Incorporating big data and IoT in intelligent ecosystems: state-of-the-arts, challenges and opportunities, and future directions

Saeed, Nauman; Malik, Hassaan; Naeem, Ahmad; Bashir, Umair

doi:10.1007/s11042-023-16328-3

Cited by 4 publications

(2 citation statements)

References 128 publications

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“…This demonstrates that the suggested model was statistically distinct from the other contributing models since it combined more information from the base classifiers and produced better predictions. In this section, we evaluate the suggested DCDD_Net model with previous research [82][83][84][85][86][87]. In comparison to prior SOTA studies, Table 8 provides an in-depth analysis of the proposed DCDD_Net model in the context of numerous performance assessment criteria, including accuracy, recall, and F1-score.…”

Section: Discussionmentioning

confidence: 99%

“…Cross-sectional images are produced using a CT scan, which combines several X-ray images collected at various angles. Scalograms represent the actual frequencies of a wave's continuous wavelet transform (CWT) factors [82][83][84][85][86][87]. Cough signals utilize CWT to convey data from the time domain to the frequency domain, as demonstrated in Figure 3.…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images

Malik,

Anees,

Al-Shamaylehs

et al. 2023

Diagnostics

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Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. The objective of the work is to identify nine different types of chest diseases, including COVID-19, edema (EDE), LC, PNEU, pneumothorax (PNEUTH), normal, atelectasis (ATE), and consolidation lung (COL). Therefore, we designed a novel deep learning (DL)-based chest disease detection network (DCDD_Net) that uses a CXR, CT scans, and cough sound images for the identification of nine different types of chest diseases. The scalogram method is used to convert the cough sounds into an image. Before training the proposed DCDD_Net model, the borderline (BL) SMOTE is applied to balance the CXR, CT scans, and cough sound images of nine chest diseases. The proposed DCDD_Net model is trained and evaluated on 20 publicly available benchmark chest disease datasets of CXR, CT scan, and cough sound images. The classification performance of the DCDD_Net is compared with four baseline models, i.e., InceptionResNet-V2, EfficientNet-B0, DenseNet-201, and Xception, as well as state-of-the-art (SOTA) classifiers. The DCDD_Net achieved an accuracy of 96.67%, a precision of 96.82%, a recall of 95.76%, an F1-score of 95.61%, and an area under the curve (AUC) of 99.43%. The results reveal that DCDD_Net outperformed the other four baseline models in terms of many performance evaluation metrics. Thus, the proposed DCDD_Net model can provide significant assistance to radiologists and medical experts. Additionally, the proposed model was also shown to be resilient by statistical evaluations of the datasets using McNemar and ANOVA tests.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images

Malik,

Anees,

Al-Shamaylehs

et al. 2023

Diagnostics

Self Cite

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High Performance Magnetic Mass‐Enhanced Triboelectric‐Electromagnetic Hybrid Vibration Energy Harvester Enabling Totally Self‐Powered Long‐Distance Wireless Sensing

Xi,

Yu,

et al. 2024

Adv Materials Technologies

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Wireless sensor networks play a significant role in various fields, and it is promising to construct a totally self‐powered wireless sensor network by harvesting unused mechanical vibration energy. Here, a magnetic mass‐enhanced triboelectric‐electromagnetic hybrid nanogenerator (MM‐HNG) is proposed for harvesting mechanical vibration energy. The additional magnets generate magnetic fields for electromagnetic power generation. As an additional mass effectively increases the membrane's amplitude, thereby enhancing the output performance of the MM‐HNG. The peak power density of TENG in the MM‐HNG reaches 380.4 W m−3, while the peak power density of EMG achieves 736 W m−3, which can charge a 0.1 F capacitor rapidly. In addition, a totally self‐powered wireless sensing system is constructed, with the integrated microcontroller unit (MCU), which detects and processes various sensing parameters and controls wireless transmission. The system features rapid transmission speeds and an extensive transmission range (up to 1 km), and its effectiveness has been validated in a practical application aboard an actual ship. The results illustrate the MM‐HNG's broad applicability across various Internet of Things (IoT) scenarios, including smart machinery, smart transportation, and smart factories.

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A Monadic Second-Order Temporal Logic framework for hypergraphs

Bhuyan,

Singh,

Tomar

et al. 2024

Neural Comput & Applic

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Incorporating big data and IoT in intelligent ecosystems: state-of-the-arts, challenges and opportunities, and future directions

Cited by 4 publications

References 128 publications

Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images

Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images

High Performance Magnetic Mass‐Enhanced Triboelectric‐Electromagnetic Hybrid Vibration Energy Harvester Enabling Totally Self‐Powered Long‐Distance Wireless Sensing

A Monadic Second-Order Temporal Logic framework for hypergraphs

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