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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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Experimental Validation of RGB Dataset Analysis for Distribution system Operator Infrastructure Inspections 11

Experimental Validation of RGB Dataset Analysis for Distribu...

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11th IEEE Workshop on Advances in information, Electronic and Electrical Engineering, AIEEE 2024

作者： Gauce, Diana Litvinenko, Anna Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia

ISBN: (纸本)9798331527761

This article focuses on experimentally validating techniques for analyzing red-green-blue (RGB) datasets in infrastructure inspections carried out by Distribution system Operators (DSOs). Given the increasing demand for efficient and precise inspection methods in the energy sector, leveraging RGB datasets presents a promising opportunity. The research investigates the effectiveness of RGB dataset analysis in identifying and evaluating critical infrastructure assets essential for DSO operations. The experimental validation underscores the potential of RGB data analysis as a valuable tool for improving infrastructure inspection procedures, streamlining maintenance schedules, and ensuring dependable energy distribution networks. © 2024 IEEE.

关键词： Decision making

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Clustering Mechanism in Particle Swarm Optimization Algorithm for Data Aggregation 13

Clustering Mechanism in Particle Swarm Optimization Algorith...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Sharmin, Sharmin Ahmedy, Ismail Noor, Rafidah Md Ismail, Habibah Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Univesrsiti Teknologi MARA Computer Science Unit Centre of Foundation Studies Selangor Dengkil43800 Malaysia

ISBN: (纸本)9798350347319

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes with distinct resources working together to achieve a common objective efficiently. A critical issue is extending the network lifetime of WSNs, especially for data transfer, as sensor nodes depend on their internal batteries to carry out essential functions and communicate with one another. Efficient energy use per sensor is essential for ensuring the overall energy efficiency of the network. Clustering has been the most energy-efficient approach but lacking a method for selecting the most effective cluster head results in decreased data aggregation efficiency, leading to the increased power consumption of sensor nodes. The project aims to enhance energy efficiency and extend network service life by using Particle Swarm optimization (PSO) for clustering in WSNs. PSO is used to select the cluster head based on member node distance from the base station and remaining energy. The proposed PSO technique was tested against Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Zonal Stable Election Protocol Z-SEP, and Extended Stable Election Protocol (EZ-SEP) and found to be more effective in reducing energy usage and prolonging the network's lifespan. The recommended technique can extend the service life of WSNs and reduce energy consumption, ultimately leading to more cost-effective and eco-friendly solutions for various applications such as environmental monitoring, surveillance, and healthcare. © 2023 IEEE.

关键词： Energy utilization

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Leveraging Social Media for Real-Time Interpretable and Amendable Suicide Risk Prediction with Human-in-The-Loop

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IEEE Transactions on Affective Computing 2024年第2期16卷 1128-1145页

作者： Dai, Yi Liu, Jinlei Cao, Lei Xue, Yuanyuan Wang, Xin Ding, Yang Tian, Junrui Feng, Ling Tsinghua University Department of Computer Science Technology Beijing China Beijing Normal University Faculty of Psychology Beijing China China Transport Telecommunications & Information Center Beijing China

Suicide presents a global health challenge, prompting the development of diverse prevention strategies. Among them, timely identification of individuals at risk of suicide remains challenging. Although social media offers potential for tracking users' mental status, harnessing collaboration between AI and human experts for real-time prediction of suicide risk is inadequately explored. This study presents a human-in-the-loop framework for real-time suicide risk prediction based on social media. Once a user made a new post on social media, the AI model assesses user's suicide risk within the next month with explanation based on the historic and new posts plus domain knowledge. Human experts on the other side look into the explanation to confirm/clarify uncertain information as feedback, enabling consistent evolution of the model. Experiments on the constructed dataset, containing 66 suicidal users and 66 non-suicidal users, show that our method achieved 82.58% prediction accuracy, outperforming competitive baselines by 6.57%. Leveraging human feedback improved prediction accuracy by 4.12%. Consultation with 18 experts (including 6 medical staff and 12 psychologists) was conducted to examine the validity of our method. Ethics considerations, as well as potential and limitations of large language models in mental condition prediction, are also discussed at the end of the paper © 2010-2012 IEEE.

关键词： Prediction models

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A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning

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Neural Computing and Applications 2024年第33期36卷 20723-20750页

作者： Abdel-salam, Mahmoud Kumar, Neeraj Mahajan, Shubham Faculty of Computer and Information Science Mansoura University Mansoura Egypt Department of CSE Thapar Institute of Engineering and Technology Deemed to Be University Patiala147004 India Delhi-NCR India

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. © The Author(s) 2024.

关键词： Support vector regression

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Whole Slide Image Annotation Support for Estimating Lesion Proportions 23

Whole Slide Image Annotation Support for Estimating Lesion P...

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23rd International Conference on Cyberworlds, CW 2024

作者： Takano, Nana Nishiguchi, Satoshi Toyoura, Masahiro Osaka Institute of Technology Graduate School of Information Science and Technology Osaka Japan Osaka Institute of Technology Faculty of Information Science and Technology Osaka Japan University of Yamanashi Department of Computer Science and Engineering Yamanashi Japan

ISBN: (纸本)9798331527174

The annotation of medical images for the purpose of segmentation demands a high level of expertise and experience, and the generation of suitable datasets represents a significant challenge. In this study, we propose a method that facilitates annotation on a whole slide image (WSI) without requiring detailed specification of the lesion area. The objective is to enable annotation without the need for specialised knowledge. In particular, the WSI is divided into smaller regions for annotation purposes, with the proportion of lesions in each region being compared with reference images of similar regions, for which the proportion of lesions is known. The proportion of lesions in the reference small region image deemed to be most analogous is then designated as the annotation. The outcomes of an annotation experiment on multiple subjects based on this approach indicated that as the level of experience of the annotator increased, the variability in the accuracy rate diminished. This suggests that the annotators had acquired knowledge and skills through the process of annotation. © 2024 IEEE.

关键词： Medical imaging

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A Comparative Analysis of Deep Learning Approaches for Fire Hotspots Prediction in Thailand 23

A Comparative Analysis of Deep Learning Approaches for Fire ...

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23rd International Symposium on Communications and information Technologies, ISCIT 2024

作者： Phawandee, Sansern Sangurai, Nuanprang Putpuek, Narongsak Cooharojananone, Nagul Rajabhat Rajanagarindra University Faculty of Science and Technology Department of Information Technology Chachoengsao Thailand Chulalongkorn University Department of Mathematics and Computer Science Facluty of Science Bangkok Thailand

ISBN: (纸本)9798350353952

Northern Thailand faces a recurring ecological threat: forest fires. These fires, prevalent during the dry season, spread rapidly and consume vital forest fuels. Human activities exacerbate the problem, with agricultural burning of forests, grasses, and crop residues contributing to the fire regime. These fires not only alter forest structure, but also produce harmful PM10 and PM2.5 particles that threaten public health. The purpose of this work is to perform a comparative analysis of two deep learning approaches, including single and hybrid, for fire hotspots prediction. The dataset was acquired from NASA's MODIS and VIIRS S-NPP satellite, covering the period from January 2012 to February 2024. The dataset values were pre-processed by coding and normalization. The pairwise correlation coefficient between each feature and the number of hotspots was calculated and then sorted in descending order. Based on these correlation values, a dimensionality reduction step was performed to reduce the number of features from 4 to 2 for each experiment. The prepared dataset was then divided into 80% and 20%. Four deep learning models were trained and three error metrics were used to objectively evaluate the model performance. The investigation showed that the single approach using LSTM algorithm had promising results in identifying fire hotspots in Thailand, especially when using data from the VIIRS S-NPP dataset with two or three attributes: confidence, bright-ti5, and frp or confident and bright-ti5. © 2024 IEEE.

关键词： NASA

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Machine Translation for Morphologically Rich Low-Resourced South African Languages 4

Machine Translation for Morphologically Rich Low-Resourced S...

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4th International Multidisciplinary information technology and Engineering Conference, IMITEC 2024

作者： Masethe, Hlaudi Daniel Mothapo, Lawrence M Ojo, Sunday O. Owolawi, Pius A. Masethe, Mosima Anna Giunchigilia, Fausto Tshwane University of Technology Department of Data Science South Africa Durban University of Technology Department of Information Technology South Africa Tshwane University of Technology Department of Computer Engineering South Africa Sefako Makgatho Health Sciences University Department of Computer Science and Information Technology South Africa University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350387988

In the context of machine translation, many of South Africa's languages are categorized as low-resourced, despite the country's vast linguistic diversity. Creating efficient multilingual translation systems that can support languages like Afrikaans, Sesotho sa Leboa (Sepedi), isiNdebele, Setswana, isiXhosa, Xitsonga, siSwati and isiZulu are severely hampered by the lack of high-quality training data. Low translation accuracy and fluency are the result of existing translation models' frequent inability to perform well in these languages. As a result, speakers of these languages encounter difficulties using increasingly accessible online resources for information and services. By utilizing the MarianMT model to create a reliable multilingual machine translation system, this work seeks to address these issues by enhancing the quality of translation for South African languages with limited resources. This study presents a comprehensive approach to multilingual machine translation focused on low-resourced South African languages, utilizing the MarianMT model. Our objective was to enhance translation quality for languages with limited training data, including Afrikaans, Sesotho sa Leboa (Sepedi), isiNdebele, Setswana, IsiXhosa, Xitsonga, siSwati and isiZulu. We conducted extensive experiments, resulting in a BLEU score of 62%, indicating a significant improvement in translation. Our evaluation metrics demonstrated strong performance, with precision at 78%, recall at 81%, F1 score at 79%, and overall accuracy at 82%. © 2024 IEEE.

关键词： Neural machine translation

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Addressing the Libyan Arabic Dialect Identification: A Comparative Study of Ensemble Classification Methods 4

Addressing the Libyan Arabic Dialect Identification: A Compa...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Ghumeid, Najah A. Essgaer, Mansour Sebha University Faculty of Sciences Computer Science Department Sebha Libya Sebha University Faculty of Information Technology Information Systems Department Sebha Libya

ISBN: (纸本)9798350372632

Dialect identification presents a challenging task within the realm of Natural Language Processing. The growing utilization of Arabic dialects in written form, particularly on social media platforms, has created new demands for accurate identification of these dialects. In multilingual datasets, distinguishing the Libyan Arabic dialect from neighboring Arabic dialects has emerged as a crucial goal. To address this, we conducted experiments using different lengths of word n-gram features to determine the most suitable combination of word n-grams and term frequency-inverse document frequency. Our aim was to capture contextual information effectively while mitigating linguistic similarities between Arabic dialects of neighboring countries. The QCRI Arabic Dialects Identification dataset was employed, and we evaluated the performance of three ensemble tree-based classification methods: Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. The evaluation, based on the f-measure metric, yielded respective scores of 68%, 37%, and 52% for Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. These results underscore the effectiveness of our approach in identifying the Libyan Arabic dialect and highlight the potential of ensemble tree-based methods in Arabic dialect identification tasks. © 2024 IEEE.

关键词： Computational linguistics

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AI-Driven Learning Management systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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