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Group-wise co-salient object detection via multi-view self-labeling novel class discovery

Frontiers of Computer science 2024年第2期18卷 259-261页

作者： Yang WU Gang DONG Lingyan LIANG Yaqian ZHAO Kaihua ZHANG Inspur Electronic Information Industry Co. Ltd.Beijing 100080China Jiangsu Key Laboratory of Big Data Analysis Technology and Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing 210044China

1 Introduction Co-salient object detection(CoSOD)aims to extract the salient object(s)that are common across a group of relevant images[1].Group-wise clue plays a crucial role in accurately predicting the co-salient ***,numerous groupwise deep models have been proposed by exploring consistency across images in unsupervised clustering manners[2-4]or the semantic connections guidance information[5].

关键词： consistency Group semantic

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Photovoltaic Power Forecasting with Weather Conditioned Attention Mechanism

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big data Mining and Analytics 2025年第2期8卷 326-345页

作者： Xuetao Jiang Yuchun Gou Meiyu Jiang Lihui Luo Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China Gansu Provincial Industry Technology Center of Intelligent Equipment&Big Data for Disaster Prevention Northwest Institute of Eco-Environmentand ResourcesChinese Academy of SciencesLanzhou 730000China

Accurate Photovoltaic(PV)generation forecasts can reduce power redeploy from the grid,thus increasing the supplier’s profit in the day-ahead electricity ***,the PV process is affected differently by various factors under different weather conditions,resulting in significantly different energy output *** this context,this paper proposes a day-ahead PV power forecasting method with weather conditioned attention *** propose a Multi-Stream Attention Fusion Network(MSAFN)which utilizes an algorithm to derive the optimal decomposition algorithm for different weather *** proposed Conditional Decomposition(CD)algorithm searches for the decomposition algorithms and corresponding hyperparameters of the prediction model,aiming to achieve the optimal prediction *** MSAFN incorporates multiple attention modules to learn the energy output patterns under various weather ***,the attention modules adeptly learn patterns under diverse conditions,while simultaneously,the sharing of weights among the remaining components of the model effectively enhances prediction accuracy and facilitates a reduction in training *** compare the state-of-the-art decomposition algorithms(VMD,EEMD,MSTL,etc.)and prediction models(BPN,LSTM,XGBoost,transformer,etc.)commonly used in PV *** results show that the MSAFN model is more accurate than the models above,which has a noticeable improvement compared to other recent day-ahead PV predictions on Desert Knowledge Australia Solar Centre(DKASC)dataset.

关键词： Photovoltaic(PV)forecasting deep learning transformer Desert Knowledge Australia Solar Centre(DKASC)

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CoRE:Constrained Robustness Evaluation of Machine Learning-Based Stability Assessment for Power Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 557-559页

作者： Zhenyong Zhang David K.Y.Yau the State Key Laboratory of Public Big Data and the College of Computer Science and Technology Guizhou UniversityGuiyang 550025China IEEE the Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingapore 487372Singapore

Dear Editor,Machine learning(ML) approaches have been widely employed to enable real-time ML-based stability assessment(MLSA) of largescale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems.

关键词： enable constraints Power

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction 3

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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A multilayer network diffusion-based model for reviewer recommendation

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Chinese Physics B 2024年第3期33卷 700-717页

作者：黄羿炜徐舒琪蔡世民吕琳媛 Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Dataspace Hefei Comprehensive National Science CenterHefei 230088China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Cyber Science and Technology University of Science and Technology of ChinaHefei 230026China

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy *** systems are powerful tools developed in computer science and information science to deal with this ***,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform *** this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual *** network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among *** results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking *** work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

关键词： reviewer recommendation multilayer network network diffusion model recommender systems complex networks

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Drug sales forecasting in the pharmaceutical market using deep neural network algorithms

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第3期8卷 63-83页

作者： Qassrawi, Noura Azzeh, Mohammad Hijjawi, Mohammad Department of Computer Science Applied Science Private University Amman Jordan Department of Data Science Princess Sumaya University for Technology Amman Jordan

Drug sales and price forecasting have become an attractive investigation topic due to their important role in the pharmaceutical industry, A sales forecast helps every business to make better business decisions in overall business planning, budgeting, marketing, and risk management. The traditional forecasting method focuses on a conventional statistical model, which highly depends on the availability of historical sales data. However, for new drug entities, where not enough historical data is available, new methods of Machine Learning are applied. The aim of this paper is to identify an efficient Deep Neural Network algorithm suitable to forecast drug sales and pricing by applying Deep Neural Network Algorithms such as Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory, which are expected to perform well on this issue. The results are carried out to determine the efficiency of these algorithms by evaluating the performances of the models using MAE and RMSE performance metrics to identify the best algorithm for Drug Sales and Price Forecasting. The accepted accuracy should be more than 80\% of the actual value for quantity which is less than three thousand by unit and less than two dollars (USD) for price, Based on the results of the experiments Long Short Term Memory performed better than MLP and CNN for generating predictions with average Root Mean Square Error of for sales is 1.28(k) and Mean Absolute Error of about 0.85(k), and with average Root Mean Square Error for USD Prices is about 0.75, and Mean Absolute Error is about 0.44. The forecasts are then used to adjust stock levels according to the predictions. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Convolutional Neural Network Drug sales Forecasting Long Short-Term Memory Multilayer Perceptron

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Multi-modal and Multi-scale Spatial Environment Understanding for Immersive Visual Text-to-Speech 39

Multi-modal and Multi-scale Spatial Environment Understandin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Rui He, Shuwei Hu, Yifan Li, Haizhou Inner Mongolia University China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)157735897X

Visual Text-to-Speech (VTTS) aims to take the environmental image as the prompt to synthesize the reverberant speech for the spoken content. The challenge of this task lies in understanding the spatial environment from the image. Many attempts have been made to extract global spatial visual information from the RGB space of an spatial image. However, local and depth image information are crucial for understanding the spatial environment, which previous works have ignored. To address the issues, we propose a novel multi-modal and multi-scale spatial environment understanding scheme to achieve immersive VTTS, termed M2SE-VTTS. The multimodal aims to take both the RGB and Depth spaces of the spatial image to learn more comprehensive spatial information, and the multi-scale seeks to model the local and global spatial knowledge simultaneously. Specifically, we first split the RGB and Depth images into patches and adopt the Gemini-generated environment captions to guide the local spatial understanding. After that, the multi-modal and multi-scale features are integrated by the local-aware global spatial understanding. In this way, M2SE-VTTS effectively models the interactions between local and global spatial contexts in the multi-modal spatial environment. Objective and subjective evaluations suggest that our model outperforms the advanced baselines in environmental speech generation. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Multi-media cleaning system based on tip-electrode optimisation and its application in energy efficient purification 4

Multi-media cleaning system based on tip-electrode optimisat...

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4th International Conference on New Energy and Power Engineering, ICNEPE 2024

作者： Liang, Tianyou Huang, Tingkun Liang, Baoer Zhu, Guanyu Liang, Yanchun Zhu, Tianyuan Zhuhai College of Science and Technology School of Computer Science Zhuhai China Zhuhai College of Science and Technology School of Life Science Zhuhai China Zhuhai College of Science and Technology School of Big Data Science Zhuhai China

ISBN: (纸本)9798331516086

In this paper, a multi-media cleaning system based on the optimisation of the tip electrode structure is developed, which significantly improves the electrochemical reaction efficiency and realises the efficient purification of air, water and industrial waste gas by adopting the carbon nanotube material and the design of awn-shaped tip electrode. The experimental results show that the system has low energy consumption, long service life and excellent purification performance, and is suitable for a variety of environmental treatment scenarios with broad application prospects. © 2024 IEEE.

关键词： Air cleaners

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Multi-view Unsupervised Feature Selection Guided by Diversity and Consensus Structure 11th

Multi-view Unsupervised Feature Selection Guided by Diversi...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Zou, Jinlin Ma, Yingcang Yang, Xiaofei Xing, Zhiwei School of Science Xi’an Polytechnic University Xi’an710048 China Xi’an International Science and Technology Cooperation Base for Big Data Analysis and Algorithms Xi’an710048 China

ISBN: (纸本)9783031927430

The core challenge in multi-view feature selection lies in identifying discriminative features that capture both consensus and diversity information. Feature selection methods based on consensus learning have garnered significant attention due to their ability to reveal the common underlying structure across different views. To effectively capture both homogeneous and heterogeneous information across views, this paper proposes a novel multi-view unsupervised feature selection method guided by diversity and consensus structure, referred to as GDCSMuFS. The diversity structure learning across multiple views is crucial for preserving the heterogeneous information in the selected features. Building on this concept, the proposed method begins by incorporating both homogeneous and heterogeneous information. It first learns the multi-view cluster structure matrix through non-negative matrix factorization. Then, multi-view spectral analysis is employed to integrate the cluster structure matrices into a consensus pseudo-label matrix, enabling the extraction of consensus information at the label level. Subsequently, a feature selection matrix is constructed under the shared constraints of consensus labels and graph structures. Finally, sparse constraints are applied to the feature selection matrix, and the model is solved using an alternating iterative algorithm. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method, with comparative results showing its superiority over existing algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Non-negative matrix factorization

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PAB-Mamba-YOLO:VSSM assists in YOLO for aggressive behavior detection among weaned piglets

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Artificial Intelligence in Agriculture 2025年第1期15卷 52-66页

作者： Xue Xia Ning Zhang Zhibin Guan Xin Chai Shixin Ma Xiujuan Chai Tan Sun Agricultural Information Institute Chinese Academy of Agricultural SciencesBeijing 100081China Key Laboratory of Agricultural Big Data Ministry of Agriculture and Rural AffairsBeijing 100081China School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China

Aggressive behavior among piglets is considered a harmful social *** weaned piglets with intense aggressive behaviors is paramount for pig breeding *** study introduced a novel hybrid model,PAB-Mamba-YOLO,integrating the principles of Mamba and YOLO for efficient visual detection of weaned piglets'aggressive behaviors,including climbing body,nose hitting,biting tail and biting *** the proposed model,a novel CSPVSS module,which integrated the Cross Stage Partial(CSP)structure with the Visual State Space Model(VSSM),has been *** module was adeptly integrated into the Neck part of the network,where it harnessed convolutional capabilities for local feature extraction and leveraged the visual state space to reveal long-distance *** model exhibited sound performance in detecting aggressive behaviors,with an average precision(AP)of 0.976 for climbing body,0.994 for nose hitting,0.977 for biting tail and 0.994 for biting *** mean average precision(mAP)of 0.985 reflected the model's overall effectiveness in detecting all classes of aggressive *** model achieved a detection speed FPS of 69 f/s,with model complexity measured by 7.2 G floating-point operations(GFLOPs)and parameters(Params)of 2.63 *** experiments with existing prevailing models confirmed the superiority of the proposed *** work is expected to contribute a glimmer of fresh ideas and inspiration to the research field of precision breeding and behavioral analysis of animals.

关键词： Aggressive behaviors Weaned piglet Mamba YOLO Hybrid detection model

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