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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40714 条记录，以下是4801-4810 订阅

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Deep learning-based Hybrid Technique for Forecasting Web Traffic 5

Deep Learning-based Hybrid Technique for Forecasting Web Tra...

引用

5th IEEE international conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Mahanand, Akash Prakash, Prathibha Devaraj, Anjuna Amrita Vishwa Vidyapeetham Department of Mathematics Amritapuri India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Sheffield Hallam University Department of Computing United Kingdom

ISBN: (纸本)9781665493604

Web traffic is a kind of time-series motion, having its highs and lows. The analysis of predicting web traffic has a greater significance for website owners, to make reliable decisions for website users. But the major gripe often faced while exploring concealed and significant details are regarding web users' different usage patterns. In this paper, we apply hybrid-based deep learning algorithms which combine two different architectures of Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). The outcome of our hybrid model is acquired by using the ensemble method of stacking. The Web Traffic Time Series Forecasting(WTTSF) dataset by Kaggle is being used to predict future traffic of Wikipedia articles. We use mean squared error, mean absolute error, and R2 as major conventional evaluation metrics and it offers less error even though it has data randomness over a large scale. © 2023 IEEE.

关键词： Long short-term memory

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Should AI Technologies Replace the Human Jobs? 2

Should AI Technologies Replace the Human Jobs?

引用

2nd international conference for Innovation in Technology, INOCON 2023

作者： Trivedi, Abhinav Kaur, Er. Kanwaldeep Choudhary, Chahil Kunal Barnwal, Priyashi Chandigarh University Computer Science Engineering Mohali India

ISBN: (纸本)9798350320923

AI (Artificial Intelligence) technologies are a broad category of computer science techniques that enable machines to perform tasks that would normally require human intelligence, such as understanding natural language, recognizing images, making decisions, and solving problems. Chatbots are a specific type of AI technology that is designed to communicate with humans in a natural, conversational way using text or voice input. Chatbots are often used in customer service and support to provide quick and convenient assistance to users. GBT (Gradient Boosting Trees) is an ensemble learning method that uses a set of decision trees to make predictions. It is a popular method for a variety of machine-learning tasks, including classification and regression. GBT is often used in combination with other AI technologies to improve the accuracy of predictions and decision-making. In this paper, we are focusing on the chat gbt and Dalle-2. Should AI will replace the human job? In the future, how much progress we can see in this field? © 2023 IEEE.

关键词： Forecasting

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Cyber Attack Detection in IoT using Deep learning Techniques 6

Cyber Attack Detection in IoT using Deep Learning Techniques

引用

6th international conference on Information Systems and computer Networks, ISCON 2023

作者： Tomar, Kartik Bisht, Krishi Joshi, Kshitiz Katarya, Rahul Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350346961

The Internet of things (IoT) consists of millions of digital devices which interact with each other through minimum user interaction. IoT is one of the most rapidly expanding computing sectors;however, it is vulnerable to many attacks. An emerging concern in the Internet of Things (IoT) space is attack and strange placement on the IoT framework. Attacks and dangers on these systems are also growing proportionally because of the expanding IoT foundation usage across all industries. In this paper, a review of previous work is conducted, and several deep learning techniques are proposed for accurately predicting attacks on IoT systems. Injection attacks, Man-in-the-middle attacks, Information gathering, Malware attacks, and DDoS/Dos attacks are such attacks and irregularities that might occur in an IoT framework. Identifying such attacks and malicious traffic is important for the Internet of things (IoT) network to block unwanted traffic and unauthorized access. The Edge-IIoTset Cyber Security Dataset and the VGG16 and VGG19 algorithms are utilized to evaluate the effectiveness of the proposed solution;F1 score, precision, recall, and accuracy are the assessment metrics used.. © 2023 IEEE.

关键词： Internet of things

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Cross-Site Request Forgery as an Example of Machine learning for Web Vulnerability Detection 3

Cross-Site Request Forgery as an Example of Machine Learning...

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3rd international conference on Smart Data Intelligence, ICSMDI 2023

作者： Rao, Mekala Srinivasa Kalyani, Birudugadda Vathsalya, Baswani Dhanunjay, Karri Narayana, Alasandalapalli Lakshmi Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9781665464871

This paper presents a strategy for discovering flaws in web applications through Machine learning (ML). Web-based applications are especially troublesome to examine attributed to their variety and extensive usage of custom development methodologies. As little more than a basis, machine learning is extremely useful in website safety: It just might combine cognitive knowledge of web app terminology with automated software approaches based on verbally reported information. Mitch tool is the foremost machine learning strategy towards black-box investigation for Cross-Site Request Forgery (C.S.R.F) problems, was built using these principles. Mitch-helped us find Thirty-five recently developed cross-site request forgeries (C.S.R. Fs) in twenty wide fields, together with 3 main C.S.R. Fs in industry applications. © 2023 IEEE.

关键词： Websites

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Predicting Sentiment on IMDB using ML and DL Techniques

Predicting Sentiment on IMDB using ML and DL Techniques

引用

2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Tomar, Ashwin Kaler, Harjot Singh Kumar, Anubhav Chandigarh University Apex Institue of Technology Department of Computer Science and Engineering Punjab Gharuan140413 India

ISBN: (纸本)9798350344387

Sentiment classification holds significant importance in the field of nlp as it focuses on determining the sentiment polarity of text, namely positive, negative, or neutral. The widespread usage of IMDB as a platform for expressing opinions and emotions has made it a valuable source of data for sentiment classification. Here, we present a comparative analysis of diverse ML and DL approaches applied to sentiment classification using IMDB data. Specifically, we evaluate the performance of Bayesian classifier, Log. Regression, SVM, Recurrent Neural Systems (RNNS), and Transfer learning with BERT. Through our experimentation, we find that while Bayesian classifier and Log. Regression exhibit reasonable accuracy in this task, SVM and RNNS surpass them, with Transfer learning using BERT achieving the highest level of accuracy. This study offers valuable insights into the efficacy of different ML and DL techniques when applied to sentiment classification on IMDB data, serving as a valuable resource for both researchers and industry practitioners in this domain. © 2023 IEEE.

关键词： Bayesian classifier BERT Deep learning IMDB data Log. Regression Machine learning Recurrent Neural Systems Sentiment classification Support Vector Machines Transfer learning

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Employing AI-Driven Drones and Advanced Cyber Penetration Tools for Breakthrough Criminal Network Surveillance 9

Employing AI-Driven Drones and Advanced Cyber Penetration To...

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9th IEEE international Women in engineering (WIE) conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Mohamed, Nachaat Ahmed, Abdussalam Ali Alsharif, Abdulgader Elkhozondar, Hala J. Rabdan Academy Homeland Security Department Abu Dhabi United Arab Emirates Bani Waleed University Mechanical Engineering Department Bani Walid Libya Faculty of Technical Sciences Sebha Sebha Libya Islamic University of Gaza Faculty of Engineering Palestine

ISBN: (纸本)9798350319651

In this paper, we introduce a groundbreaking approach to criminal network surveillance, utilizing AI-powered drones equipped with advanced cyber penetration tools. Our methodology entails the deployment of drones to intercept wireless networks of targeted criminals, facilitating the collection of crucial evidence without physical intrusion. By harnessing the capabilities of Artificial Intelligence (AI), Machine learning (ML), and Deep learning (DL), in conjunction with automated penetration systems, our drones are able to conduct thorough investigations and relay the findings directly to command centers. This innovative technique ensures the acquisition of accurate and reliable evidence, boasting a remarkable success rate of 99.8%, thereby significantly expediting the arrest and prosecution processes. The outcomes of our work underscore the potential of AI drones as vital assets in modern criminal investigations, reshaping the landscape of law enforcement and national security protocols. © 2023 IEEE.

关键词： Drones

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Coded Matrix Computations for D2D-Enabled Linearized Federated learning 48

Coded Matrix Computations for D2D-Enabled Linearized Federat...

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Das, Anindya Bijoy Ramamoorthy, Aditya Love, David J. Brinton, Christopher G. Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States Iowa State University Department of Electrical and Computer Engineering AmesIA50010 United States

ISBN: (纸本)9781728163277

Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible to straggler (slower or failed) clients. Recent work has proposed to address this through device-to-device (D2D) offloading, which introduces privacy concerns. In this paper, we propose a novel straggler-optimal approach for coded matrix computations which can significantly reduce the communication delay and privacy issues introduced from D2D data transmissions in FL. Moreover, our proposed approach leads to a considerable improvement of the local computation speed when the generated data matrix is sparse. Numerical evaluations confirm the superiority of our proposed method over baseline approaches. © 2023 IEEE.

关键词： Distributed Computing Federated learning Heterogeneous Edge Computing Privacy Stragglers

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Machine learning Based Improved Phishing Detection using Adversarial Auto Encoder

Machine Learning Based Improved Phishing Detection using Adv...

引用

2023 international conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Balaji, S. Sathishkumar, R. Sharmila, G. Arikaran, N. Nivas, K. Dhamotharan, S. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350315127

Phishing scams are frequently used by malicious actors to get login credentials from unsuspecting users. One method for detecting phishing is machine learning. Despite the fact that the accuracy of machine learning algorithms is mostly determined by the training data, there isn't much easily accessible attack data. We present a method for enhancing the datasets used by machine learning algorithms. We create samples that resemble phishing websites using an Adversarial Auto Encoder (AAE) and offer metrics to judge the effectiveness of the samples. We compare these samples to models created from actual data. Some of the created samples could be impossible for the current model to recognize. After that, training is done with the remaining samples. Improved levels of accuracy and reliability are provided by the upgraded machine learning models. In other words, phishing detection is improved during model construction by merging data from real phishing sites with AAE fake data. © 2023 IEEE.

关键词： Websites

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On Bidding Decision in Short-Term Electricity Markets: A Reinforcement learning Approach 15

On Bidding Decision in Short-Term Electricity Markets: A Rei...

引用

15th IEEE international conference on Industry Applications, INDUSCON 2023

作者： Theodoro, Edson A.R. Lopes, Yuri K. Dept. of Electrical Engineering DAELE Federal University of Technology - Parana PR Cornelio Procopio Brazil Dept. of Computer Science CCT Santa Catarina State University SC Joinville Brazil

ISBN: (纸本)9798350314182

Modern restructured power systems are identified by the establishment of competitive markets. After the 90s, many countries have been adopted offer-based short-term (day-ahead and intraday) markets to enforce competitiveness among agents. In these markets, generators can improve their profits by strategic bidding during energy auctions. This paper aims to study generators' bidding strategies using reinforcement learning techniques (Q-learning) to evaluate factors that impact operational profit. As the main result, we discuss how the generator's physical location can be a determinant of its operational profit and, consequently, investment decisions in power systems. ©2023 IEEE.

关键词： electricity markets investment power systems Q-learning reinforcement learning

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AI-Navigated Fog Computing based Portable Ambient Particulate Matter Sensor Calibration

AI-Navigated Fog Computing based Portable Ambient Particulat...

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2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sahaya Sakila, V. Manohar, S. College of Engineering and Technology Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus No. 1 Jawaharlal Nehru Road TamilNadu Vadapalani India

ISBN: (纸本)9798350313987

Bull Trench Kilns, shortly referred as BTK are one of the major sources of emitting Particulate Matter (PM) pollutants in air, which is harmful to the environment, and it has the capability to remain suspended in the air for very long duration. As conventional fixed pollution monitoring stations are sparsely located, and Brick industries are generally located in remote locations, BTK's pollutant emission rate is not exactly been recorded/monitored. In this study, portable PM sensor along with a reference sensor are mounted near the Brick Kilns and the pollutant's concentration in air along with the atmospheric temperature and humidity are measured. Since PM sensors are sensitive to atmospheric factors, its readings are calibrated using Machine learning (ML) techniques, with the help of data collected from reference sensor, to provide more reliable data. The proposed AI-navigated three phase architecture is used to calibrate the readings obtained from the sensor, where Linear Support Vector Regression (LSVR) obtained best results. © 2023 IEEE.

关键词： Artificial Intelligence Bull Trench Kiln Calibrated Sensor Readings Machine learning Particulate Matter

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