Intrusion Detection System in Wireless Sensor Network Using Conditional Generative Adversarial Network

Wireless communication networks have much data to sense, process, and transmit. It tends to develop a security mechanism to care for these needs for such modern-day systems. An intrusion detection system (IDS) is a solution that has recently gained the researcher’s attention with the application of deep learning techniques in IDS. In this paper, we propose an IDS model that uses a deep learning algorithm, conditional generative adversarial network (CGAN), enabling unsupervised learning in the model and adding an eXtreme gradient boosting (XGBoost) classifier for faster comparison and visualization of results. The proposed method can reduce the need to deploy extra sensors to generate fake data to fool the intruder 1.2–2.6%, as the proposed system generates this fake data. The parameters were selected to give optimal results to our model without significant alterations and complications. The model learns from its dataset samples with the multiple-layer network for a refined training process. We aimed that the proposed model could improve the accuracy and thus, decrease the false detection rate and obtain good precision in the cases of both the datasets, NSL-KDD and the CICIDS2017, which can be used as a detector for cyber intrusions. The false alarm rate of the proposed model decreases by about 1.827%.

     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

Investigation of machining characteristics in rotary ultrasonic machining of alumina ceramic

Alumina ceramic is well documented as a much-demanded advanced ceramic in the present competitive structure of manufacturing and industrial applications owing to its excellent and superior properties. The current article aimed to experimentally investigate the influence of several process variables, namely: spindle speed, feed rate, coolant pressure, and ultrasonic power, on considered machining characteristics of interest, i.e., chipping size and material removal rate in the rotary ultrasonic machining of alumina ceramic. Response surface methodology has been employed in the form of a central composite rotatable design to design the experiments. Variance analysis testing has also been performed with a view to observing the consequence of the considered parameters. The microstructure of machined rod samples was evaluated and analyzed using a scanning electron microscope. This analysis has revealed and confirmed the presence of plastic deformation that caused removal of material along with brittle fractures in rotary ultrasonic machining of alumina ceramic. The validity and competence of the developed mathematical model have been verified with test results. The multi-response optimization of machining responses (material removal rate and chipping size) has also been attempted by employing a desirability approach, and at an optimized parametric setting the obtained experimental values for material removal rate and chipping size were 0.4166?mm³/s and 0.5134?mm, respectively, with a combined desirability index value of 0.849.     

Effect of process parameters and optimization for photochemical machining of brass and german silver

This article focuses on parametric optimization for photochemical machining (PCM) of brass and german silver. The aim of the study is to analyze the effect of control parameters on response measures, that is, surface roughness, material removal rate, and edge deviation and optimization of parameters considering different weight percentage for each performance measure. The control parameters have been selected as etchant concentration, etching temperature, and etching time. Using full factorial method of design of experiments, PCM has been carried out using ferric chloride as etchant. Surface roughness and edge deviation should be less, while material removal rate is desired high. For satisfying this multi-objective condition, overall evaluation criteria (OEC) have been formulated by assigning different and equal weight percentage to response measures. The optimized condition for particular OEC is obtained, and analysis of variance (ANOVA) has been performed for observing effect of control parameters on response measures. Surface topography study has been performed using scanning electron microscopy, and material composition analysis has been carried out using energy dispersive spectroscopy. The surface roughness is observed lower for brass, while the edge deviation is found lesser for german silver. The material removal rate is observed higher for brass compared to german silver.     

Application of a hot-melt granulation process to enhance fenofibrate solid dose manufacturing

Evaluation of hot-melt granulation of fenofibrate and croscarmellose sodium and its cooling time for the molten mass in a ratio of 55:45 was conducted to assess the manufacturing process capability to produce an acceptable granulation which flows well on Korsch PH300 tablet compression machine. The formation of the drug-polymer eutectic mixture was investigated by differential scanning calorimetry, scanning electron microscopy and X-ray powder diffraction. The physical properties of the hot-melt was determined by examining the milled blocks after solidification and milling after cooling periods of 10, 20 and 30 d. The milled material was assessed for the effect of hold time of the blend on the solid dose compression characteristics. The impact of cooling on the processing of the blocks was assessed after 10, 20 and 30 d of cooling. The study suggests that after the hot-melt formed the fenofibrate crystallized independently and a solid solution with croscarmellose sodium was not formed. The age of the blocks determined the hardness of the crystals, changing the processing nature of the granules with respect to compression and powder flow characteristics. The blocks processed after 20 d and beyond produced granules with a characteristic suitable for holding the blend for 14 d in the bin with no impact on flow properties and compressibility of the blend. There was no chipping, capping, sticking or picking observed and a higher compression speed was achieved.     

Thermodynamic properties of alumina-saturated iron-carbon-bismuth alloys

Using direct and indirect equilibration methods, the solubilities of iron in molten bismuth and those of bismuth in molten iron-carbon alloys have been measured over the temperature range 1473-1873 K. The Henrian behaviour of bismuth in molten ferrous alloys allows the calculation of its activity coefficient, which in turn facilitates modelling as a function of temperature and carbon content, using the unified interaction parameter formalism developed by Pelton and Bale. The modelling results generate the expression: . A model has also been developed expressing bismuth content in molten iron-carbon alloys as a function of temperature and composition: .     

The concert queueing game: to wait or to be late

We introduce the concert (or cafeteria) queueing problem: A finite but large number of customers arrive into a queueing system that starts service at a specified opening time. Each customer is free to choose her arrival time (before or after opening time), and is interested in early service completion with minimal wait. These goals are captured by a cost function which is additive and linear in the waiting time and service completion time, with coefficients that may be class dependent. We consider a fluid model of this system, which is motivated as the fluid-scale limit of the stochastic system. In the fluid setting, we explicitly identify the unique Nash-equilibrium arrival profile for each class of customers. Our structural results imply that, in equilibrium, the arrival rate is increasing up until the closing time where all customers are served. Furthermore, the waiting queue is maximal at the opening time, and monotonically decreases thereafter. In the simple single class setting, we show that the price of anarchy (PoA, the efficiency loss relative to the socially optimal solution) is exactly two, while in the multi-class setting we develop tight upper and lower bounds on the PoA. In addition, we consider several mechanisms that may be used to reduce the PoA. The proposed model may explain queueing phenomena in diverse settings that involve a pre-assigned opening time.     

Classification of patterns representing Apples and Oranges in three-qubit system

The study of the classification of Apples and Oranges in a warehouse has been undertaken in a three-qubit system using the method of repeated iterations in Grover’s algorithm and Ventura’s algorithm separately. Operator describing an inversion about average has been constructed as a square matrix of order eight, the phase inversion operators and corresponding iteration operators for patterns separately representing Apples and Oranges have been derived, and various possible superpositions as the choice for search states for the classification of these patterns have been obtained for starting states consisting of two patterns and a single pattern, respectively. It has been demonstrated that on the second iteration of the exclusion superposition by the corresponding iteration operators, the patterns Apples and Oranges, respectively, are most suitably classified using the Grover’s algorithm. The probabilities of classifications of Apples have also been calculated by using Ventura’s algorithm (Ventura and Martinez in Inf Sci 124:273–296, 2000; Found Phys Lett 12:547–559, 1999) for all the possible superpositions as the search states, and the results have been compared with those of Grover’s algorithm and it has been demonstrated that in general for classification of a given pattern (Apples) in three-qubit system, the Grover’s and Ventura’s algorithms are effective in the cases where the number of patterns in the stored database is larger or smaller, respectively.     

Semantic web reasoners and languages

Semantic web reasoners and languages enable the semantic web to function. Some of the latest reasoning models developed in the last few years are: DLP, FaCT, RACER, Pellet, MSPASS, CEL, Cerebra Engine, QuOnto, KAON2, HermiT and others. Some software tools such as Protégé, Jena and others also have been developed, which provide inferencing as well as ontology development and management environments. These reasoners usually differ in their inference procedures, supporting logic, completeness of reasoning, expressiveness and implementation languages. Various semantic web languages with increasing expressive power continue to be developed for describing web services. We survey the some of the more recent languages like OWL-S (Ontology Web Language-Schema), WSML (Web Service Modeling Language), SWRL (Semantic Web Rule Language) and others that have been tested in early use. We also survey semantic web reasoners and their relationship to these languages.     

Dataflow Architectures for GALS

In Kahn process network (KPN), the processes (nodes) communicate by unbounded unidirectional FIFO channels (arcs), with the property of non-blocking writes and blocking reads on the channels. KPN provides a semantic model of computation, where a computation can be expressed as a set of asynchronously communicating processes. However, the unbounded FIFO based asynchrony is not realizable in practice and hence requires refinement in real hardware. In this work, we start with KPN as the model of computation for GALS, and discuss how different GALS architectures can be realized. We borrow some ideas from existing dataflow architectures for our GALS designs.