Mathews Correlation Research Articles

Efficient mapping of RNA-binding residues in RNA-binding proteins using local sequence features of binding site residues in protein-RNA complexes.

Protein-RNA interactions play vital roles in plethora of biological processes such as regulation of gene expression, protein synthesis, mRNA processing and biogenesis. Identification of RNA-binding residues (RBRs) in proteins is essential to understand RNA-mediated protein functioning, to perform site-directed mutagenesis and to develop novel targeted drug therapies. Moreover, the extensive gap between sequence and structural data restricts the identification of binding sites in unsolved structures. However, efficient use of computational methods demanding only sequence to identify binding residues can bridge this huge sequence-structure gap. In this study, we have extensively studied protein-RNA interface in known RNA-binding proteins (RBPs). We find that the interface is highly enriched in basic and polar residues with Gly being the most common interface neighbor. We investigated several amino acid features and developed a method to predict putative RBRs from amino acid sequence. We have implemented balanced random forest (BRF) classifier with local residue features of protein sequences for prediction. With 5-fold cross-validations, the sequence pattern derived dipeptide composition based BRF model (DCP-BRF) resulted in an accuracy of 87.9%, specificity of 88.8%, sensitivity of 82.2%, Mathew's correlation coefficient of 0.60 and AUC of 0.93, performing better than few existing methods. We further validated our prediction model on known human RBPs through RBR prediction and could map ~54% of them. Further, knowledge of binding site preferences obtained from computational predictions combined with experimental validations of potential RNA binding sites can enhance our understanding of protein-RNA interactions. This may serve to accelerate investigations on functional roles of many novel RBPs.

A deep learning model enables accurate prediction and quantification of pulmonary edema from chest X-rays

BackgroundA quantitative assessment of pulmonary edema is important because the clinical severity can range from mild impairment to life threatening. A quantitative surrogate measure, although invasive, for pulmonary edema is the extravascular lung water index (EVLWI) extracted from the transpulmonary thermodilution (TPTD). Severity of edema from chest X-rays, to date is based on the subjective classification of radiologists. In this work, we use machine learning to quantitatively predict the severity of pulmonary edema from chest radiography.MethodsWe retrospectively included 471 X-rays from 431 patients who underwent chest radiography and TPTD measurement within 24 h at our intensive care unit. The EVLWI extracted from the TPTD was used as a quantitative measure for pulmonary edema. We used a deep learning approach and binned the data into two, three, four and five classes increasing the resolution of the EVLWI prediction from the X-rays.ResultsThe accuracy, area under the receiver operating characteristic curve (AUROC) and Mathews correlation coefficient (MCC) in the binary classification models (EVLWI < 15, ≥ 15) were 0.93 (accuracy), 0.98 (AUROC) and 0.86(MCC). In the three multiclass models, the accuracy ranged between 0.90 and 0.95, the AUROC between 0.97 and 0.99 and the MCC between 0.86 and 0.92.ConclusionDeep learning can quantify pulmonary edema as measured by EVLWI with high accuracy.

Development of an intelligent full-field polarization sensitive optical coherence tomography for breast cancer classification.

Biopsy is the gold standard for cancer detection, however the surge in breast cancer cases has made manual haematoxylin and eosin stained histopathological image examination difficult. Automatic cancer diagnosis is vital for a healthy life. It allows fast diagnosis without specific skills. This research proposes an intelligent full-field polarization-sensitive optical coherence tomography (FF-PS-OCT) system for ex-vivo breast classification using ensemble model corroborated by technique for order preference by similarity to ideal solution (TOPSIS). 220 samples image were scanned using the FF-PS-OCT to extract the phase information. The multilevel ensemble classifier has 94.8% precision, 92.5% recall, 93.7% F-score and 82.3% Mathews correlation coefficient on the testing dataset. The developed ensemble model corroborated by TOPSIS, outperforms the single model in terms of performance metrics. The initial results indicate that the rapid, non-contact and label-free FF-PS-OCT imaging modality using birefringent information is beneficial for making interventional decisions by clinicians.

DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human mRNA Based on DenseNet and Bidirectional LSTM Methods.

N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification that is essential for the regulation of immune functions in organisms. Currently, the identification of ac4C is primarily achieved using biological methods, which can be time-consuming and labor-intensive. In contrast, accurate identification of ac4C by computational methods has become a more effective method for classification and prediction. To the best of our knowledge, although there are several computational methods for ac4C locus prediction, the performance of the models they constructed is poor, and the network structure they used is relatively simple and suffers from the disadvantage of network degradation. This study aims to improve these limitations by proposing a predictive model based on integrated deep learning to better help identify ac4C sites. In this study, we propose a new integrated deep learning prediction framework, DLC-ac4C. First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second, one-dimensional convolutional layers and densely connected convolutional networks (DenseNet) are used to learn local features, and bi-directional long short-term memory networks (Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to determine the importance of sequence characteristics. Finally, a homomorphic integration strategy is used to limit the generalization error of the model, which further improves the performance of the model. The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly better than the prediction accuracy of the existing methods. Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention mechanism to better capture hidden information features from a sequence perspective, and can identify ac4C sites more effectively.

DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes

B-Cell epitopes (BCEs) can identify and bind with receptor proteins (antigens) to initiate an immune response against pathogens. Understanding antigen-antibody binding interactions has many applications in biotechnology and biomedicine, including designing antibodies, therapeutics, and vaccines. Lab-based experimental identification of these proteins is time-consuming and challenging. Computational techniques have been proposed to discover BCEs, but most lack of significant accomplishments. This work uses classical and deep learning models (DLMs) with sequence-based features to predict immunity stimulator BCEs from proteomics sequences. The proposed convolutional neural network-based model outperforms other models with an accuracy (ACC) of 0.878, an F-measure of 0.871, and an area under the receiver operating characteristic curve (AUC) of 0.945. The proposed strategy achieves 58.7% better results on average than other state-of-the-art approaches based on the Mathews Correlation Coefficient (MCC) results. The established model is accessible through a web application located at http://deeplbcepred.pythonanywhere.com.

Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma: A validation of ProgTOOL

BackgroundIn recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model’s reproducibility and generalizability to a separate patient cohort (i.e., external validation). ObjectivesThis study primarily provides a validation study for a recently introduced and publicly available machine learning (ML) web-based prognostic tool (ProgTOOL) for overall survival risk stratification of oropharyngeal squamous cell carcinoma (OPSCC). Additionally, we reviewed the published studies that have utilized ML for outcome prognostication in OPSCC to examine how many of these models were externally validated, type of external validation, characteristics of the external dataset, and diagnostic performance characteristics on the internal validation (IV) and external validation (EV) datasets were extracted and compared. Methods: We used a total of 163 OPSCC patients obtained from the Helsinki University Hospital to externally validate the ProgTOOL for generalizability. In addition, PubMed, OvidMedline, Scopus, and Web of Science databases were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. ResultsThe ProgTOOL produced a predictive performance of 86.5% balanced accuracy, Mathew's correlation coefficient of 0.78, Net Benefit (0.7) and Brier score (0.06) for overall survival stratification of OPSCC patients as either low-chance or high-chance. In addition, out of a total of 31 studies found to have used ML for the prognostication of outcomes in OPSCC, only seven (22.6%) reported a form of EV. Three studies (42.9%) each used either temporal EV or geographical EV while only one study (14.2%) used expert as a form of EV. Most of the studies reported a reduction in performance when externally validated. ConclusionThe performance of the model in this validation study indicates that it may be generalized, therefore, bringing recommendations of the model for clinical evaluation closer to reality. However, the number of externally validated ML-based models for OPSCC is still relatively small. This significantly limits the transfer of these models for clinical evaluation and subsequently reduces the likelihood of the use of these models in daily clinical practice. As a gold standard, we recommend the use of geographical EV and validation studies to reveal biases and overfitting of these models. These recommendations are poised to facilitate the implementation of these models in clinical practice.

UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity.

Identification of potent peptides through model prediction can reduce benchwork in wet experiments. However, the conventional process of model buildings can be complex and time consuming due to challenges such as peptide representation, feature selection, model selection and hyperparameter tuning. Recently, advanced pretrained deep learning-based language models (LMs) have been released for protein sequence embedding and applied to structure and function prediction. Based on these developments, we have developed UniDL4BioPep, a universal deep-learning model architecture for transfer learning in bioactive peptide binary classification modeling. It can directly assist users in training a high-performance deep-learning model with a fixed architecture and achieve cutting-edge performance to meet the demands in efficiently novel bioactive peptide discovery. To the best of our best knowledge, this is the first time that a pretrained biological language model is utilized for peptide embeddings and successfully predicts peptide bioactivities through large-scale evaluations of those peptide embeddings. The model was also validated through uniform manifold approximation and projection analysis. By combining the LM with a convolutional neural network, UniDL4BioPep achieved greater performances than the respective state-of-the-art models for 15 out of 20 different bioactivity dataset prediction tasks. The accuracy, Mathews correlation coefficient and area under the curve were 0.7-7, 1.23-26.7 and 0.3-25.6% higher, respectively. A user-friendly web server of UniDL4BioPep for the tested bioactivities is established and freely accessible at https://nepc2pvmzy.us-east-1.awsapprunner.com. The source codes, datasets and templates of UniDL4BioPep for other bioactivity fitting and prediction tasks are available at https://github.com/dzjxzyd/UniDL4BioPep.

Design and Application of Deep Learning-Based Crash Damage Prediction Model for Self-Driving Cars

Abstract The collision damage of automated cars has grown in importance as self-driving car technology has advanced to the pilot operation stage. To enhance the safety of autonomous vehicles by predicting and preventing potential hazards during autonomous driving, this study presents a model for collision damage prediction in automated driving cars. The model optimizes deep convolutional neural networks using the self-attention mechanism and incorporates a degree convolutional neural network algorithm with the attention mechanism. Its application is key to reduce risks in autonomous driving. The results demonstrated that the accuracy, reliability, specificity, and Mathews correlation coefficient of the improved algorithm were 94.0%, 94.8%, 93.6%, and 0.88, respectively, resulting in a high overall performance. The prediction model's accuracy during training on the training data set and validation data set was 100% and 98%, respectively, demonstrating its efficacy. The prediction model's prediction accuracy in calculating the degree of auto collision damage for 10 working conditions in the validation data set was 83.3%. The prediction results were essentially consistent with the trend of the actual collision damage degree curve, demonstrating both the viability and high prediction accuracy of the prediction model. The aforementioned findings demonstrated the model's strong performance and great application value in the field of self-driving car collision avoidance and warning.

Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

This paper addresses the detection and identification of flaws in Plug valves. The Plug valve thermal image is acquired using a Fluke thermal camera [TiS20]. Thermal images of the Plug valve are used for the identification of flaws such as Crack, Porosity, Corrosion, and Internal defects. The thermal images detect the surface flaws and never subsurface flaws in Plug valves. The subsurface flaws detection is a challenging problem in valve inspection. In this paper, the thermal images obtained after the dye penetrates the surface valve detect the surface flaws more efficiently after applying the Fuzzy Deep Learning Algorithms. DyePenetrating Test (DPT) combined with Infrared Thermography is proposed to identify heat flux changes and flaws in the faulty metal surface of Plug valves. In DPT, thinned paint is employed on the metal surface that displays metal porosity and even fine cracks. After DPT, thermal images of the Plug valve are processed through the Fuzzy Deep Learning Algorithm to evaluate flaws. The Fuzzy Algorithm is utilized prior to Deep Learning to simplify and speed up the classification task. The flaws are identified using Slicing operation and the following parametric quantities such as Accuracy, Mathew’s Correlation Coefficient (MCC), Local Self-Similarity Descriptor (LSS), Precision/Recall, F-measure, and Jaccard Index. The parametric quantities depict corresponding variations with regard to surface coarseness and metal flaws. The DPT and Fuzzy Deep Learning Algorithm identify metal defects with 80.67% accuracy.

Spatiotemporal Detection and Localization of Object Removal Video Forgery with Multiple Feature Extraction and Optimized Residual Network

Video forgery detection and localization is one of the most important issue due to the advanced editing software that provides strengthen to tools for manipulating the videos. Object based video tampering destroys the originality of the video. The main aim of the video forensic is to eradicate the forgeries from the original video that are useful in various applications. However, the research on detecting and localizing the object based video forgery with advanced techniques still remains the open and challenging issue. Many of the existing techniques have focused only on detecting the forged video under static background that cannot be applicable for detecting the forgery in tampered video. In addition to this, conventional techniques fail to extract the essential features in order to investigate the depth of the video forgery. Hence, this paper brings a novel technique for detecting and localizing the forged video with multiple features. The steps involved in this research are keyframe extraction, pre-processing, feature extraction and finally detection and localization of forged video. Initially, keyframe extraction uses the Gaussian mixture model (GMM) to extract frames from the forged videos. Then, the pre-processing stage is manipulated to convert the RGB frame into a grayscale image. Multi-features need to be extracted from the pre-processed frames to study the nature of the forged videos. In our proposed study, speeded up robust features (SURF), principal compound analysis histogram oriented gradients (PCA-HOG), model based fast digit feature (MBFDF), correlation of adjacent frames (CAF), the prediction residual gradient (PRG) and optical flow gradient (OFG) features are extracted. The dataset used for the proposed approach is collected from REWIND of about 40 forged and 40 authenticated videos. With the help of the DL approach, video forgery can be detected and localized. Thus, this research mainly focuses on detecting and localization of forged video based on the ResNet152V2 model hybrid with the bidirectional gated recurrent unit (Bi-GRU) to attain maximum accuracy and efficiency. The performance of this approach is finally compared with existing approaches in terms of accuracy, precision, F-measure, sensitivity, specificity, false-negative rate (FNR), false discovery rate (FDR), false-positive rate (FPR), Mathew’s correlation coefficient (MCC) and negative predictive value (NPV). The proposed approach assures the performance of 96.17% accuracy, 96% precision, 96.14% F-measure, 96.58% sensitivity, 96.5% specificity, 0.034 FNR, 0.04 FDR, 0.034 FPR, 0.92 MCC and 96% NPV, respectively. Along with is, the mean square error (MSE) and peak-to-signal-noise ratio (PSNR) for the GMM model attained about 104 and 27.95, respectively.

Prediction of Enhancers in DNA Sequence Data using a Hybrid CNN-DLSTM Model.

Enhancer, a distal cis-regulatory element controls gene expression. Experimental prediction of enhancer elements is time-consuming and expensive. Consequently, various inexpensive deep learning-based fast methods have been developed for predicting the enhancers and determining their strength. In this paper, we have proposed a two-stage deep learning-based framework leveraging DNA structural features, natural language processing, convolutional neural network, and long short-term memory to predict the enhancer elements accurately in the genomics data. In the first stage, we extracted the features from DNA sequence data by using three feature representation techniques viz., k-mer based feature extraction along with word2vector based interpretation of underlined patterns, one-hot encoding, and the DNAshape technique. In the second stage, strength of enhancers is predicted from the extracted features using a hybrid deep learning model. The method is capable of adapting itself to varying sizes of datasets. Also, as proposed model can capture long-range sequencing patterns, the robustness of the method remains unaffected against minor variations in the genomics sequence. The method outperforms the other state-of-the-art methods at both stages in terms of performance metrics of prediction accuracy, specificity, Mathew's correlation coefficient, and area under the ROC curve. In summary, the proposed method is a reliable method for enhancer prediction.

An Efficient Liver Disease Prediction Using Mask-Regional Convolutional Neural Network and Pelican Optimization Algorithm

Various prediction approaches regarding liver diseases have been developed. Still, they are expensive and more complex. This work aims to design an effective method for identifying liver diseases at earlier stages. This paper presents a modified Mask-regionalconvolutional neural network (MRCNN) architecture for non-invasive liver disease prediction. The Pelican Optimization Algorithm (POA) is used to balance the bounding box regression and mask branching training losses of the RCNN model. The liver disease features are extracted from three datasets namely Indian liver patient records, Hepatitis C, and Cirrhosis Prediction datasets. The POA-modified MRCNN model is mainly used to identify the interrelationship that exists between different laboratory measurements and diagnoses. The efficiency of the proposed model is compared with different state-of-art methods such as Opposition-based Laplacian Equilibrium Optimizer, Adaptive Hybridized Deep CNN, SVM, and Tree-based classifiers in terms of Accuracy, Precision, Recall, F-measure, and Mathews Correlation Coefficient (MCC). The proposed model offers an MCC value of 94.89, an accuracy of 98%, a precision of 96.2%, an F-measure of 97.3%, and a recall of 95% respectively. The results demonstrate the efficiency of the proposed model in predicting liver disease at an early stage via automatic screening and minimizing the burden of physicians.

I-DNAN6mA: Accurate Identification of DNA N6-Methyladenine Sites Using the Base-Pairing Map and Deep Learning.

The recent discovery of numerous DNA N6-methyladenine (6mA) sites has transformed our perception about the roles of 6mA in living organisms. However, our ability to understand them is hampered by our inability to identify 6mA sites rapidly and cost-efficiently by existing experimental methods. Developing a novel method to quickly and accurately identify 6mA sites is critical for speeding up the progress of its function detection and understanding. In this study, we propose a novel computational method, called I-DNAN6mA, to identify 6mA sites and complement experimental methods well, by leveraging the base-pairing rules and a well-designed three-stage deep learning model with pairwise inputs. The performance of our proposed method is benchmarked and evaluated on four species, i.e., Arabidopsis thaliana, Drosophila melanogaster, Rice, and Rosaceae. The experimental results demonstrate that I-DNAN6mA achieves area under the receiver operating characteristic curve values of 0.967, 0.963, 0.947, 0.976, and 0.990, accuracies of 91.5, 92.7, 88.2, 0.938, and 96.2%, and Mathew's correlation coefficient values of 0.855, 0.831, 0.763, 0.877, and 0.924 on five benchmark data sets, respectively, and outperforms several existing state-of-the-art methods. To our knowledge, I-DNAN6mA is the first approach to identify 6mA sites using a novel image-like representation of DNA sequences and a deep learning model with pairwise inputs. I-DNAN6mA is expected to be useful for locating functional regions of DNA.

An Intelligent Internet-of-Things-Aided Financial Crisis Prediction Model in FinTech

Financial technology (FinTech) has become a hot research topic recently, moreover, an area of major investment for most financial institutions. As the FinTech unleashed many new strategic solutions for major financial problems. One of those pivotal strategies of FinTech is financial crisis prediction (FCP) that dictates the financial status of an institution. Also, the rise of the Internet-of-Things (IoT) technology has paved a new way for interaction between humans and the physical world. Therefore, IoT can feasibly be incorporated into the FCP model to obtain a real-time analysis of the financial data from the clients. With this perspective, we propose an intelligent IoT-aided FCP model using metaheuristic algorithms. The proposed FCP method comprises data acquisition, preprocessing, feature selection (FS), and classification. First, the financial data of the enterprises are collected using IoT devices, such as smartphones, laptops, etc. Next, the quantum artificial butterfly optimization (QABO) approach for FS is applied to choose an optimal set of features. Afterward, long short-time memory (LSTM) with recurrent neural network (RNN) model is employed to classify the collected financial data. An exhaustive experimental validation process is carried out to ensure the performance of the proposed QABO-LSTM-RNN model. The simulation results accredited the efficacy of the proposed model contrasted with other baseline methods in terms of best cost, sensitivity, specificity, accuracy, F-score, kappa, and Mathew correlation coefficient (MCC).

Deep Transfer Learning Techniques-Based Automated Classification and Detection of Pulmonary Fibrosis from Chest CT Images

Pulmonary Fibrosis (PF) is a non-curable chronic lung disease. Therefore, a quick and accurate PF diagnosis is imperative. In the present study, we aim to compare the performance of the six state-of-the-art Deep Transfer Learning techniques to classify patients accurately and perform abnormality localization in Computer Tomography (CT) scan images. A total of 2299 samples comprising normal and PF-positive CT images were preprocessed. The preprocessed images were split into training (75%), validation (15%), and test data (10%). These transfer learning models were trained and validated by optimizing the hyperparameters, such as the learning rate and the number of epochs. The optimized architectures have been evaluated with different performance metrics to demonstrate the consistency of the optimized model. At epoch 26, using an optimized learning rate of 0.0000625, the ResNet50v2 model achieved the highest training and validation accuracy (training = 99.92%, validation = 99.22%) and minimum loss (training = 0.00428, validation = 0.00683) for CT images. The experimental evaluation on the independent testing data confirms that optimized ResNet50v2 outperformed every other optimized architecture under consideration achieving a perfect score of 1.0 in each of the standard performance measures such as accuracy, precision, recall, F1-score, Mathew Correlation Coefficient (MCC), Area under the Receiver Operating Characteristic (ROC-AUC) curve, and the Area under the Precision recall (AUC_PR) curve. Therefore, we can propose that the optimized ResNet50v2 is a reliable diagnostic model for automatically classifying PF-positive patients using chest CT images.

Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study.

Physical inactivity is associated with numerous health risks, including cancer, cardiovascular disease, type 2 diabetes, increased health care expenditure, and preventable, premature deaths. The majority of Americans fall short of clinical guideline goals (ie, 8000-10,000 steps per day). Behavior prediction algorithms could enable efficacious interventions to promote physical activity by facilitating delivery of nudges at appropriate times. The aim of this paper is to develop and validate algorithms that predict walking (ie, >5 min) within the next 3 hours, predicted from the participants' previous 5 weeks' steps-per-minute data. We conducted a retrospective, closed cohort, secondary analysis of a 6-week microrandomized trial of the HeartSteps mobile health physical-activity intervention conducted in 2015. The prediction performance of 6 algorithms was evaluated, as follows: logistic regression, radial-basis function support vector machine, eXtreme Gradient Boosting (XGBoost), multilayered perceptron (MLP), decision tree, and random forest. For the MLP, 90 random layer architectures were tested for optimization. Prior 5-week hourly walking data, including missingness, were used for predictors. Whether the participant walked during the next 3 hours was used as the outcome. K-fold cross-validation (K=10) was used for the internal validation. The primary outcome measures are classification accuracy, the Mathew correlation coefficient, sensitivity, and specificity. The total sample size included 6 weeks of data among 44 participants. Of the 44 participants, 31 (71%) were female, 26 (59%) were White, 36 (82%) had a college degree or more, and 15 (34%) were married. The mean age was 35.9 (SD 14.7) years. Participants (n=3, 7%) who did not have enough data (number of days <10) were excluded, resulting in 41 (93%) participants. MLP with optimized layer architecture showed the best performance in accuracy (82.0%, SD 1.1), whereas XGBoost (76.3%, SD 1.5), random forest (69.5%, SD 1.0), support vector machine (69.3%, SD 1.0), and decision tree (63.6%, SD 1.5) algorithms showed lower performance than logistic regression (77.2%, SD 1.2). MLP also showed superior overall performance to all other tried algorithms in Mathew correlation coefficient (0.643, SD 0.021), sensitivity (86.1%, SD 3.0), and specificity (77.8%, SD 3.3). Walking behavior prediction models were developed and validated. MLP showed the highest overall performance of all attempted algorithms. A random search for optimal layer structure is a promising approach for prediction engine development. Future studies can test the real-world application of this algorithm in a "smart" intervention for promoting physical activity.

Classification of Multi-view Digital Mammogram Images Using SMO-WkNN

Breast cancer (BCa) is a leading cause of death in the female population across the globe. Approximately 2.3 million new BCa cases are recorded globally in females, overtaking lung cancer as the most prevalent form of cancer to be diagnosed. However, the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries. Early diagnosis is the only option to minimize the risks of BCa. Deep learning (DL)-based models have performed well in image processing in recent years, particularly convolutional neural network (CNN). Hence, this research proposes a DL-based CNN model to diagnose BCa from digitized mammogram images. The main objective of this research is to develop an accurate and efficient early diagnosis model for BCa detection. This proposed model is a multi-view-based computer-aided diagnosis (CAD) model, which performs the diagnosis of BCa on multi-views of mammogram images like medio-lateral-oblique (MLO) and cranio-caudal (CC). The digital mammogram images are collected from the digital database for screening mammography (DDSM) dataset. In preprocessing, median filter and contrast limited adaptive histogram equalization (CLAHE) techniques are utilized for image enhancement. After preprocessing, the segmentation is performed using the region growing (RG) algorithm. The feature extraction is carried out from the segmented images using a pyramidal histogram of oriented gradients (PHOG) and the AlextNet model. Finally, the classification is performed using the weighted k-nearest neighbor (WkNN) optimized with sequential minimal optimization (SMO). The classified images are evaluated based on accuracy, recall, precision, specificity, f1-score, and mathews correlation coefficient (MCC). Additionally, the false positive and error rates are evaluated. The proposed model obtained 98.57% accuracy, 98.61% recall, 99.25% specificity, 98.63% precision, 97.93% f1-score, 96.26% MCC, 0.0143 error rate, and 0.0075 false positive rate (FPR). Compared to the existing models, the research model has obtained better performances and outperformed the other models.

Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

Increase in the use of internet of things owned devices is one of the reasons for increased network traffic. While connecting the smart devices with publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing system. The Phishing is the slow and resilient attack stacking techniques probe the users. The proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm (NWAT). Though Predictive mapping, Predictive Validation and Predictive analysis mechanism is developed. For the test purpose Canadian Institute of cyber security (CIC) dataset is used for creating a robust prediction model. This attack generates a resilience corruption works that slowly gathers the credential information from the mobiles. The proposed Predictive analysis model (PAM) enabled NWAT algorithm is used to predict the phishing probes in the form of suspicious process happening in the IoT networks. The prediction system considers the peer-to-peer communication window open for the established communication, the suspicious process and its pattern is identified by the new approach. The proposed model is validated by finding the prediction accuracy, Precision, recalls F1score, error rate, Mathew’s Correlation Coefficient (MCC) and Balanced Detection Rate (BDR). The presented approach is comparatively analyzed with the state-of-the-art approach of existing system related to various types of Phishing probes.

Appearance Based Dynamic Hand Gesture Recognition Using 3D Separable Convolutional Neural Network

Appearance-based dynamic Hand Gesture Recognition (HGR) remains a prominent area of research in Human-Computer Interaction (HCI). Numerous environmental and computational constraints limit its real-time deployment. In addition, the performance of a model decreases as the subject’s distance from the camera increases. This study proposes a 3D separable Convolutional Neural Network (CNN), considering the model’s computational complexity and recognition accuracy. The 20BN-Jester dataset was used to train the model for six gesture classes. After achieving the best offline recognition accuracy of 94.39%, the model was deployed in real-time while considering the subject’s attention, the instant of performing a gesture, and the subject’s distance from the camera. Despite being discussed in numerous research articles, the distance factor remains unresolved in real-time deployment, which leads to degraded recognition results. In the proposed approach, the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the camera. Additionally, the capability of feature extraction, degree of relevance, and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding (t-SNE), Mathew’s Correlation Coefficient (MCC), and the McNemar test, respectively. We observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level.

Predicting Pediatric Appendicitis using Ensemble Learning Techniques

Managing appendicitis in children is still lacking international consensus. This is due to the lack of defined international pediatric appendicitis management recommendations and the unavailability of data-driven studies. In clinical practice, heuristic scoring methods are frequently employed to determine patient urgency. The use of ensemble techniques may improve prediction responses, which is the major goal of this study. This study used an ensemble of classifiers to predict three target variables in 430 children and adolescents aged 0-18 years. The ensemble approaches used in this work are majority voting and weighted averaging. In the Jupyter Python environment, these strategies’ results were confirmed in terms of accuracy, misclassification rate, sensitivity, specificity, precision, f1-score, false negative and positive rates, and Mathew's correlation coefficient. Combining machine learning and deep learning can help in diagnosing and managing pediatric appendicitis, allowing for more tailored medical decisions.