OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging by Xiaolu Ren, Xiaolu Ren, Lingxuan Hou, Shan Liu, Peng Wu, Siming Liang, Haitian Fu, Chengquan Li, Ting Li, Yongjing Cheng

“…In the unseen-data test, our model achieved an average accuracy (ACC) of 84.88% and an Area Under the Curve (AUC) of 89.11%, marking improvements over the best-performing baseline models. …”
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Enhancing DeepLabv3+ Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform by Guining Gao, Zhihan Chen, Yicheng Wei, Xicun Zhu, Xinyang Yu

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Combining semiparametric and machine learning approaches for short-term prediction of satellite clock bias by Lihong Jin, Wanzhuo Zhao, Xiong Pan, Qingsong Ai, Mao Cai, Xiaoli Ruan

“…Moreover, this model combines semiparametric techniques with optimized self-attention neural networks and is applied to predict SCB for BDS-3. …”
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PA2E: Real-Time Anomaly Detection With Hyperspectral Imaging for Food Safety Inspection by Jungi Lee, Myounghwan Kim, Jiseong Yoon, Kwangsun Yoo, Seok-Joo Byun

“…Validation on three hyperspectral datasets demonstrated that PA2E achieved an average detection capability of 29, outperforming the current state-of-the-art methods (average of 23 out of 42) with 2.6 times faster inference speed. …”
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Tunable Energy-Efficient Approximate Circuits for Self-Powered AI and Autonomous Edge Computing Systems by Shubham Garg, Kanika Monga, Nitin Chaturvedi, S. Gurunarayanan

“…Additionally, recent advancements in Deep Neural Network (DNN) running on millions of devices for various AI tasks deliver an accuracy comparable to human levels. …”
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Feature-adaptive anomaly detection model for onion inspection system by Ziwei Song, Prawit Buayai, Koji Makino, Xiaoyang Mao

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Two-Timescale Cross-Layer Design for URLLC Over Parallel Fading Channels With Imperfect CSI by Hongsen Peng, Meixia Tao

“…Furthermore, the trained network is demonstrated to be robust towards different traffic arrival models, as well as variations of the average arrival rate.…”
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A Comprehensive Framework for the Design of Modular Robotic Mobile Fulfillment Systems by Wei Wang, Yaohua Wu, Jun Zheng, Cheng Chi

“…Then analytical models, including a bottleneck-based model and an open queueing network model, are developed to estimate system throughput and average order flow time. …”
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Comparative Learning for Cross-Subject Finger Movement Recognition in Three Arm Postures via Data Glove by Lei Jiang, Fengmeng Zeng, Annie Yu

“…Training employs a 1: 2 positive-to-negative pairing strategy and an empirically optimized margin of 1.0, enabling the network to form subject-invariant, rehabilitation-relevant embeddings. …”
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Binocular stereo vision-based relative positioning algorithm for drone swarm by Qing Cheng, Yazhe Wang

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The impacts of training data spatial resolution on deep learning in remote sensing by Christopher Ardohain, Songlin Fei

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COMPARING FORECASTS OF AGRICULTURAL SECTOR EXPORT VALUES USING SARIMA AND LONG SHORT-TERM MEMORY MODELS by Aleytha Ilahnugrah Kurnadipare, Sri Amaliya, Khairil Anwar Notodiputro, Yenni Angraini, Laily Nissa Atul Mualifah

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Aircraft Multi-stage Altitude Prediction Under Satellite Signal Loss by Mengchan HUANG, Qiang MIAO

“…The relative error ratios of the LTCA–TCN model for all three phases are within 1, indicating that the model’s predictions are generally more accurate than the inertial pressure altitude.Conclusions The results indicated that the LTCA–TCN model achieves high prediction accuracy across multiple stages, outperforming commonly used neural network algorithms in the field and providing optimal predictive performance. …”
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GRE²-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning by Quanjun Li, Weixuan Li, Xiya Zheng, Junhua Zhou, Wenming Zhong, Xuhang Chen, Chao Long

“…Finally, GRE2-MDCL constructs a multidimensional contrastive loss, incorporating cross-network, cross-view, and neighbor contrast, to optimize the model. …”
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Impact of Extreme Rainstorms on Accessibility of Urban Medical Services and Layout Method of Emergency Points: A Case Study of the Shenzhen River Basin by YANG Guang, LIU Yongqiang, LIU Guoqing, LU Wenxiu, FAN Ziwu, LI Suyun, XIAO Zhiming

“…In order to control the medical service arrival time at any unsubmerged location in the study area within 15 minutes under extreme rainstorm conditions, emergency service equipment of six community hospitals can be supplemented to optimize emergency service capabilities. After optimization, the average arrival time of emergency services is shortened to less than 5 minutes, which is 2.9 minutes shorter than that of the unoptimized scheme on average. …”
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Exploration and application of deep learning based wellbore deformation forecasting model by Hui LIU, Guoqiang LI, Xiaojun ZHU, Pengfei ZHANG, Hua CHENG, Jinzheng WANG, Peishuai LI

“…The overall prediction is optimized by the 1DCNN model, and the main tilting direction (The prediction accuracy of the main inclined direction (east-west direction, which is inclined to the side of the mining area) is slightly lower than that of the direction with smaller deformation amount (north-south direction), and all of them can meet the engineering needs. ③ The overall prediction curve of the wellbore is consistent with the actual tilt direction, and the average values of EMA and ERMS of the wellhead and loose bedrock interface are 0.005 m and 0.006 m. …”
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Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy by Ansam Khraisat, Ammar Alazab, Moutaz Alazab, Areej Obeidat, Sarabjot Singh, Tony Jan

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GAPO: A Graph Attention-Based Reinforcement Learning Algorithm for Congestion-Aware Task Offloading in Multi-Hop Vehicular Edge Computing by Hongwei Zhao, Xuyan Li, Chengrui Li, Lu Yao

“…Efficient task offloading for delay-sensitive applications, such as autonomous driving, presents a significant challenge in multi-hop Vehicular Edge Computing (VEC) networks, primarily due to high vehicle mobility, dynamic network topologies, and complex end-to-end congestion problems. …”
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Cellular-free RAN slicing resource allocation algorithm based on multi-timescale collaboration by XIA Weiwei, WANG Boye, XIA Yaxing, MIAO Weiwei, WANG Dayang, JING Dongsheng, YAN Feng, SHEN Lianfeng

“…To address the issue of ensuring user quality of service in resource allocation for 6G cell-free radio access network slicing under dynamic service variations, a resource allocation algorithm based on multi-timescale collaborative optimization was proposed. …”
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AI-enhanced modelling of queueing and scheduling systems in cloud computing by Himani Chaudhary, Geetanjali Sharma, D. K. Nishad, Saifullah Khalid

“…Experimental results in a simulated cloud environment showcase remarkable improvements: a 30% decrease in average waiting time, 25% optimization in queue length, and 91% peak resource utilization compared to traditional approaches. …”
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