Preprocessing Sequence Research Articles

Performance Analysis of CNN Based Aggressive Human Behavior Detection Techniques

Aggressive Human Behavior is one of the most sophisticated concepts in social and situational contexts. A visible increase in aggressive human behavior can be seen over the news channels or in our surroundings every day. This work focuses on physical aggression in humans which includes hitting, kicking, punching etc. These aggressive events pose a direct threat to the public safety. Systems that can automatically monitor or detect surveillance videos and thus identify aggressive human activities in those videos will be of great help to the authorities. In this research, we used three different datasets i.e. hockey fights, movies and violent flow dataset. The video clips from these datasets are converted into pre-processed frame data sequences. The datasets are then divided into training dataset and validation dataset. The model through which training dataset is passed contains Convolution Neural Network (CNN) linked to convolutional LSTM (ConvLSTM) layer. The output of this model is binary classification of aggressive and non-aggressive flags. Further, the validation dataset is used to test the model efficiency. In case, the performance of the model is not satisfactory, the training of the model is re-evaluated until we achieve the desired performance. As depicted by the results, ResNet50 is the best performing CNN model with accuracy of 90%. The InceptionV3 CNN model yielded 89% of accuracy which is close to ResNet50. Further, VGG19 yielded very poor performance results of only 79% of accuracy. For future works, it is suggested to expand the model to more complex violence scenarios and appliances. Find creative solutions for data collection, advance generalization techniques and real-time optimizations.

PROSAC as a selection tool for SO-PLS regression: A strategy for multi-block data fusion

BackgroundSpectral data from multiple sources can be integrated into multi-block fusion chemometric models, such as sequentially orthogonalized partial-least squares (SO-PLS), to improve the prediction of sample quality features. Pre-processing techniques are often applied to mitigate extraneous variability, unrelated to the response variables. However, the selection of suitable pre-processing methods and identification of informative data blocks becomes increasingly complex and time-consuming when dealing with a large number of blocks. The problem addressed in this work is the efficient pre-processing, selection, and ordering of data blocks for targeted applications in SO-PLS. ResultsWe introduce the PROSAC–SO–PLS methodology, which employs pre-processing ensembles with response-oriented sequential alternation calibration (PROSAC). This approach identifies the best pre-processed data blocks and their sequential order for specific SO-PLS applications. The method uses a stepwise forward selection strategy, facilitated by the rapid Gram-Schmidt process, to prioritize blocks based on their effectiveness in minimizing prediction error, as indicated by the lowest prediction residuals. To validate the efficacy of our approach, we showcase the outcomes of three empirical near-infrared (NIR) datasets. Comparative analyses were performed against partial-least-squares (PLS) regressions on single-block pre-processed datasets and a methodology relying solely on PROSAC. The PROSAC–SO–PLS approach consistently outperformed these methods, yielding significantly lower prediction errors. This has been evidenced by a reduction in the root-mean-squared error of prediction (RMSEP) ranging from 5 to 25 % across seven out of the eight response variables analyzed. SignificanceThe PROSAC–SO–PLS methodology offers a versatile and efficient technique for ensemble pre-processing in NIR data modeling. It enables the use of SO-PLS minimizing concerns about pre-processing sequence or block order and effectively manages a large number of data blocks. This innovation significantly streamlines the data pre-processing and model-building processes, enhancing the accuracy and efficiency of chemometric models.

Multivariate data binning and examples generation to build a Diabetic Retinopathy classifier based on temporal clinical and analytical risk factors

In this paper, we explore the possibility of exploiting retrospective clinical data from Electronic Health Records (EHR) for classification tasks in chronic patients. The different intervals, short length and high class imbalance make it unfeasible to use traditional time series techniques. The first contribution of the paper is a preprocessing method to construct a multivariate time series dataset using EHR data, which infers missing data and regularizes the data frequency. The second contribution addresses class imbalance by using domain knowledge and existing short EHR series. We synthetically extrapolate patients’ data by using similar long time series and a fuzzy-based approach. The paper addresses the problem of detection of Diabetic Retinopathy (DR). Expert domain knowledge from ophthalmologists has been used in the proposed techniques to guide the processing of time series. The novelty in that case study consists in not using eye-fundus image analysis. Instead, the proposed methods are based solely on EHR data. Several multivariate multiclass time series classifiers are used to detect the four levels of DR severity from the pre-processed data sequences. Experiments prove the quality of the sequence preprocessing techniques proposed for EHR data. Results indicate that the TapNet classifier is the best one for DR grading. Despite being tested for DR detection, the proposed data preparation methods are applicable to other diseases with similar characteristics.

DNA Steganography: Embedding the Secret Messages Using Glass Stack Method and Detecting Errors Made by Different Attacks

Cyber threats pose a significant challenge to protecting cloud-based health data, including DNA sequences and patient information. Steganography is a well-known solution to this problem. Recent studies have limitations, such as not reconstructing the cover or enlarging the original sequence, increasing personal data security risks. In this study, we provide a novel data-hiding technique that uses a secret key, bit padding, and exclusive OR (XOR) operation to insert secrets into a pre-processed DNA sequence. The message encryption and pre-processing of the cover are performed using a new glass stack pattern technique. The message encryption and pre-processing processes using the new glass stack pattern method increase the security and robustness of the system. The method embeds 3 bits per nucleotide which is greater than the existing schemes. It has a relatively small DNA expansion which is 50% and has a low risk of breaking security. The method also preserves the biological properties of DNA sequences. Additionally, a parity check approach further improves security by thwarting attacks like man-in-the-middle, chosen stego, and modification attacks that alter the stego DNA. These advances improve the system's overall security capabilities. The experimental findings demonstrate that the suggested method reconstructs the cover, healthcare data, and DNA sequence properly and error-free, and outperforms competing algorithms on all performance metric dimensions. Highlights The scheme implants a maximum of 3 bits per nucleotide with a small expansion. Its cover pre-processing and message encryption using a glass stack pattern makes strong security. The scheme can detect and fix changed bits caused by attacks. A very low cracking probability shows strong security and robustness. The biological properties of the stego DNA sequence are preserved by this scheme.

Raman spectroscopy combined with partial least squares (PLS) based on hybrid spectral preprocessing and backward interval PLS (biPLS) for quantitative analysis of four PAHs in oil sludge

Polycyclic aromatic hydrocarbons (PAHs) contained in a large amount of oily sludge produced in petroleum and petrochemical production has become one of the main environmental protection concerns in the industry. The accurate determination of PAHs is of great significance in the field of petroleum geochemistry and environmental protection. In this study, Raman spectroscopy combined with partial least squares (PLS) based on different hybrid spectral preprocessing methods and variable selection strategies was proposed for quantitative analysis of phenanthrene, fluoranthrene, fluorene and naphthalene (Phe, Flt, Flu and Nap) in oil sludge. At first, PAHs in oily sludge was extracted by solid–liquid extraction with methanol as extractant, and Raman spectra of 21 oily sludge samples were collected by portable Raman spectrometer. And then, the influence of first derivative (D1st), wavelet transform (WT) and their hybrid spectral preprocessing on the predictive performance of the PLS calibration model was discussed. Thirdly, biPLS (backward interval partial least squares) was used to optimize the input variables before and after the hybrid spectral preprocessing methods, and the influence of biPLS and the hybrid spectral preprocessing sequence on the predictive performance of the PLS calibration model was discussed. Finally, the predictive performance of the PLS calibration model was optimized according to the results of leave-one-out cross-validation (LOOCV) method. The results show that the biPLS-D1st-WT-PLS calibration model established by using biPLS first to select the characteristic variables, followed by hybrid spectral preprocessing of the characteristic variables, has better prediction performance for Flt (determination coefficient of prediction (R2P) = 0.9987, and the mean relative error of prediction (MREP) = 0.0606). For Phe, Flu and Nap, the WT-biPLS-PLS calibration model has a better predictive effect (R2P are 0.9995, 0.9996 and 0.9983, and MREP are 0.0426, 0.0719 and 0.0497, respectively). In general, portable Raman spectroscopy combined with PLS calibration model based on different hybrid spectral preprocessing and variable selection strategies has achieved good prediction results for quantitative analysis of four PAHs in oily sludge. It is a new strategy to firstly select the characteristic variables of the original spectra, and secondly to preprocess the characteristic variables by the hybrid spectral preprocessing, which will provide a new idea for the establishment of quantitative analysis methods for PAHs in oily sludge.

One-dimensional deep convolutional autoencoder active infrared thermography: Enhanced visualization of internal defects in FRP composites

Fiber-reinforced polymer (FRP) composites have been widely applied in different industrial fields, thereby necessitating the employment of non-destructive testing (NDT) methods to ensure structural integrity and safety. Active infrared thermography (AIRT) is a fast and cost-efficient NDT technique for inspecting FRP composites. However, this method is easily affected by factors such as inhomogeneous heating, leading to a low level of visualization of defects. To address this issue, this study proposes a novel method called one-dimensional deep convolutional autoencoder active infrared thermography (1D-DCAE-AIRT) to enhance the visualization of internal defects in FRP composites. This method first preprocesses the thermal image sequence acquired by AIRT inspections. Subsequently, high-level thermal features at the pixel level are extracted from the aforementioned preprocessed thermal image sequence using a designed one-dimensional deep convolutional autoencoder (1D-DCAE) model. Finally, the extracted high-level thermal features are employed to generate enhanced visualization results that exhibit improved defect visibility. The results of three kinds of AIRT (eddy current pulsed thermography, flash thermography, and vibrothermography) experiments on FRP composite specimens with artificially introduced defects show that 1D-DCAE-AIRT can effectively enhance the visualization of internal defects. The enhancement effect is better than the conventional techniques of fast Fourier transform (FFT), principal component analysis (PCA), independent component analysis (ICA), and partial least-squares regression (PLSR).

A CNN based m5c RNA methylation predictor

Post-transcriptional modifications of RNA play a key role in performing a variety of biological processes, such as stability and immune tolerance, RNA splicing, protein translation and RNA degradation. One of these RNA modifications is m5c which participates in various cellular functions like RNA structural stability and translation efficiency, got popularity among biologists. By applying biological experiments to detect RNA m5c methylation sites would require much more efforts, time and money. Most of the researchers are using pre-processed RNA sequences of 41 nucleotides where the methylated cytosine is in the center. Therefore, it is possible that some of the information around these motif may have lost. The conventional methods are unable to process the RNA sequence directly due to high dimensionality and thus need optimized techniques for better features extraction. To handle the above challenges the goal of this study is to employ an end-to-end, 1D CNN based model to classify and interpret m5c methylated data sites. Moreover, our aim is to analyze the sequence in its full length where the methylated cytosine may not be in the center. The evaluation of the proposed architecture showed a promising results by outperforming state-of-the-art techniques in terms of sensitivity and accuracy. Our model achieve 96.70% sensitivity and 96.21% accuracy for 41 nucleotides sequences while 96.10% accuracy for full length sequences.

A matrixed nonlinear exponential grey Bernoulli model for interval number prediction of crude oil futures prices

PurposeThe effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high volatility and uncertainty of the crude oil futures price, a matrixed nonlinear exponential grey Bernoulli model combined with an exponential accumulation generating operator (MNEGBM(1,1)) is proposed in this paper.Design/methodology/approachFirst, the original sequence is processed by the exponential accumulation generating operator to weaken its volatility. The nonlinear grey Bernoulli and exponential function models are combined to fit the preprocessed sequence. Then, the parameters in MNEGBM(1,1) are matrixed, so the ternary interval number sequence can be modeled directly. Marine Predators Algorithm (MPA) is chosen to optimize the nonlinear parameters. Finally, the Cramer rule is used to derive the time recursive formula.FindingsThe predictive effectiveness of the proposed model is verified by comparing it with five comparison models. Crude oil futures prices in Cushing, OK are predicted and analyzed from 2023/07 to 2023/12. The prediction results show it will gradually decrease over the next six months.Originality/valueCrude oil futures prices are highly volatile in the short term. The use of grey model for short-term prediction is valuable for research. For the data characteristics of crude oil futures price, this study first proposes an improved model for interval number prediction of crude oil futures prices.

A multi-dimensional hybrid CNN-BiLSTM framework for epileptic seizure detection using electroencephalogram signal scrutiny

The proposed hybrid CNN-BiLSTM architecture aims to address the challenge of detecting epileptic seizures systematically from EEG signal analysis. The system consists of several stages, including preprocessing, feature extraction using multi-dimensional CNN, temporal feature processing using BiLSTM, and classification using fully connected layers. The first stage involves preprocessing and normalization of the raw EEG signal to prepare it for further analysis. This step helps in removing noise and standardizing the input for subsequent processing. Next, a multi-dimensional CNN is employed to effectively extract features from the preprocessed EEG sequence data. CNNs are known for their ability to capture spatial features, and in this case, they are utilized to extract relevant features from the EEG data. After the feature extraction stage, the BiLSTM component of the architecture is utilized to process the extracted features and capture temporal dependencies. BiLSTMs are well-suited for sequence modeling tasks and can effectively capture long-range dependencies in the data. By incorporating BiLSTM, the architecture aims to capture important temporal patterns related to epileptic seizures. Finally, the temporal feature values are fed into fully connected layers for classification. The system is designed to detect epileptic seizures and classify them into specific types using a 10-class classification approach. The proposed system reports high detection accuracy with an overall accuracy of 99.53% and an accuracy of 82.95% on the binary classification task. These results suggest that the system performs well in accurately identifying epileptic seizures from EEG signals. Furthermore, the proposed system demonstrates superior performance compared to other existing techniques such as K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) in terms of accuracy. It is important to note that the evaluation and comparison of the proposed system were performed on a publicly available epileptic seizures image database. Overall, the proposed hybrid CNN-BiLSTM architecture shows potential in enhancing the detection and classification of epileptic seizures from EEG signals, potentially improving the efficiency and accuracy of diagnosis and treatment in the early stages of these disorders.

Optimization of pre-processing and data fusion strategies for multi-block spectroscopic characterization of cellular growth phases in the chlorophyte, Tetraselmis suecica

In an era where phytoplankton-based technologies are expanding, optimization for rapid cellular growth detection and detailed biochemical characterization is key to enhancing productivity. Hence, the complex analysis and development of highly sensitive and rapid in-vivo methods for precise real-time molecular monitoring of phytoplankton cells is imperative. Herein, a multi-block spectroscopic based methodology for the identification and characterization of molecular changes occurring across different growth phases in the chlorophyte species, Tetraselmis suecica, is presented. Confocal Raman microscopy with near-infrared (NIR) excitation spectra and concurrent excitation-emission fluorescence matrix (EEM) measurements were taken at different intervals across a twenty-day cell growth cycle period encompassing three distinct growth phases (exponential, stationary and decline). Three different data fusion strategies were explored: low-level, mid-level and a mixed level with subsequent multi-block model building using the Common Components and Specific Weights Analysis (CCSWA) algorithm. Various pre-processing sequences in regard to the raw data and single-block exploratory methods were evaluated for all three strategies and selected based on the optimum computed salience contribution towards the multi-block global components within each model. A detailed characterization of the biochemical changes happening within cellular growth phases of the chlorophyte species was constructed. Additionally, the study establishes a novel paradigm for data manipulation within a multi-block framework for complex spectroscopic data of biological cells, such as phytoplankton.

GENERATION OF NOVEL ANTIBODY CANDIDATES USING TRANSFORMER AND GAN-BASED DEEP LEARNING ARTIFICIAL INTELLIGENCE

Abstract Introduction Conventional library-based antibody display can only explore a small fraction of the sequences generated from animal immunization, not even to exhaust the potential sequence diversity that can be turned into antibody therapies. This is because screening for antibody is limited to sequences that can be displayed, which only constitute a subset of the entire sequences generated by B cells, whereas screening for antibody directly from single B cells can be costly. Here, we introduce a novel Artificial Intelligence-enabling tool to navigate antibody discovery from a broader range of search space with reduced cost. We trained a transformer-based model from sequences of an immunized library to cluster the clones and a generative adversarial network (GAN)-based model to generate novel sequences that can be potentially developed into antibody therapies. Background and significance One limitation in the early discovery of antibody is the number of functional candidates that can be selected. Our work provides an AI-enabling tool to discover and generate a panel of antibodies of differentiated binding strengths to a broad range of epitopes to ensure functional coverage. Methods & Results We extracted 104 sequences from the FACS-enriched yeast pool from a fully immunized alpaca (Lama pacos) using Next Generation Sequencing, from which we assembled 103 unique sdAb sequences. We fine-tuned a transformer-based deep learning model, which was previously trained from our dataset containing 100,000 antibody sequences, on such pre-processed sdAb sequences giving representation that correlates to the sequence homology for the clustering of clonal types. We postulate such representation also encodes long-range amino acid interactions in the 3D structure, making the accuracy exceeds the performance of bioinformatics-based primary sequence homology analysis. This process is fully automated and optimized to require minima computational resources. We selected 15 candidates from AI-clustered clonal groups and experimentally measured their binding activity. Kd of 12 candidates were of 10−9 affinity and 1 candidates were of 10−8 affinity, the rest one candidate was non-binding (hence a hit rate of 87%). The large sequence diversity of the CDR3 show these nanobodies are potentially good binders for a wide range of epitopes. We generated a CDR-diversifying virtual library (103) of each binding candidate by training a GAN-based models using the sequences of the same clonal group of the binder sequences. This method incorporates the probability of amino acid residues on each specific location that provides a more precise mutagenesis route than PCR-based affinity maturation. The generated sequences provided a wider CDR sequence diversity for the selection of antibodies of differentiated affinity and epitopes, which could generate candidates of different functionality. Conclusion Antibody discovery is a central step in early drug development that identification of a wide range of functional candidates could increase the success rate and reduce risks in later developments. We built an AI-enabling tool for the searching and generation of functional antibodies from animal immunization library. We believe this technology would help deliver candidates of fine-tuned affinity and functionality.

Software for brain tumor diagnosis on magnetic resonance imaging

BACKGROUND: The main reason for the development and implementation of artificial intelligence (AI) technologies in neuro-oncology is the high prevalence of brain tumors reaching up to 200 cases per 100,000 population. The incidence of a primary focus in the brain is 5%10%; however, 60%70% of those who die from malignant neoplasms have metastases in the brain. Magnetic resonance imaging (MRI) is the most common method for primary non-invasive diagnosis of brain tumors and monitoring disease progression. One of the challenges is the classification of tumor types and determination of clinical parameters (size and volume) for the conduct, diagnosis, and treatment procedures, including surgery.
 AIM: To develope a software module for the differential diagnosis of brain neoplasms on MRI images.
 METHODS: The software module is based on the developed Siberian Brain Tumor Dataset (SBT), which contains information on over 1000 neurosurgical patients with fully verified (histologically and immunohistochemically) postoperative diagnoses. The data for research and development was presented by the Federal Neurosurgical Center (Novosibirsk). The module uses two- and three-dimensional computer vision models with pre-processed MRI sequence data included in the following packages: pre-contrast T1-weighted image (WI), post-contrast T1-WI, T2-WI, and T2-WI with fluid-attenuated inversion-recovery technique. The models allow to detect and recognize with high accuracy 4 types of neoplasms, such as meningioma, neurinoma, glioblastoma, and astrocytoma, and segment and distinguish components and sizes: ET (tumor core absorbing Gd-containing contrast), TC (tumor core) = ET + Necr (necrosis) + NenTu, and WT (whole tumor) = TC + Ed (peritumoral edema).
 RESULTS: The developed software module shows high segmentation results on SBT by Dice metric for ET 0.846, TC 0.867, WT 0.9174, Sens 0.881, and Spec 1.000 areas. The testing and validation were done at the international BraTS Challenge 2021 competition. The test dataset yielded DiceET 0.86588, DiceTC 0.86932, and DiceWT 0.921 values, placing the developed software module in the top ten. According to the classification, the results demonstrate high accuracy rates of up to 92% in patient analysis (up to 89% in slice analysis), a very high potential, and a perspective for future research in this area.
 CONCLUSIONS: The developed software module may be used for training specialists and in clinical diagnostics.

V-SLAM ENHANCED INS/GNSS FUSION SCHEME FOR LANE LEVEL VEHICULAR NAVIGATION APPLICATIONS IN DYNAMIC ENVIRONMENT

Abstract. With the development of different sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), LiDAR, radar and camera, more localization information is available for autonomous vehicular applications. However, each sensor has its limitations in different circumstances. For example, visual Simultaneous Localization and Mapping (SLAM) easily loses tracking in an open sky area where accurate GNSS measurements can be obtained. Sensors can complement each other by integrated their information in a multi-sensor fusion scheme. In this study, we proposed a visual-SLAM enhanced INS/GNSS localization fusion scheme for a high dynamic environment. Oriented FAST and rotated BRIEF (ORB) SLAM are used to pre-process image sequences from monocular camera, rescaled and refreshed after applying GNSS measurements, and convert to position and velocity information, which can provide updates to the system. The performance of the fusion system was verified through two field tests at different speed ranges (about 30–60 km/s), using a reliable reference system as ground-truth to assess the accuracy of the proposed localization fusion scheme. The results indicated that the proposed system could improve the navigation accuracy compared to INS/GNSS integration scheme and achieve which-lane level or even where-in-lane level.

Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model

Uncertainty in wind power is often unacceptably large and can easily affect the proper operation, quality of generation, and economics of the power system. In order to mitigate the potential negative impact of wind power uncertainty on the power system, accurate wind power forecasting is an essential technical tool of great value to ensure safe, stable, and efficient power generation. Therefore, in this paper, a hybrid intelligent model based on isolated forest, wavelet transform, categorical boosting, and quantile regression is proposed for deterministic and probabilistic wind power prediction. First, isolated forest is used to pre-process the original wind power data and detect anomalous data points in the power sequence. Then, the pre-processed original power sequence is decomposed into sub-frequency signals with better profiles by wavelet transform, and the nonlinear features of each sub-frequency are extracted by categorical boosting. Finally, a quantile-regression-based wind power probabilistic predictor is developed to evaluate uncertainty with different confidence levels. Moreover, the proposed hybrid intelligent model is extensively validated on real wind power data. Numerical results show that the proposed model achieves competitive performance compared to benchmark methods.

Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning.

Epilepsy is a neurological disorder characterized by abnormal brain activity. Epileptic patients suffer from unpredictable seizures, which may cause a loss of awareness. Seizures are considered drug resistant if treatment does not affect success. This leads practitioners to calculate the cortical thickness to measure the distance between the brain's white and grey matter surfaces at various locations to perform a surgical intervention. In this study, we introduce using machine learning as an approach to classify extracted measurements from T1-weighted magnetic resonance imaging. Data were collected from the epilepsy unit at King Abdulaziz University Hospital. We applied two trials to classify the extracted measurements from T1-weighted MRI for drug-resistant epilepsy and healthy control subjects. The preprocessing sequence on T1-weighted MRI images was performed using C++ through BrainSuite's pipeline. The first trial was performed on seven different combinations of four commonly selected measurements. The best performance was achieved in Exp6 and Exp7, with 80.00% accuracy, 83.00% recall score, and 83.88% precision. It is noticeable that grey matter volume and white matter volume measurements are more significant than the cortical thickness measurement. The second trial applied four different machine learning classifiers after applying 10-fold cross-validation and principal component analysis on all extracted measurements as in the first trial based on the mentioned previous works. The K-nearest neighbours model outperformed the other machine learning classifiers with 97.11% accuracy, 75.00% recall score, and 75.00% precision.

MLysPRED: graph-based multi-view clustering and multi-dimensional normal distribution resampling techniques to predict multiple lysine sites.

Posttranslational modification of lysine residues, K-PTM, is one of the most popular PTMs. Some lysine residues in proteins can be continuously or cascaded covalently modified, such as acetylation, crotonylation, methylation and succinylation modification. The covalent modification of lysine residues may have some special functions in basic research and drug development. Although many computational methods have been developed to predict lysine PTMs, up to now, the K-PTM prediction methods have been modeled and learned a single class of K-PTM modification. In view of this, this study aims to fill this gap by building a multi-label computational model that can be directly used to predict multiple K-PTMs in proteins. In this study, a multi-label prediction model, MLysPRED, is proposed to identify multiple lysine sites using features generated from human protein sequences. In MLysPRED, three kinds of multi-label sequence encoding algorithms (MLDBPB, MLPSDAAP, MLPSTAAP) are proposed and combined with three encoding strategies (CHHAA, DR and Kmer) to convert preprocessed lysine sequences into effective numerical features. A multidimensional normal distribution oversampling technique and graph-based multi-view clustering under-sampling algorithm were first proposed and incorporated to reduce the proportion of the original training samples, and multi-label nearest neighbor algorithm is used for classification. It is observed that MLysPRED achieved an Aiming of 92.21%, Coverage of 94.98%, Accuracy of 89.63%, Absolute-True of 81.46% and Absolute-False of 0.0682 on the independent datasets. Additionally, comparison of results with five existing predictors also indicated that MLysPRED is very promising and encouraging to predict multiple K-PTMs in proteins. For the convenience of the experimental scientists, 'MLysPRED' has been deployed as a user-friendly web-server at http://47.100.136.41:8181.

Pseudo grey metabolic Markov model and its application in urban rainfall prediction

Purpose: The purpose of this paper is to propose a pseudo-grey metabolic grey Markov model to deal with the prediction issue in which the original sequences are oscillation sequences. Design/methodology/approach: First, the original sequences were processed with the accelerated advection transformation and the weighted mean generation transformation to make them smoother. Then, the mean GM (1, 1) model was applied to the multi-step prediction of the pre-processed data sequences. Finally, with the help of the optimal partitioning method, the pseudo-grey metabolic Markov model was used to correct the prediction results and determine the final prediction values. Findings: The results demonstrate that the accuracy of this model is significantly higher than that of the traditional grey Markov model, which further verifies the rationality of the proposed model. Therefore, scientific and reasonable prediction of urban rainfall is of great theoretical significance and application value for the government and decision-making departments to formulate drought prevention and disaster mitigation measures. Originality/value: The model in this paper not only provides new ideas for the data preprocessing problem of the grey Markov model, but also solves the problem of errors due to individual subjectivity in state interval division. It provides a novel idea for the development of grey prediction models. The rationality and validity of the model are illustrated by taking the Zhengzhou City of Henan Province as examples.

REVERSE: a user-friendly web server for analyzing next-generation sequencing data from in vitro selection/evolution experiments.

Next-generation sequencing (NGS) enables the identification of functional nucleic acid sequences from in vitro selection/evolution experiments and illuminates the evolutionary process at single-nucleotide resolution. However, analyzing the vast output from NGS can be daunting, especially with limited programming skills. We developed REVERSE (Rapid EValuation of Experimental RNA Selection/Evolution) (https://www.reverseserver.org/), a web server that implements an integrated computational pipeline through a graphical user interface, which performs both pre-processing and detailed sequence level analyses within minutes. Raw FASTQ files are quality-filtered, dereplicated, and trimmed before being analyzed by either of two pipelines. The first pipeline counts, sorts, and tracks enrichment of unique sequences and user-defined sequence motifs. It also identifies mutational intermediates present in the sequence data that connect two input sequences. The second pipeline sorts similar sequences into clusters and tracks enrichment of peak sequences. It also performs nucleotide conservation analysis on the cluster of choice and generates a consensus sequence. Both pipelines generate downloadable spreadsheets and high-resolution figures. Collectively, REVERSE is a one-stop solution for the rapid analysis of NGS data obtained from in vitro selection/evolution experiments that obviates the need for computational expertise.

A New Hyperchaotic System Generated by an External Periodic Excitation and its Image Encryption Application

By using a controller of uniformly bounded sine function, the problem of chaos anti-control for continuous linear systems is studied, and the dynamic characteristics of the controlled system are analyzed via the Lyapunov exponent spectrum and bifurcation diagram. The controlled system can be at a state of periodic motion, chaos or hyperchaos with multiple positive Lyapunov exponents when the parameters of controller belong to different intervals. Based on the hyperchaotic system, a new scheme of chaotic image encryption is proposed and it is given in the following aspects: (1) five chaotic sequences are generated from the hyperchaotic system, and the preprocessed pseudo-random sequences are used in the scrambling of the pixel positions; (2) the pixel values of image are encrypted by the combination of multiple pseudo-random sequences; (3) though the double chaotic encryption, the security of the chaotic stream cipher is analyzed by means of key sensitivity analysis, histogram analysis and information entropy analysis, etc. Finally, the experimental results show the scheme is effective and feasible in image encryption, and it can resist some attacks, such as the differential attacks, chosen-plain-text attacks, and clipping attacks.

Automatic Brain Tumor Detection From MRI Using Curvelet Transform and Neural Features

The brain tumor is one of the most health hazard diseases across the world in recent time. The development of the intelligent system has extended its applications in the automated medical diagnosis domains. However, image-based medical diagnosis result strongly depends on the selection of relevant features. This research focuses on the automatic detection of brain tumors based on the concatenation of curvelet transform and convolutional neural network (CNN) features extracted from the preprocessed MRI sequence of the brain. Relevant features are selected from the feature vector using mutual information based on the minimum redundancy maximum relevance (mRMR) method. The detection is done using the ensemble classifier of the bagging method. The experiment is performed using two standard datasets of BraTS 2018 and BraTS 2019. After five-fold cross-validation, we have obtained an outperforming accuracy of 98.96%.