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Air Quality Assessment B ased on CNN Transformer Hybrid Architecture

International Journal of Advanced Computer science and Applications 2025年第4期16卷 273-279页

作者： Zhang, Yuchen Thinakaran, Rajermani Faculty of Data Science and Information Technology INTI International University Malaysia

Air quality assessment plays a crucial role in environmental governance and public health decision making. Traditional assessment methods have limitations in handling multi source heterogeneous data and complex nonlinear relationships. This paper proposes an air quality assessment model based on a CNN Transformer hybrid architecture, which achieves end to end prediction by integrating CNN's local feature extraction capability with Transformer's advantage in modeling global dependencies. The model employs a three layer CNN for local feature learning, combined with Transformer's multi head self attention mechanism to capture long range dependencies, and uses multilayer perceptrons for final prediction. Experiments on public datasets demonstrate that compared t o traditional machine learning methods and single deep learning models, the proposed hybrid architecture achieves a 10.2 percentage improvement in Root Mean Square Error (RMSE) and a 0.57 percentage point improvement in coefficient of determination (R2). T hrough systematic ablation experiments, we verify the necessity of each model component, particularly the importance of the CNN Transformer hybrid architecture, positional encoding mechanism, and multi layer network structure in enhancing prediction perfor mance. The research results provide an effective deep learning solution for air quality assessment. © (2025), (science and information Organization). All Rights Reserved.

关键词： Damage detection

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LesionAid: vision transformers-based skin lesion generation and classification – A practical review

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Multimedia Tools and Applications 2025年 1-22页

作者： K, Mallikharjuna Rao Krishna, Ghanta Sai Supriya, Kundrapu Sorgile, Meetiksha Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur India

Skin cancer is one of the most prevalent forms of human cancer. It is recognized mainly visually, beginning with clinical screening and continuing with the dermoscopic examination, histological assessment, and specimen collection. Deep convolutional neural networks (CNNs) perform highly segregated and potentially universal tasks against a classified fine-grained object. This study suggests a novel multi-class prediction framework that uses ViT and ViTGAN to categories skin lesions. To address the class disparity, GANs (Generative Adversarial Networks) based on vision transformers are used. The framework comprises four main phases: ViTGANs, Image processing, and explainable AI. Phase 1 consists of generating synthetic images using ViTGAN to balance all the classes in the dataset. To enhance the amount of the data, the second phase involves using various morphological processes and data augmentation techniques. In phases three and four, a ViT model for edge computing systems that can recognize patterns and classify skin lesions from the user's skin that is visible in the picture is developed. In phase 3, after classifying the lesions into the desired class with ViT, we will use explainable AI (XAI) that leads to more explainable results (using activation maps, etc.) while ensuring high predictive accuracy. The results demonstrate that the model used for generating synthetic images has achieved an FID score of 13.32 and the ViT model has achieved 99.2% as its training accuracy and 97.4% as its validation accuracy. The whole framework is compared with the existing frameworks for skin lesion detection. And explainable AI (XAI) is used in the proposed framework in order to increase model transparency and boost trust among users by illustrating the main factors affecting predictions. This interpretability helps physicians in making more informed decisions by providing clear insights into the model's reasoning. © The Author(s), under exclusive licence to Springer science+Bu

关键词： Generative adversarial networks

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Overview and New Opportunities for Multi-Source data Assimilation

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Journal of Meteorological Research 2025年第1期39卷 1-25页

作者： Xiaolei ZOU Joint Center of Data Assimilation for Research and Application Nanjing University of Information Science&TechnologyNanjing210044China

The world witnessed an accelerated development of various types of meteorological observing technology,an evolution of numerical weather prediction(NWP)models from single atmospheric component to coupled multi-components of the earth system,as well as the multi graphics processing unit technology in computer sciences,a new era for rapidly advancing data assimilation science and technology development has *** multi-source data assimilation is important not only for NWP but also for further understanding of global and regional weather *** article firstly selectively reviews past methods of multi-source data *** opportunities are then discussed for future development of data assimilation system framework,for innovative uses of high-resolution observations,and for applications of artificial intelligence machine learning in meteorological data assimilation.

关键词： multi-source observations data assimilation innovative use of high-resolution observations

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GOES-16 ABI Brightness Temperature Observations Capturing Vortex Rossby Wave Signals during Rapid Intensification of Hurricane Irma(2017)

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Journal of Meteorological Research 2024年第4期38卷 768-783页

作者： Yanyang HU Xiaolei ZOU Joint Center of Data Assimilation for Research and Application Nanjing University of Information Science&TechnologyNanjing 210044

Geostationary Operational Environmental Satellite-16(GOES-16) Advanced Baseline Imager(ABI) observations of brightness temperature(TB) are used to examine the temporal evolutions of convection-affected structures of Hurricane Irma(2017) during its rapid intensification(RI) period from 0600 to 1800 UTC 4 September *** ABI observations reveal that both an elliptical eye and a spiral rainband that originated from Irma's eyewall obviously exhibit wavenumber-2 TB *** elliptical eye underwent a counterclockwise rotation at a mean speed of a wavenumber-2 vortex Rossby edge wave from 0815 to 1005 UTC 4 *** the following about 2 hours(1025–1255 UTC 4 September),an inner spiral rainband originated from the eyewall and propagated at a phase speed that approximates the vortex Rossby wave(VRW) phase speed calculated from the aircraft reconnaissance *** the RI period of Irma,ABI TB observations show an on–off occurrence of low TB intrusions into the eye,accompanying a phase lock of eyewall TB asymmetries of wavenumbers 1 and 2 and an outward propagation of VRW-like inner spiral rainbands from the *** phase lock leads to an energy growth of Irma's eyewall *** the eye remained clear from 1415 to 1725 UTC 4 September,an inner spiral rainband that originated from a large convective area also had a VRW-like outward propagation,which is probably due to a vertical tilt of *** study suggests a potential link between convection sensitive GOES imager observations and hurricane dynamics.

关键词： Geostationary Operational Environmental Satellite(GOES)imager brightness temperature(TB) hurricane vortex Rossby wave(VRW)

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Stylometry-driven framework for Urdu intrinsic plagiarism detection: a comprehensive analysis using machine learning, deep learning, and large language models

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Neural Computing and Applications 2025年第9期37卷 6479-6513页

作者： Manzoor, Muhammad Faraz Farooq, Muhammad Shoaib Abid, Adnan Department of Computer Science University of Management and Technology Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the passages have been written by a single author;(2) extrinsic: where a suspicious document is compared with a given set of source documents to figure out sentences or phrases which appear in both documents. In the pursuit of advancing intrinsic plagiarism detection, this study addresses the critical challenge of intrinsic plagiarism detection in Urdu texts, a language with limited resources for comprehensive language models. Acknowledging the absence of sophisticated large language models (LLMs) tailored for Urdu language, this study explores the application of various machine learning, deep learning, and language models in a novel framework. A set of 43 stylometry features at six granularity levels was meticulously curated, capturing linguistic patterns indicative of plagiarism. The selected models include traditional machine learning approaches such as logistic regression, decision trees, SVM, KNN, Naive Bayes, gradient boosting and voting classifier, deep learning approaches: GRU, BiLSTM, CNN, LSTM, MLP, and large language models: BERT and GPT-2. This research systematically categorizes these features and evaluates their effectiveness, addressing the inherent challenges posed by the limited availability of Urdu-specific language models. Two distinct experiments were conducted to evaluate the impact of the proposed features on classification accuracy. In experiment one, the entire dataset was utilized for classification into intrinsic plagiarized and non-plagiarized documents. Experiment two categorized the dataset into three types based on topics: moral lessons, national celebrities, and national events. Both experiments are thoroughly evaluated through, a fivefold cross-validation analysis. The results show that the random forest classifier achieved an ex

关键词： Deep learning

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Towards Optimization and Assessment of the Chinese New-generation VLBI Network for Earth Orientation Parameters Monitoring

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Research in Astronomy and Astrophysics 2025年第2期25卷 256-268页

作者： Hao Xiang Zhong-Kai Zhang Lan Du Ze-Jun Liu Xu-Qiao Wang College of Geospatial Information Information Engineering University Henan Industrial Technology Academy of Spatio-Temporal Big Data College of Geography and Environmental Science Henan University

The Earth Orientation Parameters(EOP) provide a time-varying transition relationship between the International Terrestrial Reference Frame and the International Celestial Reference Frame. To support deep space exploration and the Beidou Navigation Satellite System, the Chinese New-generation Very Long Baseline Interferometry Network(CNVN) is under construction for independent monitoring of the EOP. This paper evaluates the performance of existing 4-antenna CNVN through a batch generated observation schedules followed by extensive Monte Carlo simulations. The optimal positions of the fifth and sixth antennas of CNVN are found from 24hypothetical antenna positions uniformly distributed in China. In this process, the weighted parameters are optimized, which not only reduce the possibility of large error of EOP estimation accuracy due to unreasonable combination, but also greatly reduce the calculation cost.

关键词： methods: analytical techniques: interferometric reference systems

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Partial Label Learning with Noisy Labels

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Annals of data science 2025年第1期12卷 199-212页

作者： Zhao, Pan Tang, Long Pan, Zhigeng School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing210044 China Research Institute of Talent Big Data Nanjing University of Information Science & Technology Nanjing210044 China

Partial label learning (PLL) is a particular problem setting within weakly supervised learning. In PLL, each sample corresponds to a candidate label set in which only one label is true. However, in some practical application scenarios, the emergence of label noise can make some candidate sets lose their true labels, leading to a decline in model performance. In this work, a robust training strategy for PLL, derived from the joint training with co-regularization (JoCoR), is proposed to address this issue in PLL. Specifically, the proposed approach constructs two separate PLL models and a joint loss. The joint loss consists of not only two PLL losses but also a co-regularization term measuring the disagreement of the two models. By automatically selecting samples with small joint loss and using them to update the two models, our proposed approach is able to filter more and more suspected samples with noise candidate label sets. Gradually, the robustness of the PLL models to label noise strengthens due to the reduced disagreement of the two models. Experiments are conducted on two state-of-the-art PLL models using benchmark datasets under various noise levels. The results show that the proposed method can effectively stabilize the training process and reduce the model's overfitting to noisy candidate label sets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Phase locked loops

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A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

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Software defect density prediction using grey system theory and fuzzy logic

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Soft Computing 2024年第21期28卷 12897-12916页

作者： Azzeh, Mohammad Elsheikh, Yousef Alqasrawi, Yousef Department of Data Science Princess Sumaya University for Technology Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

Defect Density (DD) is a cornerstone metric in software quality assessment, influencing decisions across quality planning, testing strategies, and resource allocation. However, inherent uncertainties within software module data significantly impede accurate prediction of DD. This paper proposes a novel predictive framework that leverages the strengths of grey system theory and fuzzy logic to address data imprecision in software defect measurement. Our approach introduces a fuzzy grey relational similarity metric that capitalizes on the benefits of both fuzzy logic membership functions and grey relational analysis for similarity assessment. We perform a rigorous evaluation comparing our model with eight established machine learning algorithms across 28 publicly available software datasets. The proposed framework demonstrates superior performance, particularly in scenarios characterized by high sparsity levels in the DD variable. We further analyze the model's efficacy across varying data sparsity levels, categorizing datasets into four groups based on sparsity ratios. Our findings reveal the model's dominance over alternative prediction methods in datasets exhibiting high and very high sparsity. Notably, traditional techniques like multi-linear regression and multi-layer perceptron exhibit limitations in handling such challenges within this specific problem domain and data landscape. Conversely, ensemble learning techniques emerge as viable alternatives for datasets with lower sparsity levels. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： System theory

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IQABC-Based Hybrid Deployment Algorithm for Mobile Robotic Agents Providing Network Coverage

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Tsinghua science and technology 2024年第2期29卷 589-604页

作者： Shuang Xu Xiaojie Liu Dengao Li Jumin Zhao College of Computer Science and Technology(College of Data Science) Taiyuan University of TechnologyTaiyuan 030024China Pengcheng Laboratory Shenzhen 518055China College of Information and Computer Taiyuan University of TechnologyTaiyuan 030024China

Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target ***,a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users,while considering the mobility of on-ground *** this paper,to solve this issue,we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage ***,we propose a hybrid deployment algorithm based on the improved quick artificial bee *** algorithm is composed of a centralized deployment algorithm and a distributed *** proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically *** results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.

关键词： wireless robotic networks network coverage deployment algorithm improved quick artificial bee colony

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