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Review of advanced drying techniques: a path to lower greenhouse gas emissions in agriculture

Discover Sustainability 2025年第1期6卷 1-26页

作者： Kaveh, Mohammad Abbaspour-Gilandeh, Yousef Nadimi, Mohammad Shahbazi, Feizollah Paliwal, Jitendra Department of Petroleum Engineering College of Engineering Knowledge University Erbil Iraq Department of Biosystems Engineering College of Agriculture and Natural Resources University of Mohaghegh Ardabili Ardabil Iran Department of Biosystems Engineering University of Manitoba Winnipeg Canada Department of Biosystems Engineering Lorestan University Khoramabbad Iran Department of Applied Computer Science The University of Winnipeg Winnipeg Canada

The agricultural sector has one of the largest carbon footprints among all industries due to the extensive use of fossil fuels, chemical fertilizers, and pesticides. Over the past century, agricultural mechanization has remarkably increased greenhouse gas (GHG) emissions, contributing to global warming and climate change. Among these gases, carbon dioxide (CO2) is the most abundant. Drying is a crucial and widely used method for preserving agricultural products, with broad applications in the food industry. Recent advancements in drying technology offer promising alternatives that enhance product quality, reduce energy use, and mitigate GHG emissions, thus promoting environmental sustainability. This review explores some of the most promising drying techniques that will shape the future of agricultural processes. Efficient and innovative drying of agri-food products can be achieved by hybridizing conventional techniques like hot-air, microwave, infrared, fluid bed, continuum, vacuum, and refractance window drying with pre-treatments such as ultrasound (US), pulsed electric fields (PEF), blanching, and cold plasma (CP). The combined use of these modalities can decrease GHG emissions while producing high-quality, nutritionally rich products. Our synthesis of published information also proposes research and development strategies to mitigate GHGs during the drying process.

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A Comparative Analysis of IoT Data Using Machine Learning Algorithms 15th

A Comparative Analysis of IoT Data Using Machine Learning Al...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Munrathnam, Meram Angeline Kavitha, M. Department of Computer Science and Applications School of CSA REVA University Bangalore India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.K. Valley Kadapa District Andhra Pradesh Idupulapaya 516330 India Department of Mathematics Voorhees College Vellore 632001 India

ISBN: (纸本)9783031889912

IoT (Internet of Things) is revolutionizing the commercial environment by acquiring vast amounts of data from various sources. However, processing and analyzing this data is challenging due to the large volume of IoT devices. AI-powered IoT creates intelligent machines that mimic human intelligence, aiding decision-making with minimal human input. Both individuals and professionals’ benefit from this combination, as IoT is concerned with communicating online, but AI allows machines to learn from data and experience. This study demonstrates the analysis of IoT data using machine learning algorithms and compares the accuracy of various machine learning algorithms used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： AI data internet IoT Machine Learning

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Enhancing Kidney Stone Detection in CT scans with Image Processing and the DCNN-RELM Model

Enhancing Kidney Stone Detection in CT scans with Image Proc...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Manjula Prabakaran Jesmin Zakaria S Kannan Santhiya P Rajasekar P M V Rathnamma Department of CSE- (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Electronics & Communication Engineering (ECE) NSHM Knowledge Campus Durgapur West Bengal India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy Tamilnadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India Department of CSE KLM College of Engineering for Women Kadapa Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

CT of the urinary system, abdomen, and kidneys appears to improve kidney stone screening and identification. Segmentation and categorization are essential for kidney stone identification and management. This work developed a semi-automatic kidney stone detection system using geometric concepts and image processing. The main steps include model training, feature extraction, segmentation, embossing, and preprocessing. Preprocessing involves trimming the input image to remove unnecessary kidney regions to focus on critical CT scan locations. Relief embossing enhanced image details. Segmentation accurately classified veins and blood arteries to define kidney stone boundaries. CNN-RELM was our training model. Compared to CNN and ELM models, the recommended system performed better. Its 93.87% kidney stone detection and localization accuracy prove its reliability. The semi-automatic system's reliable kidney stone detection and segmentation method may improve clinical decision-making. Combining CNN-RELM modeling and enhanced image processing could lead to medical imaging applications.

关键词： Training Image segmentation Accuracy Three-dimensional displays Computed tomography Embossing Data preprocessing Reliability Kidney Biomedical imaging

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Blockchain-Based IoMT Security: BSHS-EODL Method for Secure Data Storage and Analysis

Blockchain-Based IoMT Security: BSHS-EODL Method for Secure ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Biru Rajak M. Shailaja Mustafa Zuhaer Nayef Al-Dabagh Ali Ali Saber Mohammed Malik Bader Alazzam Ponni Valavan M Department of Computer Science Asansol Girls' College Under the Kazi Nazrul University Asansol India Department of Computer Science(DataScience) MallaReddy University Hyderabad India Department of Computer Science College of Science Knowledge University Erbil Iraq Department of Computer Engineering Al Kitab University Kirkuk Iraq Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The integration of the Internet of Medical Things (IoMT) has revolutionized healthcare by improving patient care and system efficiency. However, this progress introduces significant concerns about data security and privacy, particularly with medical data. Traditional methods like Decision Tree, Random Forest, and Naïve Bayes face limitations in scalability, accuracy for large datasets, and energy efficiency. To address these challenges, this research introduces a novel Blockchain-based IoMT framework, BSHS-EODL (Blockchain-Secured Healthcare System with Edge-Oriented Deep Learning). The framework employs Long Short-Term Memory (LSTM) networks for precise healthcare data analysis, optimized using the Grey Wolf Optimization (GWO) algorithm for hyperparameter tuning. The proposed system achieved outstanding results, including an accuracy of 99.42%, precision of 99.51%, recall rate of 99.47%, and F1 score of 99.39%, outperforming conventional methods. Moreover, BSHS-EODL demonstrated remarkable efficiency, this approach not only enhances healthcare data security and diagnostic accuracy The system is implemented using Python integrated with MATLAB.

关键词： Deep learning Accuracy Data security Scalability Memory Internet of Medical Things Long short term memory Optimization Tuning Random forests

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Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection

arXiv

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arXiv 2025年

作者： Hao, Pingting Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University PortlandOR97201 United States

In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and efficiency remains a significant question in MVML. Existing methods often extract information separately from the consistency part and the complementary part, which may result in noise due to unclear segmentation. In this paper, we propose a unified model constructed from the perspective of global-view reconstruction. Additionally, while feature selection methods can discern the importance of features, they typically overlook the uncertainty of samples, which is prevalent in realistic scenarios. To address this, we incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness. Thus, the global-view is reconstructed through the graph structure between samples, sample confidence, and the view relationship. The accurate mapping is established between the reconstructed view and the label matrix. Experimental results demonstrate the superior performance of our method on multi-view datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature Selection

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Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection

arXiv

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arXiv 2025年

作者： Pan, Hanlin Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University PortlandOR97201 United States

The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguation, previous methods mainly focus on utilizing the information inside the labels and the relationship between the labels and features. However, the information existing in the feature space is rarely considered, especially in partial multi-label scenarios where the noises is considered to be concentrated in the label space while the feature information is correct. This paper proposes a method based on latent space alignment, which uses the information mined in feature space to disambiguate in latent space through the structural consistency between labels and features. In addition, previous methods overestimate the consistency of features and labels in the latent space after convergence. We comprehensively consider the similarity of latent space projections to feature space and label space, and propose new feature selection term. This method also significantly improves the positive label identification ability of the selected features. Comprehensive experiments demonstrate the superiority of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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Generative Classifier for Domain Generalization

arXiv

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arXiv 2025年

作者： Long, Shaocong Zhou, Qianyu Li, Xiangtai Ying, Chenhao Tong, Yunhai Ma, Lizhuang Luo, Yuan Tao, Dacheng Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Nanyang Technological University Singapore Peking University Beijing China

Domain generalization (DG) aims to improve the generalizability of computer vision models toward distribution shifts. The mainstream DG methods predominantly focus on learning domain invariance across domains, however, such methods overlook the untapped potential inherent in domain-specific information. While the prevailing practice of discriminative linear classifier has been tailored to domain-invariant features, it struggles when confronted with diverse domain-specific information, e.g., intra-class shifts, that exhibits multi-modality. To address these issues, we explore the theoretical implications of relying on domain-invariant features, revealing the crucial role of domain-specific information in mitigating the target risk for DG. Drawing from these insights, we propose Generative Classifier-driven Domain Generalization (GCDG), introducing a generative paradigm for the DG classifier based on Gaussian Mixture Models (GMMs) for each class across domains. GCDG consists of three key modules: Heterogeneity Learning Classifier (HLC), Spurious Correlation Blocking (SCB), and Diverse Component Balancing (DCB). Concretely, HLC attempts to model the feature distributions and thereby capture valuable domain-specific information via GMMs. SCB identifies the neural units containing spurious correlations and perturbs them, mitigating the risk of HLC learning irrelevant spurious patterns. Meanwhile, DCB ensures a balanced contribution of components within HLC, preventing the underestimation or neglect of critical components. In this way, GCDG excels in capturing the nuances of domain-specific information characterized by diverse distributions. GCDG demonstrates the potential to reduce the target risk and encourage flat minima, improving the model’s generalizability. Extensive experiments show GCDG’s comparable performance on five DG benchmarks and one face anti-spoofing dataset, seamlessly integrating into existing DG methods with consistent improvements. Code will be avail

关键词： Discriminant analysis

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Autoencoder-LSTM Ensembles for Predictive Analysis of Financial Churn Patterns

Autoencoder-LSTM Ensembles for Predictive Analysis of Financ...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Raynukaazhakarsamy Rahul Pradhan Nadhema Ahmed Jaff G Venu Gopal Mariam Fadel Ali P. Nagaraj Department of Computer Applications Nehru Arts and Science College (Autonomous) Coimbatore India Department of Computer Engineering & Applications GLA University Mathura India Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Computer Science and Engineering (AI & ML) Geethanjali College of Engineering and Technology Telangana India Department of Radiology Techniques Uruk University Baghdad Iraq Department of AI & ML K. Ramakrishnan College of Engineering Trichy Tamilnadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

In the financial sector, customer churn is a significant problem that influences the organization’s revenue growth. Churn means that customers have left their current providers which is a negative factor for the providers or financial institutions; therefore, accurate churn prediction helps institutions to increase customer loyalty and satisfaction by improving the quality of service. In this paper, a new method of using Autoencoder and LSTM network for predicting financial churn patterns is introduced. The Autoencoder is used to reduce dimensions and for unsupervised learning acts as a pass through that allows the model to learn the latent patterns in customer behaviour and transactions. Since the LSTM has demonstrated its efficacy in analyzing temporal dependencies of a sequence in time series, it is used to predict churn by analyzing sequential customer activity. The ensemble approach combines the merits of the two models improving the predictive power and especially the stability in a situation of data scarcity and high noise levels. Experimental comparison with conventional predictive models such as decision trees and logistic regression reveal that the Autoencoder-LSTM is more accurate and sensitive than traditional models by comparison. In other words, the proposed model enhances the early identification of potential churners and such institutions can then take appropriate measures of containing it, and in the process, offer relevant retention solutions. The paper describes the benefits of utilizing deep learning models for churn prediction for the financial industry and provides information on how such methods might be used to improve customer retention. Possible future research avenues consist of improving the model interpretability and generalizing the approach to other industries with comparable churn prediction requirements. The accuracy obtained from the proposed method is higher at 99.7% compared to other methods.

关键词： Accuracy Computational modeling Autoencoders Time series analysis Predictive models Stability analysis Churn Predictive analytics Long short term memory Unsupervised learning

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A novel framework for efficient dominance-based rough set approximations using K-dimensional (K-D) tree partitioning and adaptive recalculations techniques

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Nawaz, Uzma Saeed, Zubair Atif, Kamran Knowledge and Data Science Research Centre Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad Pakistan Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Civil Engineering Deakin University Victoria Australia

In dynamic datasets, both entries and their possible values frequently change. This makes it difficult to update the necessary calculations for analysis. The Dominance-based Rough Set Approach (DRSA) effectively analyzes preference-based data. Still, it can be slow because it requires recalculating important values each time the data are updated. This study proposes an optimized K-dimensional (K-D) tree algorithm that integrates dynamic weighting and lazy recalculations to reduce computational cost. The K-D tree effectively organizes data, dynamic weighting prioritizes recalculations for essential nodes, and lazy recalculation postpones unnecessary computation. The proposed approach significantly reduces the execution time and memory consumption while maintaining accuracy. An ablation study was conducted to separately evaluate the impact of each component. This demonstrates the efficiency improvement achieved using the proposed approach. Compared to conventional DRSA, our optimized method reduced computational time and memory usage by 99 % across ten publicly available University of California, Irvine (UCI) datasets, which provided an efficient and scalable solution for real-time data processing in dynamic environments. © 2025 The Authors

关键词： Data reduction

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Glioblastoma tumor segmentation using an ensemble of vision transformers

Glioblastoma tumor segmentation using an ensemble of vision ...

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Medical Imaging 2025: computer-Aided Diagnosis

作者： Liu, Huafeng Dowdell, Benjamin Engelder, Todd Pulmano, Zarah Osa, Nicolas Barman, Arko Department of Computer Science Rice University Houston United States Data to Knowledge Lab Rice University Houston United States Department of Electrical and Computer Engineering Rice University Houston United States Department of Statistics Rice University Houston United States

ISBN: (纸本)9781510685925

Glioblastoma is one of the most aggressive and deadliest types of brain cancer, with low survival rates compared to other types of cancer. Analysis of Magnetic Resonance Imaging (MRI) scans is one of the most effective methods for the diagnosis and treatment of brain cancers such as glioblastoma. Accurate tumor segmentation in MRI images is often required for treatment planning and risk assessment of treatment methods. Here, we propose a novel pipeline, Brain Radiology Aided by Intelligent Neural NETworks (BRAINNET), which leverages MaskFormer, a vision transformer model, and generates robust tumor segmentation masks. We use an ensemble of nine predicted segmentation masks generated from three different models separately trained on each of the three orthogonal 2D slice directions (axial, sagittal, and coronal) of a 3D brain MRI volume. Each of the models in the ensemble consists of three modules - a pixel-level module that extracts per-pixel embeddings, a transformer module that generates per-segment embeddings, and a segmentation module that combines these embeddings to generate the segmentation mask. Two different data augmentation strategies were also implemented to improve the performance of the individual models. We trained, tuned hyperparameters, and tested our models on the publicly available UPenn-GBM dataset, consisting of 3D multi-parametric MRI (mpMRI) scans from 611 subjects. We use Dice coefficient (DC) and 95% Hausdorff distance (HD) for evaluating the performance of our models. Our models achieved state-of-the-art results in segmenting all three different tumor regions – tumor core (DC = 0.894, HD = 2.308), whole tumor (DC = 0.891, HD = 3.552), and enhancing tumor (DC = 0.812, HD = 1.608). © 2025 SPIE.

关键词： Magnetoencephalography

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