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Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky board images and an improved Cascade R-CNN

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Journal of Integrative Agriculture 2025年第1期24卷 220-234页

作者： Yufan Gao Fei Yin Chen Hong Xiangfu Chen Hang Deng Yongjian Liu Zhenyu Li Qing Yao School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China Collaborative Innovation Center Guangdong Academy of Agricultural SciencesGuangzhou 510640China

Cruciferous vegetables are important edible vegetable ***,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific *** yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow *** achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a *** the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every *** pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model *** solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop *** algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck *** results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of *** with other algorithms,our algorithm has a significant advantage in its ability to detect small target *** accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was *** algorithm performed well and achieved

关键词： vegetable pests yellow sticky boards intelligent monitoring system deep learning pest detection

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Short-Term Rolling Prediction of Tropical Cyclone Intensity Based on Multi-Task Learning with Fusion of Deviation-Angle Variance and Satellite Imagery

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Advances in Atmospheric sciences 2025年第1期42卷 111-128页

作者： Wei TIAN Ping SONG Yuanyuan CHEN Yonghong ZHANG Liguang WU Haikun ZHAO Kenny Thiam Choy LIM KAM SIAN Chunyi XIANG School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Nanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Fudan University Shanghai 200433China Pacific Typhoon Research Center Nanjing University of Information Science and TechnologyNanjing 210044China Wuxi University Wuxi 214100China National Meteorological Center Beijing 100081China

Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and ***,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging *** present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep ***,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting ***,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and *** TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial *** real-time intensity estimation,multi-task learning acts as an implicit time-series *** model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity *** multiple tasks are correlated,the loss function of 12 h and 24 h are *** testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.

关键词： tropical cyclone intensity structure rolling prediction multi-task

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Fine-Tuning a Biased Model for Improving Fairness

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IEEE Transactions on Big Data 2025年第3期11卷 1397-1410页

作者： Chen, Huiqiang Zhu, Tianqing Liu, Bo Zhou, Wanlei Yu, Philip S. University of Technology Sydney Center for Cyber Security and Privacy School of Computer Science SydneyNSW2007 Australia City University of Macau Faculty of Data Science 999078 China University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

Fairness has emerged as a crucial concern in machine learning since biased models would generate dissimilar predictions for different groups, perpetuating social inequalities. Although numerous techniques have been proposed to address the fairness issue in machine learning, most rely on incorporating fairness constraints during the training phase, rendering them ineffective once the model is deployed. This paper explores the potential of fine-tuning biased models to enhance fairness, particularly suitable for scenarios where retraining the model is not feasible. Our approach is rooted in an empirical analysis of the distribution of bias within a biased model, and we fine-tune the model parameter in a limited scope so that the performance of the original model can be maintained. We first observe that fine-tuning a biased model leads to deviations from its initial state, with deep layers undergoing the most significant changes. We then design and apply a bias-discovery algorithm, revealing that bias predominantly resides in the model’s deep layers. Based on these observations, we propose a straightforward yet highly effective method for debiasing the model: fine-tuning the classification head. We conduct a thorough theoretical analysis to justify the proposed method and provide guidance for fine-tuning. Furthermore, we experimentally validate our method on tabular and image datasets using four networks (CNN, AlexNet, VGG-11, and ResNet-18). © 2015 IEEE.

关键词： Adversarial machine learning

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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification

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Neutrosophic Sets and Systems 2025年 82卷 781-799页

作者： Abdellah, Mohamed Refaat Oudah, Hasan H. Badawy, Ahmed Mohamed Ahmed Mohamed, Abdelfattah Abdelfattah M. Bedier, Shady Ahmed Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Cybersecurity Department Faculty of Computers and Artificial Intelligence Helwan University Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Malware detection is one of the critical tasks of cybersecurity, especially considering the growing popularity of mobile devices. The integrity and security of mobile ecosystems rely on the capacity to identify malware quickly and precisely, as security hacks against these devices become increasingly frequent. Due to the requirement for massive volumes of data aggregation, traditional centralized machine learning (ML) techniques for malware detection face challenges with data sharing, computational complexity, and privacy. This research addresses these challenges by proposing a novel model, called "CNN-Fed", which is based on Convolutional Neural Networks (CNN) with Federated Learning (Fed). The main goal of this work is to create a global classifier for Android malware detection that is highly accurate and does not require centralised data aggregation. Using four benchmark datasets—Drebin, Malgenome, Kronodroid, and Tuandromd—the CNN-Fed model is trained across many clients in a federated setting under the suggested architecture. Then we use multi-criteria decision-making (MCDM) methodology to evaluate the number of clients and number of rounds. We use two MCDM methods such as Entropy methodology to compute the criteria weights and the CoCoSo methodology to rank the alternatives. We use the interval-valued neutrosophic sets (IVNSs) to deal with uncertainty and vague information. We use five criteria such as accuracy, F1-score, precision, recall, and FP and six of alternatives refer to the number of clients and number of rounds. The results show the best alternative has a larger number of clients and rounds. © 2025, University of New Mexico. All rights reserved.

关键词： Android malware

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Real-Time On-Site Ground-Motion Prediction Using ResNet

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Seismological Research Letters 2025年第3期96卷 1834-1845页

作者： Yang, Tzu-Yi Chen, Da-Yi Chin, Tai-Lin Mobile Computing and Networking Lab Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Seismological Center of Central Weather Administration Taipei Taiwan

On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023. © Seismological Society of America.

关键词： Convolutional neural networks

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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Quantum Run-length Encoding: Optimizing Data Compression on Quantum computers with Exponential Resource Efficiency

Quantum Run-length Encoding: Optimizing Data Compression on ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Jiale Che, Xilong Jin, Shiyong Pan, Kaifan Peng, Shun Hu, Juncheng College of Computer Science and Technology Jilin University Changchun China Computer Network Information Center University of Chinese Academy of Science Beijing China

ISBN: (纸本)9798350368741

Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE's application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM's superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware. © 2025 IEEE.

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Bridging the Bandwidth Gap: A Mixed Band Telephonic Urdu ASR Approach with Domain Adaptation for Banking Applications 1

Bridging the Bandwidth Gap: A Mixed Band Telephonic Urdu ASR...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Khalid, Ayesha Adeeba, Farah Sehar, Najm Ul Hussain, Sarmad Center for Language Engineering Al-Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Department of Computer Science University of Engineering and Technology New Campus Pakistan

ISBN: (纸本)9798891762015

The accuracy of Automatic Speech Recognition (ASR) systems is influenced by the quality and context of speech signals, particularly in telephonic environments prone to errors like channel drops and noise, leading to higher Word Error Rates (WER). This paper presents the development of a large vocabulary Urdu ASR system for telephonic speech, based on a corpus of 445 speakers from diverse domains. The corpus, annotated at the sentence level, is used to train and evaluate GMM-HMM and chain Time-Delay Neural Network (TDNN) models on a 10-hour test set. Results show that the TDNN model outperforms GMM-HMM. Mixing narrowband and wideband speech further reduces WER. The test sets are also evaluated for the pre-trained model Whisper for performance comparison. Additionally, system adaptation for the banking domain with a specialized lexicon and language model demonstrates the system’s potential for domain-specific applications. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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