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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Galat, Umesh Rewatkar, Rajendra Kothare, Chandrakant Datta Meghe Institute of Research and Higher Education Aids Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Shri Shankarprasad Agnihotri Collge of Engineering Department of Mechanical Engineering Maharashatra Wardha India

ISBN: (纸本)9798331523923

These due to the shift towards the cleaner energy generation and newer and sophisticated engine technologies the prediction of the performance and emission of engine is of vital importance for the designer for the purpose of ANR as well as the emission control standards. This review also assesses the performance of ANNs and the new machine learning techniques in this area. ANNs especially the single layer and multi-layer networks have shown good levels of predictions of engine performance and emission for ethanol-gasoline, bioethanol and other fuel blends. For example, the models based on Levenberg-Marquardt optimization and the Bayesian regularization have revealed essential correlation coefficients and minimized mean squared errors. Based on comparative analyses, the further improvement of the predictive ability and speed of ANNs is stressed, including applications of ANNs integrated with other approaches like Response Surface Methodology (RSM), and other developing approaches like Extreme Learning Machines (ELM) and Support Vector Regression (SVR). However, challenges are concluded to remains including data quality, model complexities and computational resources. This review reveals that, for tackling these issues and enhancing the progress in developing efficient and sustainable engine solutions, detailed databases and refined mathematical modeling approaches are required. The directions for future research are to obtain better quality data, enhance computational time, and determine more fuel blends and engine settings for the model. © 2025 IEEE.

关键词： Adversarial machine learning

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Incentivizing Crowdsensing for DT-Enabled Metaverse 20th

Incentivizing Crowdsensing for DT-Enabled Metaverse

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Ye, Dongdong Huang, Xumin Wu, Yuan Kang, Jiawen Zhong, Weifeng Niyato, Dusit School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China Department of Computer and Information Science University of Macau Taipa China Collge of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Digital Twin (DT) has been proposed as a key enabling technology to produce a digital clone of the physical world and facilitate the convergence of the physical and virtual worlds for building a real-world Metaverse. For physical-virtual synchronization, crowdsensing is applied to employ Internet of Things (IoT) devices to sense physical objects and collect status information for timely updating the associated DTs. However, an incentive mechanism is necessitated to stimulate the devices to perform sensing tasks while meeting task requirements such as Age of Information (AoI) requirements. Thus, we adopt a contract theoretic approach to design an incentive mechanism for the devices that are classified into several types and the type parameters are unknown to others. The contract consists of a set of contract items with each contract item representing a device type, which specifies the required data updating frequency and monetary reward. We formulate an optimal contract design problem subject to feasible constraints including the average AoI requirement for each device type. Furthermore, we solve the problem by transforming it into a convex optimization problem that can be directly tackled by the existing methods. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our scheme. © IFIP International Federation for Information Processing 2025.

关键词： Convex optimization

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An explainable multi-objective hybrid machine learning model for reducing heart failure mortality

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PeerJ computer science 2025年 11卷 e2682-e2682页

作者： Shamrat, F.M. Javed Mehedi Khalid, Majdi Qadah, Thamir M. Farrash, Majed Alshanbari, Hanan Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer Science and Artificial Intelligence Umm Al-Qura University Makkah Saudi Arabia Department of Computer and Network Engineering Collge of Computing Umm Al-Qura University Makkah Saudi Arabia

As the world grapples with pandemics and increasing stress levels among individuals, heart failure (HF) has emerged as a prominent cause of mortality on a global scale. The most effective approach to improving the chances of individuals’ survival is to diagnose this condition at an early stage. Researchers widely utilize supervised feature selection techniques alongside conventional standalone machine learning (ML) algorithms to achieve the goal. However, these approaches may not consistently demonstrate robust performance when applied to data that they have not encountered before, and struggle to discern intricate patterns within the data. Hence, we present a Multi-objective Stacked Enable Hybrid Model (MO-SEHM), that aims to find out the best feature subsets out of numerous different sets, considering multiple objectives. The Stacked Enable Hybrid Model (SEHM) plays the role of classifier and integrates with a multi-objective feature selection method, the Non-dominated Sorting Genetic Algorithm II (NSGA-II). We employed an HF dataset from the Faisalabad Institute of Cardiology (FIOC) and evaluated six ML models, including SEHM with and without NSGA-II for experimental purposes. The Pareto front (PF) demonstrates that our introduced MO-SEHM surpasses the other models, obtaining 94.87% accuracy with the nine relevant features. Finally, we have applied Local Interpretable Model-agnostic Explanations (LIME) with MO-SEHM to explain the reasons for individual outcomes, which makes our model transparent to the patients and stakeholders. Copyright 2025 Shamrat et al. Distributed under Creative Commons CC-BY 4.0

关键词： Heart

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Evaluating Artificial Neural Networks and Emerging Machine Learning Methods for Engine Performance and Emissions Prediction: A Review

Evaluating Artificial Neural Networks and Emerging Machine L...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Umesh Galat Rajendra Rewatkar Chandrakant Kothare Department of Computer Science and Engineering AIDS Faculty of Engineering and Technology Datta Meghe Institute of Research and higher education Wardha Maharashtra India Department of Mechanical Engineering Shri Shankarprasad Agnihotri collge of Engineering Wardha Maharashatra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

关键词： Support vector machines Accuracy Reviews Computational modeling Artificial neural networks Predictive models Mathematical models Fuels Reliability Engines

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Uncertainty Guided Refinement for Fine-Grained Salient Object Detection

arXiv

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arXiv 2025年

作者： Yuan, Yao Gao, Pan Dai, Qun Qin, Jie Xiang, Wei Collge of Computer Science and Technology Nanjing University of Aeronautics and Astronautics the Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC3086 Australia

Recently, salient object detection (SOD) methods have achieved impressive performance. However, salient regions predicted by existing methods usually contain unsaturated regions and shadows, which limits the model for reliable fine-grained predictions. To address this, we introduce the uncertainty guidance learning approach to SOD, intended to enhance the model’s perception of uncertain regions. Specifically, we design a novel Uncertainty Guided Refinement Attention Network (UGRAN), which incorporates three important components, i.e., the Multilevel Interaction Attention (MIA) module, the Scale Spatial-Consistent Attention (SSCA) module, and the Uncertainty Refinement Attention (URA) module. Unlike conventional methods dedicated to enhancing features, the proposed MIA facilitates the interaction and perception of multilevel features, leveraging the complementary characteristics among multilevel features. Then, through the proposed SSCA, the salient information across diverse scales within the aggregated features can be integrated more comprehensively and integrally. In the subsequent steps, we utilize the uncertainty map generated from the saliency prediction map to enhance the model’s perception capability of uncertain regions, generating a highly-saturated fine-grained saliency prediction map. Additionally, we devise an adaptive dynamic partition (ADP) mechanism to minimize the computational overhead of the URA module and improve the utilization of uncertainty guidance. Experiments on seven benchmark datasets demonstrate the superiority of the proposed UGRAN over the state-of-the-art methodologies. Codes will be released at https://***/I2-Multimedia-Lab/UGRAN. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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Web Application to Track Student Attentiveness during Online Class using CNN and Eye Aspect Ratio 6

Web Application to Track Student Attentiveness during Online...

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Deepa, D. Selvaraj, S. Vijaya Lakshmi, D.M. Sarneshwar, S. Vignesh Balaji, N. Vikash, M. Kongu Engineering Collge Computer Science and Engineering Tamilnadu Erode India Jayam College of Engineering and Technology Computer Science and Engineering Dhrmapuri Tamilnadu India

ISBN: (纸本)9781665410281

During this COVID-19 pandemic online class platforms are the only solution to transfer the knowledge in the field of education. Even though the physical classes are being practiced slowly in some countries, still academicians are in the need of online classes. In addition to content delivery, teachers are in the need to concern about throughout the class time whether the students are listening and be active in online classes. Due to more bandwidth consumption of the audio and video streaming, students can't be compelled to unmute the audio and video when the teacher delivers the content. So, there is no option for the teachers to observe the student's activity. With the advancement of technology and enhanced image analysis capacity of deep learning techniques, a system is proposed to compute the student's activity and can report it to the teachers during the class time itself. Drowsiness detection is tested using CNN based segmentation on our own set of 5000 images collected from 1000 students. The observed result shows 90% accuracy in predicting the drowsiness of the student by observing the face pattern of the student without streaming the video to the teacher's device. © 2022 IEEE.

关键词： COVID-19

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The applicaiton of OEA in the geometric constraint solving

The applicaiton of OEA in the geometric constraint solving

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2006 International Conference on Machine Learning and Cybernetics

作者： Cao, Chun-Hong Zhang, Bin Li, Wen-Hui Collge of Information Science and Engineering Northeastern University Shenyang 110004 China College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)1424400619

In this paper we propose a new optimization algorithm-organizational evolutionary algorithm (OEA) and apply it into the geometric constraint solving. We transfer the geometric constraint problem into a set of nonlinear equations substantially. In OEA the colony is composed of the organizations. Three organizational evolutionary operators-split operator, merging operator and coordinating operator can lead the colony to evolve. These three kinds of operators have different functions in the algorithm. The experiment shows that OEA has good capability in the geometric constraint solving. © 2006 IEEE.

关键词： Evolutionary algorithms

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The geometric constraint solving based on the quantum particle swarm 1

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5th International Conference on Rough Set and Knowledge technology, RSKT 2010

作者： Chunhong, Cao Limin, Wang Wenhui, Li Collge of Information Science and Engineering Northeastern University Shenyang 110004 China College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (数字)9783642162480

ISBN: (纸本)3642162479

Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially. The constraint problem can be transformed to an optimization problem. We can solve the problem with quantum particle swarm. The PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents a novel Quantum-behaved PSO (QPSO). The experiment shows that it can improve the algorithmic efficiency. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Nonlinear equations

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An improved particle swarm optimization algorithm for geometric constraint solving problem

An improved particle swarm optimization algorithm for geomet...

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2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

作者： Cao, Chun-Hong Zhang, Chang-Sheng Wang, Li-Min Northeastern University Collge of Information Science and Engineering Shenyang 110004 China College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)9781424465262

Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially. The constraint problem can be transformed to an optimization problem. We can solve the problem by an improved PSO algorithm (IPSO), which is based on the "alldifferent" constraint. It combines the particle swarm optimization algorithm with genetic operators together effectively. When a particle is going to stagnate, the mutation operator is used to search its neighborhood. The experiment indicates that the algorithm can be used to solve geometric constraint problem effectively. © 2010 IEEE.

关键词： Nonlinear equations

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Bibliometric review of actuators: Key automation technology in a smart city framework

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Materials Today: Proceedings 2022年 60卷 1800-1807页

作者： Rani, Sita Kumar, Raman Department of Computer Science and Engineering Gulzar Institue of Engineering and Technology Gulzar Group of Institutions Khanna-141401 Punjab India Department of Mechanical and Production Engineering Guru Nanak Dev Engineering Collge Ludhiana-141006 Punjab India

Smart cities are built in diverse mechanisms and sub-systems by integrating Information and Communication Technologies (ICT). It offers inhabitants sustainable services at a lower cost and provides full connectivity. The sensors are incorporated virtually everywhere in a smart city environment. Actuators are switching technologies that enable communication in a highly heterogeneous smart city environment and perform output functions. This article focuses on the function of actuators as a vital technology to allow different smart-city services to be automated. So, the role of actuators in the smart city was inclusively scrutinized employing bibliometric review. An organized practise was utilized to accumulate journal articles on actuators in smart cities. Screening processes have been devised for citation analyses. The utmost prolific authors, journals, articles, counties, and institutions are graded based on the overall citation count. Researching hubs and patterns were established utilizing author-keywords and index-keyword scrutiny on actuators in smart cities. The bibliometric assessment of the actuators in smart cities might deliver imperative evidence about the development of worldwide research. It is also very beneficial for upcoming researchers in the area of sensors and actuators in smart cities. © 2021

关键词： Actuators

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