Improved Chameleon Swarm Optimization-Based Load Scheduling for IoT-Enabled Cloud Environment

Internet of things (IoT) and cloud computing (CC) becomes widespread in different application domains such as business, e-commerce, healthcare, etc. The recent developments of IoT technology have led to an increase in large amounts of data from various sources. In IoT enabled cloud environment, load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization. The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics. In this view, this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling (C3SOA-LS) technique for IoT enabled cloud environment. The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished. Besides, the presented C3SOA-LS model involves the design of circle chaotic mapping (CCM) with the traditional chameleon swarm optimization (CSO) algorithm for improving the exploration process, shows the novelty of the work. The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan. The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.     

Evolution of ochratoxin A content during red and rose vinification

BACKGROUND: This work aims to investigate the effects of the common vinification steps on the fate of the ochratoxin A (OTA) during wine making. Two assays of red and rose microvinification, with artificially contaminated grapes, were performed. The content of this mycotoxin was also monitored throughout the process of red wine making from naturally contaminated grapes in a winery. RESULTS: The results from the different assays revealed that the maceration of pomace have a significant effect on the increase of OTA content in red wine (P < 0.05) whereas the alcoholic fermentation had a reducing effect. However, the spontaneous malolactic fermentation showed no significant effect on the OTA content in wine (P > 0.05). Storage of red wine in tanks followed by draining caused a significant decrease of OTA of about 55%. Clarification with a gelatin oenological fining agent contributed to the removal of up to 58% of OTA from red wine. CONCLUSION: Overall, a consistent decrease in OTA concentration was noticed throughout either red or rose vinification. This work has contributed to the understanding of the fate of OTA during different vinification processes, especially from naturally contaminated grapes. Copyright © 2008 Society of Chemical Industry     

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOML-ERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.     

Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. The CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature vectors. In addition, extreme learning machine autoencoder (ELM-AE) model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using CBA. The experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.     

Determination of Fusarium mycotoxins enniatins,beauvericin and fusaproliferin in cereals and derived products from Tunisia

In this study, 51 samples of cereals (wheat, Barley, maize and Sorghum) and by-products (mainly pasta and couscous) purchased from Tunisian supermarkets were examined for contamination with the emerging Fusarium mycotoxins: Enniatins ENs (EN A, EN A₁, EN B and EN B₁), beauvericin (BEA) and fusaproliferin (FUS).The extraction of the samples was performed with methanol using an Ultra-turrax homogenizer. Mycotoxins were analyzed with a liquid chromatography (LC) coupled to a diode array detector (DAD).The frequencies of contamination of total samples with ENs were 96%. EN A₁ was the most common EN found with the highest prevalence of 92.1%, levels ranged between 11.1 and 480 mg/kg. EN B was evidenced in 35 samples and levels ranged from 1.5 to 295 mg/kg. EN B₁ was detected in 20 samples (39.2%) and levels varied from 4.8 to 120.1 mg/kg and EN A was detected in 14 samples with contamination levels ranging between 19.6 and 121.3 mg/kg. The maximum concentration of total ENs in a single sample was 683.9 mg/kg (sorghum). The analytical results also showed that all the analyzed samples were free of BEA and FUS.The present work is the first one ever drafted on the presence of the emerging Fusarium mycotoxins in Tunisian cereals and derived products.     

Practical Implementation of Diagnosis Systems Using Timed Failure Propagation Graph Models

Timed failure propagation graphs (TFPGs) are causal models that capture the temporal aspects of failure propagation in typical engineering systems. In this paper, we present several practical modeling and reasoning considerations that have been addressed based on experience with complex real-time vehicle subsystems. These include handling intermittent faults, reasoning over dynamically commanded test sequences, dealing with the constraints of limited computational resources, and providing automated model verification. We finally present a vehicle subsystem case study.     

Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification

Atherosclerosis diagnosis is an inarticulate and complicated cognitive process. Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions. Since the medical diagnostic outcomes need to be prompt and accurate, the recently developed artificial intelligence (AI) and deep learning (DL) models have received considerable attention among research communities. This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification (MDL-BADDC) model. The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing, feature selection, classification, and parameter tuning. Besides, the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer (QOBMO) based feature selection technique. Moreover, the deep stacked autoencoder (DSAE) based classification model is designed for the detection and classification of atherosclerosis disease. Furthermore, the krill herd algorithm (KHA) based parameter tuning technique is applied to properly adjust the parameter values. In order to showcase the enhanced classification performance of the MDL-BADDC technique, a wide range of simulations take place on three benchmarks biomedical datasets. The comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.     

1,3-Dipolar cycloaddition polymerization reactions of novel macromolecules containing sym-tetrazine rings

We report the synthesis of sym-1,4-diphenyl-1,4-dihydro-1,2,4,5-polytetrazine through 1,3-dipolar cycloaddition polymerization reactions where bis-hydrazonoyl chloride was converted to a tetrazine based polymer through bis-nitrilimine intermediates. Polymer molecular weights approached 90,000 g/mol under optimized reaction conditions with low polydispersity indices of approximately 1.05. The polymers are soluble in a variety of organic solvents and the reactions were characterized through a series of spectral, thermal and chromatographic techniques. The tetrazine based polymers display high complexation potential with cobalt chloride demonstrating metal complexation capability.