Managing natural disasters: An analysis of technological advancements, opportunities, and challenges

Internet of Things and Cyber-Physical Systems, Journal Year: 2023, Volume and Issue: 4, P. 99 - 109

Published: Sept. 30, 2023

Natural disasters (NDs) have always been a major threat to human lives and infrastructure, causing immense damage loss. In recent years, the increasing frequency severity of natural highlighted need for more effective efficient disaster management strategies. this context, use technology has emerged as promising solution. survey paper, we explore employment technologies in order relieve impacts various disasters. We provide an overview how different such Remote Sensing, Radars Satellite Imaging, internet-of-things (IoT), Smartphones, Social Media can be utilized NDs. By utilizing these technologies, predict, respond, recover from NDs effectively, potentially saving minimizing infrastructure damage. The paper also highlights potential benefits, limitations, challenges associated with implementation purposes. While significantly improve NDM, there are that addressed, cost specialized knowledge skills. Overall, provides comprehensive managing sheds light on important role play NDM. exploring applications aims contribute development sustainable

Language: Английский

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The Challenges of Machine Learning: A Critical Review

Electronics, Journal Year: 2024, Volume and Issue: 13(2), P. 416 - 416

Published: Jan. 19, 2024

The concept of learning has multiple interpretations, ranging from acquiring knowledge or skills to constructing meaning and social development. Machine Learning (ML) is considered a branch Artificial Intelligence (AI) develops algorithms that can learn data generalize their judgment new observations by exploiting primarily statistical methods. millennium seen the proliferation Neural Networks (ANNs), formalism able reach extraordinary achievements in complex problems such as computer vision natural language recognition. In particular, designers claim this strong resemblance way biological neurons operate. This work argues although ML mathematical/statistical foundation, it cannot be strictly regarded science, at least methodological perspective. main reason have notable prediction power they necessarily provide causal explanation about achieved predictions. For example, an ANN could trained on large dataset consumer financial information predict creditworthiness. model takes into account various factors like income, credit history, debt, spending patterns, more. It then outputs score decision approval. However, multi-layered nature neural network makes almost impossible understand which specific combinations using arrive its decision. lack transparency problematic, especially if denies applicant wants know reasons for denial. model’s “black box” means clear breakdown how weighed decision-making process. Secondly, rejects belief machine simply data, either supervised unsupervised mode, just applying process much more complex, requires full comprehension learned ability skill. sense, further advancements, reinforcement imitation denote encouraging similarities similar cognitive used human learning.

Language: Английский

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Developments in Image Processing Using Deep Learning and Reinforcement Learning

Journal of Imaging, Journal Year: 2023, Volume and Issue: 9(10), P. 207 - 207

Published: Sept. 30, 2023

The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes 2025, represents a major challenge both for organizations society general. In addition being larger, datasets are increasingly complex, bringing new theoretical computational challenges. Alongside this evolution, science tools have exploded popularity over past two decades due their myriad applications when dealing with complex data, high accuracy, flexible customization, excellent adaptability. When it comes images, analysis presents additional challenges because as quality an image increases, desirable, so does be processed. Although classic machine learning (ML) techniques still widely used different research fields industries, there has been great interest from scientific community development artificial intelligence (AI) techniques. resurgence neural networks boosted remarkable advances areas such understanding processing images. study, we conducted comprehensive survey regarding AI design optimization solutions proposed deal Despite good results that achieved, many face field study. work, discuss main more recent improvements, applications, developments targeting propose future directions constant fast evolution.

Language: Английский

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A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 41180 - 41218

Published: Jan. 1, 2024

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and segmentation. There numerous types CNNs designed to meet specific needs requirements, including 1D, 2D, 3D CNNs, well dilated, grouped, attention, depthwise convolutions, NAS, among others. Each type CNN has its unique structure characteristics, making it suitable tasks. It's crucial gain thorough understanding perform comparative analysis these different understand their strengths weaknesses. Furthermore, studying the performance, limitations, practical applications each can aid in development new improved architectures future. We also dive into platforms frameworks that researchers utilize research or from perspectives. Additionally, we explore main fields like 6D vision, generative models, meta-learning. This survey paper provides comprehensive examination comparison architectures, highlighting architectural differences emphasizing respective advantages, disadvantages, applications, challenges, future trends.

Language: Английский

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Artificial intelligence for literature reviews: opportunities and challenges

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(10)

Published: Aug. 17, 2024

Abstract This paper presents a comprehensive review of the use Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is rigorous and organised methodology that assesses integrates prior research on given topic. Numerous tools have been developed to assist partially automate process. The increasing role AI this field shows great potential providing more effective support for researchers, moving towards semi-automatic creation literature reviews. Our study focuses how techniques are applied semi-automation SLRs, specifically screening extraction phases. We examine 21 leading using framework combines 23 traditional features with 11 features. also analyse recent leverage large language models searching assisting academic writing. Finally, discusses current trends field, outlines key challenges, suggests directions future research. highlight three primary challenges: integrating advanced solutions, such as knowledge graphs, improving usability, developing standardised evaluation framework. propose best practices ensure robust evaluations terms performance, transparency. Overall, offers detailed overview AI-enhanced researchers practitioners, foundation development next-generation solutions field.

Language: Английский

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Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

Sensors, Journal Year: 2024, Volume and Issue: 24(3), P. 877 - 877

Published: Jan. 29, 2024

The main purpose of this paper is to provide information on how create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was understand the primary aspects creating and fine-tuning CNNs various application scenarios. We considered characteristics signals, coupled with an exploration signal processing data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, dimension among others. In addition, we conduct in-depth analysis well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, combined architecture. This further offers comprehensive evaluation these covering accuracy metrics, hyperparameters, appendix that contains table outlining parameters commonly used architectures feature extraction

Language: Английский

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In-Depth Review of YOLOv1 to YOLOv10 Variants for Enhanced Photovoltaic Defect Detection

Solar, Journal Year: 2024, Volume and Issue: 4(3), P. 351 - 386

Published: June 26, 2024

This review presents an investigation into the incremental advancements in YOLO (You Only Look Once) architecture and its derivatives, with a specific focus on their pivotal contributions to improving quality inspection within photovoltaic (PV) domain. YOLO’s single-stage approach object detection has made it preferred option due efficiency. The unearths key drivers of success each variant, from path aggregation networks generalised efficient layer architectures programmable gradient information, presented latest YOLOv10, released May 2024. Looking ahead, predicts significant trend future research, indicating shift toward refining variants tackle wider array PV fault scenarios. While current discussions mainly centre micro-crack detection, there is acknowledged opportunity for expansion. Researchers are expected delve deeper attention mechanisms architecture, recognising potential greatly enhance capabilities, particularly subtle intricate faults.

Language: Английский

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A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial Applications

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 94250 - 94295

Published: Jan. 1, 2024

Quality inspection and defect detection remain critical challenges across diverse industrial applications. Driven by advancements in Deep Learning, Convolutional Neural Networks (CNNs) have revolutionized Computer Vision, enabling breakthroughs image analysis tasks like classification object detection. CNNs' feature learning capabilities made through Machine Vision one of their most impactful This article aims to showcase practical applications CNN models for surface various scenarios, from pallet racks display screens. The review explores methodologies suitable hardware platforms deploying CNN-based architectures. growing Industry 4.0 adoption necessitates enhancing quality processes. main results demonstrate efficacy automating detection, achieving high accuracy real-time performance different surfaces. However, limited datasets, computational complexity, domain-specific nuances require further research. Overall, this acknowledges potential as a transformative technology vision applications, with implications ranging control enhancement cost reductions process optimization.

Language: Английский

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Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 96893 - 96910

Published: Jan. 1, 2024

Deep learning (DL), a branch of machine (ML), is the core technology in today's technological advancements and innovations. learning-based approaches are state-of-the-art methods used to analyse detect complex patterns large datasets, such as credit card transactions. However, most fraud models literature based on traditional ML algorithms, recently, there has been rise applications deep techniques. This study reviews recent DL-based presents concise description performance comparison widely DL techniques, including convolutional neural network (CNN), simple recurrent (RNN), long short-term memory (LSTM), gated unit (GRU). Additionally, an attempt made discuss suitable metrics, common challenges encountered when training using architectures potential solutions, which lacking previous studies would benefit researchers practitioners. Meanwhile, experimental results analysis real-world dataset indicate robustness detection.

Language: Английский

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Alpha-EIOU-YOLOv8: An Improved Algorithm for Rice Leaf Disease Detection

AgriEngineering, Journal Year: 2024, Volume and Issue: 6(1), P. 302 - 317

Published: Feb. 4, 2024

Early detection of plant leaf diseases is a major necessity for controlling the spread infections and enhancing quality food crops. Recently, disease based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper utilized convolutional neural network (CNN) to improve rice efficiency. We present modified YOLOv8, which replaces original Box Loss function by our proposed combination EIoU loss α-IoU in order system. A two-stage approach achieve high accuracy identification AI (artificial intelligence) algorithms. In first stage, images field are automatically collected. Afterward, these image data separated into blast leaf, folder, brown spot sets, respectively. second after training YOLOv8 model dataset, trained deployed IoT devices detect identify diseases. assess approach, comparative study between method methods using YOLOv7 YOLOv5 conducted. The experimental results demonstrate that research reached up 89.9% dataset 3175 with 2608 training, 326 validation, 241 testing. It demonstrates achieves higher rate existing approaches.

Language: Английский

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