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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Jindal, Anshul Jose, Jiby Mariya Benedict, Shajulin Gerndt, Michael Technical University Munich Computer Architecture and Parallel Systems Germany Indian Institute of Information Technology Kottayam Department of Computer Science and Engg. India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

The ongoing accomplishments in the decades-long realization of computer vision have infused new dimensions in various research areas such as smart mobility, smart healthcare, education, finance, and so forth. Research works relating to automated object detection, deep learning-assisted data pipelines, and energy-efficient end-to-end solutions have enabled newer perceptions among researchers, albeit the existence of challenges. This paper proposes an object detection system using energy-efficient Long Range (LoRA) communication media on edge nodes such as Raspberry Pi, Coral DevBoard, and Nvidia Jetson Nano. The proposed approach utilized energy-efficient methods to collaboratively offload object detection-related tasks such as capturing images, training images, and inferring objects across a compendium of computing nodes using LoRA. In addition, this research study has attempted to reveal the inference capabilities of images on three different edge nodes. The proposed work has achieved a power difference of at least 1.2 watts during the inference period of the deep learning models without challenging the prediction accuracy with respect to the base model. © 2022 IEEE.

关键词： Object detection

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The Seeker's Dilemma: Realistic Formulation and Benchmarking for Hardware Trojan Detection

The Seeker's Dilemma: Realistic Formulation and Benchmarking...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Sarihi, Amin Patooghy, Ahmad Badawy, Abdel-Hameed A. Jamieson, Peter New Mexico State University Klipsch School of Electrical and Computer Engineering Las CrucesNM United States MIPS Architecture and Performance AustinTX United States North Carolina A and T State University Department of Computer Systems Technology GreensboroNC United States Miami University Department of Electrical and Computer Engineering OxfordOH United States

ISBN: (纸本)9798350367942

This work focuses on advancing the security field in the hardware design space by formally defining the problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e., describing the problem as "The Seeker's Dilemma"(an extension of Hide&Seek on a graph), where a detecting agent is unaware of whether HTs infect circuits or not. Using this problem formulation, we create a benchmark that consists of a mixture of HT-free and HT-infected restructured circuits while preserving their original functionalities. The restructured circuits are randomly infected by HTs, causing a situation where the defender is uncertain if a circuit is infected. Our innovative dataset will help the community better judge the detection quality of different methods by comparing their success rates in circuit classification. We use our benchmark to evaluate three state-of-the-art HT detection tools to show baseline results for this approach. We use Principal Component Analysis to assess the strength of our benchmark, where we observe that some restructured HT-infected circuits are mapped closely to HT-free circuits, leading to significant label misclassification by detectors. © 2024 IEEE.

关键词： Hardware security

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AN ACTIVE LEARNING PROCEDURE FOR THE INTERAURAL TIME DIFFERENCE DISCRIMINATION THRESHOLD 26

AN ACTIVE LEARNING PROCEDURE FOR THE INTERAURAL TIME DIFFERE...

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26th International Conference on Digital Audio Effects, DAFx 2023

作者： Gulli, Andrea Fontana, Federico Serafin, Stefania Geronazzo, Michele Department of Mathematics Computer Science and Physics Università di Udine Udine Italy Department of Architecture Design and Media Technology Aalborg University Copenhagen Denmark Department of Engineering and Management Università di Padova Padua Italy

Measuring the auditory lateralization elicited by interaural time difference (ITD) cues involves the estimation of a psychometric function (PF). The shape of this function usually follows from the analysis of the subjective data and models the probability of correctly localizing the angular position of a sound source. The present study describes and evaluates a procedure for progressively fitting a PF, using Gaussian process classification of the subjective responses produced during a binary decision experiment. The process refines adaptively an approximated PF, following Bayesian inference. At each trial, it suggests the most informative auditory stimulus for function refinement according to Bayesian active learning by disagreement (BALD) mutual information. In this paper, the procedure was modified to accommodate two-alternative forced choice (2AFC) experimental methods and then was compared with a standard adaptive "three-down, one-up" staircase procedure. Our process approximates the average threshold ITD 79.4% correct level of lateralization with a mean accuracy increase of 8.9% over the Weibull function fitted on the data of the same test. The final accuracy for the Just Noticeable Difference (JND) in ITD is achieved with only 37.6% of the trials needed by a standard lateralization test. © 2023 Andrea Gulli, Federico Fontana et al.

关键词： Inference engines

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction 2

Impurity explicit optimal Machine learning and statistical p...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Nareshkumar, R. Shalinikumari, R. Kowsalya, S. Sri Nandhini Vijay Anand, P. Mohamed, Ajmal Gayathri, S. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India Bharath Institute of Higher Education and Research Selaiyur Department of Architecture Tamilnadu Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamilnadu Chennai India CMR Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350365269

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy. © 2024 IEEE.

关键词： Machine learning

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Future connected vehicles:Communications demands,privacy and cyber-security

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Communications in Transportation Research 2022年第1期2卷 39-41页

作者： Tomas Olovsson Tommy Svensson Jiaming Wu Department of Computer Science and Engineering Chalmers University of TechnologyGothenburgSE41296Sweden Department of Electrical Engineering Chalmers University of TechnologyGothenburgSE41296Sweden Department of Architecture and Civil Engineering Chalmers University of TechnologyGothenburgSE41296Sweden

*** With the advance of data collection,data processing,telecommunication and vehicular technologies,connected vehicles(CVs)have been emerging as a crucial branch of smart mobility(Olia et al.,2015;Li et al.,2021).Its basic idea is to realize real-time exchanges and processing of essential information,such as positions and destinations,among surrounding vehicles and infrastructures.

关键词： connected branch destination

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Leveraging Machine Learning for Crime Intent Detection in Social Media Posts 1st

Leveraging Machine Learning for Crime Intent Detection in ...

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1st International Conference on AI-generated Content, AIGC 2023

作者： Bokolo, Biodoumoye George Onyehanere, Praise Ogegbene-Ise, Ebikela Olufemi, Itunu Tettey, Josiah Nii Armah Department of Computer Science Sam Houston State University HuntsvilleTX77340 United States Department of Architecture Technology and Engineering University of Brighton BrightonBN2 4AT United Kingdom Department of Computer and Software Engineering Kennesaw State University KennesawGA United States Department of Computer Science and Quantitative Methods Austin Peay State University ClarksvilleTN37044 United States Department of Computer Science and Engineering Wright State University DaytonOH45435 United States

ISBN: (纸本)9789819975860

Detecting crime intent from user-generated content on social media platforms has become increasingly important for law enforcement and crime prevention. This paper presents a comprehensive approach for crime intent detection from user tweets using machine learning techniques. The study utilizes a dataset of about 400,000 tweets and applies data preprocessing, feature selection, and model training with logistic regression, ridge regression classifier, Stochastic Gradient Descent (SGD) classifier, Random Forests, and support vector machine models. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models’ performance. The results reveal that the logistic regression model achieves the highest accuracy ratio of 0.981 in detecting crime intent from tweets. This research showcases the effectiveness of machine learning and advanced transformer-based models in leveraging social media data for crime analysis. The findings provide valuable insights into the potential for early detection and monitoring of crime intent using online platforms, contributing to the field of crime prevention and law enforcement. The utilization of machine learning techniques offers new avenues for understanding and analyzing crime-related sentiments expressed by social media users. By accurately detecting crime intent from user-generated content, law enforcement agencies can enhance their proactive measures, monitor public sentiment towards crime, and shape policies and interventions to address public concerns effectively. The research highlights the significance of leveraging social media data for crime detection and emphasizes the potential impact of advanced machine learning models in improving public safety and crime prevention efforts. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Enhancing LLM Code Generation Using Natural Language Processing in the Context of Machine Learning 28th

Enhancing LLM Code Generation Using Natural Language Process...

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28th International Symposium on Database Engineered Applications, IDEAS 2024

作者： Nelson, Jordan Pavlidis, Michalis Fish, Andrew Polatidis, Nikolaos School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9783031834714

The rapid rise in popularity of Generative AI and Large Language Models (LLMs) has brought both innovation and controversy, particularly regarding plagiarism and IP law infringements. However, one underexplored concern is the generation of code by these models, which, despite their potential, often includes errors and promotes poor programming practices. This paper explores new methods to address these issues by integrating LLMs with Automated Machine Learning (AutoML). By leveraging AutoML’s capabilities in hyperparameter tuning and model selection, we propose a novel approach for generating robust machine learning algorithms. This integration aims to enhance the accuracy and reliability of code generation while mitigating legal risks. Our findings include the application of Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques to interpret chatbot prompts, thereby improving the generation and customization of machine learning models. The proposed methodology demonstrates practical implementation and high prediction accuracy, offering a promising solution to the current challenges faced by LLM-based code generation. In summary the findings of the paper are as follows: A new implementation of natural language processing for natural language understanding in the context of chatbot prompts aims to serve as an initial step for feature extraction, which will be utilised by an AutoML system to generate machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Ambient intelligence for self-care and self-management

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Journal of Ambient Intelligence and Humanized Computing 2023年第1期14卷 1-3页

作者： Hervás, Ramón Baños, Oresti Villalonga, Claudia Department of Technologies and Information Systems Modelling Ambient Intelligence Research Group University of Castilla-La Mancha Paseo de La Universidad 4 Ciudad Real Spain Department of Computer Architecture and Computer Technology Research Center for Information and Communication Technologies University of Granada Calle Periodista Rafael Gomez Montero 2 Granada Spain

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Evaluating Machine Learning Classifiers for Predicting Liver Disease Outcomes

Evaluating Machine Learning Classifiers for Predicting Liver...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Khaled Mohamad AlMustafa Juliano Katrib Department of Electrical and Computer Engineering College of Engineering and Architecture Gulf University for Science and Technology (GUST)

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The increasing prevalence of liver disease necessitates the development of accurate predictive models to aid in early diagnosis and treatment. This study investigates the effectiveness of several machine learning classifiers, Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), and Logistic Regression, in predicting liver disease outcomes based on clinical and biochemical features. Each model's performance was rigorously evaluated using multiple metrics, including F1-score, accuracy, root mean square error (RMSE), mean absolute error (MAE), precision, recall, and cross-validation ROC-AUC scores. The Random Forest Classifier emerged as the most effective model, achieving a perfect accuracy of 100% and an F1-score of 1.00, suggesting its robustness in handling complex, high-dimensional data. The Gradient Boosting Classifier also demonstrated commendable performance with an accuracy of 89% and an F1-score of 0.85, indicating its efficacy in capturing the underlying patterns of liver disease. Conversely, Logistic Regression and SVM exhibited lower performance metrics, pointing to potential limitations in their ability to model the intricacies of liver disease data effectively. The insights from this study not only enhance our understanding of liver disease prediction using machine learning but also emphasize the need for further research to validate these models in diverse clinical populations. This study contributes to the growing body of literature advocating for the integration of machine learning techniques in clinical decision-making processes, ultimately aiming to improve patient outcomes in liver disease management.

关键词： Support vector machines Measurement Logistic regression Accuracy Liver diseases Biological system modeling Predictive models Boosting Root mean square Random forests

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Hemodynamics in renal arteries with partial coverage after endovascular aneurysm repair affected by renal orientations

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Medicine in Novel technology and Devices 2023年第2期18卷 81-90页

作者： Jun Wen Xinhui Liu Jianguo Lei Jiarong Wang Xiyang Chen Tinghui Zheng Ding Yuan School of Computer Science and Technology Southwest University of Science and TechnologyMianyang621010China Institute of Civil Engineering and Architecture Southwest University of Science and TechnologyMianyang621010China Department of Vascular Surgery West China HospitalSichuan UniversityChengdu610041China Department of Applied Mechanics Sichuan UniversityChengdu610000China

Endovascular aortic repair(EVAR)has been increasingly used to treat abdominal aortic aneurysm(AAA)due to its lower mortality and shorter recovery ***,the complications caused by renal artery coverage remained a major *** this study,three types of renal orientation and five types of partial renal coverage degree were adopted to numerically investigate the flow pattern,wall shear stress distribution,and blood perfusion during a cardiac *** results show that as for the mild partial coverage(=<30%),the renal artery with a downward orientation suffered fewer unfavorable wall shear stress(WSS)distribution and more blood *** should be noted that the renal artery with horizontal orientation suffered more unfavorable WSS *** terms of severe coverage(≥50%),all of the renal arteries displayed highly disturbed wall shear distribution,resulting in a significant reduction in blood perfusion It was interesting to note that the artery with an upward orientation had a smaller rate of reduction in blood *** can be concluded that as for the mild renal coverage after EVAR,renal arteries with downward orientation may have the advantage of reducing the possibility of renal occlusion,while renal arteries with horizontal orientation may increase the unfavorable wall shear *** the other hand,as for serious coverage(≥50%),high risk of renal ischemia remains the main concern for all types of renal arteries due to highly disturbed wall shear distribution would significantly increase regardless of renal orientation.

关键词： Hemodynamics Orientation Follow-up Numerical simulation Renal artery

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