The effect of liposome on dyeing mohair/wool blends

The aim of this study was to examine the use of liposome in the dyeing of wool and mohair fibres with acid dyestuffs. Soybean lecithin and cholesterol were used to form the liposome membrane utilised in the dyebath. Liposome production was performed according to the thin lipid layer method (Bangham Method) using a rotary evaporator. Two different forms of liposome were used for dyeing wool and mohair fibres. In its first form, liposome was utilised as an auxiliary agent, where it was added to a conventional dyebath at the beginning of the process. In its second form, dyes were encapsulated with liposome and then used in dyeing. The effects of these two different forms of liposome were compared with conventional dyeing. Dyeing was carried out at depths of shade of 0.5%, 1.0% and 2.0% using three different concentrations of liposome (0.33%, 0.66% and 1.33%). An analysis of K/S values, fastness to washing, and the alkali solubility of fibres was conducted. The fibre samples dyed in the presence of liposome exhibited very good fastness to light (grade 8). The wash fastness test results of the liposomal‐dyed samples were significantly better (grade 4‐5) than for those samples which were conventionally dyed. In the presence of liposome, the tensile strength of fibres was 20 gf, whereas it was 11 gf without liposomes.     

Study and evaluation of abrasive water jet turning process performance on AA5083

In this study, the effect of processing parameters on surface roughness and macro surface characteristics was analyzed during the machining of Ø30 mm and 300 mm aluminum alloy AA5083 abrasive water jets. As the processing parameters (up to 10 mm min⁻¹, 15 mm min⁻¹, 20 mm min⁻¹ and 25 mm min⁻¹), abrasive flow rate (50 g min⁻¹, 150 g min⁻¹, 250 g min⁻¹ and 350 g min⁻¹), the lathe chuck rotational speed (25 min⁻¹, 50 min⁻¹, 75 min⁻¹ and 100 min⁻¹) and the nozzle approach distance (2 mm, 5 mm, 8 mm and 11 mm) were used in experiments. In experimental studies, the pump pressure (360 MPa) was used as a constant, in the form of an abrasive Garnet (100 mesh), and the nozzle diameter as 0.76 mm. According to the findings, the best results in terms of surface roughness were obtained as a result of turning speed and abrasive flow rate. When the macro surface characteristics were examined, it was found that the lathe chuck rotational speed increased, the rate of nozzle progression was low, the rate of abrasive flow was high and the nozzle approach distance was lower and the smoother surfaces were obtained.     

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.     

Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.     

Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

Automatic biomedical signal recognition is an important process for several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patterns of the ECG signals. In order to raise the diagnostic accuracy and reduce the diagnostic time, automated computer aided diagnosis model is necessary. With the advancements of artificial intelligence (AI) techniques, large quantity of biomedical datasets can be easily examined for decision making. In this aspect, this paper presents an intelligent biomedical ECG signal processing (IBECG-SP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit (GRU) model is used for the feature extraction of the ECG signals. Moreover, earthworm optimization (EWO) algorithm is utilized to optimally tune the hyperparameters of the GRU model. Lastly, softmax classifier is employed to allot appropriate class labels to the applied ECG signals. For examining the enhanced outcomes of the proposed IBECG-SP technique, an extensive simulation analysis take place on the PTB-XL database. The experimental results portrayed the supremacy of the IBECG-SP technique over the recent state of art techniques.     

A review on the design and optimization of antennas using machine learning algorithms and techniques

This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.     

Decay and termite resistance of plywood treated with various fire retardants

This study evaluated the ability of white and brown rot fungi and termites to decompose fire retardant-treated (FRT) or untreated-plywood by measuring mass loss. Plywood was manufactured using Ekaba (Tetraberlinia bifoliolata Harms.) veneers which were previously treated with either boric acid, borax, mono-ammonium phosphate (MAP), or di-ammonium phosphate (DAP). Plywood specimens were then subjected to fungal decay resistance tests performed according to Japanese Industrial Standard (JIS) A-9201 method using a brown-rot fungus, Fomitopsis palustris (Berkeley et Curtis) Murrill and a white-rot fungus, Trametes versicolor (L. ex Fr.) Quel. The specimens were also assayed against the subterranean termite, Coptotermes formosanus Shiraki to determine termite resistance. Boron and phosphorus chemicals used in the study increased the resistance of plywood panels against both fungal and termite attack. Plywood panels treated with boric acid and borax resulted in less mass losses when compared to MAP and DAP-treated specimens in decay and termite resistance tests.