A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis

Journal of Engineering, Год журнала: 2024, Номер 2024, С. 1 - 16

Опубликована: Апрель 15, 2024

Intrusion detection (ID) is critical in securing computer networks against various malicious attacks. Recent advancements machine learning (ML), deep (DL), federated (FL), and explainable artificial intelligence (XAI) have drawn significant attention as potential approaches for ID. DL-based shown impressive performance ID by automatically relevant features from data but require labelled computational resources to train complex models. ML-based fewer data, their ability generalize unseen limited. FL a relatively new approach that enables multiple entities model collectively without exchanging providing privacy security benefits, making it an attractive option However, FL-based more communication additional computation aggregate models different entities. XAI understanding how AI make decisions, improving interpretability transparency. While existing literature has explored the strengths weaknesses of DL, ML, FL, XAI-based ID, gap exists comprehensive analysis specific use cases scenarios where each most suitable. This paper seeks fill this void delivering in-depth review not only highlights also offers guidance selecting appropriate based on unique context available resources. The selection depends case, work provides insights into which method best suited network sizes, availability, privacy, concerns, thus aiding practitioners informed decisions needs.

Язык: Английский

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Artificial Intelligence in Agricultural Mapping: A Review

Agriculture, Год журнала: 2024, Номер 14(7), С. 1071 - 1071

Опубликована: Июль 3, 2024

Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time increases efficiency management activities, which improves the food industry. Agricultural mapping is necessary for resource requires technologies farming challenges. The AI applications gives its subsequent use decision-making. This study analyses AI’s current state through bibliometric indicators a literature review to identify methods, resources, geomatic tools, types, their management. methodology begins with bibliographic search Scopus Web of Science (WoS). Subsequently, data analysis establish scientific contribution, collaboration, trends. United States (USA), Spain, Italy are countries that produce collaborate more this area knowledge. Of studies, 76% machine learning (ML) 24% deep (DL) applications. Prevailing algorithms such as Random Forest (RF), Neural Networks (ANNs), Support Vector Machines (SVMs) correlate activities In addition, contributes associated production, disease detection, crop classification, rural planning, forest dynamics, irrigation system improvements.

Язык: Английский

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Hyperspectral imaging with multivariate analysis for detection of exterior flaws for quality evaluation of apples and pears

Postharvest Biology and Technology, Год журнала: 2025, Номер 223, С. 113453 - 113453

Опубликована: Фев. 17, 2025

Язык: Английский

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Analysis and realization of a self-adaptive grasper grasping for non-destructive picking of fruits and vegetables

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 232, С. 110119 - 110119

Опубликована: Фев. 18, 2025

Язык: Английский

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Synchronous detection of internal and external defects of citrus by structured-illumination reflectance imaging coupling with improved YOLO v7

Postharvest Biology and Technology, Год журнала: 2025, Номер 227, С. 113576 - 113576

Опубликована: Апрель 16, 2025

Язык: Английский

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Unmanned Ground Vehicles for Continuous Crop Monitoring in Agriculture: Assessing the Readiness of Current ICT Technology

Machines, Год журнала: 2024, Номер 12(11), С. 750 - 750

Опубликована: Окт. 23, 2024

Continuous crop monitoring enables the early detection of field emergencies such as pests, diseases, and nutritional deficits, allowing for less invasive interventions yielding economic, environmental, health benefits. The work organization modern agriculture, however, is not compatible with continuous human monitoring. ICT can facilitate this process using autonomous Unmanned Ground Vehicles (UGVs) to navigate crops, detect issues, georeference them, report experts in real time. This review evaluates current state technology determine if it supports autonomous, focus on shifting from traditional cloud-based approaches, where data are sent remote computers deferred processing, a hybrid design emphasizing edge computing real-time analysis field. Key aspects considered include algorithms in-field navigation, AIoT models detecting agricultural emergencies, advanced devices that capable managing sensors, collecting data, performing deep learning inference, ensuring precise mapping sending alert reports minimal intervention. State-of-the-art research development suggest general, necessarily crop-specific, prototypes fully UGVs now at hand. Additionally, demand low-power consumption affordable solutions be practically addressed.

Язык: Английский

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Exploring Autonomous Load-Carrying Mobile Robots in Indoor Settings: A Comprehensive Review

IEEE Access, Год журнала: 2024, Номер 12, С. 131395 - 131417

Опубликована: Янв. 1, 2024

This review paper provides a detailed overview of the advancements and identifies pivotal challenges in realm autonomous load-carrying mobile robots, with particular focus on indoor applications for both ground aerial platforms. It critically examines past decade's innovations designs sensor technologies, scrutinizing their impact enhancement robotic autonomy load management. The also presents an in-depth analysis latest trends navigation control algorithms essential refining these robots' operational efficacy diverse scenarios. By evaluating current research outputs, this work critical areas future exploration, such as improving complexity, optimizing handling varying conditions, pioneering precise load-sensing techniques. proposes innovative paths designed to address identified gaps, underscoring necessity breakthroughs robot design, enhanced integration systems, increased efficiency. overarching aim is propel functionality robots new heights, ensuring they meet increasing demands various sectors including industrial, commercial, service domains.

Язык: Английский

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IoT-Enabled Smart Agriculture for Improving Water Management: A Smart Irrigation Control Using Embedded Systems and Server-Sent Events

Scientific African, Год журнала: 2024, Номер unknown, С. e02527 - e02527

Опубликована: Дек. 1, 2024

Язык: Английский

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Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots

Agronomy, Год журнала: 2025, Номер 15(1), С. 145 - 145

Опубликована: Янв. 9, 2025

Due to the short time, high labor intensity and workload of fruit vegetable harvesting, robotic harvesting instead manual operations is future. The accuracy object detection location directly related picking efficiency, quality speed fruit-harvesting robots. Because its low recognition accuracy, slow poor localization traditional algorithm cannot meet requirements automatic-harvesting increasingly evolving powerful deep learning technology can effectively solve above problems has been widely used in last few years. This work systematically summarizes analyzes about 120 literatures on three-dimensional positioning algorithms robots over 10 years, reviews several significant methods. difficulties challenges faced by current are proposed from aspects lack large-scale high-quality datasets, complexity agricultural environment, etc. In response challenges, corresponding solutions future development trends constructively proposed. Future research technological should first these using weakly supervised learning, efficient lightweight model construction, multisensor fusion so on.

Язык: Английский

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Edge Computing-Based Machine Vision for Non-Invasive and Rapid Soft Sensing of Mushroom Liquid Strain Biomass

Agronomy, Год журнала: 2025, Номер 15(1), С. 242 - 242

Опубликована: Янв. 20, 2025

Biomass monitoring of mushroom liquid strains during the fermentation process demands real-time analysis with minimal manual intervention, highlighting urgent need for intelligent surveillance. This study introduced a soft sensor method based on edge computing machine vision, termed Edge CV, in situ non-invasive estimation biomass. In our experiment, hardware CV system includes Jetson Nano 4 GB RAM, 64 ROM, and 128-core Maxwell GPU executing vision tasks, along embedded cameras image data acquisition. Furthermore, cascaded model was developed to enable biomass evaluation system. The mainly consists three steps: first, object detection task locate observation window, achieving mean Average Precision (mAP50:95) 82.3% 78.7 GFLOPs; then, segmentation extract strain within yielding intersection over union (MIoU) 85.9% 110.4 finally, calculating mycelium indices via morphological processing task. correlation between inference measurement showed an R2 0.963 RMSE 0.027 normalized indices, demonstrating robust consistent trend. Therefore, this illustrates practical application computing-based sensing process.

Язык: Английский

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