting Distributed Denial of Service Attacks Using Machine Learning

An Innovative Ensemble Approach to Detecting Distributed Den...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Rajput, Deepak Singh Upadhyay, Arvind Kumar Amity University Dept. of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798331517953

In the present era marked by rapid Internet development, there is a growing emphasis on information security. Distributed Denial of Service (DDoS) attacks poses significant threats to servers globally. Various methods and services have emerged to prevent and mitigate damage caused by these attacks. The challenge in implementing defence lies in timely attack detection, requiring a highly accurate mechanism. DDoS attack methods are diverse and evolving, rendering traditional prevention methods less effective. This research explores the application of machine learning, utilizing the CICDDoS2019 dataset to train hybrid models. The proposed ensemble model combines unsupervised K-Means for dataset labelling and supervised classifiers (Random Forest, XGBoost, AdaBoost, SVM) with Artificial Neural Network (ANN). Achieving an overall accuracy of 99.46%, this study underscores the effectiveness of deep learning in DDoS attack detection. © 2024 IEEE.

关键词： Random forests

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Retroflex: Uncovering Visual Equivalences through Reverse Image Recon 18

Retroflex: Uncovering Visual Equivalences through Reverse Im...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Yadav, Sneha M. Joshi, Karik Shailendra Mandavia, Divyah Tarun Puri, Parth Mahendra Vidyavardhini's College of Engineering and Technology Dept. Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9789380544519

This research introduces a comprehensive method for Reverse Image Search (RIS) this can be also called as CBIR that is Content Based Image Retrieval designed for large datasets with more than 50,000 photos in 25 different classes. The approach uses sophisticated deep learning feature extraction methods, such as Auto-encoders and CNNs, to extract complex visual representations for reliable content characterization. We incorporate advanced indexing techniques to address the issues of scalability and retrieval efficiency. The resultant framework exhibits higher accuracy and speed and provides an optimal and sophisticated solution for picture retrieval problems. This effort is a major step forward in improving the capabilities of RIS systems, offering a flexible tool for real-world applications that need for thorough and effective image retrieval across a range of classes. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Auto-encoders CNN Content Based Image Retrieval (CBIR) datasets Framework Reverse Image Search (RIS) Sophisticated Deep Learning Feature Extraction

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Designing of Creator's Information Hub using Machine Learning 2

Designing of Creator's Information Hub using Machine Learnin...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gabhane, Diksha Gawande, Ujwalla Gaikwad, Sanjana Gajarlewar, Swati Bhawalkar, Rita Yeshwantrao Chavan College of Engineering Dept. of Computer Science & Design Nagpur India Yeshwantrao Chavan College of Engineering Dept. of Information Technology Nagpur India NCPharmacy Wanadongri Nagpur India G H Raisoni College of Engineering and Management Dept. of Cyber Security and Data Science Pune India

ISBN: (纸本)9798331528713

The proliferation of educational content on platforms like YouTube has transformed digital learning, yet it presents challenges in effective creation, dissemination, and impact assessment. This paper proposes a strategic initiative to collaborate with 6,000 active educational YouTubers to enhance global educational outreach. Central to this initiative is the compilation and analysis of a comprehensive dataset containing vital information about content creators and their audiences. Leveraging advanced machine learning algorithms, the analysis aims to identify collaboration opportunities, optimize engagement strategies, enhance content relevance and impact, facilitate knowledge sharing, and measure outcomes. The problem statement highlights fragmentation in educational content, lack of data-driven insights, and suboptimal collaboration opportunities. Addressing these challenges requires a structured approach to connect creators, leverage data analytics, and establish a collaborative ecosystem for enriching educational experiences worldwide. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart Traffic Management System for Urban Mobility Enhancement Using RF Sensors, Cameras, and Machine Learning 4

Smart Traffic Management System for Urban Mobility Enhanceme...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Vivekanandan, G. Pathmanaban, J. James, Prabahar Godwin Barani Sree, S. Priyanka, V. Samvardhini, V.M. Sri Sai Ram Institute of Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331508845

This paper introduces a Smart Traffic Management System (STMS)employing RF sensors, cameras, and machine learning algorithms to monitor and optimize urban traffic. The system dynamically adjusts traffic signal timings, offers real-time route recommendations based on GPS data, and incorporates adaptive control mechanisms to reduce congestion and improve overall mobility. Simulation studies and real-world testing demonstrate the effectiveness of the STMS in enhancing traffic flow, minimizing waittimes, and contributing to sustainable urbandevelopment. © 2024 IEEE.

关键词： Traffic signals

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Empirical Investigation of Adversarial Attacks for Semi-Supervised Object Detection 6

Empirical Investigation of Adversarial Attacks for Semi-Supe...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Jo, Junhyung Kim, Joongsu Suh, Young-Joo Pohang Korea Republic of Dept. of Computer Science and Engineering Pohang Korea Republic of

ISBN: (纸本)9798350344349

Semi-supervised learning (SSL) techniques have been rapidly developed and adopted in various vision tasks because of their advantage of leveraging unlabeled data. However, existing works have neglected the vulnerability of SSL because most of the existing adversarial attacks are mainly discussed in a supervised learning manner. In this paper, we study the effects of adversarial examples on Semi-Supervised Object Detection (SS-OD) which is the mainstream of SSL techniques. We build our hypothesis that attacks on the supervised learning model are also effective on SSL models. Since the state-of-The-Art SS-OD methods borrow the teacher-student network, we prepared two pseudo-label based SS-OD networks to validate our hypothesis. We attempt to attack the inference model with adversarial examples crafted by using pretrained auxiliary model and found that SS-OD networks are more vulnerable to adversarial attacks. In addition, we found that selecting different loss components of SS-OD networks to generate perturbations determines the effect and performance of the attack such as misclassification or mislocalization. Visual examples are provided for a clearer understanding. To the best of our knowledge, this is the first effort to investigate the vulnerability of SS-OD. © 2024 IEEE.

关键词： Object detection

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Utilization of MFCC in Conjuction with Elaborated LSTM Architechtures for the Amplification of Lexical Descrimination in Acoustic Milieus Characterized by Pronounced Nocturnal Disturbance 2

Utilization of MFCC in Conjuction with Elaborated LSTM Archi...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Upadhyay, Deepak Malhotra, Sonal Rawat, Ramesh Singh Gupta, Mridul Mishra, Somesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350371055

In this pioneering research endeavor, we delved into the intricate realm of speech recognition technology, aiming to surmount the formidable challenges posed by acoustically complex environments, notably those marred by pronounced nocturnal disturbance. Through the synergistic amalgamation of Mel-frequency cepstral coefficients (MFCC) and meticulously crafted Long Short-Term Memory (LSTM) architectures, we charted a transformative path toward the amplification of lexical discrimination. Our multifaceted approach not only enhanced the discernment of subtle linguistic nuances but also showcased unparalleled efficacy in the face of disruptive nocturnal noise. The meticulously calibrated fusion of advanced signal processing techniques and neural network architectures culminated in a novel paradigm, revolutionizing the landscape of speech recognition technology. By pushing the boundaries of computational linguistics, this study not only advances scientific understanding but also paves the way for real-world applications in domains reliant on precise and resilient speech recognition systems. © 2024 IEEE.

关键词： AI LSTM MFCC Transfer Learning

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Plant Disease Detection using Convolutional Neural Network

Plant Disease Detection using Convolutional Neural Network

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agarwal, Jyoti Naithani, Shivansh Singh, Utkarsh Raj, Harsh Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Pest-infested plants and crops have a negative effect on the nation's agricultural output. To detect and identify diseases in plants, farmers or other experts must keep a careful watch on plants. However, this method is frequently expensive, time-consuming, and inaccurate. Finding a spot on the leaves of the infected plant is one method of detecting plant diseases. This research aims to develop a disease detection model based on the classification of leaf images. To detect plant illnesses the proposed model utilizes image processing with a convolution neural network (CNN). This research work focus on three different plant diseases: tomato, corn and potato. Experimental results show high accuracy rate of 98.8%. Proposed work is also compared with some existing work working o same plants diseases. © 2024 IEEE.

关键词： Support vector regression

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A Lightweight CNN-SVM Explainable AI Approach for Classification and Visualization of Grape Leaf Disease 3

A Lightweight CNN-SVM Explainable AI Approach for Classifica...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Peyal, Hasibul Islam Leion, Zannatul Mawa Abdal, Md. Nazmul Islam, Md. Injamul Miraz, Shafiun Remon, Mahmudur Rashid Kontho, Md. Mahin Rahaman Tasnim, Nishat Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Rajshahi Bangladesh North South University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Sher-e-Bangla Agricultural University Dept. of Agroforestry and Environmental Science Dhaka Bangladesh

ISBN: (纸本)9798350388282

Grape is highly esteemed as a significant agricultural crop in Bangladesh. Plant diseases primarily result from the presence of pathogens and pest insects, leading to a significant decline in productivity if not properly addressed. The fundamental aim of this research is to employ a streamlined CNN-SVM architecture, utilizing deep learning techniques, to accurately categorize grape leaves into three distinct disease classes and one healthy class. The proposed model surpasses the accuracy of the previously trained transfer learning models VGG-16 and VGG-19 while having approximately 257× to 267× times fewer parameters (0.537 M). On average, the proposed model achieves a classification accuracy of 99.18%, which is significantly higher than the 93.42% and 91.94% achieved by the transfer learning models, respectively. With a precision, recall, and F1 score close to 99%, the suggested model provides excellent results. The model's outstanding performance is further validated by its remarkable Area Under Curve (AUC) score of 99.98%. In addition to using less disc space (about 6 MB), the suggested model because of being lightweight shows a significant decrease in parameters. To visually display the disease identified by the proposed model, a transparent AI methodology has been utilized, specifically the Gradient Weighted Class Activation Mapping (Grad-CAM) technique. To better understand which area was responsible for the classification, a heatmap has been created. © 2024 IEEE.

关键词： Deep learning

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Detection of Airports and Runways From Aerial Imagery Using Deep Learning 8

Detection of Airports and Runways From Aerial Imagery Using ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Vasavi, S. Paul, Valeti Abhishek Abhilash, Vandanala Gnaneswar, Kotha Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331505462

Disaster management calls for as quick a detection of critical infrastructure-like airports and runways. Most of the traditional object detection models, which include CNNs, R-CNN s, SSDs, and earlier versions of YOLO, have high computational costs and low accuracy, particularly for smaller objects within aerial and satellite imagery. It presents the YOLOv9 object detection model with very significant improvements in feature extraction, bounding box prediction, and several modern techniques involving attention mechanisms and multi-scale feature fusion. For the purpose of improving model robustness, extensive data augmentation was adopted in training. The proposed model was tested and validated on the diverse dataset of aerial and satellite images, attaining an accuracy as high as 91.3% with a low false alarm rate of just 7.08%. These further improvements will make YOLOv9 highly scalable to support disaster management with faster infrastructure assessments, improvement in border surveillance, among others. © 2024 IEEE.

关键词： Disaster prevention

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Performance Evaluation of Triple-Branch Diversity Receivers in Composite Gamma-Shadowed Rician Fading Channels with AMC and Asymmetric SNR Conditions 1

Performance Evaluation of Triple-Branch Diversity Receivers ...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Yadav, Devansh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350365016

This paper provides an in-dept. analysis of the performance enhancement resulting from AMC in triple-branch diversity receivers under composite Gamma shadowed Rician fading channels with uneven SNR conditions. This paper was able to show the level of enhancement in average SNR resulting through simulations of AMC on signal quality in several receiver branches, which clearly shows AMC's ability in alleviating fading and shadowing based degradations. The analysis revealed that AMC adequately alleviates degradations by changing modulation schemes and coding rates based on the current quality of the channel. This reinforces AMC's ability to improve the link's reliability and likelihood to optimize the efficiency of the communication channel. This makes AMC a desirable design and optimization feature for wireless communication in highly fading and shadowed environments, which makes this innovation a potential fundamental technology in the realization of robust, resilient, and adaptive communication systems. © 2024 IEEE.

关键词： Signal to noise ratio

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