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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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Leveraging OpenStack-based Private Cloud for Intrusion Detection Using Deep learning Techniques 1

Leveraging OpenStack-based Private Cloud for Intrusion Detec...

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1st international conference for Women in computing, InCoWoCo 2024

作者： Roshan, Rahul G. Salanke, Vishal S Nagasundari, S. Research Center for Information Security Forensics and Cyber Resilience PES University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518943

OpenStack is a cloud platform that offers infrastructure services, providing access to computing, storage, and network resources. However, due to its open-source nature, the cloud environment can be vulnerable to security threats. OpenStack security measures are generally applied to specific services, rather than being integrated comprehensively across the entire cloud environment. Despite offering flexibility and scalability, the widespread use of OpenStack calls for robust security solutions. Current intrusion detection systems often struggle to adapt to constantly evolving traffic patterns and attack vectors in the OpenStack environment. This study introduces a two-stage deep learning framework to create a robust Intrusion Detection System (IDS) for OpenStack-based private clouds. The proposed framework efficiently analyzes network traffic data in these clouds by implementing dimensionality reduction and anomaly detection techniques. It utilizes a network-based IDS, trained on two publicly available datasets, which enables a broader detection range of known attack patterns. The results indicate that the OpenStack IDS significantly enhances OpenStack's security, addressing the limitations of generic IDS solutions. © 2024 IEEE.

关键词： Network intrusion

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A Multi-scale Perception Enhancement Network for Underwater Waste Detection 9

A Multi-scale Perception Enhancement Network for Underwater ...

引用

9th international conference on Image, Vision and computing, ICIVC 2024

作者： Li, Yangke Zhang, Xinman MOE Key Lab for Intelligent Networks & Network Security School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350385991

Underwater waste detection plays an important role in aquatic ecosystems and environmental protection. In fact, waste deposits in aquatic environments not only cause water contamination, but also pose potential risks to human health. However, underwater images often face the problem of image degradation, which makes underwater waste detection tasks more challenging. Therefore, we propose an efficient multi-scale perception enhancement network (MPE-Net) for underwater waste detection, which can provide more discriminative information to improve detection performance. On the one hand, we introduce a multi-scale semantic enhancement module (MSEM) to capture richer contextual features in different receptive fields, and enhance critical semantic information by using frequency domain information. On the other hand, we design a multi-scale boundary enhancement module (MBEM) to extract boundary information at different scales, and emphasize important detail features by using hybrid boundary perception. We conduct comprehensive experiments on two common underwater waste image datasets. Relevant experimental results show that our method outperforms other representative deep learning models. © 2024 IEEE.

关键词： Health risks

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Solar Irradiance Forecasting using Improved Sample Convolution and Interactive learning 5

Solar Irradiance Forecasting using Improved Sample Convoluti...

引用

5th international conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Subair, Ansil Gopakumar, G. Department of computer science and engineering Amrita School of Computing Amrita Viswa Vidyapeetham Amritapuri India

Renewable energy sources, driven by the global concern for climate change and the urgent need to reduce greenhouse gas emissions, are gaining increasing importance. Among them, solar energy stands out as a crucial renewable source and accurate solar irradiance forecasting is vital for its effective utilisation. Deep learning (DL) techniques have emerged as state-of-the-art solar irradiance forecasting methods in past years. However, traditional models that are widely used have inferior accuracy in forecasting. To bridge this gap, this paper presents an improved state-of-the-art model that leverages the convolutional property to forecast the time series data. We got a reduction of 8.32% in MAE compared to the base model. The paper's findings highlight the potential of DL-based approaches to significantly improve the accuracy of solar irradiance forecasts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Forecasting

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Applied Natural Language Processing: User Review Sentiment Classification Optimized by Modified Metaheuristics 5

Applied Natural Language Processing: User Review Sentiment C...

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bacanin, Nebojsa Antonijevic, Milos Perisic, Jasmina Zivkovic, Miodrag Jovanovic, Luka Adamovic, Sasa Singidunum University Informatics and Computing Belgrade Serbia

ISBN: (纸本)9798331518097

E-commerce has become increasingly popular in recent years with increasingly many retailers even conducting business fully online. With the anonymity of the internet, an inability to inspect goods directly, the experiences of other users and seller reputation plays an increasingly important role in purchasing decisions. However, the sentiment expressed in reviews may vary and can be difficult to interpret without manual inspection. This work explores the potential of coupling text processing techniques with machine learning classifiers to handle automatic review sentiment classification. Since ML algorithms are not directly well suited to working with textual data, term frequency inverse document frequency is used to encode data in to a more appropriate format. The AdaBoost classifier is used to determine sentiment expressed in reviews, however, as the AdaBoost classifier is sensitive to parameter selection a modified metaheuristic algorithm is introduced to handle tuning. A comparative analysis of selected relevant optimizers reveals that the proposed optimizer achieves superior results, reaching an objective function score with an overall accuracy of 86.35% for the most effective classification model. © 2024 IEEE.

关键词： Sales

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ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited data

ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation...

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IEEE/CIC international conference on Communications in China (ICCC)

作者： Yuan, Doncheng Xue, Jianzhe Su, Jinshan Xu, Wenchao Zhou, Haibo Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Yili Normal Univ Key Lab Vibrat Signal Capture & Intelligent Proc Yining 835000 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9798350378412

Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. While traditional on-road detectors are hindered by limited coverage and high costs, cloud computing and data mining of vehicular network data, such as driving speeds and GPS coordinates, present a promising and cost-effective alternative. Furthermore, minimizing data collection can significantly reduce overhead. However, limited data can lead to inaccuracies and instability in TFE. To address this, we introduce the spatial-temporal Mamba (ST-Mamba), a deep learning model combining a convolutional neural network (CNN) with a Mamba framework. ST-Mamba is designed to enhance TFE accuracy and stability by effectively capturing the spatial-temporal patterns within traffic flow. Our model aims to achieve results comparable to those from extensive data sets while only utilizing minimal data. Simulations using real-world datasets have validated our model's ability to deliver precise and stable TFE across an urban landscape based on limited data, establishing a cost-efficient solution for TFE.

关键词： ST-Mamba traffic flow estimation recovery method limited data

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AST-Trans: Detecting Web Tracking using Transformer-based Deep learning with Abstract Syntax Tree

AST-Trans: Detecting Web Tracking using Transformer-based De...

引用

2024 IEEE international Performance, computing, and Communications conference, IPCCC 2024

作者： Yuan, Yong Wei, Ziling Liu, Lin Chen, Shuhui Su, Jinshu National University of Defense Technology College of Computer Changsha China

ISBN: (纸本)9798350367942

Web tracking has become a key tool for service providers to collect online data and analyze user behaviors, raising concerns about the privacy of Internet users. In this paper, we propose a new web tracking detection method, namely AST-Trans, which detects and removes web tracking behavior using Transformer-based deep learning with abstract syntax trees. In the method, the abstract syntax tree is built for the detected website codes. Then, a sequence generation algorithm is proposed to convert tree-like code structures into one-dimensional sequences for deep models. To enhance training efficiency, we devise a reduction strategy to simplify the code tree structure by introducing equivalent nodes. After that, a Transformer-based deep learning algorithm is introduced to realize web tracking detection. By the proposed method, the exact tracking code blocks can be identified, and thus, we can implement the tracking code removal with minimum website breakage. To verify the effectiveness of the proposed method, an HTTPS proxy with AST-Trans on it is implemented to detect and remove tracking codes. We evaluate AST-Trans with the TrackSign-labeled dataset. The results show that the proposed method can detect the tracking behavior with high precision. In addition, we validate the feasibility of the method by measuring the website page breakage. © 2024 IEEE.

关键词： Syntactics

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Opportunities and Challenges of AI to Support Student Assessment in computing Education: A Systematic Literature Review 16

Opportunities and Challenges of AI to Support Student Assess...

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16th international conference on Computer Supported Education, CSEDU 2024

作者： dos Santos, Simone C. Junior, Gilberto A.S. Centro de Informática Federal University of Pernambuco Recife Brazil

ISBN: (纸本)9789897586972

This study investigates how Artificial Intelligence (AI) can support student assessment in computing education through a systematic literature review of twenty studies from the past decade. AI's evolution has significantly impacted various fields, including education, offering advanced capabilities for personalized teaching, continuous evaluation, and performance prediction. Analysing these studies, evidence showed a focus on undergraduate students and the employment primarily face-to-face teaching methods, with engineering education and serious games being more cited contexts. These studies also reveal AI's potential to create personalized learning experiences using techniques like fuzzy logic, KNN algorithms, and predictive models to analyse student interactions and performance, particularly in educational games and online courses. The positive findings demonstrate AI's effectiveness in classifying students' learning profiles, predicting employability, providing real-time assessments, facilitating targeted interventions, and improving learning outcomes through personalization. Automated assessments via AI have been shown to reduce teachers' workload by offering accurate, real-time feedback. However, the studies also highlighted challenges concerning student engagement, teacher material quality, model generalization, and technical obstacles such as natural language processing, algorithm stability, and data cleaning. These data-driven factors emphasize the necessity for further advancements in AI to enhance continuous and effective student assessment as part of the personalized learning process. Copyright © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Students

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ENCL: An Extensive learning Based Methodology to Predict Liver Disease by Using Enhanced Neural Classification Logic

ENCL: An Extensive Learning Based Methodology to Predict Liv...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ramachandran, R. Bhuvaneswari, J. Prasanth, K. Glory Sheela Shantha Kumari, P.K. Parvathi, A. Lakshmi Manikandan, R. Rajalakshmi Engineering College Department of Biomedical Engineering Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Department of Mathematics Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Qis College of Engineering and Technology Department of Cse AndhraPradesh Ongole India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331543617

Effective treatment and handling require precise forecasting and early diagnosis of liver diseases. This paper introduces the Enhanced Neural Classification Logic (ENCL), a novel deep learning framework that is intended to enhance the precision of liver disease predictions. The proposed model is cross-validated with the conventional learning approach, the Random Forest Classifier (RFC), to assess its efficiency. The proposed model ENCL utilizes advanced neural network architecture with optimizations a sequence, to fine-tune classification performance. Our approach includes a multi-layered neural network coupled with improved feature extraction, dynamic attention systems, and refined loss calculus for improving model sensitivity and specificity. We work on a substantially large dataset of the records pertinent to diseases of the liver, which contains both structured clinical data and unstructured imaging data for the assessment of ENCL. Results have indicated that ENCL is significantly better than traditional as well as state-of-the-art approaches regarding predictive reliability as well as classification accuracy. This paper is aimed at describing how the enhanced architecture of proposed Enhanced Neural Classification Logic can avail in order to address such complex issues of prediction within the medical domain towards effective care for the patients. © 2024 IEEE.

关键词： Decision trees

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Corporate Agility and AI: Enhancing Adaptive Strategies for a Dynamic Technological Landscape 4

Corporate Agility and AI: Enhancing Adaptive Strategies for ...

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4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Guruprasad, R. Abood, Baydaa Sh. Z. Al-Khalidi, Ayadh Abdulhasan, Maki Mahdi Thomas, Subin Naqvi, Syed Rizwan P.S.R Engineering College Department of Management Studies Sivakasi India National University of Science and Technology College of Health and Medical Technology Dhi Qar Iraq Al-Hadi University College Baghdad Iraq Technology AL-Nisour University College Baghdad Iraq Girideepam Business School Department of Management Studies Kottayam India Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350360165

Thus, in the fast-changing technology environment, the most critical question that faces any organization is not 'whether' to go for corporate agility but to 'what degree' and 'how best' to develop that flexibility. The present paper considers corporate agility to intersect with artificial intelligence (AI) and, in this regard, seek how to perfect more adaptive strategies in the dynamic business environment. In a business world characterized by an unmatched complexity and uncertainty, a constant ability to respond and adjust for changed circumstances has become a new currency to continue in success. Starting with the definition of corporate agility in the modern paradigms of business and underlining its importance, the paper defines corporate agility as an organization's capability to be aware, foresee, and respond to the shifts in markets, new technologies, and competitive pressures. It will cover the basic principles of flexibility, responsiveness, and resilience in the agility and what it may mean or imply at the organization's side of structures, processes, and cultures. It aims at establishing the transformative potential of AI technologies for organizational agility. All of this enables the organization to look for trend analysis and trends within varied, big data sets, and even anticipates strategic opportunities and threats in real time. While the former is fast emerging as a standard tool in the extraction of critically important information from data, algorithms of machine learning, advanced analytics, and predictive modeling help in empowering agile choices amidst varying uncertainties for the decision-maker to keep them informed with actionable intelligence. © 2024 IEEE.

关键词： Agribusiness

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PRACTICING THE RULE-BASED CLASSIFIER USING METAHEURISTICS GRAVITATIONAL SEARCH ALGORITHM TO GENERATE A CREDIT SCORE MODEL TO AVAIL LOANS

PRACTICING THE RULE-BASED CLASSIFIER USING METAHEURISTICS GR...

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kumar, Rohit Habelalmateen, Mohammed I. Bansal, Saloni Bhadula, Shuchi Reddy, C.H. Vijendar Kansal, Lavish Boob, Nandini Shirish School of Management Gautam Buddha University Greater Noida India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9798350355338

Recent times has witnessed a sudden surge in organizations offering micro-loans on digital media in several developing nations. Airtime lending default rates are generally lesser compared to those faced by banks and microfinance institutions (MFIs) but there is possibly more development since the service is provided ubiquitously. In order to provide an appropriate machine learning framework for airtime lending, this paper examines credit scoring methodologies. Over three million loans with a three-month repayment period that were extended to over 41,000 clients are assessed. A brand-new method for creating a fuzzy-rule-based classifier using the Gravitational Search Algorithm (GSA) metaheuristic is presented. It is evaluated for its ability to classify defaulters using a variety of cross-validation techniques, and the latter framework's performance was excellent. The newness of this technique is in the hybridization of data mining algorithms targeted at the dimensionality reduction of the data and accuracy improvement in the future behaviour prediction of corporate loans, so that a highly efficient micro and macro administration of credit risk can be facilitated. All applicants can get loans if the default rate is less than 2%. When default rates are higher, the framework offers a significant boost in profitability. The allowable degree of default rate for exceeding even is quadrupled by the framework, from 8% to 32%. Nonlinear classification methods show significant promise in credit scoring, allowing for a large number of consumers by handling higher levels of default. © 2024 IEEE.

关键词： Dimensionality reduction

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