Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu, Yanming; Wang, Min; Yin, Xuefei; Zhang, Jue; Meijering, Erik; Hu, Jiankun

doi:10.3390/s23010062

Cited by 13 publications

(4 citation statements)

References 594 publications

(657 reference statements)

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“…A large database including more than 31,000 HTDL and URL characteristics was tested, and the results showed that accuracy was 97.24%, True Positive (TP) rate was 4.99%, and False Negative (FN) rate was 2.74% [17]. Author in [18] recommended an intelligent phishing detection method based on website text features to combat zero-day phishing tactics.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Alsubaei,

Almazroi,

Ayub

2024

IEEE Access

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Protecting against interference is essential at a time when wireless communications are essential for sending large amounts of data. Our research presents a novel deep learning technique, the ResNeXt method and embedded Gated Recurrent Unit (GRU) model (RNT), rigorously developed for realtime phishing attack detection. Focused on countering the escalating threat of phishing assaults and bolstering digital forensics, our systematic approach involves SMOTE for managing data imbalance during initial data processing. The model's discriminative capability is improved, particularly in the feature extraction process, when autoencoders and ResNet (EARN) are integrated with feature engineering. The ensemble technique of feature extraction reveals crucial data patterns. At the core of our AI categorization is the RNT model, optimized using hyperparameters through the Jaya optimization method (RNT-J). Rigorously tested on real phishing attack datasets, our AI model consistently outperforms state-of-the-art algorithms by a substantial margin of 11% to 19% while maintaining exceptional computing efficiency. Furthermore, our model achieves 98% accuracy, low false positive/false negative values, and a statistical execution time with a mean of 36.99s, median of 35.99s, minimum of 34.99s, maximum of 41.99s, and a standard deviation of 1.10s. Moreover, it demonstrates superior accuracy with SMOTE (98%) and without SMOTE (83%) compared to other algorithms. This state-of-the-art AI study, which focuses on digital forensics, offers enhanced security and optimized productivity for businesses and industries, signifying a breakthrough in the continuing battle against phishing attempts. Through strengthening protection against interference in wireless communication, our AI research strives to amplify data accessibility, resilience, and trustworthiness in the face of cybersecurity threats within the organizational context.

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Section: Related Workmentioning

confidence: 99%

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Alsubaei,

Almazroi,

Ayub

2024

IEEE Access

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“…Unlike traditional machine learning algorithms that require manual feature selection and classifier choice, deep learning algorithms automatically extract features through self-learning from errors. This automatic feature extraction sets deep learning apart from the broader field of machine learning [1,92,93]. To train and evaluate a deep CNN model, each input image undergoes a sequence of convolution layers with filters, followed by flattening, pooling layers and fully connected layers.…”

Section: Learning Algorithmmentioning

confidence: 99%

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Qu,

2024

Agronomy

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Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology’s prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture.

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“…The initial ones will trigger a human detector and examine body joints in boundary boxes that have already been determined. Top-down methods include the ones described in PoseNET [25], HourglassNet [26], and Hornet [27]. There are a few other bottom-up methods, such as Open space [28] and PifPaf [29].…”

Section: A Proposed Approachmentioning

confidence: 99%

A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities

Omarov,

Nurmash,

Doskarayev

et al. 2023

IJACSA

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In the research paper, authors meticulously detail the development, testing, and application of an innovative deep learning model aimed at monitoring physical activities of students in real-time. Drawing upon the advanced capabilities of convolutional neural networks (CNNs), the proposed system exhibits an exceptional ability to track, analyze, and evaluate the physical exercises performed by students, thereby providing an unprecedented scope for customization in physical education strategies. This piece of scholarly work bridges the gap between physical education and cutting-edge technology, highlighting the burgeoning role of artificial intelligence in health and fitness sector. With an expansive study spanning various cohorts of physical culture students, the paper provides compelling empirical evidence that underlines the superiority of the deep learning system over conventional methods in aspects of accuracy, speed, and efficiency of monitoring. The authors demonstrate the transformative potential of their system, capable of facilitating personalized and optimized physical training strategies based on real-time feedback. Moreover, the potential implications of the study extend beyond the realm of education and into wider public health applications, with the possibility of fostering improved health outcomes on a larger scale. This research paper makes a significant contribution to the burgeoning field of AI in physical education, embodying a paradigm shift in the approach towards physical fitness and health monitoring. It underscores the potential of AI-driven technology to revolutionize traditional methods in physical education, paving the way for more personalized and effective teaching and training regimes, and ultimately contributing to enhanced health and fitness outcomes among students.

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Deep Learning in Diverse Intelligent Sensor Based Systems

Cited by 13 publications

References 594 publications

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities

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