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Enhance statistical Features with Changepoint Detection for Driver Behaviour Analysis 21st

Enhance Statistical Features with Changepoint Detection for ...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Maktoubian, Jamal Tran, Son N. Shillabeer, Anna Amin, Muhammad Bilal Sambrooks, Lawrence School of Information and Communication Technology University of Tasmania Hobart Australia School of Information Technology Deakin University VIC Australia School of Computing Mathematics and Engineering Charles Sturt University NSW Australia

ISBN: (纸本)9789819601240

Driver behaviour modelling is a critical field that addresses complex and dynamic driving behaviours on roads with the goal of enhancing road safety, reducing air pollution, and improving vehicle performance. Recent advancements in sensor technology and machine learning (ML) techniques have facilitated the capture and analysis of driver behaviour patterns. Nonetheless, the efficacy of ML models heavily relies on the quality of the data used. Therefore, developing feature extraction techniques that provide high-quality inputs is crucial. In this paper, we conceptualised, implemented, and evaluated a novel feature model called Changepoint-based statistical Feature (C-bSF). Initially, we extracted various statistical functions from raw sensor data, which were then aggregated using lagging windows. Following this, a changepoint detection method was used to derive the C-bSF feature. We compared the performance metrics of this new approach with other feature extraction methods, demonstrating the superiority of C-bSF in driver behaviour classification tasks across three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An Edge-oriented Deep learning Model Security Assessment Framework 21

An Edge-oriented Deep Learning Model Security Assessment Fra...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Pengbo Zhang, Boyin Zhang, Shigeng Liu, Xuan Central South University School of Computer Science and Engineering ChangSha China Hunan University College of Computer Science and Electronic Engineering ChangSha China

ISBN: (纸本)9798350329223

As computer performance advances and deep learning models proliferate, their applications expand, but concerns arise due to their opaque nature and security issues. Edge intelligence requires more from these models but faces limitations like insufficient computing power and memory. Existing research on model compression mainly focuses on the trade-off between compression and accuracy, neglecting deployment at the edge and its impact on factors beyond accuracy. Furthermore, evaluation methods for model performance and robustness in compression techniques are underdeveloped. To address these issues, this paper introduces a comprehensive framework for evaluating edge intelligence deep learning models. It conducts a thorough assessment of these models on common image classification datasets, comparing their performance with three compression algorithms and analyzing their security implications. The proposed framework aims to provide a holistic assessment of deep learning models, promoting trustworthy edge intelligence, enabling secure deployments, and enhancing understanding of model decision-making processes. © 2023 IEEE.

关键词： Classification (of information)

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CNN-KNN Model for Assessing Hair Health in Telogen Effluvium 3

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Gotte Ranjith Shubham Banerjee, Deepak School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns. © 2025 IEEE.

关键词： Nearest neighbor search

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Botnet Attack Intrusion Detection In IoT Enabled automated Guided Vehicles

Botnet Attack Intrusion Detection In IoT Enabled Automated G...

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2022 IEEE international conference on Big data, Big data 2022

作者： Shaikh, Sumaiya Rupa, Ch. Srivastava, Gautam Reddy Gadekallu, Thippa Lords Institute of Engineering and Technology Department of Cse Hyderabad India Vr Siddhartha Engineering College Department of Cse Vijayawada India Brandon University Department of Math and Computer Science Canada Vellore Institute of Technology School of Information Technology and Engineering India Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon

ISBN: (纸本)9781665480451

The Internet of Things (IoT), with the ease of access of automated Vehicles, is the most reliable technology in the 21st century making every possible thing within seconds. The people in this era are blessed with all the new technology, and new gadgets, due to which many things which used to take longer are done within nanoseconds. IoT combines computing devices, mechanical devices, digital machines, objects, and people which possess the ability to transfer data over the network with Unique identifiers (UIDs) without human intervention. Such IoT-enabled automated Guided Vehicles (AGV) are more reliable on networks for every action. Deep learning and machine learning techniques are pivotal for the successful implementation of IoT-based applications including AGVs. A botnet refers to the attacks which come from robot Network attacks. Recently, many organizations have been compromised using this attack. Most affected devices are connected to IoT as it uses automatically generated data. The ideology of this study is to propose an intrusion prediction system which can predict botnet attacks in AGVs. In this study, N - Balo dataset is used for classification, clustering and prediction. The technique implemented in this paper may provide some roots to develop the most reliable and highly secured AGV network. © 2022 IEEE.

关键词： Internet of things

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IO-SEA: storage I/O and data Management for Exascale Architectures 21

IO-SEA: Storage I/O and Data Management for Exascale Archite...

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21st ACM international conference on Computing Frontiers, CF 2024

作者： Medeiros, Daniel Gregory, Eric B. Couvee, Philippe Hawkes, James Gougeaud, Sebastien Gilliot, Maike Bressand, Olivier Valeri, Yoann Jaeger, Julien Chapon, Damien Bournaud, Frederic strafella, Loïc Caviedes-Voullième, Daniel Tashakor, Ghazal Zjupa, Jolanta Holicki, Max Ridley, Tom Müller, Yanik Guimarães, Filipe Souza Mendes Frings, Wolfgang Mirus, Jan-Oliver Zhukov, Ilya Borba, Eric Rodrigues Moti, Nafiseh Salkhordeh, Reza Derbey, Nadia Mimouni, Salim Derr, Simon Gursoy, Buket Benek Grogan, James Furmánek, Radek Golasowski, Martin Slaninová, Kateåina Martinovič, Jan Faltýnek, Jan Wong, Jenny Cakircali, Metin Quintino, Tiago Smart, Simon Iffrig, Olivier Narasimhamurthy, Sai Happ, Sonja Rauh, Michael Krempel, stephan Wiggins, Mark Nováček, Jiåí Brinkmann, André Markidis, stefano Denier, Philippe KTH Royal Institute of Technology Sweden Forschungszentrum Jülich Germany Eviden France European Centre for Medium-Range Weather Forecasts United Kingdom Commissariat À l'Énergie Atomique et Aux Énergies Alternatives France ParTec AG Germany IT4Innovations VSB-Technical University of Ostrava Czech Republic SEAGATE United Kingdom Central European Institute of Technology Czech Republic Irish Centre for High-End Computing Ireland Johannes Gutenberg University Mainz Germany

ISBN: (纸本)9798400704925

The new emerging scientific workloads to be executed in the upcoming exascale supercomputers face major challenges in terms of storage, given their extreme volume of data. In particular, intelligent data placement, instrumentation, and workflow handling are central to application performance. The IO-SEA project developed multiple solutions to aid the scientific community in adressing these challenges: a Workflow Manager, a hierarchical storage management system, and a semantic API for storage. All of these major products incorporate additional minor products that support their mission. In this paper, we discuss both the roles of all these products and how they can assist the scientific community in achieving exascale performance. © 2024 Owner/Author.

关键词： Supercomputers

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A Firmware Vulnerability Detection Method Based on Feature Filtering 21

A Firmware Vulnerability Detection Method Based on Feature F...

引用

作者： Xia, Bing Tang, Chongjun Liu, Wenbo Chu, Shihao Dong, Yu ZhongYuan University of Technology Key Laboratory of Mathematical Engineering and Advanced Computing Dept Zhengzhou China ZhongYuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Dept Zhengzhou China ZhongYuan University of Technology Dept Zhengzhou China

ISBN: (纸本)9798350329223

Open source code reuse and code cross-platform deployment accelerate the spread of software vulnerabilities, and pose challenges for accurate detection of cross-platform vulnerabilities. The binary vulnerability similarity detection method based on machine learning lacks a fine-grained vulnerability similarity feature filtering mechanism, and cannot solve cross-function vulnerabilities triggered by data dependencies, resulting in a high vulnerability false positive rate in existing solutions, which limits practical applications. Software code vulnerabilities are usually caused by function key statements. Therefore, based on binary function decompilation, we identify the key statement that causes the vulnerability, analyze the cross-function data dependency of the key statements, extract the code similarity features and code vulnerability features of the key statements, and propose a feature filtering-based Binary vulnerability detection method SimFilter. SimFilter calculates the similarity of key statements by comparing the characteristics of function key statements, and infers the similarity of binary function vulnerabilities based on the similarity of key statements. The experimental results show that the vulnerability semantic features extracted by SimFilter are highly interpretable, and the accuracy rate is increased by 10.8% compared with other models. At the same time, we analyzed and evaluated the impact of different similarity features on the accuracy of SimFilter, and gave the best empirical value of the SimFilter model in firmware vulnerability detection. © 2023 IEEE.

关键词： Firmware

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Prediction of Ground Water Level in Prosopis Juliflora Ecosystem using Machine learning 2

Prediction of Ground Water Level in Prosopis Juliflora Ecosy...

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2nd international conference on Electronics and Renewable Systems, ICEARS 2023

作者： Choudhary, Shabnam Pundir, Meena Chandigarh University Department of Computer Science and Engineering Mohali140413 India

ISBN: (纸本)9798350346640

Climatic change is one of the biggest problems faced in the 21st century. The shifting of rain patterns brings floods and draughts and increases the level of groundwater. It increases the scarcity of drinking water and its impacts affect a global level. There are several unforeseen reasons related to this climatic change. Prosopis Juliflora is an invasive exotic plant which minimizes the level of groundwater. In India, Prosopis Juliflora has become the most invasive weed which replaces the local vegetation cover. These plants create biomass and do not attract rainfall. The deep root of these trees exhausts ground water rapidly and leads to droughts. In this paper, the level of groundwater is predicted using a linear regression algorithm for the study area from 2020-2070. The LULC data is used to train the machine learning model and it is collected through spatial analysis on LANDSAT8 data using ArcGIS. It comprises the data on land cover, annual rainfall and groundwater levels over the years 1995-2020 in Tamil Nadu. The results predict that increased Prosopis land cover reduces the groundwater level to 55 meters below ground level by 2050 in Tamil Nadu. © 2023 IEEE.

关键词： Machine learning

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stC: student-Teacher Collaborative Model for Multi-Target Domain Adaptation 21

STC: Student-Teacher Collaborative Model for Multi-Target Do...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Jiayi, Xu Jiaqi, Yu Yixin, Wang Shijia, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9798331519254

Cross-domain object detection poses significant challenges due to the susceptibility of object detection models to data variance, particularly the domain shifts that can occur between different domains. To address the limitation, we draw inspiration from knowledge distillation, proposing a collaborative learning framework. Our method employs CycleGAN to generate target-style images, and during pretraining, an unsupervised domain adaptation teacher model is trained for each source-target pair. In the distillation process, our proposed algorithm implements an out-of-distribution estimation strategy to select samples that best align with the current model, thereby enhancing the cross-domain distillation process. Furthermore, each expert model is encouraged to collaborate by designating the student model as a bridge between different target domains, facilitated by the Exponential Moving Average (EMA) algorithm. Experiments show that the proposed method leverages structured information, not only does it perform well across various target domains, but it also yields favorable results compared to state-of-the-art unsupervised methods that are specifically trained on single source-target pair. © 2024 IEEE.

关键词： Contrastive learning

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Designs of Dual-band Metamaterials for Near-field Wireless Power Transmission 21

Designs of Dual-band Metamaterials for Near-field Wireless P...

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21st international System-on-Chip Design conference, ISOCC 2024

作者： Ji, Xiaozhe Wang, Jingchen Man, Ka Lok Gee Lim, Eng Yue, Yutao Zhou, Jiafeng Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou China JITRI Institute of Deep Perception Technology Wuxi China Xi'an Jiaotong-Liverpool University XJTLU-JITRI Academy of Industrial Technology Suzhou China University of Liverpool Department of Mathematical Science Liverpool United Kingdom University of Liverpool Department of Electrical Engineering and Electronics Liverpool United Kingdom

ISBN: (纸本)9798350377088

Within the ever-changing environment of Wireless Power Transmission (WPT), the goal of increased efficiency and charging range has gained significant interest. A significant difficulty is the inherent reduction in energy transmission with increasing distance and decreasing coupling coefficients. Metamaterials (MTMs), designed objects with remarkable electromagnetic characteristics, represent a possible option for overcoming these restrictions through magnetic field manipulation. Recent research has focused on multiband WPT systems, namely dual-band metamaterial (DB-MTM) that can optimize power transfer efficiency at two different frequencies at the same time. This study presents a complete overview of recent advances in DB-MTM for WPT applications. It investigates the structural complexities of these metamaterials before conducting a comparative examination of previously described DB-MTM-based WPT systems across a range of performance criteria. © 2024 IEEE.

关键词： Inductive power transmission

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Assessing the Sentiment of Book Characteristics Using Machine learning NLP Models 21st

Assessing the Sentiment of Book Characteristics Using Machin...

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21st international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Drozda, Pawel Sopyla, Krzysztof Univ Warmia & Mazury Fac Math & Comp Sci Olsztyn Poland Literacka Ltd Olsztyn Poland

ISBN: (纸本)9783031234798;9783031234804

This paper presents a new approach for processing the entire books with Natural Language Processing algorithms. In particular, we proposed methods to evaluate books in terms of assessing the intensity of the book's soft features, such as fantastic, touching, suspenseful, etc. Using Bag ofWords and TF/IDF, we embedded books and conducted classification experiments to determine the most appropriate parameters for classifying the intensity of features. The obtained results showed, that in the considered problem the Random Forests algorithm fitted the best, achieving accuracy of 95% and F1 measure of 89%. The evaluation also included the selection of the best converter and data aggregation method.

关键词： Text representation Word embeddings Long text classification Bag of words

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