Enhancing image categorization with the quantized object recognition model in surveillance systems

Wang, Jinming; Hu, Fengjun; Abbas, Ghulam; Albekairi, Mohammed; Rashid, Nasr

doi:10.1016/j.eswa.2023.122240

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…At the same time, some studies have compared artificial neural networks (ANN) and SVM algorithms to classify and predict varied kidney diseases, with ANN demonstrating the highest accuracy among them. Similarly, statistical and computational intelligence models have been juxtaposed, utilizing class-balanced order for dual classes of non-uniform distribution (Zhao and Zhang, 2008 ; Wang et al, 2023b ). An investigation by Dritsas and Trigka ( 2022 ) detailed the identification of suitable dietary plans for CKD patients, utilizing multiple classification methods, with the multi-class decision forest model achieving the highest accuracy (99.17%).…”

Section: Introductionmentioning

confidence: 99%

Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease

Khan,

Raza,

Mirjat

et al. 2024

Front. Artif. Intell.

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In today's modern era, chronic kidney disease stands as a significantly grave ailment that detrimentally impacts human life. This issue is progressively escalating in both developed and developing nations. Precise and timely identification of chronic kidney disease is imperative for the prevention and management of kidney failure. Historical methods of diagnosing chronic kidney disease have often been deemed unreliable on several fronts. To distinguish between healthy individuals and those afflicted by chronic kidney disease, dependable and effective non-invasive techniques such as machine learning models have been adopted. In our ongoing research, we employ various machine learning models, encompassing logistic regression, random forest, decision tree, k-nearest neighbor, and support vector machine utilizing four kernel functions (linear, Laplacian, Bessel, and radial basis kernels), to forecast chronic kidney disease. The dataset used constitutes records from a case-control study involving chronic kidney disease patients in district Buner, Khyber Pakhtunkhwa, Pakistan. For comparative evaluation of the models in terms of classification and accuracy, diverse performance metrics, including accuracy, Brier score, sensitivity, Youden's index, and F1 score, were computed.

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Section: Introductionmentioning

confidence: 99%

Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease

Khan,

Raza,

Mirjat

et al. 2024

Front. Artif. Intell.

Self Cite

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“…Liu et al 19 introduced a network-based cancer driver gene prediction classification method. The presented method identifies the critical data using biological information of the patients.…”

Section: Related Workmentioning

confidence: 99%

“…No. Key techniques Dataset used Advantages Disadvantages 19 network-based driver gene prediction Patient data Critical data identification Limited discussion on methodolgy 20 cancer-associated protein–protein interaction (PPI) PPI data Improved accuracy Limited explanation on deep learning 21 deep multimodal stacked generalization approach for PPI Trained protein data Reduced energy consumption Limited graph attention 22 MMR-CRC DNA-immunohistochemistry (IHC) testing Sustainability in CRC prediction Lack of detailed MMR 23 CNN based approach PPI network Improved accuracy Lack of protein sample structure 24 DNL cancer prediction Clinical data samples Improved energy efficiency Lack of feature selection technique 25 multi-gene genetic programming algorithm Biological information’s protein amino acid ratio Reduced time and energy consumption Limited information on genetic progression 26 miRNA and lncRNA Three EPs of miRNA, lncRNA and PCG in database of the cancer genome atlas (TCGA) Solves optimization problems Complexity due to bigger dataset 27 DNN based lung cancer prediction …”

Section: Related Workmentioning

confidence: 99%

Response score-based protein structure analysis for cancer prediction aided by the Internet of Things

Alruwaili,

Yousef,

Jumani

et al. 2024

Sci Rep

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Medical diagnosis through prediction and analysis is par excellence in integrating modern technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical assessments are eased with protracted computing. Specifically, cancer research through structure prediction and analysis is improved through human and machine interventions sustaining precision improvements. This article, therefore, introduces a Protein Structure Prediction Technique based on Three-Dimensional Sequence. This sequence is modeled using amino acids and their folds observed during the pre-initial cancer stages. The observed sequences and the inflammatory response score of the structure are used to predict the impact of cancer. In this process, ensemble learning is used to identify sequence and folding responses to improve inflammations. This score is correlated with the clinical data for structures and their folds independently for determining the structure changes. Such changes through different sequences are handled using repeated ensemble learning for matching and unmatching response scores. The introduced idea integrated with deep ensemble learning and IoT combination, notably employing stacking method for enhanced cancer prediction precision and interdisciplinary collaboration. The proposed technique improves prediction precision, data correlation, and change detection by 11.83%, 8.48%, and 13.23%, respectively. This technique reduces correlation time and complexity by 10.43% and 12.33%, respectively.

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Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors

Alrowaily,

Alruwaili,

Alghamdi

et al. 2024

Alexandria Engineering Journal

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Enhancing image categorization with the quantized object recognition model in surveillance systems

Cited by 11 publications

References 27 publications

Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease

Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease

Response score-based protein structure analysis for cancer prediction aided by the Internet of Things

Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors

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