The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Nasif, Ammar; Othman, Zulaiha Ali; Sani, Nor Samsiah

doi:10.3390/s21124223

Cited by 30 publications

(17 citation statements)

References 151 publications

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“…134 In recent years, the emergence of edge computing in various fields has presented great potential in reducing latency and saving cost and power. 135 It would be advantageous if more complex analysis could be performed on these devices. The emergence of DL methodologies capable of extracting features of data and augmenting the data processing capabilities in realtime continuous monitoring has enhanced the possibility of performing more complex data analysis on-site without transferring data.…”

Section: Current State and Challenges Of Rtcsm Data Transmission Data...mentioning

confidence: 99%

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A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives

Fan

Wang

Funk

et al. 2022

Environ. Sci. Technol.

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Most soil quality measurements have been limited to laboratory-based methods that suffer from time delay, high cost, intensive labor requirement, discrete data collection, and tedious sample pretreatment. Real-time continuous soil monitoring (RTCSM) possesses a great potential to revolutionize field measurements by providing first-hand information for continuously tracking variations of heterogeneous soil parameters and diverse pollutants in a timely manner and thus enable constant updates essential for system control and decision-making. Through a systematic literature search and comprehensive analysis of state-of-the-art RTCSM technologies, extensive discussion of their vital hurdles, and sharing of our future perspectives, this critical review bridges the knowledge gap of spatiotemporal uninterrupted soil monitoring and soil management execution. First, the barriers for reliable RTCSM data acquisition are elucidated by examining typical soil monitoring techniques (e.g., electrochemical and spectroscopic sensors). Next, the prevailing challenges of the RTCSM sensor network, data transmission, data processing, and personalized data management are comprehensively discussed. Furthermore, this review explores RTCSM data application for updating diverse strategies including high-fidelity soil process models, control methodologies, digital soil mapping, soil degradation, food security, and climate change mitigation. Finally, the significance of RTCSM implementation in agricultural and environmental fields is underscored through illuminating future directions and perspectives in this systematic review.

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Section: Current State and Challenges Of Rtcsm Data Transmission Data...mentioning

confidence: 99%

“…In recent years, the emergence of edge computing in various fields has presented great potential in reducing latency and saving cost and power . It would be advantageous if more complex analysis could be performed on these devices.…”

Section: Current State and Challenges Of Rtcsm Data Transmission Data...mentioning

confidence: 99%

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives

Fan

Wang

Funk

et al. 2022

Environ. Sci. Technol.

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“…Deep learning is a model of a deep neural network with many layers of mystery. With the help of multiple hidden layers, people can perform multilayer nonlinear learning on input data, and with adequate learning, they can arbitrarily approximate complex functions and compute more complex and important properties [14,15]. e comparison between shallow learning and in-depth learning is shown in Table 1.…”

Section: Deep Learningmentioning

confidence: 99%

Feature Extraction and Classification of Music Content Based on Deep Learning

Shi¹,

2022

Advances in Multimedia

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To study the use of in-depth training in extracting and classifying the content of music samples, the work offers an algorithm for identifying and classifying musical genres based on a deep network of beliefs, enabling it to be used to extract and classify traditional Chinese musical instruments, and using real-world experiments to test its performance after training. The experimental results are as follows: the improved depth confidence network algorithm has the highest accuracy for music recognition and classification, which can reach 75.8%, higher than other traditional methods. The improved depth confidence network identifies and classifies Chinese traditional musical instruments through Softmax layer, and the accuracy is even as high as 99.2%; DBN is combined with Softmax neural network algorithm when only a few labeled samples in the training set are used for network fine-tuning, and the accuracy of the algorithm can still reach more than 90%, which can reduce the workload in the early stage. This study effectively solves the problem of too much workload and low accuracy in the process of music content recognition, classification, and extraction.

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“…Deep learning is the most popular and robust technique for classification based on execution time, complexity, and performance of the system [ 9 ]. The conventional classification techniques were restricted to the transformation of raw input into a solution of predicted and classifier outcome results [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach

et al. 2022

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Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.

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The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Cited by 30 publications

References 151 publications

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives

Feature Extraction and Classification of Music Content Based on Deep Learning

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach

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