Jelajahi Repository

research

Development of an Interactive Web App for Image Segmentation Using Otsu

: Sriyadi

This study introduces a web-based image segmentation tool designed for embryo detection in egg images using the Otsu, K-Means, LAB K-Means, and Otsu +

research

COMPARISON OF LSTM AND GRU IN TRAFFIC DENSITY PREDICTION USING HISTORICAL VISUAL DATA AND YOLOV5 DETECTION

: 1Filda, A. , Nita, M.Agus, S. Rahmadya, H.

Abstract: The problem of traffic congestion in urban areas requires adaptive and data-based predictive solutions. This study aims to develop vehicle d

research

Enhancing Video Anomaly Detection through Optimized Pre-processing: ROI Cropping and Reconstruction Loss

: Dede Firmansyah Saefudin; Jasmir; Zico Pratama Putra

Abstract—Anomaly detection in surveillance videos remainschallenging because of dynamic backgrounds, dense pedestrianflows, and the instability

research

Chili Leaf Variety Identification Using Fine-Grain Deep Learning Techniques

: Luky Fabrianto

Accurate identification of chili leaf varieties is crucial for smart agriculture, supporting seed certification, crop monitoring, and breeding. This s

research

Trustworthy Multimodal Fraud Detection with Federated Learning and Computer Vision

: Dendy K Pramudito

AbstractFraud detection in financial systems is still very difficult to dodue to a vast class imbalance, how quickly fraudsters changetheir tactics an

research

Comparison of SVM and KNN for BEMD-Based Texture Analysis in Huanglongbing Disease Severity Classification

: Rachmat Adi Purnama; Dwiza Riana; Mochamad Wahyudi; Sumanto

Abstract— Oranges are one of the tropical fruit commoditieswith high economic value and play an important role in theglobal agricultural sector.

research

Comparison of SVM and KNN for BEMD-Based Texture Analysis in Huanglongbing Disease Severity Classification

: Rachmat Adi Purnama; Dwiza Riana; Mochamad Wahyudi; Sumanto

Abstract— Oranges are one of the tropical fruit commoditieswith high economic value and play an important role in theglobal agricultural sector.

research

Evaluating Deep Learning Architectures for Potato Pest Identification: A Comparative Study of NasNetMobile, DenseNet, and Inception Models

: Sri Hadianti; Dwiza Riana; Daning Nur Sulistyowati

Abstract Manual potato pest identification that is still applied today is often time-consuming and highly dependent on farmer skills in the field. Thi

Cari

Bidang Ilmu

Kategori

Kiriman Terbaru