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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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An Overview of Telemedicine: Evolution, Application, Challenges and Future Direction 3

An Overview of Telemedicine: Evolution, Application, Challen...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kawde, Dhanshri Gourshettiwar, Palash Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Telemedicine is of great importance as it increases the availability of health care for people living in remote or undeveloped areas. It reduces the cost of health care, allows for early diagnosis and treatment of chronic conditions, and saves time for both patients and healthcare providers. Telemedicine also ensures continuity of care in situations like pandemics while reducing reducing the need for face-to-face consultations can reduce the chance of catching contagious diseases.. This innovation will combine wearable devices and artificial intelligence with personalized care and mental health support. This way, healthcare services would be available and efficient for everyone around the world. However, technological challenges include those stemming from poor system architecture, poor connectivity limitation in diagnosis from a physical examination privacy issues among others and diverse legal regulations. Lack of access and integration into mainstream health systems are also among the causes of discontinuities in care. Overcoming such barriers is critical, and telemedicine needs to be easy to integrate into mainstream frameworks for better access, equity, and health outcome. © 2025 IEEE.

关键词： Telemedicine

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Explainable ensemble deep learning-based model for brain tumor detection and classification

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Neural Computing and Applications 2025年第3期37卷 1289-1306页

作者： Hosny, Khalid M. Mohammed, Mahmoud A. Salama, Rania A. Elshewey, Ahmed M. Department of Information Technology Faculty of Computers and Information Zagazig University P.O.BOX 44519 Zagazig Egypt Department of Computer Science Faculty of Computers and Information Suez University P.O.BOX 43221 Suez Egypt Department of Information Technology Faculty of Computers and Information Suez University P.O.BOX 43221 Suez Egypt

Brain tumors are very dangerous as they cause death. A lot of people die every year because of brain tumors. Therefore, accurate classification and detection in the early stages can help in recovery. Various deep learning techniques have achieved good results in brain tumor classification. The traditional deep learning methods and training the neural network from scratch are time-consuming and can last for weeks of training. Therefore, in this work, we proposed an ensemble approach depending on transfer learning that utilizes pre-trained models of DenseNet121 and InceptionV3 to detect three forms of brain tumors: meningioma, glioma, and pituitary. While developing the ensemble model, some changes were made to the architecture of pre-trained models by replacing their classifiers (fully connected and SoftMax layers) with a new classifier to adopt the recent task. In addition, gradient-weighted class activation maps (Grad-CAM) are an explainable model to verify results and achieve high confidence. The suggested model was validated using a publicly available dataset and achieved 99.02% accuracy, 98.75% precision, 98.98% recall, and a 98.86% F1 score. The suggested approach outperformed others in detecting and classifying brain tumor MRI data, and verifying results using the explainable model achieved a high degree of trust. © The Author(s) 2024.

关键词： Transfer learning

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Next-Gen Waste Management: AI as a Catalyst for Sustainability 2

Next-Gen Waste Management: AI as a Catalyst for Sustainabili...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sheikh, Nishad Kumar, Praveen Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Sawangi Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of computer science and medical engineering Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331505745

Traditional waste management techniques frequently be afflicted by inefficiencies, high operational fees, and environmental drawbacks. However, latest improvements in Artificial Intelligence, such as computer vision and Internet of Things, are remodeling waste management right into an automated and optimized procedure. This research study explores how AI-pushed technology enhance waste series, sorting, and recycling methods. Machine learning algorithms enhance waste category accuracy, computer vision permits unique fabric separation, and IoT-powered smart containers optimize collection schedules, decreasing needless trips and gasoline consumption. Additionally, AI-pushed direction optimization algorithms Significantly reduce emissions by means of designing efficient series pathways. These advancements Contribute. Not only to improve operational efficiency but also to the broader dreams of decreasing environmental impact and promoting a Circular economy. Despite its transformative potential, AI integration in waste control affords monetary, ethical, and technological challenges that require cautious attention. This examine provides a comprehensive evaluation of AI packages, their blessings, boundaries, and destiny capacity in revolutionizing waste management. With AI solutions, the enterprise can transition towards a more adaptive, efficient, and sustainable waste management ecosystem, addressing both urban and environmental demanding situations in the process. © 2025 IEEE.

关键词： Waste treatment

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PMMTalk: Speech-Driven 3D Facial Animation From Complementary Pseudo Multi-Modal Features

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IEEE Transactions on Multimedia 2025年 27卷 2570-2581页

作者： Han, Tianshun Gui, Shengnan Huang, Yiqing Li, Baihui Liu, Lijian Zhou, Benjia Jiang, Ning Lu, Quan Zhi, Ruicong Liang, Yanyan Zhang, Du Wan, Jun Macau University of Science and Technology School of Computer Science and Engineering Faculty of Innovation Engineering 999078 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Mashang Consumer Finance Company Ltd. Chongqing400000 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

Speech-driven 3D facial animation has improved a lot recently while most related works only utilize acoustic modality and neglect the influence of visual and textual cues, leading to unsatisfactory results in terms of precision and coherence. We argue that visual and textual cues are not trivial information. Therefore, we present a novel framework, namely PMMTalk, using complementary Pseudo Multi-Modal features for improving the accuracy of facial animation. The framework entails three modules: PMMTalk encoder, cross-modal alignment module, and PMMTalk decoder. Specifically, the PMMTalk encoder employs the off-the-shelf talking head generation architecture and speech recognition technology to extract visual and textual information from speech, respectively. Following this, the cross-modal alignment module aligns the audio-image-text features at temporal and semantic levels. Subsequently, the PMMTalk decoder is employed to predict lip-syncing facial blendshape coefficients. Contrary to prior methods, PMMTalk only requires an additional random reference face image but yields more accurate results. Additionally, it is artist-friendly as it seamlessly integrates into standard animation production workflows by introducing facial blendshape coefficients. Finally, given the scarcity of 3D talking face datasets, we introduce a large-scale 3D Chinese Audio-Visual Facial Animation (3D-CAVFA) dataset. Extensive experiments and user studies show that our approach outperforms the state of the art. Codes and datasets are available at PMMTalk. © 1999-2012 IEEE.

关键词： Animation

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Uncertainty and Stochasticity in Modelling of Microstructure Evolution During Hot Rolling of Steels

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Archives of Metallurgy and Materials 2025年第1期70卷 405-414页

作者： Szeliga, D. Foryś, J. Kusiak, J. Kuziak, R. Nadolski, R. Pietrzyk, M. Rauch, L. AGH University of Science and Technology Faculty of Metals Engineering and Industrial Computer Science Al . Mickiewicza 30 Kraków30-059 Poland Research Group Processes Simulation Upper-Silesian Institute of Technology ul. K. Miarki 12-14 Gliwice44-100 Poland

Advanced numerical models, which predict heterogeneity of microstructural features, are needed to design modern steels with heterogeneous microstructures. Models based on stochastic internal variables meet this requirement. Our stochastic model accounts for the random character of the recrystallization and transfers this randomness into equations describing the evolution of the dislocation populations and the grain size during the hot deformation of steels. The idea of the internal variable model, with the dislocation density and the grain size being stochastic variables, is described in the paper. The material parameters, which influence accuracy and reliability of the model, were identified. They compose shear modulus, lattice friction stress and the mean free pass for dislocations. Numerical test showing influence of these parameters on the identification of the model coefficients were performed and a hint how these parameters should be selected is given. Compression loads and histograms of the grain size measured in the experimental compression tests were used to identify the coefficients in the model. The model was applied to simulations of the industrial process of the hot strip rolling. It was shown that the model can be used to both predictions of the microstructural heterogeneity caused by the stochastic character of microstructure evolution and to the evaluation of the uncertainty of phase composition in the final product. The latter is due to the uncertainty of the boundary conditions. © 2025. The Author(s).

关键词： Stochastic models

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Evaluating BERT-based language models for detecting misinformation

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Neural Computing and Applications 2025年第16期37卷 9937-9968页

作者： Anggrainingsih, Rini Hassan, Ghulam Mubashar Datta, Amitava Information Technology and Data Science Faculty Sebelas Maret University Ir Sutami 36A Central Java Surakarta57126 Indonesia Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia

Online misinformation poses a significant challenge due to its rapid spread and limited supervision. To address this issue, automated rumour detection techniques are essential for countering the negative impact of false information. Previous research primarily focussed on extracting text features, which proved time-consuming and less effective. In this study, we contribute substantially to two domains: rumour detection on Twitter and the evaluation of text embeddings. We thoroughly analyse rumour detection models and compare the quality of text embeddings generated by various fine-tuned BERT-based models. Our findings indicate that our proposed models outperform existing techniques. Notably, when we test these models on combined datasets, we observe significant performance improvements with larger training and testing data sizes. We conclude that carefully considering the dataset, data splitting, and classification techniques is crucial for evaluating solution performance. Additionally, we find that differences in the quality of text embeddings between RoBERTa, BERT, and DistilBERT are insignificant. This challenges existing assumptions and highlights the need for future research to explore these nuances further. © The Author(s) 2025.

关键词： BERT-based models Comparative analysis Rumour detection Text embeddings

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EPR analysis and spectral deconvolution of alanine dosimetric properties

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Journal of Radioanalytical and Nuclear Chemistry 2025年第4期334卷 2657-2670页

作者： Zahiri, Fatna Mikou, Mohammed Mamadou Saidou, Abdoul Karim Belahmar, Ayoub El Baydaoui, Redouane Laboratory of Mathematics Computer Science and Engineering Sciences Faculty of Science and Technology Hassan First University of Settat B.P. 577 Settat26000 Morocco Hassan First University of Settat B.P. 555 Settat26000 Morocco

Precise dose monitoring in radiotherapy is essential to ensure treatment efficacy and the protection of healthy tissues. Although the Alanine/EPR dosimetry system has demonstrated dosimetric properties, further analysis of this dosimetry system, taking into account the characteristics of the different contributions to the EPR spectrum of irradiated alanine, will be important in determining the system's performance and ensuring its reliability for dosimetric applications in radiotherapy and hadrontherapy. In this work, we used the deconvolution method, based on the Levenberg–Marquardt algorithm, to determine the contributions constituting the irradiated alanine spectrum. Fourteen contributions were revealed. Analysis of the comportment of these contributions as a function of irradiation dose and post-irradiation storage time revealed quasi-linear evolution according to irradiation dose, and good stability of the produced free radicals during the post-irradiation storage period. This result approves the potential application of the Alanine/EPR system for dose control in radiotherapy. © Akadémiai Kiadó Zrt 2025.

关键词： Radiotherapy

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DiffuSAR: Frequency Domain-Aware Diffusion Model for SAR Image Generation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11851-11866页

作者： Ying, Zilu Ke, Wenyu Zhai, Yikui Long, Zhihao Zhou, Jianhong Zhu, Hufei Chen, C. L. Philip Wuyi University School of Electronics and Information Engineering Jiangmen529020 China South China University of Technology School of Electronics and Information Engineering Guangzhou510000 China South China University of Technology Faculty of Computer Science and Engineering Guangzhou510000 China

In recent years, deep learning-based synthetic aperture radar (SAR) image detection, recognition, and segmentation models achieve remarkable accuracy when trained on large amounts of SAR image samples. However, the acquisition of SAR images tends to be costly. A practical approach to address this issue involves the use of artificially generated training samples to supplement the dataset. Current SAR image generation methods based on generative adversarial networks have issues with generation fidelity and are challenge to converge. Moreover, most of these methods only consider the spatial domain of SAR images without exploring the characteristics of their frequency domain. To tackle the aforementioned problems, we proposed a lightweight frequency domain-aware SAR image generation model based on the denoising diffusion probabilistic model. The proposed generative model is capable of producing highly realistic artificial SAR image samples while converging stably. Meanwhile, our research reveals that high-frequency (HF) components of SAR images play a crucial role in the generation process, and a frequency adjustment module (FAM) based on the fast Fourier transform is implemented in our generation model to prevent interference of HF components by low-frequency components. Tests on the MSTAR dataset highlight our model's advantages in both generation quality and parameter efficiency compared to other cutting-edge image generation models. An additional ablation study also confirms the effectiveness of our proposed FAM, which reduces the Fréchet inception distance by 2.85%, confirming our hypothesis that HF components are more crucial to SAR image generation. © 2008-2012 IEEE.

关键词： Fast Fourier transforms

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

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

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 IEEE.

关键词： blockchain collaborative training Federated Learning heterogeneous models

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