Adaptive gradient scaling: integrating Adam and landscape modification for protein structure prediction by Vitalii Kapitan, Michael Choi

“…Despite their success, machine learning methods face fundamental limitations in optimizing complex high-dimensional energy landscapes, which motivates research into new methods to improve the robustness and performance of optimization algorithms. …”
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Sparse transformer and multipath decision tree: a novel approach for efficient brain tumor classification by Pengcheng Li, Yuqi Jin, Monan Wang, Fengjie Liu

“…With advances in medical imaging technology, automated classification algorithms face challenges due to tumor diversity. Although Swin Transformer is effective in handling high-resolution images, it encounters difficulties with small datasets and high computational complexity. …”
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A Review of Knowledge Distillation in Object Detection by Shengjie Cheng, Qiuxia Zhao, Xinyun Zhang, Nurbiya Yadikar, Kurban Ubul

“…Target detection is a revolutionary advancement in computer vision that provides the ability to identify specific targets in images for a wide variety of applications, including but not limited to video surveillance, face recognition, and autonomous driving. …”
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Evolutionary learning in neural networks by heterosynaptic plasticity by Zedong Bi, Ruiqi Fu, Guozhang Chen, Dongping Yang, Yu Zhou, Liang Tian

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YOLOv8n-SMMP: A Lightweight YOLO Forest Fire Detection Model by Nianzu Zhou, Demin Gao, Zhengli Zhu

“…Existing forest fire detection algorithms face limitations in capturing flame and smoke features in complex natural environments, coupled with high computational complexity and inadequate lightweight design for practical deployment. …”
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AI-Driven Optimization of Breakwater Design: Predicting Wave Reflection and Structural Dimensions by Mohammed Loukili, Soufiane El Moumni, Kamila Kotrasova

“…Coastal defense structures play a crucial role in mitigating wave impacts; yet, existing breakwater designs often face challenges in balancing wave reflection, energy dissipation, and structural stability. …”
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A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du, Zhengqi Wang

“…Although existing methods have achieved certain success in processing acoustic and vibration signals, they still face challenges such as insufficient feature fusion, inflexible weight allocation, lack of effective feature selection mechanisms, and low computational efficiency. …”
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Explainable AI-Based Skin Cancer Detection Using CNN, Particle Swarm Optimization and Machine Learning by Syed Adil Hussain Shah, Syed Taimoor Hussain Shah, Roa’a Khaled, Andrea Buccoliero, Syed Baqir Hussain Shah, Angelo Di Terlizzi, Giacomo Di Benedetto, Marco Agostino Deriu

“…Current artificial intelligence (AI) approaches for skin cancer detection face challenges such as computational inefficiency, lack of interpretability, and reliance on standalone CNN architectures. …”
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Intelligent Online Multiconstrained Reentry Guidance Based on Hindsight Experience Replay by Qingji Jiang, Xiaogang Wang, Yuliang Bai, Yu Li

“…Traditional guidance algorithms for hypersonic glide vehicles face the challenge of real-time requirements and robustness to multiple deviations or tasks. …”
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UAV target tracking method based on global feature interaction and anchor-frame-free perceptual feature modulation. by Yuanhong Dan, Jinyan Li, Yu Jin, Yong Ji, Zhihao Wang, Dong Cheng

“…Deep learning UAV target tracking methods based on the Siamese family have achieved significant results but still face challenges regarding accuracy and speed compatibility. …”
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Multi-Strategy Improvement of Coal Gangue Recognition Method of YOLOv11 by Hongjing Tao, Lei Zhang, Zhipeng Sun, Xinchao Cui, Weixun Yi

“…The current methods for detecting coal gangue face several challenges, including low detection accuracy, a high probability of missed detections, and inadequate real-time performance. …”
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LGR-Net: A Lightweight Defect Detection Network Aimed at Elevator Guide Rail Pressure Plates by Ruizhen Gao, Meng Chen, Yue Pan, Jiaxin Zhang, Haipeng Zhang, Ziyue Zhao

“…In elevator systems, pressure plates secure guide rails and limit displacement, but defects compromise their performance under stress. Current detection algorithms face challenges in achieving high localization accuracy and computational efficiency when detecting small defects in guide rail pressure plates. …”
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Optimizing Problem-Solving in Technical Education: An Adaptive Learning System Based on Artificial Intelligence by Rommel Gutierrez, William Eduardo Villegas-Ch, Alexandra Maldonado Navarro, Sergio Lujan-Mora

“…Based on rules or machine learning, existing adaptive systems offer varying levels of personalization and efficiency but face significant scalability and computational demand barriers. …”
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Smart Grid System Based on Blockchain Technology for Enhancing Trust and Preventing Counterfeiting Issues by Ala’a Shamaseen, Mohammad Qatawneh, Basima Elshqeirat

“…A key contribution of the system is the design of a novel consensus algorithm, Proof of Energy, which selects validators based on node roles and randomized assignment, rather than computational power or stake ownership. …”
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A critical evaluation of Pure Pursuit, MPC and MPCC: Balancing simplicity, performance and constraints by Kong Xiangyiming, Fisher Callen, Evans Benjamin

“…In contrast to the simulated findings, PP demonstrated superior performance in physical implementations compared to MPC and MPCC. The computational demands of optimization-based algorithms result in a computation delay where the algorithm calculates for the vehicle's position one iteration behind its actual movement. …”
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Bio-Inspired Hyperparameter Tuning of Federated Learning for Student Activity Recognition in Online Exam Environment by Ramu Shankarappa, Nandini Prasad, Ram Mohana Reddy Guddeti, Biju R. Mohan

“…This includes addressing key challenges such as head pose detection and estimation, which are integral for applications like automatic face recognition, advanced surveillance systems, intuitive human–computer interfaces, and enhancing driving safety measures. …”
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Stochastic Fractal Search for Bayesian Network Structure Learning Under Soft/Hard Constraints by Yinglong Dang, Xiaoguang Gao, Zidong Wang

“…Moreover, the current data learning methods also face the problems of low computational efficiency or decreased accuracy when dealing with large-scale and highly complex DAGs. …”
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Rekayasa Perangkat Lunak Aplikasi Presensi Mobile Menggunakan Metode Deep Learning by Ragil Setiawan, Nurcahya Pradana Taufik Prakisya, Rosihan Ariyuana

“…Seeing the ongoing development of facial pattern recognition algorithms such as Viola Jones, Backpropagation, this research uses the MobileFaceNet  mobile CNN model which is currently popular to be implemented in the mobile-based facial recognition presence application at the Information and Computer Engineering Education (PTIK) FKIP UNS. …”
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Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities by Renzhong Zhang, Haorui Li, Yunxiao Shen, Jiayi Yang, Wang Li, Dongsheng Zhao, Andong Hu

“…With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GANs. …”
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An IoT intrusion detection framework based on feature selection and large language models fine-tuning by Huan Ma, Wan Zhang, Dalong Zhang, Baozhan Chen

“…Abstract The rapid proliferation of Internet of Things (IoT) devices has significantly expanded the network attack surface, necessitating the deployment of advanced AI (artificial intelligence)-based intrusion detection systems (IDS) to bolster IoT security. But existing methods face two significant challenges: (1) Feature redundancy: Current approaches extract numerous flow-level features to learn attack behavior, resulting in high computational complexity and substantial redundant information. (2) Class imbalance: Limited attack traffic samples hinder models from effectively learning attack patterns. …”
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