Application of lignosulphonate—a sustainable approach towards strength improvement and swell management of expansive soils

Bulletin of Engineering Geology and the Environment - Long-term material dependency on natural resources has caused a heavy toll on the environment and biodiversity of natural systems. To explore...     

Identifying Malicious Secondary User Presence Within Primary User Range in Cognitive Radio Networks

Wireless Personal Communications - Cognitive Radio (CR) Network is a wireless communication technology, in which a detection device smartly detects occupied and unoccupied channels. During traffic,...     

Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks. Localization is the main issue in WSNs; if the sensor node location is unknown, then coverage and power and routing are not optimal. This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities. These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants, such as PM2.5 particulate matter, PM10, nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2). The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization. The dataset is divided into training and testing parts based on 10 cross-validations. The evaluation on predicting the air pollutant for localization is performed with the training dataset. Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.     

A Novel Algorithm to Design Rate-Adaptive Irregular LDPC Codes

Wireless Personal Communications - This paper proposes an algorithm to design rate-adaptive irregular LDPC codes with improved Bit error rate (BER) performance. It focuses on achieving better BER...     

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window-based attack detection in 6G

Regardless of the developments of networking and communication technologies, security is without exception a predominant feature to ensure network reliability. The future sixth-generation (6G) network is anticipated to be carried out with artificial intelligence (AI) powered communication via machine learning (ML), post-quantum cryptography, and so on. AI-powered communication has been in recent years utilized in enhancing network traffic performance with respect to resource management, optimal frequency spectrum design, security, and latency. The studies of modern wireless communications and anticipated features of 6G networks revealed a prerequisite for designing a trustworthy attack detection mechanism. In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window-based (LA-HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained as input, preprocessing is performed to normalize network traffic features. Next, the dimensionality of network traffic feature of large-scale network traffic data is reduced using the Luong Attention integrated with Long Short Term Memory (LSTM)-based Feature extraction model. Finally, with the objective of classifying network traffic samples for attack detection in 6G, we analyze the low dimensional network traffic feature set produced by Luong Attention integrated with LSTM using the Hosmer Lemeshow Logistic Regression Window-based Attack Detection model. Extensive experiments are performed with the Botnet Attack dataset to validate the efficiency of the proposed LA-HLRW method by using different parameters such as attack detection accuracy, attack detection time, precision, and recall. The overall analysis of proposed LA-HLRW results significantly reduced the attack detection time by 24%, and additionally improved attack detection accuracy, precision, and recall by 5%, 5%, and 6% as compared to existing attack detection methods respectively.     

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

The immediate and quick spread of the coronavirus has become a life-threatening disease around the globe. The widespread illness has dramatically changed almost all sectors, moving from offline to online, resulting in a new normal lifestyle for people. The impact of coronavirus is tremendous in the healthcare sector, which has experienced a decline in the first quarter of 2020. This pandemic has created an urge to use computer-aided diagnosis techniques for classifying the Covid-19 dataset to reduce the burden of clinical results. The current situation motivated me to choose correlation-based development called correlation-based grey wolf optimizer to perform accurate classification. A proposed multistage model helps to identify Covid from Computed Tomography (CT) scan image. The first process uses a convolutional neural network (CNN) for extracting the feature from the CT scans. The Pearson coefficient filter method is applied to remove redundant and irrelevant features. Finally, the Grey wolf optimizer is used to choose optimal features. Experimental analysis proves that this determines the optimal characteristics to detect the deadly disease. The proposed model’s accuracy is 14% higher than the krill herd and bacterial foraging optimization for severe accurate respiratory syndrome image (SARS-CoV-2 CT) dataset. The COVID CT image dataset is 22% higher than the existing krill herd and bacterial foraging optimization techniques. The proposed techniques help to increase the classification accuracy of the algorithm in most cases, which marks the stability of the stated result. Comparative analysis reveals that the proposed classification technique to predict COVID-19 with maximum accuracy of 98% outperforms other competitive approaches.     

EQUILIBRIUM,KINETICS, AND BREAKTHROUGH STUDIES FOR ADSORPTION OF Cr(VI) ON CHITOSAN

Wastewater containing low levels of pollutants can be effectively treated by the adsorption technique. In the present work, an adsorption study was carried out using chitosan as adsorbent in a fixed-bed column for the removal of Cr(VI) from wastewater solutions. The column performance of Cr(VI) adsorption onto chitosan was studied at different bed heights (3–9 cm), flow rates (50–200 mL/min), initial metal concentrations (2–10 mg/L), pH values (2–7), and temperatures (30°–60°C). The equilibrium data for the batch adsorption of Cr(VI) on chitosan were tested using the Langmuir, Freundlich, and BET isotherm models. The Langmuir model was found to be the most suitable, with a maximum adsorption capacity of 35.7 mg/g and a correlation coefficient (R ²) = 0.952. The experimental data were found to fit well with the pseudo-second-order kinetic model, with R ² = 0.999. The dynamics of the adsorption process was modeled using the Adams-Bohart, Thomas, and mass transfer models. The models were used to predict the breakthrough curves of adsorption systems and to determine the characteristic design parameters of the column. The adsorption data were observed to fit well with all three models. The model parameters were derived using MATLAB software. In order to compare quantitatively the applicability of adsorption dynamic models in fitting to experimental data, the percentage relative deviation (P) was calculated and found to be less than 5, confirming that the fit is good for all three models.     

Synthesis,characterization of Ag nanoparticles using the green approach towards degradation of environmental pollutant

Journal of Materials Science: Materials in Electronics - In the present study, a facile and eco-friendly method was used for the preparation of Ag nanoparticles (NPs) by simultaneous bio-reduction...     

GC-MS analysis and ecotoxicological risk assessment of triclosan, carbamazepine and parabens in Indian rivers

Pharmaceutical and personal care products are used extensively worldwide and their residues are frequently reported in aquatic environments. In this study, antiepileptic, antimicrobial and preservative compounds were analyzed in surface water and sediment from the Kaveri, Vellar and Tamiraparani rivers, and in the Pichavaram mangrove in India by gas chromatography-mass spectrometry (GC-MS). The mean concentration of carbamazepine recorded in the Kaveri River water (28.3 ng/L) was higher than in the other rivers and the mangrove. Because carbamazepine is used only in human drugs, this may reflect the relative contributions of human excretions/sewage in these rivers. The mean triclosan level in the Tamiraparani River (944 ng/L) was an order of magnitude greater than in the other water systems, and the concentrations at two of the sites reported here (3800-5160 ng/L) are, to our best knowledge, among the highest detected in surface waters. Sediment levels were, however, comparable with other sites. We conclude that industrial releases are likely major contributors of triclosan into this river system. Among parabens, ethyl paraben was predominantly observed. Hazard Quotients suggest greater environmental risks for triclosan than for carbamazepine and parabens. This is the first study on antiepileptic, antimicrobial and preservatives in rivers and mangroves from India.