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Price Prediction of Seasonal Items Using Time Series Analysis

Computer Systems science & Engineering 2023年第7期46卷 445-460页

作者： Ahmed Salah Mahmoud Bekhit Esraa Eldesouky Ahmed Ali Ahmed Fathalla Faculty of Computers and Informatics Zagazig UniversitySharkeya44523Egypt Information Technology Department the University of Technology and Applied SciencesIbriOman School of Electrical and Data Engineering University of Technology SydneySydney2007Australia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Computer Science Department Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Higher Future Institute for Specialized Technological Studies Cairo3044Egypt Department of Mathematics Faculty of ScienceSuez Canal UniversityIsmailia41522Egypt

The price prediction task is a well-studied problem due to its impact on the business *** are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal *** lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series *** proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based ***,this research tuned a set of well-known predictive models on a real-life *** models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear ***,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are ***,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined *** obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

关键词： Deep learning price prediction seasonal goods time series analysis

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Digital Twin System for Smart Campuses

Digital Twin System for Smart Campuses

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2024 IEEE International Conference on Control science and Systems Engineering, ICCSSE 2024

作者： Wang, Huijie Cai, Yuanyuan Yue, Fabu Luo, Hao Abdipoor, Sina Goh, Say Leng School of Big Data BaoShan University Yunnan BaoShan678000 China Universiti Putra Malaysia Department of Computer Science 43400 Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Labuan87000 Malaysia

ISBN: (纸本)9798331517199

In an era where educational institutions are increasingly embracing technology to improve efficiency and enhance learning environments, the concept of smart campuses has emerged as a crucial development. The traditional approach of monitoring campus environments through video footage and manual inspections is becoming outdated as technology advances. To empower smarter campus management and enhance the educational experience, this study presents a systematic approach for smart campuses built on the foundation of digital twins. This technology creates a virtual replica of the physical campus, enabling real-time monitoring of various aspects like security, teaching, maintenance, environment, assets, and energy usage. We simulate real-world campus behavior by constructing this digital counterpart. The resulting digital twin image can be visualized through various user-friendly interfaces, including virtual reality and 3D models, empowering managers to make informed decisions for a more efficient and secure campus environment. Implementing a digital twin system in a smart campus setting demonstrates significant improvements in operational efficiency, security management, and environmental sustainability. By leveraging the power of digital twins, educational institutions can transition towards smarter, more responsive, and sustainable campus environments, ultimately enriching the educational experience for students and staff. © 2024 IEEE.

关键词： 3d modeling campus management digital twin real-time monitoring

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Co-creation and application of a framework for the de-prioritization of urban communities during insecticide-treated bed net mass campaigns for malaria prevention and control in Kwara State, Nigeria

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BMC global and public health 2025年第1期3卷 11页

作者： Ifeoma D Ozodiegwu Laurette Mhlanga Eniola A Bamgboye Adeniyi F Fagbamigbe Cyril Ademu Chukwu Okoronkwo Joshua O Akinyemi Akintayo O Ogunwale IkeOluwapo O Ajayi Beatriz Galatas Department of Health Informatics and Data Science Loyola University Chicago Health Sciences Campus Maywood IL USA. iozodiegwu@luc.edu. Department of Preventive Medicine and Institute for Global Health Northwestern University Chicago IL USA. iozodiegwu@luc.edu. Department of Health Informatics and Data Science Loyola University Chicago Health Sciences Campus Maywood IL USA. lmhlanga@luc.edu. Department of Health Informatics and Data Science Loyola University Chicago Health Sciences Campus Maywood IL USA. Epidemiology and Biostatistics Research Unit Institute for Advanced Medical Research and Training (IAMRAT) College of Medicine University of Ibadan Ibadan Oyo Nigeria. Department of Epidemiology and Medical Statistics Faculty of Public Health College of Medicine University of Ibadan Ibadan Oyo Nigeria. National Malaria Elimination Programme Abuja Nigeria. Department of Public Health College of Health Sciences Bowen University Iwo Osun Nigeria. Global Malaria Programme World Health Organization Geneva Switzerland.

BACKGROUND:Malaria continues to be a major cause of illness and death worldwide, particularly affecting children under the age of five and those living in high-burden countries like Nigeria. Long-lasting insecticidal nets (LLINs) are one of the effective interventions for malaria control and prevention. In response to funding constraints in the Global Fund Grant Cycle 7, Nigeria's National Malaria Elimination Programme (NMEP) aimed to develop an approach that maximizes the impact of limited malaria interventions by focusing on areas with the greatest need. We developed an urban LLINs distribution framework and a novel strategy, which was piloted in Ilorin, the capital of Kwara State. METHODS:A participatory action research approach, combined with abductive inquiry, was employed to co-design a framework for guiding bed net distribution. The final framework consisted of three phases: planning, data review and co-decision-making, and implementation. During the framework's operationalization, malaria risk scores were computed at the ward level using four key variables, including malaria case data and environmental factors, and subsequently mapped. A multistakeholder dialogue facilitated the selection of the final malaria risk maps. Additionally, data from an ongoing study were analyzed to determine whether local definitions of formal, informal, and slum settlements could inform community-level stratification of malaria risk in cities. RESULTS:Akanbi 4, a ward located in Ilorin South and Are 2, a ward in Ilorin East consistently had lower risk scores, a finding corroborated during the multistakeholder dialogue. A map combining malaria test positivity rates among children under five and the proportion of poor settlements was identified as the most accurate depiction of ward-level malaria risk. Malaria prevalence varied significantly across the categories of formal, informal, and slum settlements, resulting in specific definitions developed for Ilorin. Thirteen communities

关键词： Bed nets De-prioritization LLINs Malaria Reprioritization

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Machine learning for modelling unstructured grid data in computational physics: a review

arXiv

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arXiv 2025年

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China Centre for Health Informatics Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1E 6BT United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada School of Mechanical Medical and Process Engineering Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025, CC BY-NC-SA.

关键词： Mesh generation

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A community bridge boosting social network link prediction model 9

A community bridge boosting social network link prediction m...

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9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

作者： Gao, Fei Musial, Katarzyna Gabrys, Bogdan Department of Informatics Faculty of Natural and Mathematical Sciences King’s College London LondonWC2R 2LS United Kingdom Data Science Institute Faculty of Science and Technology Bournemouth University PooleBH12 5BB United Kingdom

ISBN: (纸本)9781450349932

Link prediction in social networks is a very challenging research problem. The majority of existing approaches are based on the assumption that a given network evolves following a single phenomenon, e.g."rich get richer" or"friend of my friend is my friend". However, dynamics of network dynamic changes over time and different parts of the network evolve in different manner. Because of that, we hypothesise that the prediction accuracy can be improved by providing different treatment to different nodes and links. Building on that assumption, we propose a Community Bridge Boosting Prediction Model (CBBPM) that treats certain bridge nodes differently depending on their structural position. For such bridge nodes their similarity score obtained using traditional link-based prediction methods is boosted. By doing so the importance of these nodes is increased and at the same time ensuring that the CBBPM can be used with any existing link prediction method. Our experimental results show that such bridge node similarity boosting mechanism can improve the accuracy of traditional link prediction methods. © 2017 Association for Computing Machinery.

关键词： Forecasting

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Extremely Weak Feedback Method for Controlling Chaotic Resonance

Extremely Weak Feedback Method for Controlling Chaotic Reson...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Iinuma, Takahiro Ebato, Yudai Nobukawa, Sou Tran, Anh Tu Wagatsuma, Nobuhiko Inagaki, Keiichiro Doho, Hirotaka Yamanishi, Teruya Nishimura, Haruhiko Graduate School of Information and Computer Science Chiba Institute of Technology Chiba Japan Chiba Institute of Technology Department of Computer Science Japan Toho University Faculty of Science Department of Information Science Chiba Japan Chubu University Department of Artificial Intelligence and Robotics Aichi Japan Kouchi University Faculty of Education Teacher Training Division Kochi Japan Osaka Seikei University Faculty of Data Science Osaka Japan Yamato University Faculty of Informatics Osaka Japan

ISBN: (纸本)9798350337020

Chaotic resonance, resembling stochastic resonance, is induced by internal fluctuations (i.e., chaos). This phenomenon has been observed in numerous systems. The most representative form of chaotic resonance is the synchronization with respect to weak applied signals under chaos-chaos intermittency, where a chaotic orbit moves among multiple attractors. Chaotic resonance exhibits higher sensitivity than stochastic resonance, but its engineering applications are limited by concerns such as the requirement to adjust the state of chaos by internal system parameters to induce chaotic resonance rather than the external noise strength. However, achieving this adjustment is challenging, especially in biological systems. Therefore, to handle this limitation, we proposed a novel double-Gaussian-filtered reduced region of orbit (RRO) method (called the DG-RRO method) for obtaining perturbed feedback signals lower than the conventional RRO method. This DG-RRO feedback signal is determined by the inverse sign of the map function and double-Gaussian filters around the local maximum/minimum values of the map. Because of its fine local specification, the DG-RRO feedback signal induces a chaotic resonance by one-third the feedback strength of the conventional signal. This method may pave the way for using chaotic resonance in engineering applications. © 2023 IEEE.

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Need Analysis of a Rewarding System Based on Design Thinking in Respect of Waste Sorting Behavior : Case Study: Saensook City

Need Analysis of a Rewarding System Based on Design Thinking...

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International Conference on Information Technology (INCIT)

作者： Benchaporn Jantarakongkul Kanuengnij Kubola Thatsanee Charoenporn Prawit Boonmee Prajaks Jitngernmadan Faculty of Informatics Burapha University Chon Buri Thailand Faculty of Data Science Musashino University Tokyo Japan

Designing and developing a sustainable rewarding system needs users’ involvement, especially when it comes to a public information system used by a community. In Saensook municipality, a solid waste management is one of the most important problems. We attempted to design and develop a sustainable rewarding system to encourage the people both residents and visitors (tourists and excursionists) to separate their own produced solid waste. This rewarding system is based on Design Thinking framework. In this stage, we did the need analysis and present the key factors affecting design and development of such a sustainable rewarding system for solid waste separation.

关键词： Waste materials Behavioral sciences Information technology Sorting Information systems

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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A Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction 21

A Comparative Analysis of Machine Learning Techniques for He...

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21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Alqushaibi, Alawi Ragab, Mohammed Gamal Sumiea, Ebrahim Hamid Al-Selwi, Safwan Mahmood Hasan, Mohd Hilmi Abdulkadir, Said Jadid Alhussian, Hitham Al-Asbahi, Mahmoud Mustafa Universiti Teknologi PETRONAS Computer and Information Sciences Perak Malaysia Universiti Teknologi PETRONAS Centre for Research in Data Science Perak Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Terengganu Malaysia

ISBN: (纸本)9798350318821

Detection of heart disease is critical, as it is a common and serious health condition. Predicting its occurrence is vital in the healthcare domain, and machine learning techniques are increasingly being utilized to accurately identify patients at risk. To evaluate how well machine learning models can effectively predict heart disease, we used a pre-existing dataset from the UCI repository's Cleveland database. Though the dataset originally contained 303 records and 76 attributes, we followed the literature by narrowing the focus to 14 relevant attributes in order to improve the precision of various algorithmic approaches. Our study employed five machine learning classifiers, namely KNearest Neighbors (KNN), CatBoost Classifier (CBC), Decision Tree Classifier (DT), XGBClassifier (XGBC), and Support Vector Classifier (SVM). We evaluated their performance using different metrics like accuracy, precision, AUC, and F1 score on the heart disease dataset. We found that the CatBoost Classifier had the best performance, achieving high accuracy and AUC with 0.88% and 0.94% values, respectively. This suggests that machine learning algorithms could aid in identifying heart disease at an early stage and have significant implications for improving patient outcomes in healthcare. © 2023 IEEE.

关键词： Forecasting

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Multimodal E-Commerce Product Classification Using Hierarchical Fusion 2

Multimodal E-Commerce Product Classification Using Hierarchi...

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Tashu, Tsegaye Misikir Fattouh, Sara Kiss, Peter Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (Camem-BERT and FlauBERT) and visual data (SE-ResNeXt-50), using simple fusion techniques. The proposed method significantly outperformed the performance of the unimodal models, as well as the reported performance of similar models on our specific task. We made experiments with multiple fusing techniques, and found, that the best preforming technique to combine the individual embedding of the unimodal network is based on the combination of concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems. © 2022 IEEE.

关键词： Neural network models

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