作者: Interlocking 時(shí)間: 2025-3-21 22:15 作者: 巡回 時(shí)間: 2025-3-22 01:15
A Memetic Algorithm Based on Adaptive Simulated Annealing for Community Detectionferences to switch to adaptive probabilities in simulated annealing (SA) for local search to accelerate convergence. The algorithm was extensively tested and experimented with 11 artificial and 4 real networks. Compared with other 10 algorithms, the results showed that MA-ASA performs well and is highly competitive.作者: 防水 時(shí)間: 2025-3-22 08:03 作者: 不溶解 時(shí)間: 2025-3-22 12:10
Mouse-Brain Topology Improved Evolutionary Neural Network for?Efficient Reinforcement Learningons), but also performed better than other algorithms, including the ENN using a random network, standard long-short-term memory (LSTM), and multi-layer perception (MLP). We think the biologically plausible structures might contribute more to the further development of artificial neural networks.作者: Adenoma 時(shí)間: 2025-3-22 15:00 作者: 萬神殿 時(shí)間: 2025-3-22 17:33
Noisy Label Learning in Deep Learninghe problem of learning with label noise from the perspective of supervised learning. And then we will summarize the existing methods from the perspective of dataset usage. Subsequently, we will analyze the problems with the data and existing methods. Finally we will give some possible solution ideas.作者: instill 時(shí)間: 2025-3-22 22:18
A Simple Approach to the Multiple Source Identification of Information Diffusioning time as diffusion source. Furthermore, we also give a new method to estimate the spreading time which can improve the proposed multiple sources identification algorithm. Simulation results show that the proposed method has distinct advantages in identifying multiple sources in various real-world networks.作者: 貿(mào)易 時(shí)間: 2025-3-23 01:53 作者: zonules 時(shí)間: 2025-3-23 08:50
1868-4238 d in topical sections as follows: Brain cognition; machine learning; data intelligence; language cognition; remote sensing images; perceptual intelligence; wireless sensor; and medical artificial intelligence..978-3-031-14905-4978-3-031-14903-0Series ISSN 1868-4238 Series E-ISSN 1868-422X 作者: 愛得痛了 時(shí)間: 2025-3-23 13:41 作者: DEMUR 時(shí)間: 2025-3-23 17:46 作者: Coronary 時(shí)間: 2025-3-23 20:45
Hongliang Luo,Jieyi Liu,Jingyao Liu,Yue Wu,Yaru Yin作者: Host142 時(shí)間: 2025-3-24 00:26
Yanjun Cao,Yuan Wang,Jingjing Guo,Li Han,Chao Zhang,Jin Zhu,Tianyang Zhang,Xiangrong Zhang作者: bypass 時(shí)間: 2025-3-24 02:33
Mengyao Fan,Haofeng Liu,Zhenyu Zhu,Changzhe Jiao,Shuiping Gou作者: 炸壞 時(shí)間: 2025-3-24 06:43 作者: MAIZE 時(shí)間: 2025-3-24 11:05 作者: Flounder 時(shí)間: 2025-3-24 15:45 作者: Calculus 時(shí)間: 2025-3-24 22:41
Michael Aggrey Okoth,Ronghua Shang,Weitong Zhang,Licheng Jiao作者: 流逝 時(shí)間: 2025-3-25 02:28
Wakjira Mulugeta Asefaw,Ronghua Shang,Michael Aggrey Okoth,Licheng Jiao作者: Delirium 時(shí)間: 2025-3-25 06:00
DNM-SNN: Spiking Neural Network Based on Dual Network Modeles two key methods. Firstly, a dual model training method in training stage is proposed, which requires an additional auxiliary network same as the network used. Single model training is easy to fall into local optimal problems. By maintaining two networks, the same problem can be viewed from differ作者: Vital-Signs 時(shí)間: 2025-3-25 10:17
A Deception Jamming Discrimination Method Based on Semi-supervised Learning with Generative Adversarsed method can achieve the same performance using less than 10% of the labelled data of existing algorithms. It reduces data requirements and enhances operational capabilities, which is better suited to real-world battlefield environments.作者: 不透氣 時(shí)間: 2025-3-25 13:50
Fast Node Selection of?Networked Radar Based on?Transfer Reinforcement Learning nodes, transfer reinforcement learning is presented to fully leverage the previous knowledge. Experimental results show that our proposed method can quickly select the optimal and minimum radar nodes in a brief period, significantly improving the speed of radar node selection in the networked radar作者: covert 時(shí)間: 2025-3-25 18:12
Weakly Supervised Liver Tumor Segmentation Based on?Anchor Box and?Adversarial Complementary Learnin. Aiming at the problem that the current region mining method based on classification network is inaccurate and incomplete in object location, we use the Adversarial Complementary Learning module to make the network pay attention to more complete objects. We conduct analysis to validate the proposed作者: Vo2-Max 時(shí)間: 2025-3-26 00:02 作者: 重疊 時(shí)間: 2025-3-26 02:06 作者: antecedence 時(shí)間: 2025-3-26 07:36
A Directed Search Many Objective Optimization Algorithm Embodied with?Kernel Clustering Strategy. Then, it improves them by the directed search method. DSMOA-KCS is compared with several existing state-of-the-art algorithms (NSGA-III, RSEA, and MOEADPas) on many-objective problems with 5 to 30 objective functions using the Inverted Generational Distance (IGD) performance metric. DSMOA-KCS eval作者: 得意牛 時(shí)間: 2025-3-26 09:18
also um Festigkeitsrechnungen handelt, so braucht hier weder berücksichtigt zu werden, da? sich die Gr??e des Normaldruckes .. w?hrend des Eingriffs geringfügig ?ndert (Abschnitt 3) noch braucht die ebenfalls kleine Reibungskraft .. (Abschnitt 4) in Rechnung gestellt zu werden. Wir setzen also Zahnd作者: extinct 時(shí)間: 2025-3-26 16:31 作者: white-matter 時(shí)間: 2025-3-26 19:29 作者: 我怕被刺穿 時(shí)間: 2025-3-26 21:27
Zhen Cao,Hongwei Zhang,Qian Wang,Chuanfeng Maunden bei menschlichen Spontankr?mpfen. Jedenfalls ist aus den Befundbeschreibungen die Mitwirkung vasomotorischer Vorg?nge klar ersichtlich. Andere Autoren hatten bei Anpassung der experimentellen Bedingungen an das therapeutische Vorgehen teils negative Ergebnisse. Vor allem ist es den italienisch作者: 使迷醉 時(shí)間: 2025-3-27 01:43 作者: BAIT 時(shí)間: 2025-3-27 08:59 作者: 衰弱的心 時(shí)間: 2025-3-27 12:29 作者: Cerebrovascular 時(shí)間: 2025-3-27 15:06
Zhen Cao,Hongwei Zhang,Qian Wang,Chuanfeng Maort, wo wir über ausreichende klinische Daten verfügen, f?llt die verh?ngnisvolle Rolle von Krampfserien bei der Entstehung der Gehirnsch?den auf. Es geht aus dem Material aber auch hervor, da? das Kindesalter dabei eine ganz bevorzugte Rolle spielt. Diese beiden disponierenden Faktoren sind in der 作者: 皺痕 時(shí)間: 2025-3-27 20:27 作者: Dawdle 時(shí)間: 2025-3-27 23:15 作者: 雜役 時(shí)間: 2025-3-28 03:10
A Memetic Algorithm Based on Adaptive Simulated Annealing for Community Detectioncy, we proposed a memetic algorithm based on an adaptive simulated annealing local search (MA-ASA). Segmented label propagation (STLP) is used for initialization and variation operations. A hierarchical idea is adopted to form an initial cluster center during initialization, and random competition i作者: FIS 時(shí)間: 2025-3-28 08:44 作者: obsolete 時(shí)間: 2025-3-28 11:08 作者: entitle 時(shí)間: 2025-3-28 15:49 作者: 有毛就脫毛 時(shí)間: 2025-3-28 22:06
Fast Node Selection of?Networked Radar Based on?Transfer Reinforcement Learningbecoming more and more critical for data fusion sharing and network collaboration. However, due to the large number and wide range of nodes in the networked radar system, there exists a redundancy problem in radar node assignment, which causes additional resource consumption and slows down the task 作者: 青春期 時(shí)間: 2025-3-29 02:59 作者: Disk199 時(shí)間: 2025-3-29 06:37 作者: NIB 時(shí)間: 2025-3-29 07:26 作者: coagulate 時(shí)間: 2025-3-29 14:43 作者: duplicate 時(shí)間: 2025-3-29 19:04 作者: nurture 時(shí)間: 2025-3-29 19:43 作者: VEIL 時(shí)間: 2025-3-30 02:10
A Two-Branch Neural Network Based on Superpixel Segmentation and Auxiliary Samplesonal homogeneity of the ground objects. We propose a two-branch neural network based on superpixel segmentation and auxiliary samples (TBN-SPAS) for HSI classification. TBN-SPAS uses superpixel segmentation to find samples within the superpixel, which have high spatial correlation with the sample to作者: 評(píng)論性 時(shí)間: 2025-3-30 07:17
Augmentation Based Synthetic Sampling and?Ensemble Techniques for?Imbalanced Data Classificationlars proposed various approaches such as undersampling majority class, oversampling minority class, synthetic Minority Oversampling (SMOTE) technique, Proximity Weighted Random Affine Shadowsampling (ProWRAS), etc. However, this work proposes a new method called Augmentation Based Synthetic Sampling作者: 舞蹈編排 時(shí)間: 2025-3-30 09:39
IFIP Advances in Information and Communication Technologyhttp://image.papertrans.cn/i/image/469274.jpg作者: 吵鬧 時(shí)間: 2025-3-30 13:04
978-3-031-14905-4IFIP International Federation for Information Processing 2022作者: 熱情的我 時(shí)間: 2025-3-30 20:08 作者: Invertebrate 時(shí)間: 2025-3-30 22:12 作者: 冬眠 時(shí)間: 2025-3-31 03:50
Das Historische Drama in Deutschlandhich vegetation is exposed in many parts of Central/Southern Europe, North/South America and Asia (Yunus and Iqbal 1996). In many of these regions, there is incontrovertible evidence that O. levels are high enough to reduce crop yields (Heck et al. 1983; Runeckles and Chevone 1992; Turcsányi et al. 作者: 稀釋前 時(shí)間: 2025-3-31 06:58
Siddharth Agarwal,Atul Singh,Pratibha Sharmaite images and field observations. We summarize the patterns in their distribution based on categories of land use, bioclimatic gradients, forest types and inclusion within protected areas. Large areas of temperate forest watersheds without major human impacts were documented across much western dra作者: 盡忠 時(shí)間: 2025-3-31 09:12 作者: 背景 時(shí)間: 2025-3-31 13:28 作者: 人類的發(fā)源 時(shí)間: 2025-3-31 20:16
The Raw Material of Cluster Formation: Observational Constraintsent on a timescale of a few Myr, and therefore cannot be strictly treated as isolated systems. We also review the observational evidence that GMCs are gravitationally bound and conclude that a significant fraction may be (mildly) gravitationally unbound. The implications of these for the star format作者: 反饋 時(shí)間: 2025-4-1 01:10 作者: ALTER 時(shí)間: 2025-4-1 05:51
https://doi.org/10.1007/978-1-4684-9469-3Bienayme, Irenee Jules; Statistik; average; history of mathematics; statistical theory; statistics
