A Model for Anomaly Detection Using the Metaheuristic Methods

Today detection of new threats has become a need for secured communication to provide complete data confidentiality, integrity and availability. Design and development of such an intrusion detection system in the communication world, should not only be new, accurate and fast but also effective in an environment encompassing the surrounding network. In this paper, a new approach is proposed for network anomaly detection by combining neural network and clustering algorithms. We propose a modified Self Organizing Map algorithm which initially starts with null network and grows with the original data space as initial weight vector, updating neighborhood rules and learning rate dynamically in order to overcome the fixed architecture and random weight vector assignment of simple SOM. New nodes are created using distance threshold parameter and their neighborhood is identified using connection strength and its learning rule and the weight vector updating is carried out for neighborhood nodes. The Fuzzy k-means clustering algorithm is employed for grouping similar nodes of Modified SOM into k clusters using similarity measures. Performance of the new approach is evaluated with standard bench mark dataset. The new approach is evaluated using performance metrics such as detection rate and false alarm rate. The result is compared with other individual neural network methods, which shows considerable increase in the detection rate and 1.5% false alarm rate.

Chaos coordinated neural key synchronization for enhancing security of IoT

The key exchange mechanism in this paper is built utilizing neural network coordination and a hyperchaotic (or chaotic) nonlinear dynamic complex system. This approach is used to send and receive sensitive data between Internet-of-Things (IoT) nodes across a public network. Using phishing, Man-In-The-Middle (MITM), or spoofing attacks, an attacker can easily target sensitive information during the exchange process. Furthermore, minimal research has been made on the exchange of input seed values for creating identical input at both ends of neural networks. The proposed method uses a 5D hyperchaotic or chaotic nonlinear complex structure to ensure the sharing of input seed value across two neural networks, resulting in the identical input on both ends. This study discusses two ways for sharing seed values for neural coordination. The first is a chaotic system with all real variables, whereas the second is a hyperchaotic system with at least one complex variable. Each neural network has its own random weight vector, and the outputs are exchanged. It achieves full coordination in some stages by altering the neuronal weights according to the mutual learning law. The coordinated weights are utilized as a key after the neural coordination technique. The network’s core structure is made up of triple concealed layers. So, determining the inner configuration will be tough for the intruder. The efficiency of the suggested model is validated by simulations, and the findings reveal that the suggested strategy outperforms current equivalent techniques.

Integration of Delphi Technique and Analytical Hierarchy Process Method in Assessment the Groundwater Potential Influence Criteria: A Case Study of the Ba River Basin

Water is a boon for all living beings over the world and groundwater is considered one of theindispensable natural sources of potable water. It is necessary to assess and predict the groundwaterpotential to provide insights for decision-makers for proper planning and management of groundwater.The occurrence of groundwater depends on hydrological, ecological, climate, geological, andphysiographical criteria. The purpose of the present study is to choose and attribute scores to all variousfactors that affected groundwater prospects in the Ba river basin. Firstly, the Delphi method was appliedin the expert-based survey to choose six parameters that are considered as influencing factors, namely,lineament density, rainfall, slope, land cover, drainage density, and geology. Then, the weights for thevarious factors were generated using the Analytic Hierarchy Process (AHP) approach which allows thepairwise comparison of criteria influencing the potential areas. The consistency analyses show that thefindings were consistent with a previous study. The consistency and sensitivity analyses showed that theobtained results were coherent, providing the weight vector of the achievable criteria that affect thegroundwater prospect in the study area. The study reveals that lineament density and slope are criteriaaffecting the most prominent groundwater occurrence with 35.1% and 20.1%, respectively. However, theinfluence of other factors (rainfall, land cover, drainage density, and geology) is not visible. These criteriaare assigned to the small weights and do not have a significant influence on the groundwater potential.The study results provide baseline

Solving the dilemma in supplier selection by the group of weight vector with kernel

PurposeThe purpose of this paper is to solve the dilemma in the process of major selection decision-making.Design/methodology/approachFirstly, the group of weight vector with kernel has been defined. Then, the weighted comprehensive clustering coefficient vector was calculated based on the group of weight vector with kernel. Under the action of weighted comprehensive clustering coefficient vector, the information including in other components around component k and supporting object i to be classified into the k-th category has been gathered to component k. At last, a novel two-stage decision model based on the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector is put forward to solve the dilemma in grey clustering evaluation. Then the overall evaluation conclusion can be consistent with the clustering result according to the rule of maximum value.FindingsA new way to solve the dilemma in the process of major selection decision-making has been found. People can obtain a consistent result with two-stage decision model at the case of dilemma. That is, the conclusion of the overall evaluation is consistent with the clustering result according to the rule of maximum value.Practical implicationsSeveral functional groups of weight vector with kernel have been put forward. The proposed model can solve the clustering dilemma effectively and produce consistent results. A practical application of decision problem to solve the dilemma in supplier evaluation and selection of a key component of large commercial aircraft C919 have been completed by the novel two-stage decision model.Originality/valueThe two-stage decision model, the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector were presented in this paper firstly. People can solve the dilemma in grey clustering evaluation effectively by the novel two-stage decision model based on the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector.

Multifeature Metric Learning Based on Enhanced Equidistance Embedding for Electroencephalogram Recognition of Epilepsy

Mobile edge computing (MEC) has the ability of pattern recognition and intelligent processing of real-time data. Electroencephalogram (EEG) is a very important tool in the study of epilepsy. It provides rich information that can not be provided by other physiological methods. In the automatic classification of EEG signals by intelligent algorithms, feature extraction and the establishment of classifiers are both very important steps. Different feature extraction methods, such as time domain, frequency domain, and nonlinear dynamic feature methods, contain independent and diverse specific information. Using multiple forms of features at the same time can improve the accuracy of epilepsy recognition. In this paper, we apply metric learning to epileptic EEG signal recognition. Inspired by the equidistance constrained metric learning algorithm, we propose multifeature metric learning based on enhanced equidistance embedding (MMLE3) for EEG recognition of epilepsy. The MMLE3 algorithm makes use of various forms of EEG features, and the feature weights are adaptively weighted. It is a big advantage that the feature weight vector can be adjusted adaptively, without manual adjustment. The MMLE3 algorithm maximizes the distance between the samples constrained by the cannot-link, and the samples of different classes are transformed into equidistant; meanwhile, MMLE3 minimizes the distance between the data constrained by the must-link, and the samples of the same class are compressed to one point. Under the premise that the various feature classification tasks are consistent, MMLE3 can fully extract the associated and complementary information hidden between the features. The experimental results on the CHB-MIT dataset verify that the MMLE3 algorithm has good generalization performance.

Research on Optimization Path of Intelligent Pension Industry Based on Intelligent Fusion Algorithm of Multisource Information

Accurate quantitative evaluation of the supervision effect of the smart pension industry can reduce the cost of social pension. The traditional methods cannot effectively classify the regulatory risk levels of the smart pension industry. Therefore, this paper proposes a multisource information intelligent fusion algorithm based on the intelligent pension industry optimization path research. Firstly, we establish the principal model of the supervision effect system of the intelligent elderly care industry optimization path and describe the risk level of the supervision effect from different levels. We build the intelligent service platform of the intelligent elderly care training, calculate the weight vector of the supervision risk of the optimization path at all levels, and determine the attribute type of the supervision effect at all levels. Finally, we calculate the maximum influence value of the supervision effect of the intelligent elderly care industry optimization path and use this value to complete the quantitative evaluation of its supervision effect. Simulation results show that the proposed method can evaluate the regulatory effect of smart pension industry and improve the precision of the regulatory effect of smart pension industry effectively.

A Mating Selection Based on Modified Strengthened Dominance Relation for NSGA-III

In multi/many-objective evolutionary algorithms (MOEAs), to alleviate the degraded convergence pressure of Pareto dominance with the increase in the number of objectives, numerous modified dominance relationships were proposed. Recently, the strengthened dominance relation (SDR) has been proposed, where the dominance area of a solution is determined by convergence degree and niche size (θ¯). Later, in controlled SDR (CSDR), θ¯ and an additional parameter (k) associated with the convergence degree are dynamically adjusted depending on the iteration count. Depending on the problem characteristics and the distribution of the current population, different situations require different values of k, rendering the linear reduction of k based on the generation count ineffective. This is because a particular value of k is expected to bias the dominance relationship towards a particular region on the Pareto front (PF). In addition, due to the same reason, using SDR or CSDR in the environmental selection cannot preserve the diversity of solutions required to cover the entire PF. Therefore, we propose an MOEA, referred to as NSGA-III*, where (1) a modified SDR (MSDR)-based mating selection with an adaptive ensemble of parameter k would prioritize parents from specific sections of the PF depending on k, and (2) the traditional weight vector and non-dominated sorting-based environmental selection of NSGA-III would protect the solutions corresponding to the entire PF. The performance of NSGA-III* is favourably compared with state-of-the-art MOEAs on DTLZ and WFG test suites with up to 10 objectives.

Bi-Objective Reliability-Cost Interactive Optimization Model for Series-Parallel System

The paper aims are to determine the bi-objective reliability-cost problem of a series-parallel system by employing an interactive approach. Multi-objective optimization is a design methodology that optimizes a combination of objective functions orderly and concurrently. The fuzzy membership functions have been designated to settle the contrary nature of the objectives. Based on these functions and the moment of the objectives in the form of the weight vector, a crisp optimization design is formed. Lastly, the inherited problem is determined with the aid of the PSO (Particle Swarm Optimization) algorithm and confronted with the genetic algorithm. The solution resembling the various choices of the decision-makers towards the evaluation of their decision are listed. A decision-maker can pick an immeasurable one according to his requirement to reach at the aspired goal.

Pneumonia detection in chest X-ray images using an ensemble of deep learning models

Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure. Pneumonia causes pleural effusion, a condition in which fluids fill the lung, causing respiratory difficulty. Early diagnosis of pneumonia is crucial to ensure curative treatment and increase survival rates. Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We employed deep transfer learning to handle the scarcity of available data and designed an ensemble of three convolutional neural network models: GoogLeNet, ResNet-18, and DenseNet-121. A weighted average ensemble technique was adopted, wherein the weights assigned to the base learners were determined using a novel approach. The scores of four standard evaluation metrics, precision, recall, f1-score, and the area under the curve, are fused to form the weight vector, which in studies in the literature was frequently set experimentally, a method that is prone to error. The proposed approach was evaluated on two publicly available pneumonia X-ray datasets, provided by Kermany et al. and the Radiological Society of North America (RSNA), respectively, using a five-fold cross-validation scheme. The proposed method achieved accuracy rates of 98.81% and 86.85% and sensitivity rates of 98.80% and 87.02% on the Kermany and RSNA datasets, respectively. The results were superior to those of state-of-the-art methods and our method performed better than the widely used ensemble techniques. Statistical analyses on the datasets using McNemar’s and ANOVA tests showed the robustness of the approach. The codes for the proposed work are available at https://github.com/Rohit-Kundu/Ensemble-Pneumonia-Detection.