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In today's generation, many healthcare organizations moving towards patient-centred quality assessment, transparency strategies, and practices. Therefore, online reviews are the part of everyday life and millions of online reviews being generated daily; unfortunately, all online reviews are not genuine do happen negative reviews written by malicious users are everywhere on the social media platform such as Google, Yelp, Facebook, TripAdvisor, Amazon, Healthgrades, etc. Every fake, spam, and negative online review can directly impact a potential customer's purchasing decision, company's reputation, profitability and misleading the consumers, and damage the reputation of review websites. Hence, detecting fake online reviews has become a big challenge in the rapid development of e-commerce but it can be more dangerous when it comes to medical products, e-healthcare platforms to classify online reviews correctly due to the high false-positive rate (FPR) and the false-negative rate (FNR). In the medical environment, trust is the best reason to pursue transparency and deserve their confidence then patients have made online reviews an integral part of their decision-making process. Patient online reviews and ratings can be a good indicator of clinical effectiveness, efficiently control their health and patient safety. Researchers performed various investigations to predict fake online reviews, and their models could have provided low prediction accuracy due to the selection of lesser features and lack of essential prediction labels. To handle this issue, this paper presents an efficient approach to specifically predict fake online reviews with multiple feature analyses in an automated way with different perspectives of patient safety using qualitative methods. The primary aim of this paper is to provide a solid and complete comparative study of present research on detecting fake online reviews designed a deep learning index system to examine multiple features such as sentiment, test, and behaviour features of the online reviewers. The input samples are trained with the novel dense network model to extract essential features. And the labels are provided tentatively, and the model adopts some initial samples to train the proposed classifier model as the model intends to give better accuracy. The model's novelty relies on the feature selection and label classification of reviews. The experimental results indicated that fake review detection yields better accuracy of 99.5% on the anticipated model than other baseline approaches across the Amazon and medical datasets, using deep learning techniques to reduce the FPR, and FNR then improves fake review detection capabilities. The proposed model significantly improved the classifier's performance compared to existing state-of-the-art techniques in the Amazon and medical domains, demonstrating the effectiveness and relevance of the method. [ABSTRACT FROM AUTHOR]

Retinal diseases are most widely affected by various types of people without any proper reason. Nowadays, many retinal diseases are identified by experts. Detecting retinal diseases in the early stages is very important with better accuracy. Deep learning (DL) techniques are commonly used in the early prediction of retinal disorders. In DL, multiple layers accurately detect abnormalities in the retinal images. Various datasets are also present for this research work. This paper uses a hybrid multilayered classification (HMLC/CNN-VGG19). This system is developed to categorize four kinds of retinal disorders (age-related macular degeneration, choroidal neovascularization, Drusen, diabetic retinopathy, as well as typical cases). The proposed H.M.L.C. is applied to OCT to gather pictures from different data sources, like the U.C.I. repository, Kaggle, etc. The CNN and VGG-19 models used in the H.M.L.C. are implemented in Python over the datasets. The experimental results in terms of classification accuracy are verified. The classification accuracy is high since the H.M.L.C. used the advanced features from CNN and VGG-19 models. The performance is calculated using sensitivity, specificity, F1 score, and accuracy. [ABSTRACT FROM AUTHOR]

Heartbeat classification plays an important role in identifying cardiac arrhythmias. Although automated heartbeat classification approaches have been broadly reported, they still suffer from some disadvantages, such as the inescapable domain knowledge and human ingenuity for hand-crafted heartbeat feature engineering, and the slow learning speed for iterative deep learning heartbeat classification approach. To address the above issues, for the first time, this paper proposes a novel multilayer extreme learning machine (ML-ELM) heartbeat classification approach for feature representation and classification of the heartbeat signals. Different from the iterative optimization scheme in traditional neural networks, ELM autoencoder (ELM-AE) is employed in ML-ELM for random mapping of the input weights and non-iterative learning is implemented for output weights. To realize unsupervised self-encoding heartbeat feature mapping, the output of ELM-AE is consistent with the incoming heartbeat signal, and the optimization objective is to minimize the reconstruction error. Heartbeat feature extraction is efficiently performed by layer-by-layer ELM-AE stacking. In the stage of heartbeat classification, to solve the unstable performance caused by random parameter initialization in ELM hidden nodes, bagging-based ensemble learning is employed to combine several ELM classifiers and thus develops a well-performed heartbeat classification approach. The proposed approach is applied to two-lead ECG signals, which is obtained from the MIT-BIH arrhythmia public dataset. The experimental results show that the ELM-AE based feature extraction can effectively characterize the characteristics of the heartbeat signal with high efficiency compared with other state-of-the-art approaches. Applying the ensemble decision fusion to two leads, the final classification accuracy reaches 99.41%. [ABSTRACT FROM AUTHOR]

Deep learning grabs a center attraction in industries, deep learning techniques are having great potential and recently these potentials are applied to healthcare problems, including computer-aided detection/diagnosis, disease prediction. Deep learning techniques are playing an important role in the classification and prediction of the diseases. The popularity of deep learning approaches is because of their ability to handle a large amount of data related to the patients with accuracy, reliability in a short span of time. However, the practitioners may take time in analyzing and generating the reports. In this paper, we have proposed a Deep Neural Network-based classification model for the classification of Parkinson's disease. Our proposed method is one such good example giving faster and more accurate results for the classification of Parkinson's disease patients with excellent accuracy of 94.87%. We have also compared the results with other existing approaches like linear discriminant analysis, support vector machine, K-nearest neighbor, decision tree, classification and regression trees, random forest, linear regression, logistic regression, multi-layer perceptron, and Naive Bayes. [ABSTRACT FROM AUTHOR]

In this world, travelling is the daily routine to reach the desired places such as schools, hospitals, offices and so on. Everyone makes use of their vehicles like two-wheelers or four-wheelers to reach the destination. Vehicle condition plays a major role which includes many parameters such as tyre condition, engine overheating, low fuel mileage. This paper concentrates on the examination of tyre track design for deformities. The mileage of a vehicle relies upon different components. However, it depends on different parameters like street condition, tyre weight and numerous others. For smooth driving and for safe traffic reasons, the auspicious maintenance of tyres plays a vital task. Consequently, the likelihood of prediction of tyre imperfections is assumed to play a vital role to fleet proprietors, government and tyre makers. Researchers make use of recent technologies such as machine learning and deep learning concepts to provide a solution to various real-life problems. The creation of a weighted quality-based convolutional neural network with the purpose of discovery of imperfections on tyres by the examination of pictures of tyre track designs is being proposed. The proposed system with the accessible data-set made public by different tyre producers is included for evaluation. The consequences of our tests demonstrate that it is conceivable to predict the deformities and durability of a tyre from its track designs through a weighted quality-based convolutional neural network. The proposed methodology is used to identify the group of feature values from the tyre image. These kinds of feature values are utilized to implement the dissimilarity of the feature related pixels. The number of epochs is varied as a callback mechanism has been used to validate the proposed model. Binary accuracy and mean absolute error are the best metrics to evaluate the performance which are discussed in the results section. Also, precision, recall and F-measure are used as validation parameters for the proposed model. [ABSTRACT FROM AUTHOR]

In-domain adaptation (DA), the knowledge trained in one domain, is used to test an unknown domain. Existing approaches use limited efforts on DA in sentiment classification (SC) using neural networks. The challenging task here is the dissimilarity in the semantic behavior across domains. In this paper, convolutional neural networks (CNNs) learn the knowledge of a particular domain using Doc2Vec feature representation which provides good performance for DA in SC for the target domain. Our empirical analysis with one-layer CNN exhibits significant change in the accuracy by tuning the hyperparameters involved with the CNN. This paper derives into a suitable CNN architecture accompanying hyperparameters which favor DA between different domains. Our empirical analysis with multi-domain dataset demonstrates that with suitable hyperparameters, CNN works well for DASC problems. The comparative study shows that CNN with Doc2Vec model provides a strong capability of learning large data representation semantically with other state-of-the-art methods for the DASC. [ABSTRACT FROM AUTHOR]

In modern times, digital medical images play a significant progression in clinical diagnosis to treat the populace earlier to hoard their lives. Magnetic resonance imaging (MRI) is one of the most advanced medical imaging modalities that facilitate scanning various parts of the human body like the head, chest, abdomen, and pelvis and identify the diseases. Numerous studies on the same discipline have proposed different algorithms, techniques, and methods for analyzing medical digital images, especially MRI. Most of them have mainly focused on identifying and classifying the images as either normal or abnormal. Computing brainpower is essential to understand and handle various brain diseases efficiently in critical situations. This paper knuckles down to design and implement a computer-aided framework, enhancing the identification of humans' cognitive power from their MRI Images. The proposed framework converts the 3D DICOM images into 2D medical images, pre-processing, enhancement, learning, and extracting various image information to classify it as normal or abnormal and provide the brain's cognitive power. This study widens the efficient use of machine learning methods, voxel residual network (VRN), with multimodality fusion architecture to learn and analyze the image to classify and predict cognitive power. The experimental results denote that the proposed framework demonstrates better performance than the existing approaches. [ABSTRACT FROM AUTHOR]

An intrusion detection system is a security system that aims to detect sabotage and intrusions on networks to inform experts of the attack and abuse of the network. Different classification methods have been used in the intrusion detection systems such as fuzzy, genetic algorithms, decision trees, artificial neural networks, and support vector machines. Moreover, ensemble classifiers have shown more robust and effective performance for various tasks in the field. In this paper, we adopt ensemble models in order to improve the performance of intrusion detection and, at the same time, decrease the false alarm rate. We use kNN for multi-class classification, as well as SVM to approach the classification problem in normal-based detection. In order to combine multiple outputs, we use the Dempster–Shafer method in which there is the possibility of explicit retrieval of uncertainty. Moreover, we utilize deep learning for extracting features to train the samples, selected by the sample selection algorithm based on ensemble margin. We compare our results with state-of-the-art methods on benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSL-KDD. Our proposed method indicates the superiority in terms of prominent metrics Accuracy, Precision, Recall, and F-measure. [ABSTRACT FROM AUTHOR]

Stock market timing is regarded as a challenging task of financial prediction. An accurate prediction of stock trend can yield great profits for investors. At present, recurrent neural networks (RNNs) have a good performance in stock market forecasting. However, there has been a relative lack of research in the stock market timing using RNNs. In this paper, a novel model named hybrid RNN model is proposed for stock market timing by incorporating multi-layer long short-term memory, multi-layer gated recurrent unit and one-layer ReLU layer. Moreover, based on five popular benchmark datasets from UCI Machine Learning Repository and six daily securities from Shanghai Stock Exchange, comparisons with 12 state-of-the-art models are conducted to verify the superiority of the proposed hybrid RNN model in terms of nine technical indicators. The findings from the experiment demonstrate that: (1) as opposed to 12 models, the average accuracy, MSE and AUC of hybrid RNN model (0.7406, 0.2592, 0.7368) are significantly better than other comparison models, and (2) the proposed hybrid RNN classification procedure can be considered as a feasible and effective tool for stock market timing. [ABSTRACT FROM AUTHOR]

Nowadays, big data plays a substantial part in information knowledge analysis, manipulation, and forecasting. Analyzing and extracting knowledge from such big datasets are a very challenging task due to the imbalance of data distribution, which could lead to a biased classification results and wrong decisions. The standard classifiers are not capable of handling such datasets. Hence, a new technique for dealing with such datasets is required. This paper proposes a novel classification framework for big data that consists of three developed phases. The first phase is the feature selection phase, which uses the Whale optimization algorithm (WOA) for finding the best set of features. The second phase is the preprocessing phase, which uses the SMOTE algorithm and the LSH-SMOTE algorithm for solving the class imbalance problem. Lastly, the third phase is WOA + BRNN algorithm, which is using the Whale optimization algorithm for training a deep learning approach called bidirectional recurrent neural network for the first time. Our proposed algorithm WOA-BRNN has been tested against nine highly imbalanced datasets one of them is big dataset in terms of area under curve (AUC) against four of the most common use machine learning algorithms (Naïve Bayes, AdaBoostM1, decision table, random tree), in addition to GWO-MLP (training multilayer perceptron using Gray Wolf Optimizer), then we test our algorithm over four well-known datasets against GWO-MLP and particle swarm optimization (PSO-MLP), genetic algorithm (GA-MLP), ant colony optimization (ACO-MLP), evolution strategy (ES-MLP), and population-based incremental learning (PBIL-MLP) in terms of classification accuracy. Experimental results proved that our proposed algorithm WOA + BRNN has achieved promising accuracy and high local optima avoidance, and outperformed four of the most common use machine learning algorithms, and GWO-MLP in terms of AUC. [ABSTRACT FROM AUTHOR]

Recent researches have shown that deep forest ensemble achieves a considerable increase in classification accuracy compared with the general ensemble learning methods, especially when the training set is small. In this paper, we take advantage of deep forest ensemble and introduce the dense adaptive cascade forest (daForest). Our model has a better performance than the original cascade forest with three major features: First, we apply SAMME.R boosting algorithm to improve the performance of the model. It guarantees the improvement as the number of layers increases. Second, our model connects each layer to the subsequent ones in a feed-forward fashion, which enhances the capability of the model to resist performance degeneration. Third, we add a hyper-parameter optimization layer before the first classification layer, making our model spend less time to set up and find the optimal hyper-parameters. Experimental results show that daForest performs significantly well and, in some cases, even outperforms neural networks and achieves state-of-the-art results. [ABSTRACT FROM AUTHOR]

Invasive ductal carcinoma (IDC) is a common form of breast cancer that affects women. In traditional medical practice, physicians have to manually test and classify areas which are suspected to be cancerous. However, the literature strongly indicates that the manual segmentation process performed by medical practitioners is neither time efficient nor accurate, as it relies on their subjective judgment. This paper introduces a model called residual attention neural network breast cancer classification (RANN-BCC) to help medical practitioners in the cancer diagnostic process. RANN-BCC utilizes residual neural network (ResNet) as an expert-supportive method to aid medical practitioners in cancer diagnosis. The implementation of RANN-BCC can support the classification of whole slide imaging (WSI) into non-IDC and IDC without prior information about the presence of a cancerous lesion. The classification results demonstrate that the RANN-BCC model has achieved 92.45% accuracy, 0.98 recall, 0.91 precision, and 0.94 F-score which has outperformed other models such as CNN, AlexNet, Residual Neural Network 34 (ResNet34), and Feed-Forward Neural Network. The developed RANN-BCC model aims to help medical experts to classify IDC and non-IDC of breast cancer by learning the feature content of medical images. [ABSTRACT FROM AUTHOR]

Today, biomedical technology is essential for the early diagnosis and effective management of a wide range of illnesses, from mild to fatal. Brain tumours, a mass multiplication of abnormal brain cells, are one of the worst diseases. Early detection and treatment can save a person's life by preventing the spread of abnormal cells. To provide an efficient diagnosis technique, the healthcare sector has recently undergone significant improvement. Finding a specific image categorization method based on tumour cell areas is crucial in the medical industry. After choosing the tumour site, the segmentation and classification processes are carried out. In a shorter amount of time, the identification-based method aids in both the restriction of the image's region and the identification of its border. Automatic brain tumour classification is hard because the area around the brain tumour is so different regarding geography and structure. This paper makes a case for the automatic detection of brain tumours using deep neural network classification. MRI-based image segmentation for fuzzy-based cell detection concerning image extraction by utilizing deep learning model. The results show that the performance levels of the suggested model are superior to those of the existing models. [ABSTRACT FROM AUTHOR]

Diagnostic image volume and complexity in healthcare system increases in rapid pace where available human proficiency may not sufficient for interpreting this much capacity of image data. Machine learning approaches exposed excessive potential to knob huge amount of two-dimensional annotated images of common illnesses from large databases. Deep learning imitates human for extracting knowledge from dataset and favourable to data scientists for accumulating, analysing, interpreting and predictive modelling. In this paper, organ inflammation disease is addressed with deep learning neural network (DLNN)-based classification scheme is incorporated to diagnose or prognoses the patient from severity, based on their historical database. In pandemic environment collecting histopathology tissue score is time-consuming process due to a smaller number of physician availability, by implementing proposed DLNN algorithm suits for collecting organ inflammation score and categorizing its brutality by classification of pancreatitis, duodenum and appendix. In order to achieve accuracy and sensitivity of various stages soreness DLNN-based algorithm is developed and it supports by classifying the datasets. [ABSTRACT FROM AUTHOR]

Dermatological infections are among the most frequent ailments on the planet. Despite their high prevalence, assessment is challenging due to the intricacies of complexions, colour, and hair appearance. Skin diseases are a significant public health hazard worldwide. These infections become dangerous when they reach the invasive stage. The medical community faces a massive challenge in recognizing and monitoring skin diseases. The number of people suffering from skin diseases is expanding faster because of increased pollution and poor nutrition. People frequently underestimate the consequences of skin diseases in their early stages. The present treatment process involves examining a biopsy procedure, and doctors manually administer drugs. This paper offers a hybrid method to circumvent human examination and provide optimistic timely results. In-depth research methods and deep learning approaches may aid in creating capable frameworks for categorizing various forms of skin diseases. To begin identifying skin disorders, it is necessary to distinguish between the skin and nonskin tissue. This research suggested a computerized classification method for skin diseases based on Deep Convolutional Neural Networks (DCNN) and Binary Butterfly Optimization Algorithm (BBOA). This approach aims to maximize the accuracy of skin disease prognosis while retaining a high level of effectiveness in collecting stateful knowledge for precise predictions. [ABSTRACT FROM AUTHOR]