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检索条件"任意字段=8th International Conference on Data Science and Machine Learning Applications, CDMA 2025"

共 718 条记录，以下是61-70 订阅

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Explainable Artificial Intelligence for Dependent Features: Additive Effects of Collinearity 24

Explainable Artificial Intelligence for Dependent Features: ...

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8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Salih, Ahmed M. Department of Population Health Sciences University of Leicester Leicester United Kingdom William Harvey Research Institute Queen Mary University of London London United Kingdom Barts Heart Centre St Bartholomew’s Hospital Barts Health NHS Trust London United Kingdom Department of Computer Science University of Zakho Kurdistan region Zakho Iraq

ISBN: (纸本)9798400718014

Explainable Artificial Intelligence (XAI) emerged to reveal the internal mechanism of machine learning models and how the features affect the prediction outcome. Collinearity is one of the big issues that XAI methods face when identifying the most informative features in the model. Current XAI approaches assume the features in the models are independent and calculate the effect of each feature toward model prediction independently from the rest of the features. However, such assumption is not realistic in real life applications. We propose an Additive Effects of Collinearity (AEC) as a novel XAI method that aim to considers the collinearity issue when it models the effect of each feature in the model on the outcome. AEC is based on the idea of dividing multivariate models into several univariate models in order to examine their impact on each other and consequently on the outcome. the proposed method is implemented using simulated and real data to validate its efficiency comparing with the a state of arts XAI method. the results indicate that AEC is more robust and stable against the impact of collinearity when it explains AI models compared with the state of arts XAI method. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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

Integrating DDP to Enhance Cryptographic Potential of Latin ...

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conference on data science and machine learning applications (cdma)

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

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

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.

关键词： Resistance machine learning data science Robustness Diamond Encryption Complexity theory Cryptography Proposals Multiaccess communication

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A Comparative Study of Short Text Classification Methods based on Word Vector Fusion 24

A Comparative Study of Short Text Classification Methods bas...

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8th international conference on Computing and data Analysis, ICCDA 2024

作者： Yan, Duanwu He, Junjie Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798400710445

Currently, there are many algorithms that can be used for text multicategorization, but each algorithm has its own specific assumptions, advantages, and disadvantages. Aiming at the multiclassification problem of short text, this paper crawls the title and abstract text of blogs on 51CTO blog website and its domain classification as a dataset, proposes a feature representation method of fusing Word2Vec word vectors with Bert characterised text vectors after weighting them with TF-IDF, based on the three commonly used machine learning base classifiers and their deep learning models, namely, Decision Tree, Random Forest and KNN BERT, and introduce the integration algorithm for the calculation of the F1 value of the model, which is used as an evaluation index of the merits of text classification algorithms, and compared with the experimental results of the single text feature representation method, with a view to improving the performance of the classification model through the fusion of word vectors. the experiment shows that compared with the single text feature representation method, the model performance is improved under the fusion feature vector representation, and its F1 value reaches 86.6% on the integration algorithm in traditional machine learning, which is improved by 0.6%∼1% compared with the original one;the splicing of fused features with BERT makes its F1 value also improved by about 2%, which indicates that the fusion features proposed in this experiment can be more comprehensive, accurately describe the semantic information of text, and can more effectively characterize short texts. © 2024 Copyright held by the owner/author(s).

关键词： Vectors

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8th EAI international conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2024

8th EAI International Conference on Smart Grid and Innovativ...

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8th EAI international conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2024

ISBN: (纸本)9783031788055

the proceedings contain 11 papers presendted at a virtual meeting. the special focus in this conference is on Smart Grid and Innovative Frontiers in Telecommunications. the topics include: On IT and OT Cybersecurity datasets for machine learning-Based Intrusion Detection in Industrial Control Systems;rate Distortion Analysis of Wavefield Coding in Wireless Geophone Networks;ECIC: A Content and Context Integrated data Acquisition Method for Artificial Internet of things;A Two-Stage Inference Method Based on Graph Neural Network for Wind Farm SCADA data;A Comprehensive Evaluation Method for KG-Augmented Large Language Models;collaborative-Based Batch Verification Scheme for Event-Driven Messages in Vehicular Ad Hoc Networks;enhancing Mobile Communication System Security via Neural Cryptography applications;EcoIntegrity: AI-Augmented Blockchain Framework for Carbon Footprint Tracking and Incentives in IoT;Development Pitfalls: A Case Study in Developing a Smart Grid Co-simulation Platform Based on HELICS.

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Dynamic Hover Gesture Classification using Photovoltaic Sensor and machine learning 24

Dynamic Hover Gesture Classification using Photovoltaic Sens...

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8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Almania, Nora Abdullah Alhouli, Sarah Yousef Sahoo, Deepak Ranjan Department of Computer Science Swansea University Swansea United Kingdom Department of Computer Science Shaqra University Shaqra Saudi Arabia Shuwaikh Kuwait

ISBN: (纸本)9798400718014

Self-powered photovoltaic sensor technology has been presented as a gestural interface that could recognise time-series data of dynamic hover gestures using machine learning (ML). To further expand and improve the classification system of dynamic hover hand gestures, we discovered time-series data from multiple subjects and explored the systems’ performance through ML algorithms. In this paper, we collected our own time-series dataset of 3,696 hand gesture samples from 48 multiple subjects using an off-the-shelf photovoltaic sensor and analysed them by applying different preprocessing techniques such as smoothing, augmentation, normalisation, and resampling. then, we used different ML algorithms such as K-Nearest Neighbors (KNN), Gradient Boosting (GB), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) to classify 11 dynamic hover hand gestures. Our findings indicated that RF achieved the highest accuracy among all other ML classifiers, with more than 97% accuracy. the implications of our study could inform researchers of the potential of this technology for recognising accurate hand gestures trained with multiple subjects, which could help them establish gesture recognition systems for improved accuracy and usability. © 2024 Copyright held by the owner/author(s).

关键词： Random forests

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Interpretable Grade Prediction Based on ECOC and Shapley theory 24

Interpretable Grade Prediction Based on ECOC and Shapley The...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Yang, Dapeng Cao, Longhan Pan, Lifang Xie, Shutong School of Computer Engineering Jimei University Fujian Xiamen China School of Science Jimei University Fujian Xiamen China

ISBN: (纸本)9798400710094

In recent years, blended learning models combining online and offline instructional methods have been widely implemented in higher education institutions. Online learning platforms accumulate a large number of fine-grained records of students’ learning behavior, offering an excellent opportunity to analyze students’ learning behavior. Firstly, a year’s worth of student behavior log data was collected from the "Java Language Programming" blended learning course at a college. Secondly, we propose an interpretable grade prediction method based on the error correcting output code (ECOC) with Shapley theory. this approach employs Recursive Feature Elimination (RFE) to select features from the original features, and creates an ECOC classification matrix to predict student performance. Finally, the Shapley values for each feature were used to conduct an explainable analysis of the prediction results. Experimental results show that the proposed method outperforms other classical classification algorithms. this study provides a visual and educational analysis of the major factors affecting academic performance, thereby offering educational administrators more intuitive decision support. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Evaluating the Effectiveness of Pre-trained Neural Networks in Automatic Sit-up Counter for Civil Servant Physical Test 24

Evaluating the Effectiveness of Pre-trained Neural Networks ...

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8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Hutahaean, Andrew Daud Tjahyadi, Hendra Faculty of Computer Science Universitas Pelita Harapan Banten Tangerang Indonesia

ISBN: (纸本)9798400718014

In the domain of machine learning and computer vision, employing deep learning models for real-time object detection tasks, especially in low-resource environments, poses significant challenges. this study aimed to assess the effectiveness and efficiency of five pretrained neural network architectures—SqueezeNet, MobileNetV3, ResNet50, VGG16, and InceptionNet—for detecting and counting sit-ups from video footage. the evaluation criteria included accuracy in sit-up counting compared to manual methods and the computational resources utilized. Among the models examined, SqueezeNet demonstrated notable performance, closely aligning with manual counts and outperforming larger, presumably more complex networks. Despite its compact size of 3.33 MB, SqueezeNet showcased the potential for achieving high detection accuracy without the need for extensive network complexity. Conversely, larger networks such as VGG16, MobileNetV3, ResNet50, and InceptionNet exhibited significant discrepancies in sit-up counts, indicating potential overfitting during training and a lack of generalizability to real-world data. Regarding computational resources, all networks displayed minimal memory usage and CPU utilization, highlighting the feasibility of deploying them on low-resource platforms like Raspberry Pi. this study underscores the promise of lightweight deep learning models like SqueezeNet for real-time object detection tasks and provides valuable insights for future research and applications in health and fitness monitoring. © 2024 Copyright held by the owner/author(s).

关键词： Object detection

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An empirical study on the application of KANs for classification 24

An empirical study on the application of KANs for classifica...

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8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Costa, Samuel Sampaio Pato, Matilde Datia, Nuno Politécnico de Lisboa ISEL Lisbon Lisbon Portugal LASIGE Faculdade de Ciencias Universidade de Lisboa LASIGE Lisbon Lisbon Portugal NOVA LINCS NOVA School of Science and Technology NOVA LINCS Setubal Caparica Portugal

ISBN: (纸本)9798400718014

Kolmogorov-Arnold Networks (KANs) represent a breakthrough in deep learning, diverging from Multi-Layer Perceptrons (MLPs) by generalizing the Kolmogorov-Arnold representation theorem (KAT) to networks of arbitrary depth and width. this theorem facilitates the decomposition of multivariate functions into constituent one-dimensional elements, with learnable activation functions on weights and the sum operator on nodes. KANs have been shown to exhibit robust performance in function approximation, validated across mathematical, physical, and practical domains such as traffic prediction and medical diagnostics. Our study investigates KANs’ efficacy through comprehensive evaluations on OpenML, Kaggle and UCI datasets, with a focus on enhancing Human Activity Recognition systems. they demonstrate high classification performance compared to conventional machine learning approaches and MLPs. these findings underscore KANs’ potential as scalable, interpretable tools in modern machine learning applications given their favorable neural scaling laws. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Novel Methods for Enhancing Statistical Randomness Characteristic Using Chaotic Systems

Novel Methods for Enhancing Statistical Randomness Character...

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conference on data science and machine learning applications (cdma)

作者： Idris Olcay Erkan Tanyildizi Fatih Özkaynak Ministry of Education Van Private Education Vocational High School Van Turkiye Department of Spftware Engineering Firat University Elazlg Turkiye

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

this study explores various methods for enhancing the statistical randomness of sequences generated from chaotic systems. While the modulus function is commonly employed to improve randomness by addressing the clustering of chaotic outputs, it has certain limitations. To address these, we investigated alternative methods, including the fractional part method, threshold functions, trigonometric transformations, and linear transformations with scaling and shifting. the results of this study demonstrated that while the modulus function is effective, alternative methods like the fractional part method and threshold functions can further enhance the distribution of values, reducing bias and improving the overall randomness. these findings indicate that the proposed methods are valuable for applications requiring high-quality random sequences, including cryptographic systems, data science, and large-scale simulations.

关键词： Chaotic communication machine learning data science data models Robustness Nonlinear dynamical systems Random number generation Random sequences Logistics Testing

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Evaluating the Effectiveness of machine learning, Deep learning, and Evolutionary Algorithms in Intrusion Detection Systems 8th

Evaluating the Effectiveness of Machine Learning, Deep Learn...

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8th international conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Anandpwar, Winit Barhate, Shweta Dhore, Mahendra RTM Nagpur University Maharashtra Nagpur India Department of Electronics and Computer Science PGTD RTM Nagpur University Maharashtra Nagpur India Shri Shivaji Science College RTM Nagpur University Maharashtra Nagpur India

ISBN: (纸本)9789819784219

Intrusion detection systems (IDS) are a key component in protecting computer systems and networks from cyber threats. In the past few years, the application of algorithms based on machine learning (ML) and deep learning (DL) methodologies for IDS has garnered a significant amount of interest due to their capacity to analyse enormous amounts of data and identify complex patterns of harmful activity. this ability has contributed to the rise in popularity of these techniques. this paper reviews the current state of ML and DL-based IDS, including both traditional and emerging techniques. It also discusses the key challenges and opportunities in this area, including the need for diverse and robust training data, the balance between accuracy and efficiency, and the integration of IDS with other security measures. Additionally, this paper outlines future directions for research on ML and DL-based IDS, including the potential for real-time and adaptive learning, the incorporation of domain knowledge, and the use of interpretability techniques to improve transparency and accountability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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