User Purchase Intention Prediction Based on Improved Deep Forest

Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)model.This model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns.Moreover,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive accuracy.Results demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline model.Furthermore,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.

Recommendation Method for Contrastive Enhancement of Neighborhood Information

Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.

粮食安全背景下甘肃省天水市粮食保险问题研究

干旱少雨,地质灾害和气象灾害频发给广大农民带来严重经济损失和生命威胁。粮食安全自然成为我国最重要的国家安全战略。面对每年的农业灾害对粮食生产造成重大损失,现行的粮食灾害保险业务发展缓慢且存在粮食保险定位模糊;粮食保险创新不足;政策支持不完善;自然灾害分散频发,风险防范困难;保险服务能力不足等问题。本文通过对天水市麦积区、清水县的6个乡镇12个村360户农民进行问卷调查,提出了从顶层设计重塑粮食保险功能;完善粮食保险制度;推动粮食保险转型升级;提升粮食保险服务农业全产业链能力等措施,为促进粮食安全,提升农民种植粮食积极性,提高农民收入保驾护航。

Chromosome-scale genome assembly of sweet cherry(Prunus avium L.)cv.Tieton obtained using long-read and Hi-C sequencing

Sweet cherry(Prunus avium)is an economically significant fruit species in the genus Prunus.However,in contrast to other important fruit trees in this genus,only one draft genome assembly is available for sweet cherry,which was assembled using only Illumina short-read sequences.The incompleteness and low quality of the current sweet cherry draft genome limit its use in genetic and genomic studies.A high-quality chromosome-scale sweet cherry reference genome assembly is therefore needed.A total of 65.05 Gb of Oxford Nanopore long reads and 46.24 Gb of Illumina short reads were generated,representing~190x and 136x coverage,respectively,of the sweet cherry genome.The final de novo assembly resulted in a phased haplotype assembly of 344.29 Mb with a contig N50 of 3.25 Mb.Hi-C scaffolding of the genome resulted in eight pseudochromosomes containing 99.59%of the bases in the assembled genome.Genome annotation revealed that more than half of the genome(59.40%)was composed of repetitive sequences,and 40,338 protein-coding genes were predicted,75.40%of which were functionally annotated.With the chromosomescale assembly,we revealed that gene duplication events contributed to the expansion of gene families for salicylic acid/jasmonic acid carboxyl methyltransferase and ankyrin repeat-containing proteins in the genome of sweet cherry.Four auxin-responsive genes(two GH3s and two SAURs)were induced in the late stage of fruit development,indicating that auxin is crucial for the sweet cherry ripening process.In addition,772 resistance genes were identified and functionally predicted in the sweet cherry genome.The high-quality genome assembly of sweet cherry obtained in this study will provide valuable genomic resources for sweet cherry improvement and molecular breeding.

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources.Although leaf image-based methods have been widely adopted in plant species identification,they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars.Here,we propose an automatic leaf image-based cultivar identification pipeline called MFCIS(Multi-feature Combined Cultivar Identification System),which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network(CNN).Persistent homology,a multiscale and robust method,was employed to extract the topological signatures of leaf shape,texture,and venation details.A CNN-based algorithm,the Xception network,was fine-tuned for extracting high-level leaf image features.For fruit species,we benchmarked the MFCIS pipeline on a sweet cherry(Prunus avium L.)leaf dataset with>5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%.For annual crop species,we applied the MFCIS pipeline to a soybean(Glycine max L.Merr.)leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods.The identification models for each growth period were trained independently,and their results were combined using a score-level fusion strategy.The classification accuracy after score-level fusion was 91.4%,which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods.To facilitate the adoption of the proposed pipelines,we constructed a user-friendly web service,which is freely available at http://www.mfcis.online.