Artificial Immune System Approach Research Articles

Artificial Immune System Implementation for Predicting WM Presence from MYD88 and CXCR4

Waldenstrom’s Macroglobulinemia (WM) is a rare malignancy that affects human blood cells and spreads slowly. The development of WM occurs whenever the blood cells undergo genetic changes. Better therapies can be offered by the healthcare sector to get rid of the symptoms that cannot be cured. Everyone in the healthcare sector is aware that genetic abnormalities cause WM, but they are unsure of what causes the alterations. The risk factors that increase the number of WM's aberrant cells have been found. The greatest risk variables have a fatal impact on humans. The healthcare sector is working to save lives by offering better care. Only when WM is discovered earlier when it is treatable with better care and potent medications, is it very likely. For analysing the healthcare data associated with WM, a number of prior research studies have suggested both standard and unique software models and techniques. However, the accuracy is subpar and inefficient in terms of both time and money. To analyse the genomic dataset and detect Waldenstrom's Macroglobulinemia or its symptoms, this research explored this issue and suggested an Artificial Immune System (AIS) approach. Software written in Python is used to conduct the experiment and validate the findings. by contrasting the trial outcomes with other performance assessment techniques. The analysis reveals that the suggested AIS algorithm works better than the others.

Artificial immune system approach for optimizing abrasive water jet machining process on super alloys

Artificial immune system approach for optimizing abrasive water jet machining process on super alloys

Development of a unified artificial immune system for complex objects control within the framework of the Industry 4.0 concept

The article is devoted to the creation of an intelligent decision-making technology for complex objects control using the bioinspired approach of artificial immune systems (AIS) and the principles of immunological homeostasis for the implementation of the Industry 4.0 program. The bases for creating a unified artificial immune system (UAIS) consisting of modified algorithms of clonal selection, negative selection and immune network have been developed in order to identify the most effective multidimensional dynamic data for processing and predicting. The unification of AIS algorithms based on systematization and classification is used to reduce the variety of modifications and to find the best solution for a specific data set. A technique for evaluating the effectiveness of modified UAIS algorithms based on the principles of immunological homeostasis has been developed. The main stages of the functioning of the proposed technology for complex objects of the oil and gas industry and the advantages of this approach are considered.

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

This research attempts to reinforce the cultivating expression of radial basis function neural network (RBFnet) through computational intelligence (CI) and swarm intelligence (SI) learning methods. Consequently, the artificial immune system (AIS) and ant colony optimization (ACO) approaches are utilized to cultivate RBFnet for function approximation issue. The proposed hybridization of AIS and ACO approaches optimization (HIAO) algorithm combines the complementarity of exploitation and exploration to realize problem solving. It allows the solution domain having the advantages of intensification and diversification, which further avoids the situation of immature convergence. In addition, the empirical achievements have confirmed that the HIAO algorithm not only obtained the best accurate function approximation for theoretically standard nonlinear problems, it can be further applied on the instance solving for practical crude oil spot price prediction.

Special issue editorial on emerging trends in internet of things for e‐health and medical supply chain systems

Special issue editorial on emerging trends in internet of things for e‐health and medical supply chain systems

An Artificial Immune System Approach to Automated Program Verification: Towards a Theory of Undecidability in Biological Computing

An Artificial Immune System Approach to Automated Program Verification: Towards a Theory of Undecidability in Biological Computing

Development of theoretical foundations for the creation of intelligent technology based on a unified artificial immune system for complex objects control of the oil and gas industry

The article is devoted to the current and promising direction of the implementation of the Industry 4.0 program based on the introduction of artificial intelligence (AI) approaches in the industrial production of the oil and gas industry. A review of the application of modern bioinspired AI approaches for solving problems of control and diagnostics of industrial equipment is carried out. The application of the approach of artificial immune systems (AIS) and various modified AIS algorithms is substantiated in the creation of innovative and competitive applications for solving various specific engineering problems and creating a modern highly efficient technology for automating production processes at oil refineries. The theoretical foundations for the creation of an intelligent innovative technology based on a unified artificial immune system (UAIS) for distributed systems for complex objects control using modern computer technology have been developed. Definitions of UAIS and immunological homeostasis are given. Unification is carried out with the aim of reducing the variety of modified AIS algorithms and their optimal use for solving the assigned tasks for the technological processes control. The classification and assessment of the effectiveness of the application of modified AIS algorithms is carried out. The block diagram of an intelligent control system based on the developed UAIS is presented.

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

The fast growth of the Internet of Things (IoT) and its diverse applications increase the risk of cyberattacks, one type of which is malware attacks. Due to the IoT devices’ different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion-detecting algorithms inspired by the human body’s adaptive immune system techniques. Most of these algorithms imitate the human’s body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions.

A Flow-based Multi-agent Data Exfiltration Detection Architecture for Ultra-low Latency Networks

Modern network infrastructures host converged applications that demand rapid elasticity of services, increased security, and ultra-fast reaction times. The Tactile Internet promises to facilitate the delivery of these services while enabling new economies of scale for high fidelity of machine-to-machine and human-to-machine interactions. Unavoidably, critical mission systems served by the Tactile Internet manifest high demands not only for high speed and reliable communications but equally, the ability to rapidly identify and mitigate threats and vulnerabilities. This article proposes a novel Multi-Agent Data Exfiltration Detector Architecture (MADEX), inspired by the mechanisms and features present in the human immune system. MADEX seeks to identify data exfiltration activities performed by evasive and stealthy malware that hides malicious traffic from an infected host in low-latency networks. Our approach uses cross-network traffic information collected by agents to effectively identify unknown illicit connections by an operating system subverted. MADEX does not require prior knowledge of the characteristics or behavior of the malicious code or a dedicated access to a knowledge repository. We tested the performance of MADEX in terms of its capacity to handle real-time data and the sensitivity of our algorithm’s classification when exposed to malicious traffic. Experimental evaluation results show that MADEX achieved 99.97% sensitivity, 98.78% accuracy, and an error rate of 1.21% when compared to its best rivals. We created a second version of MADEX, called MADEX level 2, that further improves its overall performance with a slight increase in computational complexity. We argue for the suitability of MADEX level 1 in non-critical environments, while MADEX level 2 can be used to avoid data exfiltration in critical mission systems. To the best of our knowledge, this is the first article in the literature that addresses the detection of rootkits real-time in an agnostic way using an artificial immune system approach while it satisfies strict latency requirements.

A proactive model to predict osteoporosis: An artificial immune system approach

AbstractOsteoporosis disease is caused by hormonal changes, vitamin D, and calcium deficiency. With current technologies, the identification of osteoporosis requires many tests with the support of medications. Bone mineral density is a typical measure implemented using a DEXA scan which can be very costly. Such high technology equipment is usually not accessible for remote people, and thus a low‐cost screening system is very appealing. This article proposes an osteoporosis prediction system that effectively determines its possibility of occurrence based on essential factors such as smoking habits and calcium level so that the people at high risk can be referred to access the DEXA scanner. Our proposed system is implemented by an improved version of the artificial immune system, enabling care providers to take precautionary measures at the right time to avoid the early development of osteoporosis. The experiments demonstrated a promising result of 94% prediction accuracy that proved its usefulness in identifying people with potential osteoporosis in the future.

Developing a new integrated artificial immune system and fuzzy non-discretionary DEA approach

As a useful performance evaluation and decision-making tool, data envelopment analysis (DEA) has been proven to be an excellent data-oriented efficiency analysis method when there are multiple inputs and outputs. However, when working with large datasets, DEA requires more time to solve and calculate the optimal values for each decision-making unit (DMU). To close this methodological gap, this study proposes a new integrated fuzzy nondiscretionary DEA (FNDEA) model and artificial immune system (AIS) to predict and find the optimal values of DMUs. In so doing, we first modify an FNDEA model to classify the set of all DMUs into efficient and inefficient, and immune system (AIS) is used to predict and find the optimal values of DMUs. Then, a modified fuzzy nondiscretionary additive DEA model, which is designed as a middle chain, is used for sensitivity analysis of inefficient DMUs to determine their target outputs, which are called antigens. Finally, we try to reduce the distance between these two steps in order to predict the optimal values (with nearest distance to antigens) of inefficient DMUs and improve their efficiency by using a combined AIS and FNDEA model called the FNDEA–AIS approach. To illustrate the advantages of the proposed FNDEA–AIS approach, a dataset from 24 Iranian forest management units is collected; the results indicated that our new FNDEA–AIS approach (in comparison with other well-established performance prediction techniques) exhibits better convergent validity and high correlation with low error rate to predict optimal values of inefficient DMUs. The main conclusion is that our new applied method, developed for the first time herein, provides a suitable improvement over previously developed methods, such as artificial neural networks (ANNs), to detect inefficiency and to improve the overall performance when analyzing different types of real-life problems.

Development of a cognitive mnemonic scheme for an optical Smart-technology of remote learning based of Artificial Immune Systems

The article discusses current issues related to the development of an information optical Smart technology for distance learning of Honeywell's distributed Experion PKS control system for the oil and gas industry. About 70 % of industrial accidents are caused by the human factor through the fault of operators. The work of operators consists in monitoring and managing high-tech proc-esses through mnemonic scheme circuits and is characterized by increased tension in the visual apparatus, as well as general fatigue and loss of concentration. The innovative personalized tech-nology of distance learning takes into account the peculiarities of students' vision by adjusting the color supply of educational material and the dynamic presentation of information depending on the person's psychotype and is based on the use of cognitive, optical, multi-agent technologies, as well as ontological and immuno-network approaches. The development of cognitive mnemonic schemes is carried out taking into account these features, which allows one to reduce the load on the visual apparatus and increase the effectiveness of teaching practical skills when working with mnemonic schemes. An artificial immune systems approach is used to predict and evaluate the learning process and promptly adjust the knowledge obtaining process. A modified algorithm for the functioning of a distance learning system based on the use of optimization algorithms for arti-ficial intelligence and an algorithm for immuno-network modeling has been developed. General principles of creating mimic diagrams and existing Honeywell mnemonic schemes are considered. An example of the implementation of the proposed remote technology is presented and results of the simulation of cognitive mnemonic scheme for various categories of students with special needs are discussed.

Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems

BackgroundCurrently, due to the huge progress in the field of information technologies and computer equipment, it is important to use modern approaches of artificial intelligence in order to process extensive chemical information at creating new drugs with desired properties.The interdisciplinary of research creates additional difficulties in creating new drugs. Currently, there are no universal algorithms and software for predicting the “structure-property” dependence of drug compounds that can take into account the needs of specialists in this field.In this regard, the development of a modern Smart-system based on the promising bio-inspired approach of artificial immune systems for predicting the structure-property dependence of drug compounds is relevant.The aim of this work is to develop a multi-agent Smart-system for predicting the “structure-property” dependence of drug compounds using the ontological approach and modified algorithms of artificial immune systems using the example of drug compounds of the sulfonamide group. The proposed system makes it possible to increase the accuracy of prediction models of the “structure-property” dependence, to reduce the time and financial costs for obtaining candidate drug compounds.MethodsDuring the creation of a Smart-system, there are used multi-agent and ontological approaches, which allow to structure input and output data, optimally to distribute computing resources and to coordinate the work of the system. As a promising approach for processing a large amount of chemical information, extracting informative descriptors and for the creation of an optimal data set, as well as further predicting the properties of medicinal compounds, there are considered modified algorithms of artificial immune systems and various algorithms of artificial intelligence.ResultsThere was developed an ontological model of a multi-agent Smart-system. There are presented the results of the «structure-property» dependence simulation based on a modified grey wolf optimization algorithm and artificial immune systems. During the simulation, there was used information from the Mol-Instincts sulfonamide descriptor database.ConclusionThe developed multi-agent Smart-system using ontological models allows visually to present the structure and interrelationships of agents functioning, which greatly facilitates the development of software and reduces time and financial costs during the development of new drugs.

Margin setting algorithm for pattern classification via spheres

Margin setting algorithm (MSA) is a new sphere-based classification algorithm. It employs an artificial immune system approach to construct a number of hyperspheres that cover each class of a given set of data. To gain insights into the classification performance of MSA, it is the first work to analyze two important fundamental problems of MSA as a sphere-based classifier. First, single sphere or multiple spheres are needed to achieve good classification performance in MSA? This problem was presented as sphere analysis, which was experimentally carried out on simulation data sets using Monte Carlo method. The results demonstrated that MSA employs a multiple-sphere strategy instead of one-sphere strategy as its decision boundaries. This strategy allows MSA to achieve lower probabilities of classification error rate. Second, how to adapt the location and size of the hypersphere to achieve good classification performance? This problem was presented as adaption analysis, which was experimentally carried out on real-world data sets compared to the support vector machine and the artificial neural network. The results demonstrated that MSA employs an artificial immune system approach to optimize the locations of the hyperspheres and to shrink the radius of the hypersphere in a certain range using margin as an algorithm parameter. Overall, computational results indicate the advantages of MSA in classification performance.

Incorporating shifting bottleneck identification in assembly line balancing problem using an artificial immune system approach

The manufacturing industry has evolved in the past few years due to the competitive global economy where the performance of its assembly line operations is primarily dependent upon optimum resource utilization. The assembly line operations are balanced among the available resources to obtain an equal amount of workload to achieve optimum resource utilization, called the assembly line balancing (ALB) problem. Various approaches have been proposed to solve the ALB problem, which is broadly categorized as exact, heuristic, and meta-heuristic approaches. Although solving the ALB problem is crucial, a bottleneck may still occur over the next operation stages. By using problem-specific information (bottleneck identification), it is expected to improve the solution quality of the ALB problem. As such, the contribution of this study is the computational method, namely as the swarm of immune cells with bottleneck identification (SIC+) approach, where both the ALB and bottleneck identification problems are addressed. In addition to the flexible problem representation, the SIC+ approach is equipped with a discrete bottleneck simulator to simulate the bottleneck scenario and bottleneck-specific operators to redistribute the machine workload of the identified bottleneck machine. The approach was tested on 24 benchmark data sets of the ALB problem, and the impact of incorporating bottleneck identification was illustrated. The experimental results show that the proposed SIC+ approach has achieved a total of 66.12% optimal solution over all instances of the benchmark data sets and has been compared with approaches from the literature where high-quality solutions were statistically justified.

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms; Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

An Artificial Immune System Approach for a Multi-compartment Queuing Model for Improving Medical Resources and Inpatient Bed Occupancy in Pandemics

In the context of the Covid-19 pandemic the pressure that is put on the medical systems is increasing exponentially. Healthcare systems resources are in general scarce, and hence they ...

A novel parallel learning algorithm for pattern classification

In today’s data-intensive applications, machine learning constructs algorithms that are capable of learning and making predications on the data. Margin setting algorithm (MSA) is a novel machine learning algorithm for pattern classification. It employs an artificial immune system approach to generates prototype regions as the classification boundaries. However, its computation time limited its applications in real-world application. When the datasets grow in size and algorithm complexity increases, it is necessary to spread the work among multiple cores and processors. To reduce the execution time during classification, a parallel implementation of MSA, called PMSA is proposed for multicore and multiprocessor system. It is the first work to scale up the classification time of MSA using parallel implementation. To evaluate the proposed PMSA algorithm, we used standard image datasets of 512 × 512 pixels and 321 × 481 pixels. Besides, benchmark datasets from University of California, Irvine Machine Learning Repository are also used. They are 768 data samples from dataset Pima Indian Diabetes, 683 data samples from dataset Wisconsin Breast Cancer, 690 data samples from dataset Australian Credit Approval, 178 data samples from dataset Wine and 391 data samples from dataset Svmguide2. The classification performance is compared with another two state-of-the-art classification algorithms: the artificial neural network and the support vector machine. The results show the proposed PMSA gains significant improvements in terms of execution time, with a promising speedup compared to the single-threaded CPU counterpart.

An artificial immune systems approach to Case-based Reasoning applied to fault detection and diagnosis

This work presents a hybrid model of Case-based Reasoning (CBR) and artificial immune systems (AIS), which is able to manage the processes of recovery, adaptation (reuse and revision) and retention of cases. The developed model also provides an alternative way of clustering cases, identifying high density areas, improve search efficiency in the case space and store relationships among similar cases. The proposed model is applied to a fault detection and diagnosis problem of direct currnet motor nenchmark and the obtained results are compared using specific CBR performance metrics showing promising perspectives for the proposed model.

Development of Smart Technology for Complex Objects Prediction and Control on the Basis of a Distributed Control System and an Artificial Immune Systems Approach

Development of Smart Technology for Complex Objects Prediction and Control on the Basis of a Distributed Control System and an Artificial Immune Systems Approach