Intelligent Computational Model Research Articles

Regularized deep clustering approach for effective categorization of maize diseases

The disease prevalence among different plant species reduces the grain production, significantly affects the growth in terms of quantity and quality. Early diagnosis of the ailment effectively minimizes the plant devastation and improves the condition. Mostly, people involved in the agriculture-related activity identifies the abnormalities in the plants. However, in some cases, the chance of manual error is high during the diagnosis of the plant status. It further leads to economic deprivation, affects the livelihood of the agriculturalists. Effective monitoring and diagnosing plant conditions can be automated with available tools and applications. Such systems serve as virtual assistance to the needy anytime, which fills the gap of the presence of domain expertise. Moreover, automated screening tools contribute to elevated agricultural activities and ensures security upon food production and availability. These applications are powered by advanced, intelligent computational models for an accurate diagnosis. Deep learning algorithms are gaining more attention in the field of real-time monitoring and assistance due to its higher precision over complex applications. However, not all the models fit for applications with different environmental conditions. In this paper, a deep cluster-based plant disease categorization system is proposed to find the discriminative patterns from the images of different categories of maize crop. The proposed regularized deep clustering (RDC) algorithm combines the effectiveness of the convolutional autoencoder model alongside local structure preservation constraints and regularization. The proposed model is compared with state-of-the-art deep clustering algorithms to exhibit the efficacy of the RDC under different modes of clustering performance evaluation. This system acts as an effective tool for targeted users by providing them better assistance on plant disease diagnosis.

Early and accurate detection and diagnosis of heart disease using intelligent computational model

Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalities. Early and on-time diagnosing of this problem is very essential for preventing patients from more damage and saving their lives. Among the conventional invasive-based techniques, angiography is considered to be the most well-known technique for diagnosing heart problems but it has some limitations. On the other hand, the non-invasive based methods, like intelligent learning-based computational techniques are found more upright and effectual for the heart disease diagnosis. Here, an intelligent computational predictive system is introduced for the identification and diagnosis of cardiac disease. In this study, various machine learning classification algorithms are investigated. In order to remove irrelevant and noisy data from extracted feature space, four distinct feature selection algorithms are applied and the results of each feature selection algorithm along with classifiers are analyzed. Several performance metrics namely: accuracy, sensitivity, specificity, AUC, F1-score, MCC, and ROC curve are used to observe the effectiveness and strength of the developed model. The classification rates of the developed system are examined on both full and optimal feature spaces, consequently, the performance of the developed model is boosted in case of high variated optimal feature space. In addition, P-value and Chi-square are also computed for the ET classifier along with each feature selection technique. It is anticipated that the proposed system will be useful and helpful for the physician to diagnose heart disease accurately and effectively.

A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19.

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19, with the model parameters enabling an evaluation of the impact of NPIs. By representing the number of daily confirmed cases (NDCC) as a time-series, we assume that, with or without NPIs, the pattern of the pandemic satisfies a series of Gaussian distributions according to the central limit theorem. The underlying pandemic trend is first extracted using a singular spectral analysis (SSA) technique, which decomposes the NDCC time series into the sum of a small number of independent and interpretable components such as a slow varying trend, oscillatory components and structureless noise. We then use a mixture of Gaussian fitting (GF) to derive a novel predictive model for the SSA extracted NDCC incidence trend, with the overall model termed SSA-GF. Our proposed model is shown to accurately predict the NDCC trend, peak daily cases, the length of the pandemic period, the total confirmed cases and the associated dates of the turning points on the cumulated NDCC curve. Further, the three key model parameters, specifically, the amplitude (alpha), mean (mu), and standard deviation (sigma) are linked to the underlying pandemic patterns, and enable a directly interpretable evaluation of the impact of NPIs, such as strict lockdowns and travel restrictions. The predictive model is validated using available data from China and South Korea, and new predictions are made, partially requiring future validation, for the cases of Italy, Spain, the UK and the USA. Comparative results demonstrate that the introduction of consistent control measures across countries can lead to development of similar parametric models, reflected in particular by relative variations in their underlying sigma, alpha and mu values. The paper concludes with a number of open questions and outlines future research directions.

Recognition of building group patterns using graph convolutional network

ABSTRACT Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic data to extract spatial features is critical. To this end, we propose a novel deep neural network based on graph convolutions to automatically identify building group patterns with arbitrary forms. The method first models buildings by a general graph, and then the neural network simultaneously learns the structural information as well as vertex attributes to classify building objects. We apply this method to real building data, and the experimental results show that the proposed method can effectively capture spatial information to make more accurate predictions than traditional methods.

Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations

N6-methyladenosine (m6A) is a well-studied and most common interior messenger RNA (mRNA) modification that plays an important function in cell development. N6A is found in all kingdoms​ of life and many other cellular processes such as RNA splicing, immune tolerance, regulatory functions, RNA processing, and cancer. Despite the crucial role of m6A in cells, it was targeted computationally, but unfortunately, the obtained results were unsatisfactory. It is imperative to develop an efficient computational model that can truly represent m6A sites. In this regard, an intelligent and highly discriminative computational model namely: m6A-word2vec is introduced for the discrimination of m6A sites. Here, a concept of natural language processing in the form of word2vec is used to represent the motif of the target class automatically. These motifs (numerical descriptors) are automatically targeted from the human genome without any clear definition. Further, the extracted feature space is then forwarded to the convolution neural network model as input for prediction. The developed computational model obtained 83.17%, 92.69%, and 90.50% accuracy for benchmark datasets S1, S2, and S3, respectively, using a 10-fold cross-validation test. The predictive outcomes validate that the developed intelligent computational model showed better performance compared to existing computational models. It is thus greatly estimated that the introduced computational model “m6A-word2vec” may be a supportive and practical tool for elementary and pharmaceutical research such as in drug design along with academia.

Voltage unbalance evaluation in the intelligent recognition of induction motor rotor faults

Induction motors are widely used in several industrial applications due to their factors of favouritism already consolidated, such as robustness, low cost and high reliability. Early detection and proper fault diagnosis reduce the maintenance cost and also increase process effectiveness. Therefore, this paper presents a method for fast classification of rotor faults in line-connected induction motors operating at steady state, under unbalanced voltages and load conditions. Hence, the amplitude of the stator’s current signal in the time domain is presented as input to intelligent computational models for the classification of rotor’s faults. After a proper discretization of the current signal, the points extraction technique is applied allowing a reduction in the classifier’s complexity. Results from 900 experimental tests are provided and compared to validate this study. The results indicate that this approach can be employed to proper classify rotor broken bars in induction motors operating under unbalanced voltage and different load conditions.

An intelligent computational model for prediction of promoters and their strength via natural language processing

In DNA, a promoter is an essential part of genes that controls the transcription of specific genes in a particular tissue or cells. The combination of RNA polymerase and a number of various proteins named "σ-factors” can define the transcription start site (TSS) by inducing RNA holoenzyme. Further, Promoter is categorized into strong and weak promoters on the basis of promoter strength. Owing to exponential increase of RNA/DNA and protein samples in the post-genomic era, developing a simple and efficient sequential-based intelligent computational model for the discrimination of promoters is a challenging job. An intelligent computational model namely: 2L-iPSW(word2vec) was introduced for discrimination of promoters and their strength, in this regard. Machine learning and Deep learning algorithms in conjunction with natural language processing method i.e., “word2vec” are used. The proposed computational model 2L-iPSW(word2vec) achieved 91.42% of accuracy for 1st layer contains promoters and non-promoters which is 8.29% higher than the existing model, whereas 82.42% of accuracy for 2nd layer identifies strong promoter and weak promoter which is 11.22% advanced than the present model. Proposed 2L-iPSW(word2vec) model obtained efficient success rates than the present models in terms of all assessment metrics. It is thus greatly observed that the 2L-iPSW(word2vec) model will lead a useful tool for academic research on promoter identification.

IRNA-PseTNC: identification of RNA 5-methylcytosine sites using hybrid vector space of pseudo nucleotide composition

RNA 5-methylcytosine (m5C) sites perform a major role in numerous biological processes and commonly reported in both DNA and RNA cellular. The enzymatic mechanism and biological functions of m5C sites in DNA remain the focusing area of researchers for last few decades. Likewise, the investigators also targeted m5C sites in RNA due to its cellular functions, positioning and formation mechanism. Currently, several rudimentary roles of the m5C in RNA have been explored, but a lot of improvements are still under consideration. Initially, the identification of RNA methylcytosine sites was carried out via experimental methods, which were very hard, erroneous and time consuming owing to partial availability of recognized structures. Looking at the significance of m5C role in RNA, scientists have diverted their attention from structure to sequence-based prediction. In this regards, an intelligent computational model is proposed in order to identify m5C sites in RNA with high precision. Three RNA sequences formulation methods namely: pseudo dinucleotide composition,pseudo trinucleotide composition and pseudo tetra nucleotide composition are applied to extract variant and high profound numerical features. In a sequel, the vector spaces are fused to build a hybrid space in order to compensate the weakness of each other. Various learning hypotheses are examined to select the best operational engine, which can truly identify the pattern of the target class. The strength and generalization of the proposed model are measured using two different cross validation tests. The reported outcomes reveal that the proposed model achieved 3% better accuracy than that of the highest present approach in the literature so far.

IDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule

DNA methylation is an elementary epigenetic process. The N6-methyladenine is related to a large kind of biological processes i.e., transcription, DNA replication, and repair. In genome, the N6-methyladenine (6 mA) site distribution is non-random; therefore, precise discrimination of 6 mA is necessary to understand its biological functions. Through biochemical experiments, the N6-methyladenine produced a positive outcome, still, these wet lab processes are very time consuming and high pricy. In view of this, it is of high priority to introduce a powerful, accurate, and fast computational model to identify N6-methyladenine sites. In this connection, we propose an intelligent computational model called iDNA6mA (5-step rule) using deep learning approach to identify N6-methyladenine sites from DNA sequences in the rice genome. Existing methods used handcrafted features to identify N6-methyladenine sites; however, the proposed computational model automatically extracts the key features from DNA input sequences via the proposed convolution neural network (CNN) model. The intelligent computational model iDNA6mA (5-step rule) obtained 86.64% of accuracy, 86.70% of sensitivity, 86.59% of specificity, 0.732 of MCC, and 0.931 of auROC. The results demonstrate that the proposed intelligent computational model achieved better performance in terms of all evaluation parameters than existing techniques. It is observed that iDNA6mA (5-step rule) model will become a useful tool in the fields of computational biology, bioinformatics, and for the academic research on N6-methyladenine sites prediction. A user-friendly webserver has been established and freely accessible at https://home.jbnu.ac.kr/NSCL/iDNA6mA.htm.

A Computational Model Based on Intelligence Computation for Classifying Enhancers

As a crucial DNA element, enhancer usually can be classified as strong enhancer or weak enhancer. To make some improvement for identifying enhancers and their subsets, the paper constructed an intelligent computational model by extracting features from bi-profile Bayes and pseudo amino acid components. Moreover, the combination of features, jackknife cross validation and Support Vector Machine were tested on the rigorous dataset. It has been achieved that accuracyof 77.30% on identifying enhancers and non-enhancers, and accuracy of 68.22% on classifying strong enhancers and weak enhancers. Eventually, the performance we achieved is satisfactory.

IMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences

N6- methyladenosine (m6A) is a vital post-transcriptional modification, which adds another layer of epigenetic regulation at RNA level. It chemically modifies mRNA that effects protein expression. RNA sequence contains many genetic code motifs (GAC). Among these codes, identification of methylated or not methylated GAC motif is highly indispensable. However, with a large number of RNA sequences generated in post-genomic era, it becomes a challenging task how to accurately and speedily characterize these sequences. In view of this, the concept of an intelligent is incorporated with a computational model that truly and fast reflects the motif of the desired classes. An intelligent computational model “iMethyl-STTNC” model is proposed for identification of methyladenosine sites in RNA. In the proposed study, four feature extraction techniques, such as; Pseudo-dinucleotide-composition, Pseudo-trinucleotide-composition, split-trinucleotide-composition, and split-tetra-nucleotides-composition (STTNC) are utilized for genuine numerical descriptors. Three different classification algorithms including probabilistic neural network, Support vector machine (SVM), and K-nearest neighbor are adopted for prediction. After examining the outcomes of prediction model on each feature spaces, SVM using STTNC feature space reported the highest accuracy of 69.84%, 91.84% on dataset1 and dataset2, respectively. The reported results show that our proposed predictor has achieved encouraging results compared to the present approaches, so far in the research. It is finally reckoned that our developed model might be beneficial for in-depth analysis of genomes and drug development.

Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling.

The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and computational methods. However, it is currently hindered by two limitations: (1) the lack of high-quality comprehensive data for computational modeling and (2) the lack of an effective modeling method for the complex nanomaterial structures. In this study, we tackled both issues by first synthesizing a large library of nanoparticles and obtained comprehensive data on their characterizations and bioactivities. Meanwhile, we virtually simulated each individual nanoparticle in this library by calculating their nanostructural characteristics and built models that correlate their nanostructure diversity to the corresponding biological activities. The resulting models were then used to predict and design nanoparticles with desired bioactivities. The experimental testing results of the designed nanoparticles were consistent with the model predictions. These findings demonstrate that rational design approaches combining high-quality nanoparticle libraries, big experimental data sets, and intelligent computational models can significantly reduce the efforts and costs of nanomaterial discovery.

Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins

Mycobacterium is a pathogenic bacterium, which is a causative agent of tuberculosis (TB) and leprosy. These diseases are very crucial and become the cause of death of millions of people every year in the world. So, the characterize structure of membrane proteins of the protozoan play a vital role in the field of drug discovery because, without any knowledge about this Mycobacterium's membrane protein and their types, the scientists are unable to treat this pathogenic protozoan. So, an accurate and competitive computational model is needed to characterize this uncharacterized structure of mycobacterium. Series of attempts were carried out in this connection. Split amino acid compositions, Unbiased-Dipeptide peptide compositions (Unb-DPC), Over-represented tri-peptide compositions, compositions & translation were the few recent encoding techniques followed by different researchers in their publications. Although considerable results have been achieved by these models, still there is a gap which is filled in this study. In this study, an evolutionary feature extraction technique position specific scoring matrix (PSSM) is applied in order to extract evolutionary information from protein sequences. Consequently, 99.6% accuracy was achieved by the learning algorithms. The experimental results demonstrated that the proposed computational model will lead to develop a powerful tool for anti-mycobacterium drugs as well as play a promising rule in proteomic and bioinformatics.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

PurposeAs far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients.Design/methodology/approachOn the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator.FindingsOn applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty.Originality/valueThe originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

IACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers.

Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition

Background and ObjectivesEnhancers are pivotal DNA elements, which are widely used in eukaryotes for activation of transcription genes. On the basis of enhancer strength, they are further classified into two groups; strong enhancers and weak enhancers. Due to high availability of huge amount of DNA sequences, it is needed to develop fast, reliable and robust intelligent computational method, which not only identify enhancers but also determines their strength. Considerable progress has been achieved in this regard; however, timely and precisely identification of enhancers is still a challenging task. MethodsTwo-level intelligent computational model for identification of enhancers and their subgroups is proposed. Two different feature extraction techniques including di-nucleotide composition and tri-nucleotide composition were adopted for extraction of numerical descriptors. Four classification methods including probabilistic neural network, support vector machine, k-nearest neighbor and random forest were utilized for classification. ResultsThe proposed method yielded 77.25% of accuracy for dataset S1 contains enhancers and non-enhancers, whereas 64.70% of accuracy for dataset S2 comprises of strong enhancer and weak enhancer sequences using jackknife cross-validation test. ConclusionThe predictive results validated that the proposed method is better than that of existing approaches so far reported in the literature. It is thus highly observed that the developed method will be useful and expedient for basic research and academia.

Intelligent computational model for classification of sub-Golgi protein using oversampling and fisher feature selection methods.

Golgi is one of the core proteins of a cell, constitutes in both plants and animals, which is involved in protein synthesis. Golgi is responsible for receiving and processing the macromolecules and trafficking of newly processed protein to its intended destination. Dysfunction in Golgi protein is expected to cause many neurodegenerative and inherited diseases that may be cured well if they are detected effectively and timely. Golgi protein is categorized into two parts cis-Golgi and trans-Golgi. The identification of Golgi protein via direct method is very hard due to limited available recognized structures. Therefore, the researchers divert their attention toward the sequences from structures. However, owing to technological advancement, exploration of huge amount of sequences was reported in the databases. So recognition of large amount of unprocessed data using conventional methods is very difficult. Therefore, the concept of intelligence was incorporated with computational model. Intelligence based computational model obtained reasonable results, but the gap of improvement is still under consideration. In this regard, an intelligent automatic recognition model is developed in order to enhance the true classification rate of sub-Golgi proteins. In this approach, discrete and evolutionary feature extraction methods are applied on the benchmark Golgi protein datasets to excerpt salient, propound and variant numerical descriptors. After that, an oversampling technique Syntactic Minority over Sampling Technique is employed to balance the data. Hybrid spaces are also generated with combination of these feature spaces. Further, Fisher feature selection method is utilized to reduce the extra noisy and redundant features from feature vector. Finally, k-nearest neighbor algorithm is used as learning hypothesis. Three distinct cross validation tests are used to examine the stability and efficiency of the proposed model. The predicted outcomes of proposed model are better than the existing models in the literature so far. Finally, it is anticipated that the proposed model will provide the foundation to pharmaceutical industry in drug design and research community to innovate new ideas in the area of computational biology and bioinformatics.

A Novel Intelligent Computational Model for Analyzing and Predicting Coal Mine Water Inrush

A Novel Intelligent Computational Model for Analyzing and Predicting Coal Mine Water Inrush

Fault detection and identification spanning multiple processes by integrating PCA with neural network

This paper proposes an effective fault detection and identification method for systems which perform in multiple processes. One such type of system investigated in this paper is COSMED K4b2. K4b2 is a standard portable electrical device designed to test pulmonary functions in various applications, such as athlete training, sports medicine and health monitoring. However, its actual sensor outputs and received data may be disturbed by Electromagnetic Interference (EMI), body artifacts, and device malfunctions/faults, which might cause misinterpretations of activities or statuses to people being monitored. Although some research is reported to detect faults in specific steady state, normal approach may yield false alarms in multi-processes applications. In this paper, a novel and comprehensive method, which merges statistical analysis and intelligent computational model, is proposed to detect and identify faults of K4b2 during exercise monitoring. Firstly the principal component analysis (PCA) is utilized to acquire main features of measured data and then K-means is combined to cluster various processes for abnormalities detection. When faults are detected, a back propagation (BP) neural network is constructed to identify and isolate faults. The effectiveness and feasibility of the proposed model method is finally verified with experimental data.

On-line pattern analysis by evolving self-organizing maps

Many real world data processing tasks demand intelligent computational models with good efficiency and adaptability in their on-line operations. Consequently, neural algorithms with constructive network structure and incremental learning ability are of increasing interest. In this paper we present an algorithm of evolving self-organizing map (ESOM), which features an evolving network structure and fast on-line learning. Experiments have been carried out on some benchmark data sets for vector quantisation and classification tasks. Compared with other methods, ESOM achieved better or comparable performance with a much shorter learning process. Our results show that ESOM is a promising computational model for on-line pattern analysis in real world problems.