A novel method for power transformer fault diagnosis considering imbalanced data samples by Jun Chen, Yong Wang, Lingming Kong, Yilong Chen, Mianzhi Chen, Qian Cai, Gehao Sheng

“…We derive a sample parameter correlation quantization matrix from oil chromatography fault data using association rules, which serves as the initial value for the NCA algorithm’s training metric matrix. …”
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Retracted: Computer Medical Image Segmentation Based on Neural Network by Xiaopeng Wang, Lei Gu, Zhongyi Wang

“…This method introduces a non-linear template and data quantization on the basis of a basic network model, which greatly reduces the computational complexity while maintaining the accuracy of image segmentation. …”
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Hospital performance evaluation models: A scoping review by Shirin Alsadat Hadian, Reza Rezayatmand, Saeedeh Ketabi, Nasrin Shaarbafchizadeh, Ahmad Reza Pourghaderi

“…The DEA model, which includes extensive functions, was the most common model used for evaluating hospital performance. Quantitative models for evaluating hospital performance focused on performance and efficiency terms. …”
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Adaptive Lossy Color Image Compression System Based on Hybrid Algorithm by Husam Khalid Khammas, Ayça Kurnaz Türkben

“…To implement this proposed method three experiments were performed by using images of varying dimensions, namely (256 * 256), (512 * 512), and (1024 * 1024) and four different quantization coefficients. For efficiency performance measurement of the proposed system, three metrics were used: Peak signal-to-noise ratio (PSNR), Structural similarity index (SSIM), and compression ratio (CR). …”
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Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma. by José Fernando García Molina, Lei Zheng, Metin Sertdemir, Dietmar J Dinter, Stefan Schönberg, Matthias Rädle

“…First- and second-order angular independent statistical approaches and rotation invariant local phase quantization (RI-LPQ) were utilized to quantify texture information. …”
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On-Chip Photonic Convolutional Processing Lights Up Fourier Neural Operator by Zilong Tao, Hao Ouyang, Qiuquan Yan, Shiyin Du, Hao Hao, Jun Zhang, Jie You

“…Fourier Neural Operators (FNOs) have gained increasing attention for their effectiveness in extracting frequencydomain features and efficiently approximating functions, making them wellsuited for classification tasks. …”
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Microcontroller-Based EdgeML: Health Monitoring for Stress and Sleep via HRV by Priyanshu Srivastava, Namita Shah, Kavita Jaiswal

“…However, traditional ML systems often face challenges in real-time processing and resource efficiency, limiting their application in life-critical scenarios. …”
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Deploying AI on Edge: Advancement and Challenges in Edge Intelligence by Tianyu Wang, Jinyang Guo, Bowen Zhang, Ge Yang, Dong Li

“…Although edge intelligence methods have been proposed to alleviate the computational and storage burdens, they still face multiple persistent challenges, such as large-scale model deployment, poor interpretability, privacy and security vulnerabilities, and energy efficiency constraints. This article systematically reviews the current advancements in edge intelligence technologies, highlights key enabling techniques including model sparsity, quantization, knowledge distillation, neural architecture search, and federated learning, and explores their applications in industrial, automotive, healthcare, and consumer domains. …”
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The influence of digital transformation on the total factor productivity of enterprises: the intermediate role of human-machine cooperation by Qi Xiong, Jingyi Yang, Xiuwu Zhang, Yarui Deng, Yao Gui, Xiaoyang Guo

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Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite by Fabrizio Maria Aymone, Danilo Pietro Pau

“…With the exponential growth of edge devices, efficient local processing is essential to mitigate economic costs, latency, and privacy concerns associated with the centralized cloud processing. …”
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IMPACT OF ECONOMIC REFORM ON THE AMOUNT AND FFICIENCY OF AGRICULTURAL LENDING IN SELECTED COUNTRIES by Hanaa Dawood

“…And was relying on two-way link in analysis, the first is based on theoretical studies which addressed the same subject, the second, quantitative methods based, on econometric, and then interpret the results of the quantitative method for assessing the theoretical side in the study. …”
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Single nucleotide variants in transcription factors associate more tightly with phenotype than with gene expression. by Priya Sudarsanam, Barak A Cohen

“…We addressed this question using four naturally occurring Quantitative Trait Nucleotides (QTN) in three transcription factors that affect sporulation efficiency in wild strains of the yeast, Saccharomyces cerevisiae. …”
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An autonomous vehicles’ test case extraction method: Example of vehicle-to-pedestrian scenarios by Zhengping Tan, Qian Wang, Wenhao Hu, Pingfei Li, Liangliang Shi, Hao Feng

“…This paper proposes a novel method to enhance scenario adaptation by integrating quantization weights with a new clustering algorithm. …”
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Empowering Healthcare: TinyML for Precise Lung Disease Classification by Youssef Abadade, Nabil Benamar, Miloud Bagaa, Habiba Chaoui

“…Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. …”
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Lightweight deep learning method for end-to-end point cloud registration by Linjun Jiang, Yue Liu, Zhiyuan Dong, Yinghao Li, Yusong Lin

“…Specifically, our approach utilizes pruning and weight-sharing quantization techniques to reduce model size and simplify the network structure. …”
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Binary-Weighted Neural Networks Using FeRAM Array for Low-Power AI Computing by Seung-Myeong Cho, Jaesung Lee, Hyejin Jo, Dai Yun, Jihwan Moon, Kyeong-Sik Min

“…Furthermore, the combination of binary weight quantization and in-memory computing enables energy-efficient inference without significant loss in recognition accuracy, as validated using MNIST datasets. …”
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A DSP–FPGA Heterogeneous Accelerator for On-Board Pose Estimation of Non-Cooperative Targets by Qiuyu Song, Kai Liu, Shangrong Li, Mengyuan Wang, Junyi Wang

“…Optimization techniques, including batch normalization fusion, ReLU integration, and linear quantization, are applied to improve inference efficiency. …”
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CNN-Based Optimization for Fish Species Classification: Tackling Environmental Variability, Class Imbalance, and Real-Time Constraints by Amirhosein Mohammadisabet, Raza Hasan, Vishal Dattana, Salman Mahmood, Saqib Hussain

“…Optimization techniques, including pruning and quantization, reduced model size by 73.7%, enabling real-time deployment on resource-constrained devices. …”
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A seed-epidermis-feature-recognition-based lightweight peanut seed selection method for embedded systems by Dehao Li, Jinlong Huang, Xincheng Li, Zhaolei Yang, Xueke An, Pengfei Xu, Yuliang Yun

“…The model was based on deep learning and model quantization techniques. The transfer learning method was used to use four pre-trained models, EfficientNet_b0, EfficientNetv2-b0, MobileNet_v2_35_224, and NasNet_Mobile, as feature extraction layers, the input layer was added before the feature extraction layer, and the dropout and dense layers were added after the feature extraction layer to construct a classifier. …”
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DEVELOPMENT OF THE METHOD OF THE CHOICE OF THE TYPE OF PASSENGER TRANSPORT DEPENDING ON RANGE OF TRANSPORTATIONS by A. A. Prytov

“…The article presents the results of calculations confirming efficiency of using transport depending on range of a trip taking into account requirements of passengers which have no accurate quantitative assessment and quantitative interrelation (duration, cost, comfort, reliability and safety of a trip).…”
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