Artificial Immune Recognition System Research Articles

Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions

Educational institutions are increasingly focused on supporting students who may be facing academic challenges, aiming to enhance their educational outcomes through targeted interventions. Within this framework, leveraging advanced deep learning techniques to develop recommendation systems becomes essential. These systems are designed to identify students at risk of underperforming by analyzing patterns in their historical academic data, thereby facilitating personalized support strategies. This research introduces an innovative deep learning model tailored for pinpointing students in need of academic assistance. Utilizing a Gated Recurrent Neural Network (GRU) architecture, the model is rich with features such as a dense layer, max-pooling layer, and the ADAM optimization method used to optimize performance. The effectiveness of this model was tested using a comprehensive dataset containing 15,165 records of student assessments collected across several academic institutions. A comparative analysis with existing educational recommendation models, like Recurrent Neural Network (RNN), AdaBoost, and Artificial Immune Recognition System v2, highlights the superior accuracy of the proposed GRU model, which achieved an impressive overall accuracy of 99.70%. This breakthrough underscores the model’s potential in aiding educational institutions to proactively support students, thereby mitigating the risks of underachievement and dropout.

Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review.

BackgroundAmerican Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.ObjectiveWe aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA.MethodsWe searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study.ResultsOur search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression.ConclusionsAlthough high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition.Trial RegistrationPROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339

Application of Artificial Immune Recognition System for Monitoring the Brake System Using Vibration-Based Statistical Learning

Infotainment systems is one of the standard elements used in all ranges of motor vehicles. These elements act as driver assistance systems that offer numerous advantages to the drivers to ensure safe driving. The infotainment systems indicate the monitoring information based on which the preventive measures can be initiated. In this study, we made an attempt to display the faults that occur in a brake system using vibration analysis through an artificial intelligence (AI) technique called Artificial Immune Recognition System (AIRS). The vibration signals captured under various brake faults were processed and the statistical information was extracted. Three versions of the AIRS were used for the classification. Compared to AIRS 1 and AIRS 2 Parallel, the AIRS 2 version produced the maximum accuracy of 97.82 %. Based on the outcome, an electronics-based diagnostic module (EBD) is proposed for categorizing and displaying the nature of brake faults in the infotainment system.

Making Teaching and Learning Effective Using Analytics

In this paper, an empirical comparison of three state-of-the-art classifier methods (artificial immune recognition systems, Lazy-K Star, and random tree) to predict teachers’ ability to adapt in a classroom environment is carried out. Two educational databases are used for this task. First, measures collected in an academic context, especially from classroom visits, are used (database 1). Then, the three classifiers quantify the acts, behaviors, and characteristics of teaching effectiveness and the teacher’s “ability to adapt in the classrooms.” Professional classrooms visits to more than 200 teachers are used as the second database (database 2). An interactive grid gathering 63 educational acts and behaviors is conceived as an observation instrument for those visits. Within the Waikato Environment for Knowledge Analysis library environment, and with the progressive enhancement of the raw database, the utilization of state-of-the-art classification methods when predicting teaching effectiveness shows promising results, especially when data quality issues are considered.

Sequential Feature Maps with LSTM Recurrent Neural Networks for Robust Tumor Classification

In the field of biomedicine, applications of the identification of biomarkers require a robust gene selection mechanism. For identifying the characteristic marker of an observed event, the selection of attributes becomes important. The robustness of gene selection methods affects detecting biologically meaningful genes in tumor diagnosis. For mapping, sequential features Long-short-term memory (LSTM) was used with Artificial Immune Recognition Systems (AIRS) to remember gene sequences and effectively recall of learned sequential patterns. An attempt was made to improve AIRS with LSTM which is a type of RNNs to produce discriminative gene subsets for finding biologically meaningful genes in tumor diagnosis. The algorithms were evaluated using six common cancer microarray datasets. By converging to the intrinsic information of the microarray datasets, specific groups like functions of the co-regulated groups were observed. The results showed that the LSTM based AIRS model can successfully identify biologically significant genes from the microarray datasets. Also the predictive genes for biological sequences areimportant in gene expression microarrays.This study confirms that different genes can be found in the same pathways. It was also found that the gene subsets selected by the algorithms were involved in important biological pathways.In this work we try an LSTM on our learning problem. We suspected that recurrent neural networks would be a good architecture for making predictions.The results show that the optimal gene subsets were based on the suggested framework, so they should have rich biomedical interpretability.

Retraction Note to: Diagnosing breast cancer with an improved artificial immune recognition system

Breast cancer is the top cancer in women worldwide. Scientists are looking for early detection strategies which remain the cornerstone of breast cancer control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose this disease. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. To increase the classification accuracy, this study introduces a new hybrid system that incorporates support vector machine, fuzzy logic, and real tournament selection mechanism into AIRS. The Wisconsin Breast Cancer data set was used as the benchmark data set; it is available through the machine learning repository of the University of California at Irvine. The classification performance was measured through tenfold cross-validation, student’s t test, sensitivity and specificity. With an accuracy of 100 %, the proposed method was able to classify breast cancer dataset successfully.

Stacked Autoencoder based Feature Compression for Optimal Classification of Parkinson Disease from Vocal Feature Vectors using Immune Algorithms

Parkinson’s disease (PD) is a neurological progressive disorder and is most common among people who are above 60 years old. It affects the brain nerve cells due to the deficiency of dopamine secretion. Dopamine acts as a neurotransmitter and helps in the movement of the body parts. Once brain cells/neurons start dying due to aging, then it will lead to a decrease in dopamine levels. The symptoms of Parkinson’s are difficultly in doing regular/habitual movements, uncontrollable shaking of hands and limbs may encounter memory loss, stiff muscles, sudden temporary loss of control, etc. The severity of the disease will be worse if not diagnosed and treated at the early stages. This paper concentrates on developing Parkinson’s disease diagnosing system using machine learning techniques and algorithms. Machine Learning is an integral part of artificial intelligence it takes huge data as input and train by making use of existing algorithms to understand the pattern of the data. Based on the recognized pattern, the machine will act accordingly without any human intervention. In this work, two major approaches have been employed to diagnose PD. Initially, 26 vocal data of PD affected and healthy individual datasets are obtained from the UCI Machine Learning data repository, are taken as initial raw data/features. In pre-processing, the mRMR feature selection algorithm is employed to minimize the feature count and increase the accuracy rate. The selected features will further be extracted using the Stacked Autoencoder technique to improve and increase the accuracy rate and quality of classification with reduced run time. K-fold cross-validation is used to evaluate the predictive capability of the model and the effectiveness of the extracted features. Artificial Immune Recognition System – Parallel (AIRS-P), an immune inspired algorithm is employed to classify the data from the extracted features. The proposed system attained 97% accuracy, outperforms the benchmarked algorithms and proved its significance on PD classification.

Background subtraction using Artificial Immune Recognition System and Single Gaussian (AIRS-SG)

Background subtraction is an essential step in the video monitoring process. Several models have been proposed to differentiate background pixels from foreground pixels. However, most of these methods fail to distinguish them in highly dynamic environments. In this paper, we propose a new method robust and more efficient for distinguishing moving objects from static objects in dynamic scenes. For this purpose, we propose to use a bio-inspired approach based on the Artificial Immune Recognition System (AIRS) as a classification tool. AIRS separates antibodies, represented by the pixels of the background model, from the antigens that model foreground pixels representing moving objects. Each pixel is modeled by a feature vector containing the attributes of a Gaussian. Only the pixels classified as background are taken into account by the system and updated in the model. This combination has allowed to benefit from two advantages: the power of AIRS to provide an online update of system parameters and the ability of Gaussians to adapt to scene variations at the pixel level. To test the proposed approach, six videos representing the dynamic background category of the CDnet 2014 dataset are selected. Obtained results proved the effectiveness of this new process in terms of quality and complexity compared to other state-of-the-art methods.

A novel possibilistic artificial immune-based classifier for course learning outcome enhancement

In this paper, we propose PAIRS3: a possibilistic classification approach based on artificial immune recognition system (AIRS) and the possibility theory. PAIRS3 is applied to address shortcomings in student attainment rates of course learning outcomes by predicting effective remedial actions through learning from assessment rubrics instances. For most of assessment rubric instances, it is difficult to determine the unique most effective remedial action to take. Consequently, each rubric instance will be labeled with uncertain remedial actions which are elicited from quality assurance experts. Elements from possibility theory are used to (1) model the uncertainty about the most effective remedial action labeling each rubric instance and (2) adapt several parts of the standard AIRS algorithm in order to address the uncertainty in class labels. The performance of the proposed method is evaluated against an academic, university level, assessment dataset that has been built progressively over multiple academic semesters. Despite the uncertainty related to the class labels in the dataset, PAIRS3 showed a good performance in terms of accuracy level (close to 75%). Also, when compared to existing state-of-the-art possibilistic classifiers such as PAIRS2, non-specificity possibilistic decision trees (NSPDT), and cluster-based possibilistic decision trees (Clust-PDT), PAIRS3 achieved better accuracy improvement ranging from 10% (in case of Clust-PDT) to 17% (in case of PAIRS2).

Automatic skull-face overlay and mandible articulation in data science by AIRS-Genetic algorithm

Craniofacial superimposition (CFS) is an innovative method of identifying the missing skull of a human and has received serious attention in forensic research. Optimal overlaying of skull face is one of the fundamental issues found on forensic technology. CFS methodology generally initiates with the marking the accurate landmarking of skull and face extracted from the scanning device which is termed as ground truth data. These extracted features of skull and face will be in crisp image as 3D and 2D. Consequent of this advanced technology, CFS has upgraded to automatic level to investigate different cases. Regardless, the forensic expert invests more for investigation and it consumes large time for completing it. Most of the review study proofs that the computer-aided methods can improve the efficiency of the CFS technique can improve much better than other techniques. The introduction of computerized 3D-2D CFS in forensic research proves to be a novel one and define as most advance CFS technique in research today. This methodology can automate the operation of CFS by reducing the error on over-layered model. Due to the involvement of machine learning process in forensic field, executing the CFS problem is much easier. Coevolutionary algorithms (CEAs) are nature-inspired and very optimistic in solving complex problems by measuring the fitness of the problem. In this research work, automatic CFS is proposed for skull-face overlay and mandible articulation. Conventionally, a fuzzy set is proposed for extracting the features of skull especially the landmarking. In this work the Artificial Immune Recognition System (AIRS) model is implemented for distance calculated for crisp point of the extracted image. Due to the increase of computation time for crisp landmarks, an efficient algorithm is needed. With the point of decreasing the time of computation not by losing the accuracy the cooperative coevolutionary algorithm is proposed. Similar to the AIRS function, the Euclidean distance metrics are calculated between the AIRS set and crisp points. Hence the fitness is calculated form the algorithm for better layout of landmarking for better skull face overlying. Ultimately, the outcome of the progression is compared with the existing methodologies for measuring the level of effectiveness respectively.

Breast Cancer Classification using Nature-inspired Algorithm

Breast Cancer is one among the dangerous ailments that roots up of deaths among women worldwide. Lots of risk factors have been identified through research though the exact reasons of breast cancer are not yet fully understood The Artificial Immune Recognition System classifier helps to classify the type of breast cancer on Wisconsin dataset which provides accurate prediction of the classes of breast cancer. i.e, Benign and Malignant. This system focuses on supervised classification with the help of clonal selection algorithm, Hierarchical Learning Vector Quantization, Multipass Self Organizing Map. The goal of this system can implement the algorithm to classify the cancer accurately and to compare the error rate, f-measure with previous classification algorithms.

IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment.

Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and plantar warts. There are various treatment methods for this disease, including the popular immunotherapy and cryotherapy methods. Manual evaluation of the WD treatment response is challenging. Furthermore, traditional machine learning methods are not robust enough in WD classification as they cannot deal effectively with small number of attributes. This study proposes a new evolutionary-based computer-aided diagnosis (CAD) system using machine learning to classify the WD treatment response. The main architecture of our CAD system is based on the combination of improved adaptive particle swarm optimization (IAPSO) algorithm and artificial immune recognition system (AIRS). The cross-validation protocol was applied to test our machine learning-based classification system, including five different partition protocols (K2, K3, K4, K5 and K10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using the K10 protocol that provided the precision, recall, F-measure and accuracy values of 0.8908, 0.8943, 0.8916 and 90%, respectively. Our IAPSO system showed the reliability of 98.68%. It was implemented in Java, while integrated development environment (IDE) was implemented using NetBeans. Our encouraging results suggest that the proposed IAPSO-AIRS system can be employed for the WD management in clinical environment.

Prediction Model on Student Performance based on Internal Assessment using Deep Learning

Educational Data Mining plays a crucial role in identifying academically weak students of an institute and helps to develop different recommendation system for them. Students from three colleges of Assam, India were considered in our research which their records were run on deep learning using sequential neural model and adam optimization method. The paper compared other classification methods such as Artificial Immune Recognition System v2.0 and Adaboost, to find out the prediction of the results of the students. The highest classification rate was 95.34% produced by the deep learning techniques. The Precision, Recall, F-Score, Accuracy, and Kappa Statistics Performance were calculated as a statistics decisions to find the best classification methods. The dataset used in this paper was 10140 student records. Directing the student for their future plan comes from discovering the hidden patterns by using Data Mining techniques.

Using Resources Competition and Memory Cell Development to Select the Best GMM for Background Subtraction

Background subtraction is an essential step in the process of monitoring videos. Several works have proposed models to differentiate the background pixels from the foreground pixels. Mixtures of Gaussian (GMM) are among the most popular models for a such problem. However, the use of a fixed number of Gaussians influence on their results quality. This article proposes an improvement of the GMM based on the use of the artificial immune recognition system (AIRS) to generate and introduce new Gaussians instead of using a fixed number of Gaussians. The proposed approach exploits the robustness of the mutation function in the generation phase of the new ARBs to create new Gaussians. These Gaussians are then filtered into the resource competition phase in order to keep only ones that best represent the background. The system tested on Wallflower and UCSD datasets has proven its effectiveness against other state-of-art methods.

Robust Feature Selection With LSTM Recurrent Neural Networks for Artificial Immune Recognition System

Stability and robustness of feature selection techniques have great importance in the high dimensional and small sample data. The neglected subject in the feature selection is solving the instability problem. Therefore, an ensemble gene selection framework is used in order to provide stable and accurate results of feature selection algorithms. Sequence modeling from high-dimensional data is an important research area for the discovery of biomarkers. Identifying biomarkers requires robust gene selection methods, which makes it possible to find important tumor-related genes with high accuracy. The main issue of this paper is creating a model in order to learn long sequences with the artificial immune recognition system (AIRS) for robust feature selection. Long short-term memory (LSTM) recurrent neural networks are trained with the AIRS in order to obtain the long-lived unit cells for use in the feature selection process. LSTM was used to be better understanding the mechanisms involving the “remember” feature of the immunological behavior of the immune response. We tried to apply a theory suggested by immunologists in order to develop stable associative memory, which capable of solving robustness and optimization tasks. We examined the initial gene selection step based on the different types of group formation algorithm for analysis of the most informative selected features. Microarray datasets are showing remarkable increases in their robustness and classification accuracy. The suggested framework is evaluated on six commonly used microarray datasets.

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions.

Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions

Abstract The explosive growth of data in volume, velocity and diversity that are produced by medical applications has contributed to abundance of big data. Current solutions for efficient data storage and management cannot fulfill the needs of heterogeneous data. Therefore, by applying computational intelligence (CI) approaches in medical data helps get better management, faster performance and higher level of accuracy in detection. This paper aims to investigate the state-of-the-art of computational intelligence approaches in medical data and to categorize the existing CI techniques, used in medical fields, as single and hybrid. In addition, the techniques and methodologies, their limitations and performances are presented in this study. The limitations are addressed as challenges to obtain a set of requirements for Computational Intelligence Medical Data (CIMD) in establishing an efficient CIMD architectural design. The results show that on the one hand Support Vector Machine (SVM) and Artificial Immune Recognition System (AIRS) as a single based computational intelligence approach were the best methods in medical applications. On the other hand, the hybridization of SVM with other methods such as SVM-Genetic Algorithm (SVM-GA), SVM-Artificial Immune System (SVM-AIS), SVM-AIRS and fuzzy support vector machine (FSVM) had great performances achieving better results in terms of accuracy, sensitivity and specificity.

Handwritten signature verification using the quad-tree histogram of templates and a Support Vector-based artificial immune classification

This work proposes a novel system for off-line handwritten signature verification. A new descriptor founded on a quad-tree structure of the Histogram Of Templates (HOT) is introduced. For the verification step, we propose a robust implementation of the Artificial Immune Recognition System (AIRS). This classifier is inspired from the natural immune system, which generates antibodies to protect the human body against antigens. The AIRS training develops new memory cells that are subsequently used to recognize data through a k Nearest Neighbor (kNN) classification. Presently, to get a robust verification, the kNN classification is substituted by a Support Vector (SV) decision, yielding the AIRSV classifier. Experiments are performed on three datasets, namely, MCYT-75, GPDS-300 and GPDS-4000. AIRSV performance is assessed comparatively to both conventional AIRS as well as SVM. Obtained results demonstrated that AIRSV is more effective than classical AIRS. Moreover, the proposed signature verification system gives similar and sometimes better performance than SVM as well as the state-of-the-art methods.

Wave Height Prediction Using Artificial Immune Recognition Systems (AIRS) and Some Other Data Mining Techniques

Significant wave height (SWH) prediction is an important task in coastal engineering activities. Due to difficulties and complexities in predicting SWH using traditional numerical and empirical models, in the past decades, a high tendency to use soft computing approaches has been observed. In this paper, an artificial immune recognition system (AIRS), which is a recently developed data mining approach, is utilized for SWH prediction. The results of AIRS model are compared with those of artificial neural networks (ANN), regression tree induction (named M5P), Rough Set Theory, Bayesian networks and support vector regression models. The AIRS has been successfully employed in some engineering applications, but its efficiency and applicability for SWH prediction have not been investigated. In this paper, the AIRS model is utilized for the SWH prediction in the Lake Superior in North America. Comparing the results of this model and five other data mining techniques shows that the AIRS and ANN can outperform other models.

Machine learning based decision support systems (DSS) for heart disease diagnosis: a review

The current review contributes with an extensive overview of decision support systems in diagnosing heart diseases in clinical settings. The investigators independently screened and abstracted studies related to heart diseases-based clinical decision support system (DSS) published until 8-June-2015 in PubMed, CINAHL and Cochrane Library. The data extracted from the twenty full-text articles that met the inclusion criteria was classified under the following fields; heart diseases, methods for data sets formation, machine learning algorithms, machine learning-based DSS, comparator types, outcome evaluation and clinical implications of the reported DSS. Out of total of 331 studies 20 met the inclusion criteria. Most of the studies relate to ischemic heart diseases with neural network being the most common machine learning (ML) technique. Among the ML techniques, ANN classifies myocardial infarction with 97% and myocardial perfusion scintigraphy with 87.5% accuracy, CART classifies heart failure with 87.6%, neural network ensembles classifies heart valve with 97.4%, support vector machine classifies arrhythmia screening with 95.6%, logistic regression classifies acute coronary syndrome with 72%, artificial immune recognition system classifies coronary artery disease with 92.5% and genetic algorithms and multi-criteria decision analysis classifies chest-pain patients with 91% accuracy respectively. There were 55% studies that validated the results in clinical settings while 25% validated the results through experimental setups. Rest of the studies (20%) did not report the applicability and feasibility of their methods in clinical settings. The study categorizes the ML techniques according to their performance in diagnosing various heart diseases. It categorizes, compares and evaluates the comparator based on physician’s performance, gold standards, other ML techniques, different models of same ML technique and studies with no comparison. It also investigates the current, future and no clinical implications. In addition, trends of machine learning techniques and algorithms used in the diagnosis of heart diseases along with the identification of research gaps are reported in this study. The reported results suggest reliable interpretations and detailed graphical self-explanatory representations by DSS. The study reveals the need for establishment of non-ambiguous real-time clinical data for proper training of DSS before it can be used in clinical settings. The future research directions of the ML-based DSS is mostly directed towards development of generalized systems that can decide on clinical measurements which are easily accessible and assessable in real-time.