Kinetic Investigation of Rosemary Essential Oil by Two Methods: Solvent-Free Microwave Extraction and Hydrodistillation

Depending on the use area of rosemary essential oil, pharmaceutical, cosmetic, or in food, the choice of both method and extraction time is fundamental, and the sample characterization is mainly based on comparing a few compounds that act as markers of a defined quality for a precise application. In the present work, the kinetic study of major components of Rosmarinus officinalis L. essential oils obtained by solvent-free microwave extraction (SMFE) and by hydrodistillation (HD) has been followed in order to determine the optimum extraction time of a defined bioactive molecule or its chemical family as well as its recommended extraction technique, either SFME or HD. The monoterpene hydrocarbons were nearly extracted completely in the first instants by SFME and progressively by HD. In contrast, the oxygenated monoterpenes were substantially extracted during the first minutes by HD and progressively by SFME. While the extraction of trans-caryophyllene was achieved at the beginning with SFME, it required more than 2 h with HD. A substantial gain in the extraction time has been obtained using SFME. Even though the essential oils extracted by SFME (30 min) and HD (3 h) are qualitatively similar, the oil fractions obtained during extraction time are very different.     

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon installation and operation are high, whereas the technology is relevant only for indoor environments. Currently, commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements. Against this background, the current study develops an Improved Whale Optimization with Deep Learning-Enabled Fall Detection for Disabled People (IWODL-FDDP) model. The presented IWODL-FDDP model aims to identify the fall events to assist disabled people. The presented IWODL-FDDP model applies an image filtering approach to pre-process the image. Besides, the EfficientNet-B0 model is utilized to generate valuable feature vector sets. Next, the Bidirectional Long Short Term Memory (BiLSTM) model is used for the recognition and classification of fall events. Finally, the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method, which shows the novelty of the work. The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.     

Adaptive integral-type terminal sliding mode control: Application to trajectory tracking for mobile robot

This paper presents an Adaptive Terminal Integral Sliding Mode Control (ATISMC) scheme for trajectory tracking problems applied to a differential Wheeled Mobile Robot (WMR). First, a terminal integral sliding variable is designed. Based on the finite-time concept, an adaptive control law has been developed in which a switching gain is adjusted adaptively by using a novel strategy. This control method aims to deal with unknown bounded disturbances and uncertainties. Moreover, it allows fast convergence of the system states to an equilibrium point. The main features of the proposed ATISMC are its robustness, fast convergence rate, and chattering avoidance. To realize trajectory tracking for WMR, the ATISMC is incorporated into a double closed loop scheme. Stability analysis is performed using the Lyapunov stability theory. Numerical simulations and real-time experiments demonstrate the feasibility of the proposed controller scheme. A comparison study with the classical ISMC was performed to show the superiority of the developed method.     

The First Application of Moroccan Perlite as Industrial Dyes Removal

Silicon - A local perlite mineral (P1) was used to remove the industrial dyes Remazole Brilliant 19 (RB19) and Drimarene Yellow (YD) from an aqueous solution. Prior to any investigation, P1 was...     

Hybrid 802.11p-cellular architecture for NDN-based VANET

Vehicular network communications (VANET) face multiple challenges due to their intermittent connections and the rapid changes in their topologies. In recent years, several research efforts have explored the use of content-centric approaches to alleviate some of these challenges. One of these promising network designs is Named Data Networking (NDN), which has become a valid solution to support VANET applications. However, in the NDN architecture, the main forwarding mechanism for the interest packets is flooding. This forwarding mechanism will result in excessive collisions, which will lead to the broadcast storm problem. In this paper, we propose VC-NDN: a hybrid and hierarchical Named Data Networking architecture for VANETs. VC-NDN improves content retrieval efficiency through an adapted NDN-based communication model. VC-NDN includes a new interest forwarding scheme to reduce packet collision in the network and an efficient mechanism to support push-based traffic. Furthermore, to reduce communication costs, VC-NDN uses two communication technologies in parallel, namely, IEEE 802.11p and cellular communications, while keeping the usage of the cellular network at a minimum level. Finally, to reduce the impact on vehicle mobility, VC-NDN follows a hierarchical clustering architecture. Specifically, a density-based clustering algorithm is designed to create and maintain stable clusters with multihop communication capability. Our performance study shows that VC-NDN outperforms the basic VNDN solutions in terms of data retrieval delay and packet delivery ratio while minimizing the usage of the cellular network.     

Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

With the flexible deployment and high mobility of Unmanned Aerial Vehicles (UAVs) in an open environment, they have generated considerable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications. The difficulty stems from features other than mobile ad-hoc network (MANET), namely aerial mobility in three-dimensional space and often changing topology. In the UAV network, a single node serves as a forwarding, transmitting, and receiving node at the same time. Typically, the communication path is multi-hop, and routing significantly affects the network’s performance. A lot of effort should be invested in performance analysis for selecting the optimum routing system. With this motivation, this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication (COAER-UAVC) technique. The presented COAER-UAVC technique establishes effective routes for communication between the UAVs. It is primarily based on the coati characteristics in nature: if attacking and hunting iguanas and escaping from predators. Besides, the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay. A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system. The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.     

Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

Autism Spectrum Disorder (ASD) refers to a neuro-disorder where an individual has long-lasting effects on communication and interaction with others. Advanced information technology which employs artificial intelligence (AI) model has assisted in early identify ASD by using pattern detection. Recent advances of AI models assist in the automated identification and classification of ASD, which helps to reduce the severity of the disease. This study introduces an automated ASD classification using owl search algorithm with machine learning (ASDC-OSAML) model. The proposed ASDC-OSAML model majorly focuses on the identification and classification of ASD. To attain this, the presented ASDC-OSAML model follows min-max normalization approach as a pre-processing stage. Next, the owl search algorithm (OSA)-based feature selection (OSA-FS) model is used to derive feature subsets. Then, beetle swarm antenna search (BSAS) algorithm with Iterative Dichotomiser 3 (ID3) classification method was implied for ASD detection and classification. The design of BSAS algorithm helps to determine the parameter values of the ID3 classifier. The performance analysis of the ASDC-OSAML model is performed using benchmark dataset. An extensive comparison study highlighted the supremacy of the ASDC-OSAML model over recent state of art approaches.     

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining (OM) concepts become familiar due to advances in networking technologies and social media. Recently, massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult. Since OM find useful in business sectors to improve the quality of the product as well as services, machine learning (ML) and deep learning (DL) models can be considered into account. Besides, the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process. Therefore, in this paper, a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory (AFSO-BLSTM) model has been developed for OM process. The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data. In addition, the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process. Besides, BLSTM model is employed for the effectual detection and classification of opinions. Finally, the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model, shows the novelty of the work. A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.