A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

Many countries developed and increased greenery in their country sights to attract international tourists. This planning is now significantly contributing to their economy. The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment; it is only possible if an upcoming number of tourists’ arrivals are accurately predicted. But accurate prediction is not easy as empirical evidence shows that the tourists’ arrival data often contains linear, nonlinear, and seasonal patterns. The traditional model, like the seasonal autoregressive fractional integrated moving average (SARFIMA), handles seasonal trends with seasonality. In contrast, the artificial neural network (ANN) model deals better with nonlinear time series. To get a better forecasting result, this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model. Then, we have used the proposed model to predict the tourists’ arrival in New Zealand, Australia, and London. Empirical results showed that the proposed hybrid model outperforms in predicting tourists’ arrival compared to the traditional SARFIMA and ANN models. Moreover, these results can be generalized to predict tourists’ arrival in any country or region with a complicated data pattern.     

Adaptive Fuzzy Logic Controller for Harmonics Mitigation Using Particle Swarm Optimization

An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance. In this research, a novel control technique-based Hybrid-Active Power-Filter (HAPF) is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor (PF) and Total–Hormonic Distortion (THD) and the performance of a system. This work proposed a soft-computing technique based on Particle Swarm-Optimization (PSO) and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods. Moreover, the control algorithms are implemented for an instantaneous reactive and active current (I_d-I_q) and power theory (Pq0) in SIMULINK. To prevent the degradation effect of disturbances on the system's performance, PS0-PI is applied in the inner loop which generate a required dc link-voltage. Additionally, a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions. The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system. Therefore, the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller (FLC) are optimal that will detect reactive power and harmonics much faster and accurately. The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI (Voltage Source Inverter), and thus the simulation has been taken in SIMULINK/MATLAB. The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation. As a result of the comparison, it can be concluded that the PSO-based Adaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.     

Emotion Recognition from Occluded Facial Images Using Deep EnsembleModel

Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency of the proposed model, we use two wild benchmark datasets Real-world Affective Faces Database (RAF-DB) and AffectNet for facial expression recognition. The proposed model classifies the emotions into seven different categories namely: happiness, anger, fear, disgust, sadness, surprise, and neutral. Furthermore, the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost, convergence and accuracy based on a standard problem specific to classification applications.     

Systems pharmacology approach to investigate the molecular mechanisms of herb Rhodiola rosea L. radix

Rhodiola rosea L. radix (RRL) is one of the most popular medical herb which has been widely used for the treatment of different diseases effectively, including cardiovascular diseases and nerve system diseases. However, due to the multiple compounds in RRL, the underlying molecular mechanisms of RRL are remained unclear. To decipher the action mechanisms of RRL from a systematic perspective, a systems pharmacology approach integrated absorption, distribution, metabolism, and excretion (ADME) system, drug targeting, and network analysis was introduced. First, by the ADME screening system and the target fishing process, 56 potential active compounds and 62 targets were obtained, respectively. In addition, compound-target network demonstrated that most compounds interacted with multiple targets, indicating that RRL may enhance its therapeutic effects probably through hitting on multiple targets in a holistic level. Moreover, target-pathway network and gene ontology analysis showed that multiple targets of RRL were involved in several biological pathways, i.e. Neuroactive ligand-receptor interaction, calcium signaling pathway, adrenergic signaling in cardiomyocytes, and VEGF signaling pathway, which dissecting the therapeutic effects of RRL on various diseases, such as cardiovascular diseases, depression, adaptation diseases, etc. In summary, this work successfully explains the potential active compounds and the multi-scale curative action mechanisms of RRL for treating various diseases; meanwhile, it implies that RRL could be applied as a novel therapeutic agent in arthritic diseases. Most importantly, this work provides an in silico strategy to understand the action mechanisms of herbal medicines from molecular/system levels, which will promote the new drug development of traditional Chinese medicine.     

A comprehensive survey of AI-enabled phishing attacks detection techniques

In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

     

Phase Engineering of Nickel Sulfides to Boost Sodium- and Potassium-Ion Storage Performance

Sulfides are promising anode candidates because of their relatively large theoretical discharge/charge specific capacity and pretty small volume changes, but suffers from sluggish kinetics and structural instability upon cycling. Phase engineering can be designed to overcome the weakness of the electrochemical performance of sulfide anodes. By choosing nickel sulfides (α-NiS, β-NiS, and NiS₂) supported by reduced graphene oxide (rGO) as model systems, it is demonstrated that the nickel sulfides with different crystal structures show different performances in both sodium-ion and potassium-ion batteries. In particular, the α-NiS/rGO display superior stable capacity (≈426 mAh g⁻¹ for 500 cycles at 500 mA g⁻¹) and exceptional rate capability (315 mAh g⁻¹ at 2000 mA g⁻¹). The combined density functional theory calculations and experimental studies reveal that the hexagonal structure is more conducive to ion absorption and conduction, a higher pseudocapacitive contribution, and higher mechanical ability to relieve the stress caused by the volume changes. Correspondingly, the phase engineered nickel sulfide coupled with the conducting rGO network synergistically boosts the electrochemical performance of batteries. This work sheds light on the use of phase engineering as an essential strategy for exploring materials with satisfactory electrochemical performance for sodium-ion and potassium-ion batteries.     

Electrical properties of Li1.3Ge1.4Ti0.3Al0.3(PO4)3 superionic ceramics

Preparation and electrical characterization of NASICON-type compound, Li_1.3Ge_1.4Ti_0.3Al_0.3(PO₄)₃, are described. The solid solution is obtained with Ge⁴⁺ → Ti⁴⁺ and Ge⁴⁺ → Al³⁺ substitutions in LiGe₂(PO₄)₃. The powder has been fabricated by a solid state reaction and the structural characteristics of it have been studied by X-ray. Ceramic samples have been sintered by varying the sintering duration from 1 to 3 h. Samples were studied by complex impedance spectroscopy in the frequency range 1 MHz-1.2 GHz and temperature range 300-600 K. Two regions of relaxation dispersion were found. The dispersions were related to the fast Li⁺ ion transport in the grains and grain boundaries. Variation of the sintering duration has no considerable effect on electrical properties of the ceramics.     

Modeling of dynamic mechanical properties of vulcanized fluoroelastomer

The dynamic mechanical properties of a vulcanized fluoroelastomer (FKM) were studied over a range of temperatures and shear frequencies. Dynamic mechanical analysis and differential scanning calorimetry were used for the purpose of the study. A model was developed in order to describe FKM's viscoelastic behavior at various temperatures. The model was fitted to experimental data using an algorithm, which was developed for this purpose. As a result the FKM discrete relaxation spectrum at two reference temperatures was obtained, as well as the Williams‐Landel‐Ferry (WLF) equation parameters or the activation energy equivalent. Further on, the model was applied on storage modulus and loss tangent values obtained from the experiments, during which the temperature increased linearly. It was observed that the WLF equation fits well with the results during the glass transition, while the Arrhenius‐type relationship predicted too rapid decrease of the storage modulus during the glass transition. The master curves were constructed using the previously calculated WLF parameters and the activation energy equivalent. The developed model may be readily applied for the prediction of the numerous FKM compounds' frequency–temperature behavior using the dynamic mechanical properties obtained from either isothermal or low linear heating rate program measurements. POLYM. ENG. SCI., 47:2085–2094, 2007. © 2007 Society of Plastics Engineers     

Biosynthesis of essential oils in aromatic plants: A review

In aromatic plants species, biosynthesis of essential oils occurs through two complex natural biochemical pathways involving different enzymatic reactions. Isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP) are the universal precursors of essential oil biosynthesis and are produced by the cytosolic enzymatic MVA (mevalonic acid) pathway or by plastidic and enzymatic 1-deoxy-d-xylolose-5-phosphate (DXP) pathway, also called the 2-C-methylerythritol-4-phosphate (MEP) pathway. In the particular plant cell part, prenyl diphosphate synthases condense isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) further to form prenyl diphosphates, which are used as substrates for geranyl diphosphate (GPP; C₁₀) or for fernesyl diphosphate (FPP; C₁₅). Essential oils are final terpenoid products and are formed by a huge group of enzymes known as terpene synthases (TPS). Essential oils are important secondary metabolites of plants and have been used not only in different industries but also in ethnobotanical medicines for centuries. Hence, considerable research has been undertaken to understand the essential oil biosynthetic pathways. This review will be a valuable source of information in the field of natural products, as we give detailed insights about biosynthesis of essential oils in plants and thus indicate also new unexplored horizons for further research.