A Novel Resource Oriented DMA Framework for Internet of Medical Things Devices in 5G Network

Hasan, Mohammad Kamrul; Islam, Shayla; Memon, Imran; Ismail, Ahmad Fadzil; Abdullah, Salwani; Budati, Anil Kumar; Nafi, Nazmus Shaker

doi:10.1109/tii.2022.3148250

Cited by 69 publications

(41 citation statements)

References 26 publications

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“…The next largest amount of variation is defined by the succeeding principal component, and this is orthogonal/ independent to the principal component that precedes it. The main advantage of PCA is that it reduces the redundancy of data [33], [40][41][42][43][44][45]. However, the disadvantage of PCA-based change detection is that it cannot provide complete change information but requires the threshold of the image to identify the changes that occurred in the area.…”

Section: ) Principal Component Analysis (Pca)mentioning

confidence: 99%

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

et al. 2022

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Change detection (CD) is the process of detecting changes from multi-temporal satellite images that have undergone spatial changes due to natural phenomena and/or human-induced activities. Mango is a major fruit crop in India, but detection of changes in mango crops remains a challenging task because the reason that many perennial crops have similar reflectance profiles. Therefore, a potent change detection technique is required for different applications such as the rate of deforestation, urban developments, damage evaluation, and resource monitoring. Compared to annual and seasonal crops, relatively few studies have been conducted on change detection in perennial fruit crops. In this study, a novel change detection technique (i.e., LR-PCA) is developed using the fusion of log-ratio (LR) and principal component analysis (PCA) images derived from bi-temporal soil adjusted vegetation index (SAVI) images to extract meaningful information and detect temporal changes in the mango fruit crop areas with high change detection accuracy. The proposed approach comprised two steps: (1) SAVI images from 2015 and 2019 were used to retrieve the log-ratio (LR) and principal component (PC) images, respectively, and both the images were fused by applying the pixel-by-pixel fusion approach. (2) Fused images were classified into three classes: "positive change", "no change", and "negative change" using a derived threshold value. The results show that the LR-PCA method of change detection yields a high change detection accuracy of 92% in comparison with the other change detection methods viz. vegetation image differencing, image ratioing, PCA, and log-ratio. To validate the adaptability of the proposed algorithm, experiments with two sets of bi-temporal SAVI indices images belonging to the Sitapur district of Uttar Pradesh State determine that the proposed change detection method performs well as compared to the existing individual methods for detection of changes in mango fruit crop. The proposed method is expected to be useful for detecting changes in the area of perennial crops. In the future, an accurate and efficient change detection analysis may be helpful for developing a real-time mango fruit crop monitoring system at the national level.INDEX TERMS Landsat 8 OLI, change detection, log ratio, principal component analysis, mango crop.

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Section: ) Principal Component Analysis (Pca)mentioning

confidence: 99%

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

et al. 2022

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“…Automated image examination clearly has the significance of assisting pathologists and clinicians in the earlier detection of OSCC and decision-making in management. The considerable heterogeneity in the presence of oral cancer makes the detection highly complex for healthcare professionals and common cause of delays in inpatient referral to oral lesion specialists [ 9 ]. In addition, early-stage OSCC lesions and OPMD are generally asymptomatic and might seem like small, harmless lesions, leading to late presentation of the patient and eventually leading to diagnosis delay [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Deep Learning Enabled Oral Squamous Cell Carcinoma Detection and Classification Using Biomedical Images

Alanazi

Khayyat

et al. 2022

Computational Intelligence and Neuroscience

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Oral cancer is one of the lethal diseases among the available malignant tumors globally, and it has become a challenging health issue in developing and low-to-middle income countries. The prognosis of oral cancer remains poor because over 50% of patients are recognized at advanced stages. Earlier detection and screening models for oral cancer are mainly based on experts’ knowledge, and it necessitates an automated tool for oral cancer detection. The recent developments of computational intelligence (CI) and computer vision-based approaches help to accomplish enhanced performance in medical-image-related tasks. This article develops an intelligent deep learning enabled oral squamous cell carcinoma detection and classification (IDL-OSCDC) technique using biomedical images. The presented IDL-OSCDC model involves the recognition and classification of oral cancer on biomedical images. The proposed IDL-OSCDC model employs Gabor filtering (GF) as a preprocessing step to eliminate noise content. In addition, the NasNet model is exploited for the generation of high-level deep features from the input images. Moreover, an enhanced grasshopper optimization algorithm (EGOA)-based deep belief network (DBN) model is employed for oral cancer detection and classification. The hyperparameter tuning of the DBN model is performed using the EGOA algorithm which in turn boosts the classification outcomes. The experimentation outcomes of the IDL-OSCDC model using a benchmark biomedical imaging dataset highlighted its promising performance over the other methods with maximum accu y , prec n , reca l , and F score of 95%, 96.15%, 93.75%, and 94.67% correspondingly.

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“…Traditional methods like angiography are regarded as the most precise practice when it comes to detecting heart abnormalities but still facing certain limitations, such as high costs, various other side effects, and a high level of technical expertise is required, and most importantly it is much expensive, computationally difficult, and take time to assess [11,12], to overcome the limitations of conventional invasive-based approaches for detecting cardiac disease. Predictive machine learning and deep learning algorithms were used to construct noninvasive Internet of Medical ing (IoMT) [13][14][15][16], smart healthcare systems such as KNN, SVM, NB, DT, LR, RF, and ANN [17][18][19][20][21][22]. As a result, the death rate among individuals with heart disease has exponentially dropped per year.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication

Ullah

Chen

Rajab

et al. 2022

Computational Intelligence and Neuroscience

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Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example,take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset'sfeatures as well as a subset of them. The reduction of features has an impact on theperformance of classifiers in terms of the evaluation matrix and execution time. Experimental results of the support vector machine, K-nearest neighbor, and logistic regression are 97.5%,95 %, and 93% (accuracy) with reduced computation timesof 4.4, 7.3, and 8seconds respectively.

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A Novel Resource Oriented DMA Framework for Internet of Medical Things Devices in 5G Network

Cited by 69 publications

References 26 publications

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

Intelligent Deep Learning Enabled Oral Squamous Cell Carcinoma Detection and Classification Using Biomedical Images

An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication

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