[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2141":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"view_count":15,"doi":26,"paper":27,"created_at":46},2141,"Next-Generation Deep Learning: A Comprehensive Survey on Explainable, Efficient, Privacy-Preserving and Multimodal Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.17148\u002Fijarcce.2026.15912","The continuous evolution of deep learning has significantly expanded the capabilities of artificial intelligence, enabling intelligent systems to solve increasingly complex problems across healthcare, computer vision, natural language processing, cybersecurity, finance, autonomous systems, and smart environments.Beginning with artificial neural networks and progressing through convolutional and recurrent networks to Transformers, Graph Neural Networks, Vision Transformers, and Large Language Models, deep learning has achieved remarkable improvements in feature representation, prediction accuracy, and knowledge transfer.Nevertheless, the growing complexity of these models introduces several challenges, including limited model transparency, high computational and energy requirements, data privacy risks, and the effective utilization of heterogeneous multimodal information.Unlike conventional surveys that primarily classify deep learning according to network architectures or application domains, this work presents a capability-oriented taxonomy that organizes recent developments into five major research directions: Explainable Deep Learning, Efficient Deep Learning, Privacy-Preserving Deep Learning, Green Deep Learning and Multimodal Deep Learning.Based on this framework, representative architectures are critically examined with respect to their operating principles, strengths, limitations, and suitability for diverse real-world applications.The survey also analyses emerging application areas, identifies unresolved challenges related to fairness, robustness, scalability, and trustworthy artificial intelligence, and discusses promising research opportunities involving Federated Large Language Models, Edge AI, Green AI, Self-supervised Learning, and Multimodal Foundation Models.The proposed framework provides a structured understanding of current advances while offering practical insights for the design of transparent, efficient, secure, and sustainable next-generation deep learning systems.","深度学习的持续演进显著拓展了人工智能的能力，使智能系统能够在医疗健康、计算机视觉、自然语言处理、网络安全、金融、自主系统和智能环境等领域解决日益复杂的问题。从人工神经网络起步，历经卷积网络和循环网络，发展到Transformer、图神经网络、视觉Transformer和大语言模型，深度学习在特征表示、预测精度和知识迁移方面取得了显著提升。然而，这些模型日益增长的复杂性也带来了若干挑战，包括模型透明度有限、计算和能耗需求高、数据隐私风险以及异构多模态信息的有效利用等问题。与主要按网络架构或应用领域对深度学习进行分类的传统综述不同，本文提出了一种以能力为导向的分类体系，将近期发展归纳为五大研究方向：可解释深度学习、高效深度学习、隐私保护深度学习、绿色深度学习和多模态深度学习。基于该框架，本文对代表性架构进行了批判性审视，分析其运行原理、优势、局限性以及对多样化实际应用的适用性。本综述还分析了新兴应用领域，指出了在公平性、鲁棒性、可扩展性和可信人工智能方面尚未解决的挑战，并讨论了涉及联邦大语言模型、边缘人工智能、绿色人工智能、自监督学习和多模态基础模型等有前景的研究机遇。所提出的框架为理解当前进展提供了结构化视角，同时为设计透明、高效、安全和可持续的下一代深度学习系统提供了实践指导。",null,"IJARCCE","2026-09-10T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"通用深度学习综述，未涉及农业场景，与三农及农业信息化无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24,25],"可解释AI","深度学习","多模态","绿色AI","隐私计算","10.17148\u002Fijarcce.2026.15912",{"doi":26,"openalex_id":28,"authors":29,"venue":10,"cited_by_count":15,"oa_url":6,"card":38,"direction":44,"ingested_from":45},"W7212125020",[30,32,34,36],{"name":31,"orcid":9},"K. Ramalakshmi",{"name":33,"orcid":9},"S. Prema",{"name":35,"orcid":9},"M. Ajithaveni",{"name":37,"orcid":9},"M Ramya",{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"综述可解释、高效、隐私保护、绿色与多模态五大深度学习方向，提出能力导向分类框架。","文献综述，按能力分类代表性架构，分析原理、优缺点与应用。","五大方向各有挑战，未来机会在联邦大模型、边缘AI、绿色AI、自监督与多模态基础模型。","农业人工智能与决策模型","可将可解释、高效、隐私保护与多模态深度学习迁移到农业场景，构建可信农业AI系统。","智慧农业 \u002F 农业物联网","openalex","2026-09-11T23:30:10.233616Z"]