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.     

Population dynamics of Salmonella spp. and Shigella spp. in ready-to-eat Mediterranean vegetable salads

This study evaluated the behavior of Salmonella and Shigella (5–6 log CFU/g) in tomato–cucumber (TC) salad without additives (control), TC with 1.0% lemon juice and 0.5% salt, TC with 10% wt/wt tahini, coleslaw, and toum sauce at 4, 10, or 24°C for 5 days. At 4°C, both pathogens survived well in all salads, with a 0.2–1.6 log CFU/g reduction after 5 days (except for toum sauce with >3.5 log CFU/g reduction after 4 days). At 10°C, Salmonella in the different TC salads remained constant, whereas Shigella numbers significantly increased by 1.0–1.7 log CFU/g after 5 days. Yet, both pathogens significantly decreased by 1.2–1.4 log CFU/g in coleslaw after 5 days and by >3.5 log CFU/g in toum sauce after 3 days. At 24°C, Salmonella significantly increased in TC salad without additives by 1.4 log CFU/g after 5 days and were below the detection level in the other types of salad after 5 days. However, Shigella numbers significantly increased by 1.0 log CFU/g in TC with tahini, but they significantly declined by 1.9–2.9 log CFU/g in TC salads after 5 days, and the pathogen was not detected in coleslaw and toum sauce after 4 days.     

Textural and microstructural properties of Kradi cheese (an indigenous cheese of Jammu and Kashmir,India)

The texture and microstructure of Kradi cheese, an indigenous fresh unripened cheese of Jammu and Kashmir, India, were studied. There were significant differences (P < 0.05) for hardness, adhesiveness, springiness, cohesiveness, chewiness and resilience between samples from four different market areas. The optimised products made in the laboratory from cultures NCDC‐167 and NCDC‐144 showed significant differences (P < 0.05) in the textural and mechanical properties of tensile strength. The microstructure of the market samples was distinguished by the greater size of the voids present in three‐dimensional casein network. The optimised laboratory product had a more compact and fibrous structure compared to the market samples, which was attributed to its higher protein content.     

A complementary study on novel PdAuCo catalysts: Synthesis,characterization, direct formic acid fuel cell application,and exergy analysis

At present, Pd containing (10–40 wt%) multiwall carbon nanotube (MWCNT) supported Pd monometallic, Pd:Au bimetallic, and PdAuCo trimetallic catalysts are prepared via NaBH₄ reduction method to examine their formic acid electrooxidation activities and direct formic acid fuel cell performances (DFAFCs) when used as anode catalysts. These catalysts are characterized by advanced analytical techniques as N₂ adsorption and desorption, XRD, SAXS, SEM-EDX, and TEM. Electronic state of Pd changes by the addition of Au and Co. Moreover, formic acid electrooxidation activities of these catalysts measured by CV indicates that particle size changes in wide range play a major role in the formic acid electrochemical oxidation activity, ascribed the strong structure sensitivity of formic acid electrooxidation reaction. PdAuCo (80:10:10)/MWCNT catalyst displays the most significant current density increase. On the other hand, lower CO stripping peak potential obtained for PdAuCo (80:10:10)/MWCNT catalyst, attributed to the awakening of the Pd-adsorbate bond strength down to its optimum value, which favors higher electrochemical activity. DFAFCs performance tests and exergy analysis reveal that fuel cell performances increase with the addition of Au and Co which can be attributed to synergetic effect. Furthermore, temperature strongly influences the performance of formic acid fuel cell.