Maximizing influence via link prediction in evolving networks by Kexin Zhang, Mo Li, Shuang Teng, Lingling Li, Yi Wang, Xuezhuan Zhao, Jinhong Di, Ji Zhang

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Using single-sample networks and genetic algorithms to identify radiation-responsive genes in rice affected by heavy ions of the galactic cosmic radiation with different LET values by Yan Zhang, Wei Wang, Meng Zhang, Binquan Zhang, Shuai Gao, Meng Hao, Dazhuang Zhou, Lei Zhao, Guenther Reitz, Guenther Reitz, Yeqing Sun

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Machine learning models for predicting morphological traits and optimizing genotype and planting date in roselle (Hibiscus Sabdariffa L.) by Fazilat Fakhrzad, Warqaa Muhammed ShariffAl-Sheikh, Mohammed M. Mohammed, Heidar Meftahizadeh

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Attention-based hybrid deep learning model with CSFOA optimization and G-TverskyUNet3+ for Arabic sign language recognition by Ahmed A. Mohamed, Abdullah Al-Saleh, Sunil Kumar Sharma, Ghanshyam Tejani

“…In addition, employing a novel metaheuristic algorithm, the Crisscross Seed Forest Optimization Algorithm, which combines the Crisscross Optimization and Forest Optimization algorithms to determine the best features from the extracted texture, color, and deep learning features. …”
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Restoring Anomalous Water Surface in DOM Product of UAV Remote Sensing Using Local Image Replacement by Chunjie Wang, Ti Zhang, Liang Tao, Jiayuan Lin

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Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights by V. S. Shaisundaram, P. V. Elumalai, S. Padmanabhan, U. Nalini Ramachandran, Abhishek Kumar Tripathi, Cui Yaping, B. Nagaraj Goud, S. Prabhakar

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Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction by Yixuan Wang, Chuang Huang, Dawei Li

“…Second, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed. The network first predicts the edge scores for the facets to determine the seed facets. …”
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Adaptação da função dielétrica {épsilon"/[épsilon'(a f épsilon' - épsilon"]} para determinação do teor de água em sementes de feijão por radiofreqüências Adjustment of the microwav... by Pedro A. Berbert, Daniel M. de Queiroz, Elias F. de Sousa, Edenio Detmann, Alexandre P. Viana, Rafael G. Dionello

“…Measurement of dielectric parameters was performed using samples varying in moisture content from 11.5 to 20.6% w.b., and bulk densities in the range from 756 e 854 kg m-3. The adaptation to radiofrequencies of a microwave dielectric model derived from the density independent function zeta produced a model capable of estimating the moisture content (w.b.) of common bean seeds with a standard error of estimate and maximum error of 0.6 and 1.4 percentage points.…”
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MDM-TDM PON Utilizing Self-Coherent Detection-Based OLT and RSOA-Based ONU for High Power Budget by Yuanxiang Chen, Juhao Li, Peng Zhou, Paikun Zhu, Yu Tian, Zhongying Wu, Jinglong Zhu, Ke Liu, Dawei Ge, Jingbiao Chen, Yongqi He, Zhangyuan Chen

“…Due to the high gain of RSOA and high receiver sensitivity of self-coherent detection, a 30-dB bidirectional power budget is achieved after 10-km few-mode fiber and a 20-km standard single-mode fiber at the bit error rate (BER) of 10^−3. Optimal seed power and signal power that input to the RSOA are investigated in this paper.…”
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Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network by Amit Kumar Rai, Nirupama Mandal, Krishna Kant Singh, Ivan Izonin

“…The trained network can discover hidden data patterns in unseen data. The learning algorithm and seed selection play a vital role in the performance of the network. …”
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Batch generated strongly nonlinear S-Boxes using enhanced quadratic maps by Mohammad Mazyad Hazzazi, Farooq E Azam, Rashad Ali, Muhammad Kamran Jamil, Sameer Abdullah Nooh, Fahad Alblehai

“…According to the cryptanalysis result of the S-box construction in AES: (1) the number of irreducible polynomials can be increased to 30; (2) the affinity transformation constant c can be chosen from all elements if the existence of fixed points and reverse fixed points in an S-box is ignored; and (3) the S-box in AES is fixed, which poses possible security risks to the AES algorithm. …”
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Improved YOLOv8 Model for Phenotype Detection of Horticultural Seedling Growth Based on Digital Cousin by Yuhao Song, Lin Yang, Shuo Li, Xin Yang, Chi Ma, Yuan Huang, Aamir Hussain

“…Moreover, a case study of watermelon seedings is examined, and the results of the 3D reconstruction of the seedlings show that our model outperforms classical segmentation algorithms on the main metrics, achieving a 91.0% mAP50 (B) and a 91.3% mAP50 (M).…”
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Targeted Influential Nodes Selection in Location-Aware Social Networks by Susu Yang, Hui Li, Zhongyuan Jiang

“…Experimental study over three real-world social networks verified the seed quality of our framework, and the coarsening-based algorithm can provide superior efficiency.…”
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A New Discrete Grid-Based Bacterial Foraging Optimizer to Solve Complex Influence Maximization of Social Networks by Yichuan Zhang, Yibo Yong, Shujun Yang, Tian Zhang

“…In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. …”
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Method to improve edge coverage in fuzzing by Chunfu JIA, Shengbo YAN, Zhi WANG, Chenlu WU, Hang LI

“…Aiming at the problems of incomplete edge coverage,insufficient uses of edge coverage information and valid bytes information in AFL (American fuzz lop),a novel method was proposed.Firstly,a new seed selection algorithm was introduced,which could completely cover all edges discovered in one cycle.Secondly,the paths were scored according to the frequency of edges,to adjust the number of tests for each seed.Finally,more mutations were crafted on the valid bytes of AFL.Based on the method above,a new fuzzing tool named efuzz was implemented.Experiment results demonstrate that efuzz outperforms AFL and AFLFast in the edge coverage,with the increases of 5% and 9% respectively.In the LAVA-M dataset,efuzz found more vulnerabilities than AFL.Moreever,in real world applications efuzz has found three new security bugs with CVEs assigned.The method can effectively improve the edge coverage and vulnerability detection ability of fuzzer.…”
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DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants by Yixin Guo, Jinchao Pan, Xueying Wang, Hong Deng, Hong Deng, Mingliang Yang, Mingliang Yang, Enliang Liu, Qingshan Chen, Qingshan Chen, Rongsheng Zhu, Rongsheng Zhu

“…The correlation coefficients between the number of one-seed pods, the number of two-seed pods, the number of three-seed pods, the number of four-seed pods and the total number of pods extracted by the algorithm and the manual measurement results were 92.62%, 95.17%, 96.90%, 94.93%, 96.64%,respectively. …”
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Channel Optimization of Marketing Based on Users’ Social Network Information by Chaolin Peng

“…To solve the NP-hard problem of maximizing influence, this paper uses Monte Carlo sampling to calculate high-influence users. Next, a seed user selection algorithm based on NSGA-II is proposed to optimize the above three objective functions and find the optimal solution. …”
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Graph cut-based segmentation for intervertebral disc in human MRI by Leena Silvoster, R. Mathusoothan S. Kumar

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Snake-Like Robot Workspace Solving Method Based on Improved Monte Carlo Method by ZhiYong Yang, Wang Tian, HaoYang Wang, Xu Liu, DaoDe Zhang, Yu Yan, ShaoSheng Fan

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Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR Spectroscopy by Jasciane da Silva Alves, Bruna Parente de Carvalho Pires, Luana Ferreira dos Santos, Tiffany da Silva Ribeiro, Kerry Brian Walsh, Ederson Akio Kido, Sergio Tonetto de Freitas

“…A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. …”
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