Perceptually Correlated Parameters of Musical Instrument Tones by James W. BEAUCHAMP

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Method for calculating the parameters of the rotary-impact mechanism by L. A. Sladkova, D. I. Skripnikov

“…The proposed method for evaluating the resistance features of structural elements allows determining the P value of the non-destruction force to provide a safe impact on the groove base during drilling tool operations.…”
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A study for method-level code smells detection using machine learning algorithms by Rajwant Singh Rao, Seema Dewangan, Alok Mishra, Manjari Gupta

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Label dependency modeling in Multi-Label Naïve Bayes through input space expansion by PKA Chitra, Saravana Balaji Balasubramanian, Omar Khattab, Mhd Omar Al-Kadri

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Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

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Decoupled pixel-wise correction for abdominal multi-organ segmentation by Xiangchun Yu, Longjun Ding, Dingwen Zhang, Jianqing Wu, Miaomiao Liang, Jian Zheng, Wei Pang

“…Attention-based deep neural networks (ADNNs) fundamentally engage in the iterative computation of gradients for both input layers and weight parameters. …”
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Computing Genomic Signatures Using de Bruijn Chains by Lenwood S. Heath, Amrita Pati

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CURRENT STATE AND PROSPECTS OF COMPUTER TERMINOLOGY ONLINE LEXICOGRAPHY by Iryna B. Mentynska

“…A number of modern online lexicographic sources that record computer terms are analyzed, their features from the point of view of practical application are noted, and the most current trends related to modern lexicography processes are highlighted. …”
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Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…It enhances multi-scale feature fusion capability via weighted feature fusion, significantly reducing parameter count while improving small target detection capability. …”
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Spontaneous Ad Hoc Mobile Cloud Computing Network by Raquel Lacuesta, Jaime Lloret, Sandra Sendra, Lourdes Peñalver

“…Smart devices are becoming one of the main information processing devices. Their computing features are reaching levels that let them create a mobile cloud computing network. …”
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Computational studies of the rotors aerodynamic characteristics of multirotor drones by K. G. Kosushkin, B. S. Kritsky, R. M. Mirgazov

“…Depending on the mode and rotors location, the average rotor thrust coefficient can vary approximately twice. The computations showed that with the similar geometric parameters and kinematics characteristics, rotors thrust is substantially subject to variation, which causes destabilizing moments to a significant degree without additional control input. …”
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Cone-beam computed tomography prescription by Brazilian orthodontists by Karine EVANGELISTA, Grasielle Di Manoel CAIADO, Maria do Carmo Matias FREIRE, José VALLADARES-NETO, Maria Alves Garcia SILVA

“…The variables were the orthodontists’ demographic features and the CBCT prescription in clinical practice. …”
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BSE-YOLO: An Enhanced Lightweight Multi-Scale Underwater Object Detection Model by Yuhang Wang, Hua Ye, Xin Shu

“…Firstly, we replace the original neck with an improved Bidirectional Feature Pyramid Network (Bi-FPN) to reduce parameters. …”
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The Cart-Pole Application as a Benchmark for Neuromorphic Computing by James S. Plank, Charles P. Rizzo, Chris A. White, Catherine D. Schuman

“…Finally, we perform a detailed examination of eight example networks from this experiment, that achieve our goals on the difficulty levels, and comment on features that enable them to be successful. Our goal is to help researchers in neuromorphic computing to utilize the cart-pole application as an effective benchmark.…”
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A New Efficient Hybrid Technique for Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame Selection by Majid Joudaki, Mehdi Imani, Hamid R. Arabnia

“…Recognizing human actions through video analysis has gained significant attention in applications like surveillance, sports analytics, and human–computer interaction. While deep learning models such as 3D convolutional neural networks (CNNs) and recurrent neural networks (RNNs) deliver promising results, they often struggle with computational inefficiencies and inadequate spatial–temporal feature extraction, hindering scalability to larger datasets or high-resolution videos. …”
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Steel Defect Detection Based on YOLO-SAFD by Feihong Yu, Jinshan Zhang, Dingdiao Mu

“…The proposed model incorporates two key innovations: 1) the Squeezed and Excited Asymptotic Feature Pyramid Network (SAFPN), which enhances multi-scale feature fusion and improves the detection of small defects, increasing the mean Average Precision (mAP) from 0.78 (YOLOv5 baseline) to 0.84; 2) the Diverse Branch Block (DBB), which replaces conventional convolutions to enrich feature diversity while reducing computational complexity, cutting the model parameters from 13.8M to 4.82M. …”
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Underwater image enhancement using hybrid transformers and evolutionary particle swarm optimization by Ajay Kumar, Gagandeep Berar, Manmohan Sharma, Sakshi, Ajit Noonia, Gunjan Verma

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Research on UAV aerial imagery detection algorithm for Mining-Induced surface cracks based on improved YOLOv10 by Jiayong An, Siyuan Dong, Xuanli Wang, Chenlei Li, Wenpei Zhao

“…However, detection is challenged by small crack size, complex morphology, large scale variation, and uneven spatial distribution, further exacerbated by UAVs’ limited onboard computational capacity. To tackle these issues, we introduce an efficient and lightweight small-target detection model, namely YOLO-LSN, which is built upon the optimized YOLO architecture.Firstly, we introduce a Lightweight Dynamic Alignment Detection Head (LDADH) for multi-scale feature fusion, precise alignment, and dynamic receptive field adjustment, optimizing crack feature extraction. …”
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DSF-YOLO for weld defect detection in X-ray images with dynamic staged fusion by Meng Zhang, Yanzhu Hu, Binbin Xu, Lisha Luo, Song Wang

“…Additionally, DSF-YOLO significantly reduces computational complexity, achieving a 75% reduction in FLOPs and a 47.5% decrease in parameters compared to YOLOv8-X. …”
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Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization by Eman Ali Aldhahri, Abdulwahab Ali Almazroi, Monagi Hassan Alkinani, Nasir Ayub, Elham Abdullah Alghamdi, Nourah Fahad Janbi

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