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 Title First Name Last Name Institution/Organization Country Domain Subdomain/Subject/Service Area Selected for
Mr.BenjaminKwakye DansoUniversity of Science and Technology of ChinaChinaComputer ScienceMetaheuristic algorithmsBest Paper Award
Dr.ArgyroKliniIESL-FORTHGreecePhysics and AstronomyMaterials ScienceBest Researcher Award
Dr.Jin-LuHuangShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineChinaPharmacology, Toxicology and Pharmaceutical ScienceHospital Pharmacy, Clinical pharmacyBest Scholar Award
Prof.SjaakBloemNyenrode Business UniversityNetherlandsEconomics, Econometrics and FinanceHealth EconomicsBest Researcher Award
Assoc. Prof. Dr.DanfengZhangAssociate dean of School of Biomedical & Chemical Engineering at Liaoning Institue of Science and TechnologyChinaChemistry•Photocatalysis oxidation/reduction on environmental pollutants by functional materials./ Analysis and detection of organic pollutants and chemical colorants by MSPE-HPLC method.Best Researcher Award
Assoc. Prof. Dr.YanLiFudan UniversityChinaChemistryComputational Chemistry, CADD/AIDDBest Researcher Award
Dr.SamAmosaThe National University of SamoaSamoaArts and HumanitiesPublic TheologyBest Researcher Award
Prof. Dr.WeiQiuTianjin UniversityChinaPhysics and Astronomysemiconductor & aerospaceBest Researcher Award
Dr.ŞenolYağdıUniversity of ViennaAustriaSocial SciencesReligious EducationBest Researcher Award
Prof.paulfentonSorbonne UniversitéFranceArts and HumanitiesHistory of ReligionsExcellence in Research Award
Prof.DanielaTarantinoUniversity of Genoa, Department of Political and International SciencesItalySocial SciencesCanon and Ecclesiastical Law, Comparative Law of ReligionsBest Researcher Award
Dr.S. Azhagu MadhavanSaveetha University, ChennaiIndiaBiochemistry, Genetics and Molecular BiologyPharmacology, BiomarkersBest Researcher Award
Prof. Dr.EduardoDelgado-OruscoUniversidad de ZaragozaSpainArts and HumanitiesProyecto ArquitectónicoBest Researcher Award
Mr.lubinwangGuilin Institute of Information TechnologyChinaComputer ScienceComputer VisionBest Researcher Award
Dr.HuangXiaosanNanjing Agricultural universityChinaAgricultural and Biological SciencesPomologyBest Researcher Award
Prof.JianghongZhaoDear Author, We are pleased to inform you that your recent publication has been provisionally selected for the "Best Researcher Award." Submit your profile via Nomination Link:  https://jut.li/RGKJl Regards, Organizing Committee, International Research Excellence Awards-Book of Award. contact@bookofaward.com Reference: To bridge the modality gap between camera images and LiDAR point clouds in autonomous driving systems—a critical challenge exacerbated by current fusion methods’ inability to effectively integrate cross-modal features—we propose the Cross-Modal Fusion (CMF) framework. This attention-driven architecture enables hierarchical multi-sensor data fusion, achieving state-of-the-art performance in semantic segmentation tasks.The CMF framework first projects point clouds onto the camera coordinates through the use of perspective projection to provide spatio-depth information for RGB images. Then, a two-stream feature extraction network is proposed to extract features from the two modalities separately, and multilevel fusion of the two modalities is realized by a residual fusion module (RCF) with cross-modal attention. Finally, we design a perceptual alignment loss that integrates cross-entropy with feature matching terms, effectively minimizing the semantic discrepancy between camera and LiDAR representations during fusion. The experimental results based on the SemanticKITTI and nuScenes benchmark datasets demonstrate that the CMF method achieves mean intersection over union (mIoU) scores of 64.2% and 79.3%, respectively, outperforming existing state-of-the-art methods in regard to accuracy and exhibiting enhanced robustness in regard to complex scenarios. The results of the ablation studies further validate that enhancing the feature interaction and fusion capabilities in semantic segmentation models through cross-modal attention and perceptually guided cross-entropy loss (Pgce) is effective in regard to improving segmentation accuracy and robustness. https://www.mdpi.com/1424-8220/25/8/2474ChinaEngineeringSurveying and Mapping Science and TechnologyBest Researcher Award
Dr.FabioMatiussiImaging Institute of Southern SwitzerlandSwitzerlandComputer ScienceArtificial intelligenceInnovative Research Award
Prof. Dr.QudongWangShanghai Jiao Tong UniversityChinaMaterials ScienceMaterials Processing EngineeringBest Researcher Award
Prof.kunyangnanjing university of aeronautics and astronauticsChinaEngineeringnuclear engineeringBest Researcher Award
Ms.YeeshtdevisinghHosaneeUniversity of TechnologyMauritiusComputer ScienceSoftware and Artificial IntelligenceWomen Research Award
 Title First Name Last Name Institution/Organization Country Domain Subdomain/Subject/Service Area Selected for

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