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Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model

Gondwana Research 2024年

作者： Kumar Lilhore, Umesh Simaiya, Sarita Dalal, Surjeet Radulescu, Magdalena Balsalobre-Lorente, Daniel Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Amity University Gurugram Haryana India Institute of Doctoral and Post-Doctoral Studies University Lucian Blaga of Sibiu Sibiu Romania UNEC Research Methods Application Center Azerbaijan State University of Economics (UNEC) Baku 1001 Istiqlaliyyat Str. 6 Azerbaijan Department of Applied Economics I University Castilla-La Mancha Spain Department of Management Faculty of Economics and Management Czech University of Life Sciences Prague Prague 16500 Czech Republic Economic Research Center (WCERC) Western Caspian University Baku Azerbaijan

The significance of waste disposal, classification, and monitoring has dramatically increased due to the increase in industrial development and the progress of intelligent urbanization. Since the last few decades, the utilization of Deep learning techniques has grown increasingly in waste management research. The efficiency of a waste reuse and recycling process relies on its capacity to restore resources to their original state, thereby minimizing pollution and promoting an ecologically sustainable framework. Selecting the optimal deep-learning method for classifying and predicting waste is challenging and time-consuming. This paper proposed intelligent garbage categorization using Bidirectional Long Short-Term Memory (Bi-LSTM) and CNN-based transfer learning to improve environmental sustainability. Organic and recyclable garbage are separated. To simplify trash categorization, a hybrid model combines TL-based CNN and Bi-LSTM. This study extensively examined the suggested technique with numerous CNN computational methods, including VGG-19, ResNet-34, AlexNet, ResNet-50, and VGG-16, using the ’TrashNet Waste’ database. Key findings show that our hybrid model outperforms existing models. Our classification accuracy is 96.78 %, 5.27 % higher than the best model. Our model also reduces misclassification by 7.25 %, proving its reliability. This comprehensive examination examined the computer models’ trash classification performance and provided specific viewpoints. The results explain each technique's pros and cons and show how useful they are in real-world circumstances. Waste classification is practical and sophisticated with a hybrid model. The effectiveness and cleverness of this model improve sustainable environmental practices. The proposed method's excellent performance suggests its seamless integration into practical waste management solutions. © 2024 International Association for Gondwana Research

关键词： Convolutional neural network (CNN) Deep learning Smart Waste Management Transfer Learning Waste classification

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Development of software for calculating the results of medicinal substances bioequivalence 2

Development of software for calculating the results of medic...

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2nd International Scientific Conference on Metrological Support of Innovative Technologies, ICMSIT II-2021

作者： Shalaginova, N.V. Mikhlyakova, E.A. Gribkov, D.N. Mikhailina, A.V. Lebedeva, E.V. Department of Applied Mathematics and Computer Science Vyatka State University Kirov Russia Secondary School with In-depth Study of Individual Subjects of Stulovo Kirov Russia Vyatka State University Kirov Russia Department of Informatics and Records Management Orel State Institute of Culture Orel Russia Department of Economics and Management Orel State Institute of Culture Orel Russia Department of Algebra and Mathematical Methods in Economy of Orel State University Named after I.S. Turgenev Orel Russia

International organizations have adopted and recognized drug standards, which should be guided by developers in the field of pharmacology. In practice, there is a problem: how to determine whether a reproduced drug sample really satisfies the world standard for its pharmacological action. It is required to implement an algorithm for checking and confirming the bioequivalence of drugs. The purpose of the study is to automate the verification of standards using an appropriate algorithm, i.e. to develop an application for calculating the results of medicinal substances bioequivalence. The methodology is based on the assumption that the identity in the sense of the created effect of the pharmacokinetic curves of the drug concentration in the blood versus time for the test drug and the standard means their therapeutic equivalence. Research methods include Python programming, use of libraries for data visualization;Visual Studio is an application development environment. The desired algorithm and requirements for the developed application are formulated in the results of the study, based on the study of theoretical issues of determining the medicinal substances bioequivalence. Finally, conclusions about the efficiency of the software are made and options for its improvement are proposed. © Published under licence by IOP Publishing Ltd.

关键词： Data visualization

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Model for Analysis of High-Frequency Modulated Random Signals

Model for Analysis of High-Frequency Modulated Random Signal...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Ihor Javorskyj Roman Yuzefovych Oleh Lychak Pavlo Semenov Roman Sliepko Yulia Romanyshyn Department of Methods and Facilities for Acquisition and Processing Diagnostic Signals Karpenko Physico-mechanical Institute of NAS of Ukraine Lviv Ukraine Institute of Telecommunication and Computer Sciences Bydgoszcz University of Science and Technology Bydgoszcz Poland Department of Applied Mathematics Lviv Polytechnic National University Lviv Ukraine Department of Hoisting and Transport Machines and Engineering of Port Technological Equipment Odesa National Maritime University Odesa Ukraine Department of Records Management and Information Activity Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine

The use of the Hilbert transform for the analysis of periodically non-stationary random signals (PNRSs), whose carrier harmonics are modulated by jointly stationary high-frequency random processes is discussed. A representation of the signal in the form of a superposition of high-frequency components is obtained and it is shown that these components are jointly periodically non-stationary random processes. The properties of the band-pass filtered signals are examined, and it is shown that band-pass filtering can reduce both the number of signal variance cyclic harmonics and their amplitudes. We show that it is possible to extract the quadratures of narrow-band high-frequency modulation processes using the Hilbert transform.

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MetAug: Contrastive Learning via Meta Feature Augmentation

arXiv

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Xiong, Hui Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Guangdong China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Guangzhou China Department of Computer Science Engineering The Hong Kong University of Science and Technology Hong Kong

What matters for contrastive learning? We argue that contrastive learning heavily relies on informative features, or "hard" (positive or negative) features. Early works include more informative features by applying complex data augmentations and large batch size or memory bank, and recent works design elaborate sampling approaches to explore informative features. The key challenge toward exploring such features is that the source multi-view data is generated by applying random data augmentations, making it infeasible to always add useful information in the augmented data. Consequently, the informativeness of features learned from such augmented data is limited. In response, we propose to directly augment the features in latent space, thereby learning discriminative representations without a large amount of input data. We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder. However, insufficient input data may lead the encoder to learn collapsed features and therefore malfunction the augmentation generator. A new margin-injected regularization is further added in the objective function to avoid the encoder learning a degenerate mapping. To contrast all features in one gradient back-propagation step, we adopt the proposed optimization-driven unified contrastive loss instead of the conventional contrastive loss. Empirically, our method achieves state-of-the-art results on several benchmark datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Backpropagation

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Interventional Contrastive Learning with Meta Semantic Regularizer

arXiv

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arXiv 2022年

作者： Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Su, Bing Xiong, Hui Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Guangdong China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner. Although the prevailing CL model has achieved great progress, in this paper, we uncover an ever-overlooked phenomenon: When the CL model is trained with full images, the performance tested in full images is better than that in foreground areas;when the CL model is trained with foreground areas, the performance tested in full images is worse than that in foreground areas. This observation reveals that backgrounds in images may interfere with the model learning semantic information and their influence has not been fully eliminated. To tackle this issue, we build a Structural Causal Model (SCM) to model the background as a confounder. We propose a backdoor adjustment-based regularization method, namely Interventional Contrastive Learning with Meta Semantic Regularizer (ICL-MSR), to perform causal intervention towards the proposed SCM. ICL-MSR can be incorporated into any existing CL methods to alleviate background distractions from representation learning. Theoretically, we prove that ICL-MSR achieves a tighter error bound. Empirically, our experiments on multiple benchmark datasets demonstrate that ICL-MSR is able to improve the performances of different state-of-the-art CL methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Satellite-based InSAR Geodesy and Collocation with GNSS

Satellite-based InSAR Geodesy and Collocation with GNSS

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2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Papco, Juraj Bakon, Matus Kubica, Lukas Belicova, Gabika Droscak, Branislav Ferianc, Martin Rovnak, Martin Ruiz, Antonio M. Sousa, Joaquim J. Department of Theoretical Geodesy and Geoinformatics Faculty of Civil Engineering Slovak University of Technology in Bratislava Radlinskeho 11 Bratislava813 62 Slovakia Insar.sk Ltd Konstantinova 3 Presov080 01 Slovakia Department of Finance Accounting and Mathematical Methods Faculty of Management and Business University of Presov Konstantinova 16 Presov080 01 Slovakia Department of Engineering School of Sciences and Technology Universidade de Tras-os-Montes e Alto Douro Quinta de Prados Vila Real5000-801 Portugal Geodetic and Cartographic Institute Bratislava Chlumeckeho 4 Bratislava827 45 Slovakia Department of Cartographic Geodetic and Photogrammetry Engineering University of Jaen Campus Las Lagunillas s/n Jaen23071 Spain University of Jaen Campus Las Lagunillas s/n Jaen23071 Spain Research Group RNM-282 Microgeodesia Jaen University of Jaen Campus Las Lagunillas s/n Jaen23071 Spain Institute for Systems and Computer Engineering Technology and Science INESC-TEC Porto4200-465 Portugal

Satellite radar interferometry (InSAR) enables large-scale monitoring of displacement with precision akin to ground-based methods. Using measurements from Sentinel-1 satellites, persistent scatterer networks cover areas up to hundreds of kilometers in size. One of the current challenges for the InSAR community is to tie these networks into a current and country-specific global terrestrial reference system through collocation with other geodetic techniques, especially Global Navigation Satellite Systems (GNSS). In this work, an optimal procedure for assessing a suitable collocation properties for InSAR and GNSS techniques is discussed, along with the proposal for the methodology to determine the spatial relationship between the reference points of both techniques. The study analyzes three collocation stations in Slovakia using their radar backscattering characteristics. InSAR achieves an intrinsic accuracy of up to 1 mm but has uncertainties several orders of magnitude larger in 3D space. A collocation of InSAR measurements with GNSS can tie relative InSAR displacement estimates to an absolute reference frame. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Geodesy

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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Fast Quaternion Product Units for Learning Disentangled Representations in SO(3)

TechRxiv

引用

TechRxiv 2022年

作者： Qin, Shaofei Zhang, Xuan Xu, Hongteng Xu, Yi MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Computer Science & Engineering Texas A&M University College StationTX77843 United States Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Real-world 3D structured data like point clouds and skeletons often can be represented as data in a 3D rotation group (denoted as SO(3)). However, most existing neural networks are tailored for the data in the Euclidean space, which makes the 3D rotation data not closed under their algebraic operations and leads to sub-optimal performance in 3D-related learning tasks. To resolve the issues caused by the above mismatching between data and model, we propose a novel non-real neuron model called quaternion product unit (QPU) to represent data on 3D rotation groups. The proposed QPU leverages quaternion algebra and the law of the 3D rotation group, representing 3D rotation data as quaternions and merging them via a weighted chain of Hamilton products. We demonstrate that the QPU mathematically maintains the SO(3) structure of the 3D rotation data during the inference process and disentangles the 3D representations into "rotation-invariant" features and "rotation-equivariant" features, respectively. Moreover, we design a fast QPU to accelerate the computation of QPU. The fast QPU applies a tree-structured data indexing process, and accordingly, leverages the power of parallel computing, which reduces the computational complexity of QPU in a single thread from O(N) to O(log N). Taking the fast QPU as a basic module, we develop a series of quaternion neural networks (QNNs), including quaternion multi-layer perceptron (QMLP), quaternion message passing (QMP), and so on. In addition, we make the QNNs compatible with conventional real-valued neural networks and applicable for both skeletons and point clouds. Experiments on synthetic and real-world 3D tasks show that the QNNs based on our fast QPUs are superior to state-of-the-art real-valued models, especially in the scenarios requiring the robustness to random rotations. © 2022, CC BY.

关键词： 3D modeling

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Accuracy of the TOPSIS Method with Different Input Data

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Procedia computer science 2022年 207卷 4574-4583页

作者： Robert Król Jakub Więckowski Jarosław Wątróbski Doctoral School of University of Szczecin ul. Mickiewicza 16 70-383 Szczecin Poland Institute of Management University of Szczecin ul. Cukrowa 8 71-004 Szczecin PolandS Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland

Using fuzzy logic in multi-criteria problems allows real-world problems to be modeled with greater accuracy. In addition, it is possible to solve problems in which all data are incomplete. The difficulty may be the choice of an appropriate technique leading to a ranking calculation. methods fully integrated with fuzzy logic can be used. It is also possible to convert fuzzy data to crisp values and then apply traditional MCDA methods. In this paper, the performance of the TOPSIS method in a crisp and fuzzy environment was analyzed. The approaches of operating fully on uncertain data when calculating preference values were compared with converting data to crisp values using selected membership functions. The research has shown that significant differences and discrepancies in how the alternatives are classified are noticeable in the rankings.

关键词： MCDA TOPSIS Fuzzy TOPSIS Comparative analysis Power plants

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Towards robust results in Multi-Criteria Decision Analysis: ranking reversal free methods case study

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Procedia computer science 2022年 207卷 4584-4592页

作者： Jakub Więckowski Robert Król Jarosław Wątróbski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland Doctoral School of University of Szczecin ul. Mickiewicza 16 70-383 Szczecin Poland Institute of Management University of Szczecin ul. Cukrowa 8 71-004 Szczecin Poland

The practical capabilities of decision support systems are beneficial in many complex problems. They aim to equip the decision-maker with knowledge about the preferred choices from the set under consideration. Often, there are minor differences between the alternatives analyzed, making it necessary to determine whether changes in the parameters characterizing them can influence their final position. For this purpose, a sensitivity analysis of the solutions can be used to examine the resistance of the results to changes. In this paper, we address the problem of performing the sensitivity analysis using MCDA methods. Most studies focus on testing the sensitivity of rankings of popular methods. However, their main disadvantage is their susceptibility to the phenomenon of ranking reversal. It may distort the results obtained in a sensitivity analysis. Therefore, it is worth using methods resistant to the ranking reversal phenomenon for this purpose. The SPOTIS and COMET methods were used in a theoretical multi-criteria problem, where the calculated results were then subjected to sensitivity analysis. The study showed that the SPOTIS method tends to be more robust to the computed results.

关键词： MCDA SPOTIS COMET Sensitivity Analysis

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