1. Auxiliary Deep Dense Network (ADDN) Based Plant Growth Estimation
基于辅助深度密集网络（ADDN）的植物生长估计
Neeta B. Bankhele, Rekha P. Labade, Sachin V. Chaudhari
妮塔·B·班赫莱，蕾卡·P·拉巴德，萨钦·V·乔达里
Abstract: ccurate plant phenotyping is essential for plant breeding and understanding environmental impacts on plant growth and yield. This paper proposes an Auxiliary Deep Dense Network (ADDN)-based deep learning framework for plant growth estimation using image segmentation, feature extraction, and deep neural networks. The proposed approach improves the accuracy of plant growth estimation and supports precision agriculture by enabling efficient monitoring of plant health and development.
摘要 准确的植物表型分析对于植物育种以及研究环境对植物生长和产量的影响至关重要。本文提出了一种基于辅助深度密集网络（ADDN）的深度学习框架，通过图像分割、特征提取和深度神经网络实现植物生长估计。该方法提高了植物生长估计的准确性，并为精准农业中的植物健康监测和生长分析提供了有效支持。。
2. Multimodal Biometric Identification Using YOLOv7 With Integrated Face, Palmprint, Fingerprint, And Iris Features
基于YOLOv7融合人脸、掌纹、指纹和虹膜特征的多模态生物特征识别
Sheetal Shrikant Shevkari, Dr. Kelapati, Anita Vinayak Atiwadkar
希塔尔·什里坎特·谢夫卡里，凯拉帕蒂博士，安妮塔·维纳亚克·阿提瓦德卡尔
Abstract: Feature-based image classification is widely used in multimodal biometric identification systems. This paper presents a hybrid YOLOv7-based biometric recognition framework that integrates face, palmprint, fingerprint, and iris features to improve identification accuracy, reliability, and security. Experimental results demonstrate high performance in terms of accuracy, precision, specificity, and sensitivity, making the proposed system suitable for high-security authentication applications.
摘要: 基于特征的图像分类是多模态生物特征识别系统中常用的方法。本文提出了一种基于YOLOv7的混合多模态生物特征识别框架，将人脸、掌纹、指纹和虹膜特征进行融合，以提高身份识别的准确性、可靠性和安全性。实验结果表明，该方法在准确率、精确率、特异性和敏感性方面均表现优异，适用于高安全性身份认证场景。
3. A Spatial Data Mining Framework for Large-Scale Geospatial Big Data Analysis
面向大规模地理空间大数据分析的空间数据挖掘框架
Ravi Kelthor, Arjun Patel, Manish Sharma
拉维·凯尔索, 阿尔琼·帕特尔, 马尼什·夏尔马
Abstract: This paper proposes a scalable spatial data mining framework designed for distributed geospatial analytics.
摘要: 本文提出了一种可扩展的空间数据挖掘框架，专为分布式地理空间分析设计。
4. A Novel GNSS Positioning Algorithm for High Precision Navigation
一种用于高精度导航的新型GNSS定位算法
Takuro Hasejima, Hiroshi Tanaka
長島拓郎, 田中宏
Abstract: This paper proposes a new positioning algorithm designed to improve the accuracy and reliability of GNSS-based navigation systems.
摘要: 本文提出了一种新的定位算法，旨在提高基于GNSS的导航系统的精度和可靠性。
5. Satellite-Based Monitoring of Environmental Changes
基于卫星的环境变化监测
Maria Gonzalez, Elena Petrova
玛丽亚·冈萨雷斯, 埃琳娜·彼得罗娃
Abstract: This study utilizes multispectral satellite imagery to analyze environmental changes in rapidly urbanizing regions.
摘要: 本研究利用多光谱卫星影像分析快速城市化地区的环境变化。
6. Automated Building Detection from Aerial Images
基于航拍影像的自动建筑物检测
Daniel Robertson, Michael Torrin
丹尼尔·罗伯逊, 迈克尔·托林
Abstract: This research presents a computer vision-based framework for detecting buildings from high-resolution aerial images.
摘要: 本研究提出了一种基于计算机视觉的框架，用于从高分辨率航拍影像中检测建筑物。
7. Spatial Analysis of Climate Change Impacts Using Geospatial Data Modeling
利用地理空间数据建模的气候变化影响空间分析
Farid Al-Khazem, Ahmed Varkonis
法里德·卡泽姆, 艾哈迈德·瓦尔科尼斯
Abstract: This study proposes a geospatial data modeling framework for analyzing climate change impacts using satellite observations.
摘要: 本研究提出了一个地理空间数据建模框架，利用卫星观测分析气候变化影响。
8. Intelligent GIS-Based Decision Support System for Smart City Planning
面向智慧城市规划的智能GIS决策支持系统
Chenrui Xian, Adrian Veltrini
陈睿贤, 阿德里安·韦尔特里尼
Abstract: This paper presents an intelligent GIS-based framework designed to assist urban planners in analyzing infrastructure development.
摘要: 本文提出了一个基于GIS的智能框架，旨在协助城市规划者分析基础设施发展。
