Ufuk Avrupa · Health
Advancing promising results from Pillar II collaborative research through the innovation journey
Çağrı özeti
Resmi metinden, ilk bölüm
Expected Outcome: Project results are expected to contribute to all the following expected outcomes: Research and Innovation (R&I) solutions funded under Horizon Europe Pillar II are further advanced along the innovation pathway towards uptake and deployment in the EU and/or Associated Countries, including their translation into high value products, services, processes or business models, strengthening the capacity of European public and private organisations to use innovative solutions. End users [1] and market actors actively co-develop and validate highly innovative solutions in real-world settings, ensuring demand-side needs are integrated, helping to de-risk the innovation journey. The EU and Associated Countries benefit from accelerated commercialisation, adoption and deployment of Horizon Europe results, thereby reinforcing societal benefits, sustainable competitiveness, resilienc… devamı
AeronauticsAgricultural biotechnologyAgricultural engineering, food safetyAgriculture related to crop production, soil biology and cultivation, applied plant biologyAgriculture, Forestry, and FisheriesAgro-forestryAgroecologyAgroindustryAgronomyAlternative fuelsAnimal and Dairy scienceAnimal healthArchitecture, smart buildings, smart cities, urban engineeringArtificial intelligenceArts (arts, history of arts, performing arts, music)Audiovisual Festivals / Events / Markets / Training - Media
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Konu uyumuna göre
19-
%54ERDAL BAŞARAN
Ortak konular: Smart Agriculture and AI
Örtüşen yayınlar: Classification of Walnut Leaf Images Using a Hybrid CNN-Based Deep Learning Approach (2026) · Advanced gastrointestinal image classification based on vision transformer, hypercolumn and biological optimization (2026)
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%52CİHAT GEDİK
Ortak konular: Smart Agriculture and AI · Remote Sensing in Agriculture · Agricultural Engineering and Mechanization
Örtüşen yayınlar: Evaluation of Plant Residues: Samsun Province (2023)
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%50CEMALETTİN AKDOĞAN
Ortak konular: Smart Agriculture and AI
Örtüşen yayınlar: Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques (2024)
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%50GÜLŞEN AYDIN KESKİN
Ortak konular: Agricultural and Rural Development Research
Örtüşen yayınlar: An extensive and systematic literature review for hybrid flowshop scheduling problems (2022)
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%49YASİN ÇİÇEK
Ortak konular: Smart Agriculture and AI
Örtüşen yayınlar: Classifying Weed Development Stages Using Deep Learning Methods (2025)
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%47ÇETİN CEM BÜKÜCÜ
Ortak konular: Smart Agriculture and AI
Örtüşen yayınlar: A NEW PROTOTYPE THAT PERFORMS REAL-TIME ERROR DETECTION IN GLASS PRODUCTS (2020)
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%46METİN TUNAY
Ortak konular: Agricultural and Rural Development Research · Agriculture and Farm Safety · Remote Sensing in Agriculture
Örtüşen yayınlar: EFFECTS OF SEASONAL CHANGES ON MICROBIAL BIOMASS AND RESPIRATION OF FOREST FLOOR AND TOPSOIL UNDER BORNMULLERIAN FIR STAND (2015) · The investigation of the ergonomics aspects of the noise caused by agricultural tractors used in Turkey (2010)
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%45MUHAMMAD UMER KHAN
Ortak konular: Smart Agriculture and AI
Örtüşen yayınlar: TobSet: A New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots (2022) · Mobile Robot Navigation Using Reinforcement Learning in Unknown Environments (2019)
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%44FERHAT SADAK
Ortak konular: Microfluidic and Bio-sensing Technologies
Örtüşen yayınlar: Strawberry Ripeness Assessment Via Camouflage-Based Data Augmentation for Automated Strawberry Picking Robot (2022) · A Deep Learning-Based Sensor Modeling for Smart Irrigation System (2022)
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%43SEVGİ AKTEN KARAKAYA
Ortak konular: Smart Agriculture and AI
Örtüşen yayınlar: pycellga: A Python package for improved cellular genetic algorithms (2025)
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%43ŞENOL ALTAN
Ortak konular: Smart Agriculture and AI
Örtüşen yayınlar: Otonom Kara ve Hava Araçları ile Akıllı Tarım: Hasat Optimizasyonu Üzerine Bir Uygulama (2020)
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%42AYHAN İSTANBULLU
Ortak konular: Smart Agriculture and AI
Örtüşen yayınlar: Morphological Analysis Of Blood Cell And Leykemia Diagnosis Based On The Yolo V11 Model And The Neyman-Pearson Hypothesis (2025)
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%42FARUK AYATA
Ortak konular: Fungal Biology and Applications
Örtüşen yayınlar: A stacking based deep learning framework integrating random search neural architecture search for meniscus tear diagnosis (2026)
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%42İSMAİL ÖZTÜRK
Ortak konular: Agricultural Engineering and Mechanization
Örtüşen yayınlar: Thermal conductivity of safflower (Carthamus tinctorius L.) seeds (2011) · Bant İlaçlaması ve Direkt İlaçlama Yapan Üniteler ve Kullanım Alanları / Band Spraying and Direct Spraying Units and Utilization Fields of This Methods (2007)
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%41BURAK SALTUK
Ortak konular: Agricultural and Rural Development Research
Örtüşen yayınlar: Determination of the Environmental Effects of Plant Protection Products in Fighting Pests in Greenhouse Vegetable Production: Batman Province Example (2021) · Effects of vermicompost application on growth and yield of hot pepper (Capsicum frutescens) (2024)
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%41FERİT ÇOBANOĞLU
Ortak konular: Agricultural and Rural Development Research · Phytochemistry and biological activities of Ficus species
Örtüşen yayınlar: Analysis of the Impact of Sustainability-Based Agricultural Certificates: The Case of Fig Growing in Aydın Provinc (2024) · The Link Between Sustainable Development Goals and Agricultural Production Systems: Türkiye Analysis (2024)
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%40CANER BALIM
Ortak konular: Smart Agriculture and AI
Örtüşen yayınlar: A CNN–NCP Based Hybrid Deep Learning Model for Speech-Driven Gender Classification (2026) · Automatic multi-language analysis of SOLID compliance via machine learning algorithms (2026)
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%40EZGİ ÇOLAK
Ortak konular: Urban Agriculture and Sustainability
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%40MURAT GÖKŞİN BAKIR
Ortak konular: Smart Agriculture and AI
Örtüşen yayınlar: Integrating Multiple Methodologies, Segment Anything Model and AlexNet, for Enhanced Accuracy in Garbage Classification (2024)
Uyum oranı kodla, her istekte hesaplanır. Çağrı metni (başlık, anahtar kelimeler, resmi açıklamanın başı) şu üç kaynakla karşılaştırılır: akademisyen profiliyle anlamsal yakınlık (%50), YÖKSİS anahtar kelimeleri ve OpenAlex konularındaki ortak ayırt edici terimler (%25) ve yayın içeriği — başlıklar, İngilizce özetler ve bunların Türkçe çevirileri (%25). Çeviriler sayesinde Türkçe çağrılar İngilizce yayınlarla da eşleşir. Yaygın kelimeler düşük ağırlıklıdır; %40 altı gösterilmez. Bir konu önerisidir; başvuru uygunluğu, kapasite ya da başarı değerlendirmesi değildir. Gizli profiller listelenmez.