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Digital technology is so broad today as to encompass almost everything. It eases the difficulties of day-to day life with internet. E-commerce plays an important role in digital age of technology. Instead of many benefits of online system, it faces many types of threats. Sometime, choosing the best e-commerce security has a lot of uncertainties and ambiguities to reduce the effect of threats. To reduce these uncertainties, this article developed a new concept of complex cubic picture fuzzy set (CoCPFS) of fuzzy algebra that explains the positive effect, neutral effect, and negative effect of any object and additionally discussed the Cartesian product between two CoCPFSs, CoCPF relations, and their types. The relationship among the e-commerce securities and threats is investigated for the first time in the history of fuzzy algebra. These relations show the ways of disabling the effects of a threat by an effective security technique. Furthermore, the advantages and benefits of CoCPFS are explained through comparison tests with preexisting frameworks of fuzzy sets. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as the coronavirus disease 2019 (COVID-19), has threatened many human beings around the world and capsized economies at unprecedented magnitudes. Therefore, the detection of this disease using chest X-ray modalities has played a pivotal role in producing fast and accurate medical diagnoses, especially in countries that are unable to afford laboratory testing kits. However, identifying and distinguishing COVID-19 from virtually similar thoracic abnormalities utilizing medical images is challenging because it is time-consuming, demanding, and susceptible to human-based errors. Therefore, artificial-intelligence-driven automated diagnoses, which excludes direct human intervention, may potentially be used to achieve consistently accurate performances. In this study, we aimed to (i) obtain a customized dataset composed of a relatively small number of images collected from publicly available datasets; (ii) present the efficient integration of the shallow handcrafted features obtained from local descriptors, radiomics features specialized for medical images, and deep features aggregated from pre-trained deep learning architectures; and (iii) distinguish COVID-19 patients from healthy controls and pneumonia patients using a collection of conventional machine learning classifiers. By conducting extensive experiments, we demonstrated that the feature-based ensemble approach provided the best classification metrics, and this approach explicitly outperformed schemes that used only either local, radiomic, or deep features. In addition, our proposed method achieved state-of-the-art multi-class classification results compared to the baseline reference for the currently available COVID-19 datasets. [ABSTRACT FROM AUTHOR]

Human detection in real scenes with high complexity is a crucial problem in computer vision research. For tackling the human detection task, several existing methods adopt one feature or combination of features to detect human objects. In this work, we propose a new combination of features based algorithm for human detection, which identifies the presence of a human, in complex real scenes. In the combination, the two features (i) biorthogonal wavelet transform (BWT) and (ii) Zernike moments (ZM) have been used. The approximate shift-invariance and symmetry properties of BWT facilitate the human detection in the wavelet domain. Specifically, the shift-invariance property of BWT is effective for translated object representation whereas the symmetry property yields perfect reconstruction for retaining object boundaries (i.e., edges). Moreover, translation and rotation-invariance properties of ZM are especially beneficial for the representation of varying pose and orientation of the human objects. For these reasons, the composite of the two features brings about significant synthesized benefits over each single feature and the other widely used features. In the experiments for human detection, we used two classifiers, AdaBoost and support vector machines, respectively, for the comparative study purpose, and the standard INRIA dataset and DaimlerChrysler dataset were used for the evaluations. Experimental results demonstrated the significant outperformance of the proposed method through quantitative evaluations and also suggest that the proposed hybridization of features is preferable for the classification problem. [ABSTRACT FROM AUTHOR]

A crossover operator in genetic algorithms (GAs) plays an essential role as the main search operator to breed offspring by exchanging information between individuals. Although different types of crossover operators have been developed for real-coded GAs (RCGAs), there has been very little research on combining different crossover operators to build more effective and efficient RCGAs. In this work, we propose new steady-state generation alternation-based RCGAs (SSGAs) ameliorated with (i) an ensemble of different probabilistic variable-wise crossover strategies, which is realized by the corresponding parallel populations, to utilize synergetic and complementary effect with their efficient operations, and (ii) efficient operation at each evolution step to obtain further performance enhancement. To investigate the performance of this ensemble with respect to search abilities and computation time, we compare the proposed algorithms against various SSGAs when running 27 benchmark functions. Empirical studies showed that the proposed algorithms exhibit better performance than the contestant SSGAs on these functions. Moreover, a comparison with the state-of-the-art evolutionary algorithms on eight difficult benchmark functions clearly demonstrated outperformance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average PFS ft PA , weighted picture fuzzy soft power average WPFS ft PA and ordered weighted picture fuzzy soft power average OWPFS ft PA aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric PFS ft PG , weighted picture fuzzy soft power geometric WPFS ft PG and ordered weighted picture fuzzy soft power geometric OWPFS ft PG aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions. [ABSTRACT FROM AUTHOR]

In the current competitive market, green products are becoming more and more in demand across all customer demographics due to environmental contamination. Consequently, this has a direct impact on the notable rise in product demand. Conversely, a product cannot be used or repurposed if its maximum useful life has already elapsed. Therefore, the product's maximum lifespan plays a crucial role in inventory management. Advance payment is a critical component of the supplier–retailer relationship for a seasonal product with high demand. By integrating these issues, an inventory model for perishable goods is created, wherein the demand for the good is impacted by both its selling price and its green level. The model also includes linearly time‐dependent holding costs. According to this model, product greenness has an increasing effect on per‐unit costs. Different numerical examples are studied for supporting this model. Optimality of the objective function is shown graphically as well as numerically with the help of MATHEMATICA software. Finally, a fruitful conclusion is reached after performing a sensitivity analysis and visually depicting the impact of changing various factors. [ABSTRACT FROM AUTHOR]

In recent years, deep learning models have been used successfully in almost every field including both industry and academia, especially for computer vision tasks. However, these models are huge in size, with millions (and billions) of parameters, and thus cannot be deployed on the systems and devices with limited resources (e.g., embedded systems and mobile phones). To tackle this, several techniques on model compression and acceleration have been proposed. As a representative type of them, knowledge distillation suggests a way to effectively learn a small student model from large teacher model(s). It has attracted increasing attention since it showed its promising performance. In the work, we propose an ensemble model that combines feature-based, response-based, and relation-based lightweight knowledge distillation models for simple image classification tasks. In our knowledge distillation framework, we use ResNet−20 as a student network and ResNet−110 as a teacher network. Experimental results demonstrate that our proposed ensemble model outperforms other knowledge distillation models as well as the large teacher model for image classification tasks, with less computational power than the teacher model. [ABSTRACT FROM AUTHOR]

Accurate brain tumor segmentation is a labor-intensive and time-consuming task that requires automation to enhance its efficacy. Recent advanced techniques have shown promising results in segmenting brain tumors; however, their dependency on extensive multimodal magnetic resonance imaging (MRI) data limits their practicality in clinical environments where such data may not be readily available. To address this, we propose a novel multi-teacher cross-modal knowledge distillation framework, which utilizes the privileged multimodal data during training while relying solely on unimodal data for inference. Our framework is tailored to the unimodal segmentation of the T 1ce MRI sequence, which is prevalently available in clinical practice and structurally akin to the T 1 modality, providing ample information for the segmentation task. Our framework introduces two learning strategies for knowledge distillation (KD): (1) performance-aware response-based KD and (2) cooperative deep supervision fusion learning (CDSFL). The first strategy involves dynamically assigning confidence weights to each teacher model based on its performance, ensuring that the KD is performance-driven, and the CDSFL module augments the learning capabilities of the multi-teacher models by fostering mutual learning. Moreover, the fused information is distilled into the student model to improve its deep supervision. Extensive experiments on BraTS datasets demonstrate that our framework achieves promising unimodal segmentation results on the T 1ce and T 1 modalities and outperforms previous state-of-the-art methods. Code is available at https://github.com/ami-lab-knut/mtcm_kd. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the novel concept of Complex q-rung orthopair fuzzy set (Cq-ROFS) which is a useful tool to cope with unresolved and complicated information. It is characterized by a complex-valued membership grade and a complex-valued non-membership grade, the distinction of which is that the sum of q-powers of the real parts (imaginary parts) of the membership and non-membership grades is less than or equal to one. To explore the study, we present some basic operational laws, score and accuracy functions and investigate their properties. Further, to aggregate the given information of Cq-ROFS, we present several weighted averaging and geometric power aggregation operators named as complex q-rung orthopair fuzzy (Cq-ROF) power averaging operator, Cq-ROF power geometric operator, Cq-ROF power weighted averaging operator, Cq-ROF power weighted geometric operator, Cq-ROF hybrid averaging operator and Cq-ROF power hybrid geometric operator. Properties and special cases of the proposed approaches are discussed in detail. Moreover, the VIKOR ("VIseKriterijumska Optimizacija I Kompromisno Resenje") method for Cq-ROFSs is introduced and its aspects discussed. Furthermore, the above mentioned approaches apply to multi-attribute decision-making problems and VIKOR methods, in which experts state their preferences in the Cq-ROF environment to demonstrate the feasibility, reliability and effectiveness of the proposed approaches. Finally, the proposed approach is compared with existing methods through numerical examples. [ABSTRACT FROM AUTHOR]

Tumor-infiltrating lymphocytes, specialized immune cells, are considered an important biomarker in cancer analysis. Automated lymphocyte detection is challenging due to its heterogeneous morphology, variable distribution, and presence of artifacts. In this work, we propose a novel Boosted Channels Fusion-based CNN "BCF-Lym-Detector" for lymphocyte detection in multiple cancer histology images. The proposed network initially selects candidate lymphocytic regions at the tissue level and then detects lymphocytes at the cellular level. The proposed "BCF-Lym-Detector" generates diverse boosted channels by utilizing the feature learning capability of different CNN architectures. In this connection, a new adaptive fusion block is developed to combine and select the most relevant lymphocyte-specific features from the generated enriched feature space. Multi-level feature learning is used to retain lymphocytic spatial information and detect lymphocytes with variable appearances. The assessment of the proposed "BCF-Lym-Detector" show substantial improvement in terms of F-score (0.93 and 0.84 on LYSTO and NuClick, respectively), which suggests that the diverse feature extraction and dynamic feature selection enhanced the feature learning capacity of the proposed network. Moreover, the proposed technique's generalization on unseen test sets with a good recall (0.75) and F-score (0.73) shows its potential use for pathologists' assistance. [ABSTRACT FROM AUTHOR]

Tumor-infiltrating lymphocytes, specialized immune cells, are considered an important biomarker in cancer analysis. Automated lymphocyte detection is challenging due to its heterogeneous morphology, variable distribution, and presence of artifacts. In this work, we propose a novel Boosted Channels Fusion-based CNN "BCF-Lym-Detector" for lymphocyte detection in multiple cancer histology images. The proposed network initially selects candidate lymphocytic regions at the tissue level and then detects lymphocytes at the cellular level. The proposed "BCF-Lym-Detector" generates diverse boosted channels by utilizing the feature learning capability of different CNN architectures. In this connection, a new adaptive fusion block is developed to combine and select the most relevant lymphocyte-specific features from the generated enriched feature space. Multi-level feature learning is used to retain lymphocytic spatial information and detect lymphocytes with variable appearances. The assessment of the proposed "BCF-Lym-Detector" show substantial improvement in terms of F-score (0.93 and 0.84 on LYSTO and NuClick, respectively), which suggests that the diverse feature extraction and dynamic feature selection enhanced the feature learning capacity of the proposed network. Moreover, the proposed technique's generalization on unseen test sets with a good recall (0.75) and F-score (0.73) shows its potential use for pathologists' assistance. [ABSTRACT FROM AUTHOR]

Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance. • The automated detection of COVID-19 using chest X-rays. • A novel MTL-based adversarial semi-supervised framework to address the smaller size of data. • We focus to utilize the unlabeled data to improve the performance as well as the generalization of the system. • Model was developed using publicly available datasets i.e., CheXpert and COVIDx. • The proposed method outperforms the standard state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Very recently, MXene‐based wearable hydrogels have emerged as promising candidates for epidermal sensors due to their tissue‐like softness and unique electrical and mechanical properties. However, it remains a challenge to achieve MXene‐based hydrogels with reliable sensing performance and prolonged service life, because MXene inevitably oxidizes in water‐containing system of the hydrogels. Herein, catechol‐functionalized poly(vinyl alcohol) (PVA‐CA)‐based hydrogels is proposed to inhibit the oxidation of MXene, leading to rapid self‐healing and superior strain sensing behaviors. Sufficient interaction of hydrophobic catechol groups with the MXene surface reduces the oxidation‐accessible sites in the MXene for reaction with water and eventually suppresses the oxidation of MXene in the hydrogel. Furthermore, the PVA‐CA‐MXene hydrogel is demonstrated for use as a strain sensor for real‐time motion monitoring, such as detecting subtle human motions and handwriting. The signals of PVA‐CA‐MXene hydrogel sensor can be accurately classified using deep learning models. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage to blood vessels in the retina. Early retinal screening can avoid the severe consequences of DR and enable timely treatment. Nowadays, researchers are trying to develop automated deep learning-based DR segmentation tools using retinal fundus images to help Ophthalmologists with DR screening and early diagnosis. However, recent studies are unable to design accurate models due to the unavailability of larger training data with consistent and fine-grained annotations. To address this problem, we propose a semi-supervised multitask learning approach that exploits widely available unlabelled data (i.e., Kaggle-EyePACS) to improve DR segmentation performance. The proposed model consists of novel multi-decoder architecture and involves both unsupervised and supervised learning phases. The model is trained for the unsupervised auxiliary task to effectively learn from additional unlabelled data and improve the performance of the primary task of DR segmentation. The proposed technique is rigorously evaluated on two publicly available datasets (i.e., FGADR and IDRiD) and results show that the proposed technique not only outperforms existing state-of-the-art techniques but also exhibits improved generalisation and robustness for cross-data evaluation. [ABSTRACT FROM AUTHOR]

Breast tumor is the second deadliest disease among women around the world. Earlier tumor diagnosis is extremely important for improving the survival rate. Recent deep-learning techniques proved helpful in the timely diagnosis of various tumors. However, in the case of breast tumors, the characteristics of the tumors, i.e., low visual contrast, unclear boundary, and diversity in shape and size of breast lesions, make it more challenging to design a highly efficient detection system. Additionally, the scarcity of publicly available labeled data is also a major hurdle in the development of highly accurate and robust deep-learning models for breast tumor detection. To overcome these issues, we propose residual-attention-based uncertainty-guided mean teacher framework which incorporates the residual and attention blocks. The residual for optimizing the deep network by enabling the flow of high-level features and attention modules improves the focus of the model by optimizing its weights during the learning process. We further explore the potential of utilizing unlabeled data during the training process by employing the semi-supervised learning (SSL) method. Particularly, the uncertainty-guided mean-teacher student architecture is exploited to demonstrate the potential of incorporating the unlabeled samples during the training of residual attention U-Net model. The proposed SSL framework has been rigorously evaluated on two publicly available labeled datasets, i.e., BUSI and UDIAT datasets. The quantitative as well as qualitative results demonstrate that the proposed framework achieved competitive performance with respect to the previous state-of-the-art techniques and outperform the existing breast ultrasound masses segmentation techniques. Most importantly, the study demonstrates the potential of incorporating the additional unlabeled data for improving the performance of breast tumor segmentation. • Devised an efficient uncertainty-guided MT model for SSL breast tumor segmentation. • Adopted the uncertainty consistency loss function to improve segmentation performance. • Utilized the supervised and unsupervised loss functions to optimize the student model. [ABSTRACT FROM AUTHOR]

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%. [ABSTRACT FROM AUTHOR]

The early diagnosis of Alzheimer's Disease (AD) plays a central role in the treatment of AD. Particularly, identifying the preclinical AD (pAD) stage could be crucial for timely treatment in the elderly. However, screening participants with pAD requires a series of psychological and neurological examinations. Thus, an efficient diagnostic tool is needed. Here, we recruited 91 elderly participants and collected 1 min of resting-state electroencephalography data to classify participants as normal aging or diagnosed with pAD. We used deep neural networks (Deep ConvNet, EEGNet, EEG-TCNet, and cascade CRNN) in the within- and cross-subject paradigms for classification and found individual variations of classification accuracy in the cross-subject paradigm. Further, we proposed an individualized diagnostic strategy to identify neurophysiological similarities across participants and the proposed approach considering individual characteristics improved the diagnostic performance by approximately 20%. Our findings suggest that considering individual characteristics would be a breakthrough in diagnosing AD using deep neural networks. [ABSTRACT FROM AUTHOR]

Several radiologists have paid attention to computer‐aided detection (CAD) systems which assist in classifying diseases on chest x‐ray (CXR). Recently, with the outbreak of COVID‐19, CAD based on deep learning has an important role in screening COVID‐19 on CXR. However, imbalanced training datasets such as COVID‐19 datasets, COVID‐19 (473), pneumonia (5458), and normal (7966) cause difficulty in classification. In this paper, we suggest a new evaluation approach, OVASO, that selectively combines one‐versus‐all (OVA) classifier and one‐versus‐one (OVO) to overcome class imbalance caused by the lower number of COVID‐19 training datasets. In addition, as part of efforts to properly apply transfer learning, we initialized batch normalization (BN) values including γ and β from the viewpoint of transfer learning and found that appropriate initialization at all binary models, OVASO's components, usually increased the binary models' performance. As a result, the proposed OVASO model achieved improved accuracy and F1‐score of 95.33% and 95.88%, respectively. Furthermore, the suggested OVASO performed similarly to COVID‐Net, which is the current state‐of‐the‐art model for classifying COVID‐19 on CXR. [ABSTRACT FROM AUTHOR]

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks. [ABSTRACT FROM AUTHOR]

A strategic decision-making technique can help the decision maker to accomplish and analyze the information in an efficient manner. However, in our real life, an uncertainty will play a dominant role during the information collection phase. To handle such uncertainties in the data, we present a decision-making algorithm under the single-valued neutrosophic (SVN) environment. The SVN is a powerful way to deal the information in terms of three degrees, namely, "truth," "falsity," and "indeterminacy," which all are considered independent. The main objective of this study is divided into three folds. In the first fold, we state the novel concept of complex SVN hesitant fuzzy (CSVNHF) set by incorporating the features of the SVN, complex numbers, and the hesitant element. The various fundamental and algebraic laws of the proposed CSVNHF set are described in details. The second fold is to state the various aggregation operators to obtain the aggregated values of the considered CSVNHF information. For this, we stated several generalized averaging operators, namely, CSVNHF generalized weighted averaging, ordered weighted average, and hybrid average. The various properties of these operators are also stated. Finally, we discuss a multiattribute decision-making (MADM) algorithm based on the proposed operators to address the problems under the CSVNHF environment. A numerical example is given to illustrate the work and compare the results with the existing studies' results. Also, the sensitivity analysis and advantages of the stated algorithm are given in the work to verify and strengthen the study. [ABSTRACT FROM AUTHOR]

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets. [ABSTRACT FROM AUTHOR]

Medical image fusion has been used to improve useful and relevant information (e.g., precise localization of abnormalities) of multimodal medical images, such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) images, and the information obtained from the fused images can then be utilized as an assistive tool for better diagnosis and treatment. In this work, we propose an algorithm for fusing multimodal medical images using lifting scheme-based biorthogonal wavelet transform. The multiscale fusion scheme is performed for the multimodal medical images in the wavelet domain at multiple scales by adopting either average or absolute maximum fusion rules. To verify the effectiveness of the proposed method, a series of visual and quantitative performance evaluations of the proposed method are compared with those of five representative wavelet-based fusion methods including contourlet transform (CLT), nonsubsampled CLT (NSCLT), lifting wavelet transform (LWT), multiwavelet transform (MWT), and stationary wavelet transform (SWT). For the quantitative performance evaluations, we adopted five metrics: fusion factor, fusion symmetry, entropy, standard deviation and edge strength. The experimental results demonstrated that the proposed method could yield better results than other wavelet transform-based fusion methods. Furthermore, from the additional comparison study of fusing noise-contaminated images, we could conclude that the proposed method is noise resilient in fusing images corrupted by Gaussian and speckle noise with varying variances. [ABSTRACT FROM AUTHOR]

Deep learning, hydrogels, MXenes, oxidation, poly(vinyl alcohol), sensors Sufficient interaction of hydrophobic catechol groups with the MXene surface reduces the oxidation-accessible sites in the MXene for reaction with water and eventually suppresses the oxidation of MXene in the hydrogel. Keywords: deep learning; hydrogels; MXenes; oxidation; poly(vinyl alcohol); sensors EN deep learning hydrogels MXenes oxidation poly(vinyl alcohol) sensors 1 1 1 06/14/23 20230612 NES 230612 B Hydrogels b In article number 2213382, Soo-Jin Park, Insik In, Dong-Yeun Koh, Albert S. Lee, and co-workers propose, catechol-functionalized poly(vinyl alcohol)-based hydrogel to inhibit the oxidation of MXene nanofiller while maintaining excellent mechanical and electrical properties suitable for strain sensors. [Extracted from the article]

In this paper, a model based on discrete wavelet transform and convolutional neural network for brain MR image classification has been proposed. The proposed model is comprised of three main stages, namely preprocessing, feature extraction, and classification. In the preprocessing, the median filter has been applied to remove salt-and-pepper noise from the brain MRI images. In the discrete wavelet transform, discrete Harr wavelet transform has been used. In the proposed model, 3-level Harr wavelet decomposition has been applied on the images to remove low-level detail and reduce the size of the images. Next, the convolutional neural network has been used for classifying the brain MR images into normal and abnormal. The convolutional neural network is also a prevalent classification method and has been widely used in different areas. In this study, the convolutional neural network has been used for brain MRI classification. The proposed methodology has been applied to the standard dataset, and for performance evaluation, we have used different performance evaluation measures. The results indicate that the proposed method provides good results with 99% accuracy. The proposed method results are then presented for comparison with some state-of-the-art algorithms where simply the proposed method outperforms the counterpart algorithms. The proposed model has been developed to be used for practical applications. [ABSTRACT FROM AUTHOR]

The symptoms of Alzheimer's disease (AD), a major cause of dementia in older adults, are linked directly with neuronal cell death, which is thought to be due to aberrant neuronal inflammation. Autoantibodies formed during neuronal inflammation show excellent stability in blood; therefore, they may be convenient blood-based diagnostic markers of AD. Here, we performed microarray analysis of 29,240 unbiased random peptides to be used for comprehensive screening of AD-specific IgG and IgM antibodies in the blood. The results showed that (1) sequence-specific and isotype-specific antibodies are regulated differentially in AD, and combinations of these antibodies showing high area under the receiver operating characteristic curve values (0.862-0.961) can be used to classify AD, (2) AD-specific IgG antibodies arise from IgM antibody-secreting cells that existed before disease onset and (3) target protein profiling of the antibodies identified some AD-related proteins, some of which are involved in AD-related signalling pathways. Therefore, we propose that these epitopes may facilitate the development of biomarkers for AD diagnosis and form the basis for a mechanistic study related to AD progression. [ABSTRACT FROM AUTHOR]