1. YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review

   http://arxiv.org/abs/2501.13400v4 · arxiv

   Note: This is a preliminary version of the manuscript. The final, peer-reviewed, and substantially revised version has been published in Jurnal RESTI. Readers are encouraged to access and cite the published version: DOI: https://doi.org/10.29207/resti.v10i2.6598 In the field of deep learning-based

2. Challenges in teaching Real Analysis classes at the University of PGRI, South Sumatra, Indonesia

   http://arxiv.org/abs/2008.00182v1 · arxiv

   This paper discusses our experiences and challenges in teaching advanced undergraduate Real Analysis classes for Mathematics Education students at the University of PGRI (Persatuan Guru Republik Indonesia, Indonesian Teachers Association) Palembang, South Sumatra, Indonesia. We observe that the syll

3. Context in object detection: a systematic literature review

   http://arxiv.org/abs/2503.23249v1 · arxiv

   Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated obj

4. The effects of ENSO, climate change and human activities on the water level of Lake Toba, Indonesia: a critical literature review

   http://arxiv.org/abs/2105.08918v1 · arxiv

   This research quantitatively and qualitatively analyzes the factors responsible for the water level variations in Lake Toba, North Sumatra Province, Indonesia. According to several studies carried out from 1993 to 2020, changes in the water level were associated with climate variability, climate cha

5. A Brief Review of Hypernetworks in Deep Learning

   http://arxiv.org/abs/2306.06955v3 · arxiv

   Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and mod

6. GIS-AHP Multi-Decision-Criteria-Analysis for the Optimal Location of Solar Energy Plants at Indonesia

   http://arxiv.org/abs/2007.15351v1 · arxiv

   A reliable tool for site-suitability assessment of solar power plants capable to account for the sustainable development and protection of cultural and biodiversity conservation areas is proposed. We present a novel Analytic Hierarchy Process (AHP) based approach for the Multi-Decision Criteria Anal

7. Fluctuations and Higgs mechanism in Under-Doped Cuprates: a Review

   http://arxiv.org/abs/1906.10146v1 · arxiv

   The physics of the pseudo-gap phase of high temperature cuprate superconductors has been an enduring mystery in the past thirty years. The ubiquitous presence of the pseudo-gap phase in under-doped cuprates suggests that its understanding holds a key in unraveling the origin of high temperature supe

8. Predicting Tourism Demand in Indonesia Using Google Trends Data

   http://arxiv.org/abs/2211.13938v1 · arxiv

   Tourism data is one of the strategic data in Indonesia. In addition, tourism is one of the ten priority programs of national development planning in Indonesia. BPS-Statistics Indonesia has collected data related to tourism demand in Indonesia, but these data have different time period. Several data

9. Impacts of the Madden Julian Oscillation on Precipitation Extremes in Indonesia

   http://arxiv.org/abs/2007.10574v1 · arxiv

   The influence of the Madden Julian Oscillation (MJO) on the precipitation extremes in Indonesia during the rainy season (October to April) has been evaluated using the daily station rain gauge data and the gridded Asian Precipitation Highly Resolved Observational Data Integration Toward Evaluation o

10. YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series

    http://arxiv.org/abs/2406.19407v8 · arxiv

    This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv12 and pr

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