Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

The recently developed machine learning (ML) models have the ability to obtain high detection rate using biomedical signals. Therefore, this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography (EEG) Biomedical Signals, named OSAE-SSCEEG technique. The major intention of the OSAE-SSCEEG technique is to find the sleep stage disorders using the EEG biomedical signals. The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach. Moreover, the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization (SAE-SR) with softmax (SM) approach. Finally, the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm (COA) and it leads to boosted classification efficiency. In order to ensure the enhanced performance of the OSAE-SSCEEG technique, a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG technique over the recent methods.     

Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

Accurate soil prediction is a vital parameter involved to decide appropriate crop, which is commonly carried out by the farmers. Designing an automated soil prediction tool helps to considerably improve the efficacy of the farmers. At the same time, fuzzy logic (FL) approaches can be used for the design of predictive models, particularly, Fuzzy Cognitive Maps (FCMs) have involved the concept of uncertainty representation and cognitive mapping. In other words, the FCM is an integration of the recurrent neural network (RNN) and FL involved in the knowledge engineering phase. In this aspect, this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classification (FCMCSO-ASC) technique. The goal of the FCMCSO-ASC technique is to identify and categorize seven different types of soil. To accomplish this, the FCMCSO-ASC technique incorporates local diagonal extrema pattern (LDEP) as a feature extractor for producing a collection of feature vectors. In addition, the FCMCSO model is applied for soil classification and the weight values of the FCM model are optimally adjusted by the use of CSO algorithm. For examining the enhanced soil classification outcomes of the FCMCSO-ASC technique, a series of simulations were carried out on benchmark dataset and the experimental outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.     

Generic methodology for the probabilistic reliability assessment of some structural properties: a graph theoretical approach

The graphical characterisation of many important structural properties, such as controllability, observability, diagnosability of many kinds of structured systems, uses mainly four types of elementary graphical conditions: connectivity, complete matching, linking and distance conditions. Since structural properties depend on different associations of elementary conditions, it is interesting to study elementary conditions. This paper is the first part of this global approach based on elementary graphical conditions and we choose to study the so-called connectivity and complete matching conditions. Starting from the graphical representation associated with a system, the paper provides Boolean expressions of the connectivity and complete matching conditions based on the edges validity, which can be linked to the physical components operating state. These expressions can then be used to define and compute the reliability of a structural property knowing the reliability of the system physical components. This knowledge can be important during both conception and exploitation stages. The proposed methods are quite intuitive and simple to implement and have basically polynomial complexity orders. This makes our approach well suited to analyse large-scale systems.     

Towards an automatic extraction of synonyms for Quranic Arabic WordNet

In this paper, we developed an automatic extraction model of synonyms, which is used to construct our Quranic Arabic WordNet (QAWN) that depends on traditional Arabic dictionaries. In this work, we rely on three resources. First, the Boundary Annotated Quran Corpus that contains Quran words, Part-of-Speech, root and other related information. Second, the lexicon resources that was used to collect a set of derived words for Quranic words. Third, traditional Arabic dictionaries, which were used to extract the meaning of words with distinction of different senses. The objective of this work is to link the Quranic words of similar meanings in order to generate synonym sets (synsets). To accomplish that, we used term frequency and inverse document frequency in vector space model, and we then computed cosine similarities between Quranic words based on textual definitions that are extracted from traditional Arabic dictionaries. Words of highest similarity were grouped together to form a synset. Our QAWN consists of 6918 synsets that were constructed from about 8400 unique word senses, on average of 5 senses for each word. Based on our experimental evaluation, the average recall of the baseline system was 7.01 %, whereas the average recall of the QAWN was 34.13 % which improved the recall of semantic search for Quran concepts by 27 %.     

Nitrogen pick-up of steels through a plasma process

Powdered austenitic steel (16/10/2 Cr/Ni/Mo) with nitrogen contents varying between 0.02 and 0.70 wt.% has been produced by the “Plasma Rotating Electrode Powder (PREP)” process from Cr/Ni/Mo steel rods. The nitrogen pick-up was found to increase with decreasing particle size of the steel powder produced and with increasing partial pressure of nitrogen in the plasma gas up to a certain limit, beyond which no effect of could be observed. The effect of nitrogen content on the intergranular corrosion rate was investigated by exposure of the powder to 0.65 % nitric acid solution. The corrosion rate was found to increase with decreasing solution heat treatment temperature of the steel from 1100 to 1000°C and increasing nitric acid temperature from 95 to 126°C. Nitrogen was found to decrease the corrosion rates appreciably. The susceptibility of the tested steels to intergranular corrosion was investigated by scanning electron microscopy and by X-ray scan spectra analysis after electrolytical etching in 10 % oxalic acid etch solution.     

Cover Image,Volume 92, Issue 5

The cover image, by Manal Ali et al., is based on the Research Article Potential of using non‐inoculated self‐aerated immobilized biomass reactor for post‐treatment of upflow anaerobic staged reactor treating high strength industrial wastewater, DOI: 10.1002/jctb.5082 .

     

Oxidative stability of black cumin (Nigella sativa L.), coriander (Coriandrum sativum L.) and niger (Guizotia abyssinica Cass.) crude seed oils upon stripping

Information on stability of edible oils is important for predicting the quality deterioration of the oil during storage and marketing. Stripping of crude oils removes most of non‐triacylglycerol components, including polar lipids and phenolics. Oxidative stability of black cumin (Nigella sativa L.), coriander (Coriandrum sativum L.) and niger (Guizotia abyssinica Cass.) crude and stripped seed oils was investigated and compared. The factors influencing the oxidative stability of different seed oils were also discussed. Oil samples were stored under accelerated oxidation conditions for 21 d. The progress of oxidation at 60 °C was followed by recording the ultraviolet absorptivity and measuring the formation of oxidative products (peroxide and p‐anisidine values). Inverse relationships were noted between peroxide values and oxidative stabilities and also between secondary oxidation products, measured by p‐anisidine value and stabilities at termination of the storage. Absorptivity at 232 nm and 270 nm increased gradually with the increase in time, due to the formation of conjugated dienes and polyenes. In general, oxidative stabilities of crude oils were stronger than their stripped counterparts and the order of oxidative stability was as follows: coriander > black cumin > niger seed. Levels of polar lipids in crude oils correlated with oxidative stability. Thus, the major factor that may contribute to the better oxidative stability of crude oils was the carry‐over of their polar lipids.     

Adaptive fuzzy APSO based inverse tracking-controller with an application to DC motors

This paper introduces the use of the adaptive particle swarm optimization (APSO) for adapting the weights of fuzzy neural networks (FNN) on line. The fuzzy neural network is used for identification of the dynamics of a DC motor with nonlinear load torque. Then the motor speed is controlled using an inverse controller to follow a required speed trajectory. The parameters of the DC motor are assumed unknown as well as the nonlinear load torque characteristics. In the first stage a nonlinear fuzzy neural network (FNN) is used to approximate the motor control voltage as a function of the motor speed samples. In the second stage, the above mentioned approximator is used to calculate the control signal (the motor voltage) as a function of the speed samples and the required reference trajectory. Unlike the conventional back-propagation technique, the adaptation of the weights of the FNN approximator is done on-line using adaptive particle swarm optimization (APSO). The APSO is based on the least squares error minimization with random initial condition and without any off-line pre-training. Simulation results are presented to prove the effectiveness of the proposed control technique in achieving the tracking performance.     

Anomaly Detection for Internet of Things Cyberattacks

The Internet of Things (IoT) has been deployed in diverse critical sectors with the aim of improving quality of service and facilitating human lives. The IoT revolution has redefined digital services in different domains by improving efficiency, productivity, and cost-effectiveness. Many service providers have adapted IoT systems or plan to integrate them as integral parts of their systems’ operation; however, IoT security issues remain a significant challenge. To minimize the risk of cyberattacks on IoT networks, anomaly detection based on machine learning can be an effective security solution to overcome a wide range of IoT cyberattacks. Although various detection techniques have been proposed in the literature, existing detection methods address limited cyberattacks and utilize outdated datasets for evaluations. In this paper, we propose an intelligent, effective, and lightweight detection approach to detect several IoT attacks. Our proposed model includes a collaborative feature selection method that selects the best distinctive features and eliminates unnecessary features to build an effective and efficient detection model. In the detection phase, we also proposed an ensemble of learning techniques to improve classification for predicting several different types of IoT attacks. The experimental results show that our proposed method can effectively and efficiently predict several IoT attacks with a higher accuracy rate of 99.984%, a precision rate of 99.982%, a recall rate of 99.984%, and an F1-score of 99.983%.     

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections, for a traveler. With this motivation, the current research paper presents an intelligent DoS attack detection with Congestion Control (IDoS-CC) technique for VANET. The presented IDoS-CC technique involves two-stage processes namely, Teaching and Learning Based Optimization (TLBO)-based Congestion Control (TLBO-CC) and Gated Recurrent Unit (GRU)-based DoS detection (GRU-DoSD). The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network. TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization. TLBO is applied to avoid congestion on roadways. Besides, GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network. The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.