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Healthcare services become increasingly technology dependent every passing day such as the Internet of Things (IoT), Fog Computing, 5th generation (5G) and beyond communications, etc. They enable the processing and exchange of huge volumes of healthcare data whose integrity and real-time delivery are critical for healthcare services. Optimal power consumption in such essential healthcare infrastructure is critical for the well-being of patients and crucial to reduce the operational cost of healthcare facilities. In this paper, a Fog node has been introduced in an IoT healthcare infrastructure with power consumption as a key deciding factor. This work proposed a mathematical formulation to decide the deployment of two heterogeneous gateways in the healthcare infrastructure. The target of optimization is to minimize transmission power and infrastructure costs. Two swarm intelligence-based algorithms have been used to solve the computationally challenging optimization problem. These evolutionary algorithms are a discrete fireworks algorithm and a discrete artificial bee colony algorithm with an ensemble of local search methods. Their performance is compared against the genetic algorithm. The simulation results demonstrate a saving of up to 33 percent in power consumption in the proposed healthcare infrastructure that can significantly improve healthcare data communications and its operational costs. [ABSTRACT FROM AUTHOR]

Transmitting electronic medical records (EMR) and other communication in modern Internet of Things (IoT) healthcare ecosystem is both delay and integrity-sensitive. Transmitting and computing volumes of EMR data on traditional clouds away from healthcare facilities is a main source of trust-deficit using IoT-enabled applications. Reliable IoT-enabled healthcare (IoTH) applications demand careful deployment of computing and communication infrastructure (CnCI). This paper presents a FOG-assisted CnCI model for reliable healthcare facilities. Planning a secure and reliable CnCI for IoTH networks is a challenging optimization task. We proposed a novel mathematical model (i.e., integer programming) to plan FOG-assisted CnCI for IoTH networks. It considers wireless link interfacing gateways as a virtual machine (VM). An IoTH network contains three wirelessly communicating nodes: VMs, reduced computing power gateways (RCPG), and full computing power gateways (FCPG). The objective is to minimize the weighted sum of infrastructure and operational costs of the IoTH network planning. Swarm intelligence-based evolutionary approach is used to solve IoTH networks planning for superior quality solutions in a reasonable time. The discrete fireworks algorithm with three local search methods (DFWA-3-LSM) outperformed other experimented algorithms in terms of average planning cost for all experimented problem instances. The DFWA-3-LSM lowered the average planning cost by 17.31%, 17.23%, and 18.28% when compared against discrete artificial bee colony with 3 LSM (DABC-3-LSM), low-complexity biogeography-based optimization (LC-BBO), and genetic algorithm, respectively. Statistical analysis demonstrates that the performance of DFWA-3-LSM is better than other experimented algorithms. The proposed mathematical model is envisioned for secure, reliable and cost-effective EMR data manipulation and other communication in healthcare. [ABSTRACT FROM AUTHOR]

Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists use automated tools for more precise opinion. Automated detection of the affected lung nodules is complicated because of the shape similarity among healthy and unhealthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer effectively. In this article, we have proposed a framework to precisely detect lungs cancer to classify the benign and malignant nodules. The proposed framework is tested using the subset of the publicly available dataset, i.e., the Lung Image Database Consortium image collection (LIDC-IDRI). We applied filtering and noise removal in the pre-processing phase. Furthermore, the adaptive thresholding technique (OTSU) and the semantic segmentation are used to accurately detect the unhealthy lung nodules. Overall, 13 nodules features have extracted using principal components analysis algorithm. In addition, four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are employed for the experimentation. Empirical analysis shows that the proposed system outperformed other techniques and provides 99.23% accuracy using a logit boost classifier. [ABSTRACT FROM AUTHOR]

Background: In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms. Methods: A gender-matched case–control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train–test split (70:30) and tenfold cross-validation approaches. Results: Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train–test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation. Conclusion: The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a common type of liver cancer worldwide. Patients with HCC have rare chances of survival. The chances of survival increase, if the cancer is diagnosed early. Hence, different machine learning-based methods have been developed by researchers for the accurate detection of HCC. However, high dimensionality (curse of dimensionality) and lower prediction accuracy are the problems in the automated detection of HCC. Dimensionality reduction-based methods have shown state-of-the-art performance on many disease detection problems, which motivates the development of machine learning models based on reduced features dimension. This paper proposes a new hybrid intelligent system that hybridizes three algorithms, i.e., linear discriminant analysis (LDA) for dimensionality reduction, support vector machine (SVM) for classification and genetic algorithm (GA) for SVM optimization. Consequently, the three models are hybridized and one black box model, namely LDA–GA–SVM, is constructed. Experimental results on publicly available HCC dataset show improvement in the HCC prediction accuracy. Apart from performance improvement, the proposed method also shows lower complexity from two aspects, i.e., reduced processing time in terms of hyperparameters optimization and training time. The proposed method achieved accuracy of 90.30%, sensitivity of 82.25%, specificity of 96.07% and Matthews Correlation Coefficient (MCC) of 0.804. [ABSTRACT FROM AUTHOR]

Paralytic ileus (PI) is the pseudo-obstruction of the intestine secondary to intestinal muscle paralysis. Causes of PI include electrolyte imbalances, gastroenteritis (inflammation or infection of the stomach or intestines), overuse of medications, abdominal surgery, etc. Predicting mortality in PI patients hospitalized to ICU is crucial for assessing the severity of illness and adjudicating the value of treatment strategy and resource planning. We have developed a Statistically Robust Machine Learning based Mortality Prediction framework namely SRML-MortalityPredictor that could potentially help intensivists, surgeons, and other medical professionals to carefully plan treatment strategies for critically ill PI patients. We used MIMIC III v1.4, a publicly available ICU database to extract patients data (with age > 18 years old) admitted to the ICU with paralytic ileus (PI) as their primary illness. At phase 1, the SRML-MortalityPredictor framework uses univariate statistical analysis to filter out those risk factors which are not associated with the label of the data. Subsequently, it uses the risk survival statistical methods such as cox-regression and Kaplan–Meier survival analyses. The cox-regression analysis provides the hazard ratio about the potential PI risk factors that later used in conjunction with the Kaplan–Meier analysis to generate a rank order list (highest to lowest risk factors). At phase 2, we used several machine learning classification approaches such as linear discriminant analysis (LDA), Gaussian naive bayes (GNB), decision tree (DT) model, k-nearest neighbor (KNN), and support vector machine (SVM) to find the one with highest predictive power using the rank order features extracted at phase 1. We have evaluated the SRML-MortalityPredictor framework and recorded the accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and area under the curve (AUC) scores for each model. The SRML-MortalityPredictor framework with support vector machine (using RBF kernel) showed better performance and yielded an accuracy score: 81.30% and AUC score: 81.38% while predicting mortality in PI patients. We demonstrated a feasible framework for the mortality risk prediction in PI patients admitted to the ICU. The proposed framework could potentially be helpful for intensivists in clinical decision making. Further research is necessary to incorporate more risk factors associated with PI patients to ensure the adaptability of SRML-MortalityPredictor at the bedside. [ABSTRACT FROM AUTHOR]

Malignant melanoma, not belongs to a common type of skin cancers but most serious because of its growth—affecting large number of people worldwide. Recent studies proclaimed that risk factors can be substantially reduced by making it almost treatable, if detected at its early stages. This timely detection and classification demand an automated system, though procedure is quite complex. In this article, a novel strategy is adopted, which not only diagnoses the skin cancer but also assigns a proper class label. The proposed technique is principally built on saliency valuation and the selection of most discriminant deep features selection. The lesion contrast is being enhanced using proposed Gaussian method, followed by color space transformation from RGB to HSV. The new color space facilitates the saliency map construction process, utilizing inner and outer disjoint windows, by making the foreground and background maximally differentiable. From the segmented images, deep features are extracted by utilizing inception CNN model on two basic output layers. These extracted set of features are later fused using proposed decision-controlled parallel fusion method, prior to feature selection using proposed window distance-controlled entropy features selection method. The most discriminant features are later subjected to classification step. To demonstrate the efficiency of the proposed methods, three freely available datasets are utilized such as PH2, ISBI 2016, and ISBI 2017 with achieve accuracy is 97.74%, 96.1%, and 97%, respectively. Simulation results clearly reveal the improved performance of proposed method on all three datasets compared to existing methods. [ABSTRACT FROM AUTHOR]

Facial recognition is a popular biometric technique to recognize an individual by comparing the facial features of a given photograph or a sketch to the digitally stored photographs. One of the important applications of facial recognition is to determine the identity of criminals through their hand-drawn or composite sketches. Despite of the development made in sketch-based facial recognition, available approaches are facing various challenges. The component-based approach (CBA) measures the similarity between each facial component of a sketch and a mugshot photograph. The major challenge in this approach is to determine which facial components are crucial in the identification process. Certain facial components provide better recognition clue than others while matching with mugshot photographs and considerably accurate identification results could be achieved to incorporate such crucial components in the recognition process. In this article, we propose a novel methodology which is based on computable weights to find the most discriminative facial components by using the Weighted Component-Based Approach (WCBA). The weight vector is used during the similarity score measurement to enhance the accuracy and performance of the facial recognition system. Experimental results on matching 50 facial images from 1193 subjects of Multiple Encounter Dataset II (MEDS-II) and 85 facial images from CHUK face sketch database (CUFS) show that the proposed method achieves promising performance (accuracies of 58.33% and 88.23%, respectively) as compared to other leading facial recognition techniques (accuracies of 52% and 80%). We believe our prototype approach will be of great value to law enforcement agencies in the apprehension of culprits in a timely fashion. [ABSTRACT FROM AUTHOR]

Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance. [ABSTRACT FROM AUTHOR]

Ontologies are not only crucial for extending the traditional web into the Semantic Web but also for developing intelligent applications, by converting the raw data into smart data, through semantic enrichment. However, crisp Ontologies are not able to represent fuzzy knowledge which is often encountered in real-world applications. Fuzzy Ontology introduces fuzzy logical rules in Ontology for representing imprecise domain concepts such as darkness, hotness, thickness, creamy etc. in a machine-readable and interoperable format. The performance of fuzzy Ontology decreases with the increase of fuzziness in the domain knowledge. Type-2 fuzzy Ontologies (T2FO) were introduced to represent the domain knowledge where the concepts are either extremely vague or their vagueness increases gradually. The type-2 fuzzy Ontology domain is continuously expanding and there is a need to provide a comprehensive review incorporating the literature of T2FO development approaches, its applications in different domains, reasoners developed for inferencing on type-2 fuzzy Ontology, and evaluation approaches. To perform a comprehensive survey about the T2FO, we used Google Scholar as the main literature research tool to review papers published between 1998 to 2018. We then summarized the published approaches by comparing their features proposed for T2FO development, reasoning or inference, and evaluation approaches. This paper also identifies the domains wherein the past T2FO has been used to develop real-world applications. We conclude this paper by summarizing the previous work, and by identifying the research gaps for investigators. [ABSTRACT FROM AUTHOR]

In the original publication of the article, the author names Fahad Shabbir Ahmad and Raza-Ul-Mustafa was incorrectly published. [ABSTRACT FROM AUTHOR]