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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Ishimizu, Yusei Li, Jialong Yamauchi, Takuto Chen, Sinan Cai, Jinyu Hirano, Takanori Tei, Kenji School of Computing Tokyo Institute of Technology Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan Kobe University Center of Mathematical and Data Sciences Kobe Japan

ISBN: (纸本)9798331530839

Discrete Controller Synthesis (DCS) is an automated generation technique to generate controllers from environment models and requirements. The time and space complexities of DCS are critical because they directly govern the feasibility of using DCS for real-time generation of controllers to cope with environmental changes. Stepwise DCS is considered a promising approach to prevent the construction of ineffective state spaces in the subsequent analysis of the full controller by analyzing sub-controllers that meet part of the requirements at an early stage. However, constructing a stepwise policy is a challenge, as the current methods often require domain experts to manually build it by understanding and reasoning the semantics of each sub-environment model and sub-requirement. To reduce the effort of manual construction, this paper introduces a method that leverages large language models (LLMs) to automate the generation of stepwise policies by utilizing the LLM's ability to understand and reason about the semantics of state machines. The evaluation using GPT-4 on a drone control scenario demonstrates the effectiveness of our method, with an average reduction of 82.2% in states and 91.2% in transitions. © 2024 IEEE.

关键词： Control system synthesis

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ACSLs for Large-Scale Bounded Single Depot Multiple Travelling Salesman Problem 13

ACSLs for Large-Scale Bounded Single Depot Multiple Travelli...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Verma, Shanu Pant, Millie Indian Institute of Technology Roorkee Department of Applied Mathematics & Scientific Computing Uttarakhand Roorkee247667 India Mehta Family School of Data Science & Artificial Intelligence Indian Institute of Technology Roorkee Department of Applied Mathematics & Scientific Computing Uttarakhand Roorkee247667 India

ISBN: (纸本)9798350308365

Bounded single depot multiple travelling salesman problem (BSD-MTSP) optimizes salesmen tours with constraints on the number of cities visiting per salesman, which ensures balanced routes. BSD-MTSP presents challenges due to its combinatorial nature and additional complexities arising from multiple salesmen and depot-related constraints. Exact solutions for BSD-MTSP via classical methods are computationally expensive, prompting the use of heuristics and metaheuristics for practical solutions. This paper presents ACSLs, a hybrid algorithm combining initialization heuristic, Ant colony system, and Local search operators. ACSLs efficiently tackles large-scale BSD-MTSPs, demonstrating a superiority of up to 9.964% over the most advanced algorithms through comparative analysis. © 2024 IEEE.

关键词： Traveling salesman problem

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Enhancing Phishing Website Detection with Machine Learning Algorithms

Enhancing Phishing Website Detection with Machine Learning A...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Weijie Pang Ayrton Joseph DiPina Ashar Neyaz School of Computing & Data Science Wentworth Institute of Technology Boston MA USA

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

This paper examines the impact of feature selection on phishing website detection using machine learning techniques. We utilize the UCI Phishing Website dataset, which consists of 11,055 instances and 30 features with no missing values. Our results show that the random forest outperforms logistic regression, achieving an accuracy of 97.11%. By analyzing feature importance, we optimize the model by removing detrimental features, further improving accuracy to 97.74%. This study highlights the effectiveness of the random forest in phishing detection and underscores the importance of feature selection in enhancing model performance.

关键词： Logistic regression Analytical models Accuracy Machine learning algorithms Phishing Digital forensics Feature extraction Security Random forests

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A generalized software reliability prediction model for module based software incorporating testing effort with cost model

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Life Cycle Reliability and Safety Engineering 2024年第4期13卷 419-436页

作者： Yadav, Akshay Kumar Srivastava, Shilpa Pant, Millie Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee 247001 India Christ University Karnataka Bengaluru 560029 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee 247667 India

As software innovation has advanced, it has been noted that the testing effort function (TEF) is one of the key factors influencing the improvement of software reliability. This paper presents a simplified model which incorporates the testing effort for the reliability growth of a software. A closed form solution has been derived for the reliability of the software. This study examines the impact of testing efforts on a software reliability model based on NHPP. The sensitivity analysis has been made available to investigate how the created model's system parameters affect the cost function, mean value function, and software’s reliability. The parameters of the model have been estimated using the non-linear least square estimation (MLE) method in MATLAB software. Additionally, a warranty cost model is constructed to assess the optimal release policy for the software. The general form of the reliability expression involves elliptic integrals, which can be computed easily through a software like Mathematica. We have derived analytical solutions for reliability pertaining to several particular cases. Optimal release time for the software product has been calculated for some particular cost-sets. Goodness of fit curves have been plotted to compare the proposed model with some well-known existing SRGMs. Numerical illustrations are provided to bolster the analytical outcomes. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.

关键词： Maximum likelihood estimation (MLE) Non-homogeneous Poisson process (NHPP) Optimal release time (ORT) Software reliability growth model (SRGM) Testing effort function (TEF) Warranty cost model

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EmoSync: Facial Emotion Detection for Adaptive Music Playback 3

EmoSync: Facial Emotion Detection for Adaptive Music Playbac...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Devarajan, Devikanniga Rajkumar, K. GITAM Deemed to be University Computer Science and Engineering Nagadenahalli Doddabalpur Karnataka Bangalore561203 India SRM Institute of Science and Technology Department of Data Science and Business Systems School of Computing Tamil Nadu Kattankulathur603 203 India

ISBN: (纸本)9798350372120

This work investigates using Convolutional Neural Networks (CNN) and VGGNet architecture, for realtime emotion detection. Raw data from the FER-2013 dataset is transformed from CSV format into organized image directories, segmented by distinct emotions. The resulting model, equipped with convolutional, pooling, and dense layers, effectively classifies emotions from 48x48 grayscale images. In application, the model overlays detected emotions onto a webcam stream. An integrated music player module synchronizes music with the subject's emotions. Overall, this research explores a comprehensive interactive system focused on recognizing and responding to human emotions. Without further training data, our model, to the best of our understanding, achieves modern single-network accuracy of 74.57% on FER2013 © 2024 IEEE.

关键词： Python

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Machine Learning and OpenCV: A Powerful Combination for Driver Drowsiness Detection

Machine Learning and OpenCV: A Powerful Combination for Driv...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Siva Kumar, A. Gopirajan, P.V. Ramasamy, Jayaraj Jackson, Beulah School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur Chennai603203 India School Of Computing Srm Institute Of Science And Technology Department Of Computational Intelligence Kattankulathur Campus Chennai603203 India Botho University Faculty Of Engineering And Technology Department of IT Botswana Vel Tech Rangarajan Dr. Sagunthala R & D Institute Of Science And Technology Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

Driver fatigue is a significant contributor to road accidents worldwide, and there is a need for efficient driver drowsiness detection systems to prevent such accidents. Computer vision techniques like OpenCV have recently been utilized to develop such strategies. This proposed work investigates the use of machine learning models and OpenCV Python package for driver drowsiness detection. The proposed system uses a camera to continuously capture the driver's face and applies image processing techniques to identify drowsiness. The system detects blinks by monitoring the driver's eyes and measuring the frequency and duration of eyelid closures. The real-time experiments conducted with the proposed method showed high accuracy in detecting drowsiness, and the system can be implemented in various settings, including cars and heavy vehicles. Ultimately, the system has some potential to reduce accidents caused by driver fatigue, thereby enhancing road safety. © 2024 IEEE.

关键词： Support vector machines

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Predictive Modeling of Cardiovascular Disease Using ML Algorithms

Predictive Modeling of Cardiovascular Disease Using ML Algor...

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Vijayalakshmi, M. Cherukuvada, Srikanth Chinnappa, A. Kavitha, G. Soujanya, A. Nareshkumar, R. Care College of Engineering Artificial Intelligence and Data Science Trichy India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chennai Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Kattankulathur India Department of Mathematics Namakkal India Ibrahimpatnam India

ISBN: (纸本)9798350348910

One of the most significant contributors to morbidity in the global population is the ability to forecast a heart attack. Clinical data analysis is considered one of the most significant aspects for making a forecast, and that aspect is cardiovascular illness, a highly important disease. This can be a very useful tool. Using ML to its full potential, this investigation aims to achieve improved accuracy in diagnosing cardiac disease. In this research, we provide a machine knowledge heart attack diagnosis (MLHAP) technique that involves the study of different risk indicators, and prediction for heart attacks is carried out via ML methodologies, including Support Vector Machines, Logistic Regression, Naive Bayes, Knn, and XGBoost. In this method, the ML-HAP method predicts heart attacks. This approach aims to reduce the mortality rate from heart attacks. The forecast has been optimized as much as possible based on many parameters, which has been the primary focus. KNN, with its parameters optimized, made the most accurate prediction of any of the algorithms. The accuracy obtained using KNN Optimization is 85.24, while the accuracy obtained using Logistic Regression is 78.68. The prediction made by machine learning models for recognizing the signs of a heart attack is extremely effective, particularly when using boosting algorithms. © 2023 IEEE.

关键词： Support vector machines

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RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer 39

RNTrajRec: Road Network Enhanced Trajectory Recovery with Sp...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Chen, Yuqi Zhang, Hanyuan Sun, Weiwei Zheng, Baihua Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Shanghai Institute of Intelligent Electronics & Systems China Singapore Management University School of Computing and Information Systems Singapore

ISBN: (纸本)9798350322279

GPS trajectories are the essential foundations for many trajectory-based applications. Most applications require a large number of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means to increase the sample rate of low sample trajectories. Most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and less spatial consistent. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model to a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach. © 2023 IEEE.

关键词： Recovery

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3D Based CNN for Improved Segmentation from the Hyperspectral Images 1

3D Based CNN for Improved Segmentation from the Hyperspectra...

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1st International Conference on Computer science and Emerging Technologies, CSET 2023

作者： Narmadha, T. Arockia Raj, A. Saravanan, P. Kumar, A. Suresh Sekaran, Ramesh Gowri Shankar, J. School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering - Data Science Madanapalle India School of Computing Science and Engineering Galgotias University Delhi NCR India

ISBN: (纸本)9798350341737

In this paper, we develop a 3D based CNN for Improved Segmentation of Paddy Fields from the HSI. Within the scope of this research, we will investigate a unique deep learning model, specifically 3D-CNN. In order to evaluate the validity of the model, it is applied to three separate datasets, each of which is then subjected to one of four distinct dimensionality reduction strategies. The purpose of this is to determine which of these four models is the most effective at reducing the number of dimensions. According to the findings, the 3D-CNN model is superior to both the AlexNet and CNN models in terms of the accuracy of its segmentations. In addition to this, the findings demonstrate that the 3D-CNN models perform better than the other approaches in terms of segmentation accuracy with a smaller number of datasets. © 2023 IEEE.

关键词： Image enhancement

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Cross-domain NER with Generated Task-Oriented Knowledge: An Empirical Study from Information Density Perspective

Cross-domain NER with Generated Task-Oriented Knowledge: An ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Zhihao Lee, Sophia Yat Mei Wu, Junshuang Zhang, Dong Li, Shoushan Cambria, Erik Zhou, Guodong School of Computer Science & Technology NLP Lab Soochow University China Department of Chinese and Bilingual Studies The Hong Kong Polytechnic University Hong Kong Beijing Jinghang Research Institute of Computing and Communication China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph (KG) construction and natural language processing (NLP), enabling learning from source to target domains with limited data. Previous studies often rely on manually collected entity-relevant sentences from the web or attempt to bridge the gap between tokens and entity labels across domains. These approaches are time-consuming and inefficient, as these data are often weakly correlated with the target task and require extensive pre-training. To address these issues, we propose automatically generating task-oriented knowledge (GTOK) using large language models (LLMs), focusing on the reasoning process of entity extraction. Then, we employ task-oriented pre-training (TOPT) to facilitate domain adaptation. Additionally, current cross-domain NER methods often lack explicit explanations for their effectiveness. Therefore, we introduce the concept of information density to better evaluate the model's effectiveness before performing entity recognition. We conduct systematic experiments and analyses to demonstrate the effectiveness of our proposed approach and the validity of using information density for model evaluation. © 2024 Association for Computational Linguistics.

关键词： Domain Knowledge

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