DFTD-YOLO: Lightweight Multi-Target Detection From Unmanned Aerial Vehicle Viewpoints by Yuteng Chen, Zhaoguang Liu

“…In this study, we propose an improved algorithm, DFTD-YOLO, based on YOLOv8n. We designed a new neck feature fusion network. …”
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Investigation of a Novel Common Subexpression Elimination Method for Low Power and Area Efficient DCT Architecture by M. F. Siddiqui, A. W. Reza, J. Kanesan, H. Ramiah

“…Furthermore, the proposed technique has significant advantages over recent well-known methods along with accuracy in terms of power reduction, silicon area usage, and maximum operating frequency by 41%, 15%, and 15%, respectively.…”
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User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices by Muhamad Hanif Jofri, Ida Aryanie Bahrudin, Noor Zuraidin Mohd Safar, Juliana Mohamed, Abdul Halim Omar

“…We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. …”
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Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. by Shayla Naznin, Md Jamal Uddin, Ishmam Ahmad, Ahmad Kabir

“…While the Linear Regression model demonstrated exceptional accuracy in forecasting trends, long-term predictions should be interpreted cautiously due to inherent uncertainties in socio-economic conditions. …”
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Modeling and optimization of power consumption for multi-core processors: A thermodynamic perspectiveModeling-and-Optimization-of-Power-Consumption-for-Multi-core-Processors-A-Ther... by Xiaoxuan Peng, Litao Yan, Qiang Li, Xiaohu Ge, Yonghui Li

“…Based on the relationship between processor power consumption and temperature, a spatio-temporal optimization algorithm is designed to reduce the power consumption of multi-core processors, which achieved maximum 22.2% power consumption reduction compared with unoptimized processors. …”
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Supporting self-management with an internet intervention for low back pain in primary care: a RCT (SupportBack 2) by Adam W A Geraghty, Taeko Becque, Lisa C Roberts, Jonathan Hill, Nadine E Foster, Lucy Yardley, Beth Stuart, David A Turner, Gareth Griffiths, Frances Webley, Lorraine Durcan, Alannah Morgan, Stephanie Hughes, Sarah Bathers, Stephanie Butler-Walley, Simon Wathall, Gemma Mansell, Malcolm White, Firoza Davies, Paul Little

“…At 12 months, there were small but significant reductions in kinesiophobia in both intervention arms, compared to usual care. …”
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Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy by Jose Manuel Gaspar Sanchez, Leonard Bruns, Jana Tumova, Patric Jensfelt, Martin Torngren

“…Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. …”
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Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024 by Paolo Sernani, Angela Cossiri, Giovanni Di Cosimo, Emanuele Frontoni

“…We introduce a novel dataset—Political Popularity Campaign—which comprises social media posts, engagement metrics, and multimedia content from the electoral period. By applying predictive modeling, we estimate key indicators such as post popularity and assess their influence on campaign outcomes. …”
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Scalable data assimilation with message passing by Oscar Key, So Takao, Daniel Giles, Marc Peter Deisenroth

“…Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed across increasingly many compute nodes; yet, existing approaches suffer from synchronization overhead in this setting. …”
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An Efficient Trajectory Negotiation and Verification Method Based on Spatiotemporal Pattern Mining by Yongqi Liu, Miao Wang, Zhaohua Zhong, Kelin Zhong, Guoqing Wang

“…Therefore, this paper develops a framework for trajectory negotiation and verification and describes the trajectory prediction, negotiation, and verification processes based on a four-dimensional trajectory. …”
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Navigating Samarinda's climate: A comparative analysis of rainfall forecasting models by Mislan, Andrea Tri Rian Dani

“…The superior points in this research are: • Modeling rainfall data in Samarinda City using several forecasting methods: Exponential Smoothing, ARIMA, and Neural Network. • The Neural-Network algorithm used is Backpropagation with data standardization. • Information about predicted high rainfall can be used to issue early warnings of floods or landslides. …”
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Ship path planning and tracking based on rapidly exploring random tree star and convex optimization by Chang ZHOU, Te YU, Jiapeng LIU, Dihua LU, Qingshan ZENG

“…Finally, model predictive control (MPC) algorithm is employed to generate ship control output sequences, enabling safe and efficient navigation around obstacles from the starting point to the destination. …”
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Vaginal dysbiosis as a factor contributing to the development of recurrent lower urinary tract infections by I. V. Kosova, V. A. Barsegian, L. A. Sinyakova, I. V. Lukianov, D. N. Kolbasov

“…The examination was performed according to the algorithm developed in the Department of Urology and Surgical Andrology, Russian Medical Academy of Continuous Professional Education. …”
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Buoy and Winch Collaborative Control System Based on Deep Reinforcement Learning by Yang Gu, Jianjun Ni, Zaiming Geng, Bing Zhao, Haowen Yang

“…The main contributions include the development of two simulation environments for training DRL agents, designing a reward function to guide the exploration process, proposing a buoy control algorithm based on the discrete soft actor-critic (SAC) algorithm, and proposing a winch cable length prediction algorithm based on a lightweight transformer model. …”
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An Optical Sensor for Measuring In-Plane Linear and Rotational Displacement by Suhana Jamil Ahamed, Michael Aaron McGeehan, Keat Ghee Ong

“…Variations in reflected R, G, B and C light intensities, caused by displacements, were used to predict linear and rotational motion via a polynomial regression algorithm. …”
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Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis by Ioannis D. Apostolopoulos, Nikolaos I. Papandrianos, Dimitrios J. Apostolopoulos, Elpiniki Papageorgiou

“…These results highlight a potential synergistic relationship between human expertise and advanced algorithmic predictions, suggesting a hybrid approach as a promising direction for enhancing CAD diagnostics.…”
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Telomerase inhibiting phytochemicals derived from Blumea eriantha for cancer treatment: A comprehensive computational analysis by Rahil Ummar Faruk Abbu, Shaik Mohammad Abdul Fayaz, Divyashree M S, Raghu Chandrashekhar H, Prasanna Kumar Reddy Gayam, Archana Mahadev Rao

“…To further assess the interaction and stability of these phytochemicals and predict their behavior in a biological environment when targeting telomerase, we conducted molecular dynamic simulations using Desmond–Schrödinger. …”
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RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot by Nattapong Ruenruedeepan, Sujin Bureerat, Natee Panagant, Nantiwat Pholdee

“…A key feature of the method is its generation of a target Pareto front, in which a z-surrogate model makes predictions to guide design solutions toward achieving optimal performance. …”
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Impact Assessment of Various Methods for Control of Synchronous Generator Excitation on Quality of Transient Processes by Y. D. Filipchik

“…The algorithm makes it possible to improve quality of the transient processes due to reduction of oscillation range concerning as an active power so a δ-angle as well.…”
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Regulating neural data processing in the age of BCIs: Ethical concerns and legal approaches by Hong Yang, Li Jiang

“…Brain–computer interfaces (BCIs) have seen increasingly fast growth under the help from AI, algorithms, and cloud computing. While providing great benefits for both medical and educational purposes, BCIs involve processing of neural data which are uniquely sensitive due to their most intimate nature, posing unique risks and ethical concerns especially related to privacy and safe control of our neural data. …”
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