Authors:Vinita Abhishek Gupta et al. Abstract: Timely identification of insects and their management play a significant role in sustainable agriculture development. The proposed hybrid model integrates a weighted multipath convolutional neural network and generative adversarial network to identify insects efficiently. To address the shortcomings of single-path networks, this novel model takes input from numerous iterations of the same image to learn more specific features. To avoid redundancy produced due to multipath, weights have been assigned to each path. For Xie2 dataset, the model shows 3.75%, 2.74%, 1.54%, 1.76%, 1.76%, 2.74 %, and 2.14% performance improvement from AlexNet, ResNet50, ResNet101, GoogleNet, VGG-16, VGG-19, and simple CNN respectively. To the best of our knowledge, no researchers have used a multipath convolution neural network in insect identification. PubDate: Fri, 27 Jan 2023 02:07:56 PST
Authors:Stitapragyan Lenka et al. Abstract: Development of an accurate forecasting model for effective prediction of Quality of Service (QoS) parameters of inter-net of things (IoT) based web services is highly desired, such that it improves service management and user experience. Mostly, QoS parameters are volatile in nature which make the IoT based service and recommendation process chal-lenging. Artificial neural network (ANN) based models are found to be worthy in modeling and forecasting nonlinear QoS parameter sequences. However, improper tuning of ANN parameters with conventional training algorithms may lead to a suboptimal model. Nature-inspired optimization methods are found suitable in fine tuning ANN parameters and have shown proficient results on real-world data mining problems. There is lack of such models for QoS parameters prediction that need to be explored. We develop an Artificial Electric Field Algorithm (AEFA) trained ANN (AEFANN) model for effective and accurate prediction of QoS parameters where AEFA is used to search an optimal ANN structure. The optimal ANN structure is achieved by AEFA through an evolutionary process. Two real-world IoT enabled web service datasets are used for evaluating effectiveness of AEFANN in terms of three performance metrics. Experimental procedures and comparative studies are conducted to establish the superiority of the proposed approach over four other similar forecasts. AEFANN obtained relative worth values of 4.13% ~ 69.12 % (5-min granularity) and 43.32% ~ 80.3 % (1-hr granularity) from SERVICE 1 dataset. Similarly, it obtained relative worth values of 7.25% ~ 65.57 % (5-min granularity) and 43.38% ~ 72.43 % (1-hr granularity) from SERVICE 2 dataset when compared to oth-er models. This is a significant improvement over comparative existing similar model. PubDate: Thu, 19 Jan 2023 02:09:03 PST
Authors:Arshad Mahdi Hamad et al. Abstract: Severe acute respiratory syndrome type 2 caused by coronavirus 2 is responsible for SARS that led to the emergence of coronavirus disease 2019 (COVID-19). Recent studies have demonstrated a high correlation between secondary bacterial infections and worse outcomes and death in COVID-19 patients. The extensive use of medicines during the last SARS-CoV epidemic led to an increase in the prevalence of multi-drug-resistant germs. Nanoparticles have important characteristics and applications in health, industry, and applied fields, etc. In medical fields, they curb and stop antibiotic-resistant diseases and pathogens. In this study, strawberry leaf extract was used to synthesize copper nanoparticles. The benefits of copper nanoparticles in inhibiting the growth of Pseudomonas aeruginosa and S.aureus bacteria isolated from COVID-19 patients' sputum were tested using the agar well diffusion method. Pseudomonas aeruginosa and S.aureus bacteria play a significant part in the series of bacterial infections that arise with COVID-19 infection. (1 ml) of strawberry leaf extract was mixed with (50 ml) of copper chloride solution prepared at a concentration of 2mM at room temperature. The mixture was blended for 7 hours to produce copper nanoparticles with a concentration of 2 mM as a stock solution in an environment-friendly manner. The first indication of the production of copper nanoparticles was the increase in the color intensity of the mixture after 7 hours. The nanoparticles were detected using UV spectrophotometers, and a scanning electron microscope SEM, XRD, FTIR, and UV-VIS spectral, which appeared at the absorbance of two absorptive peaks, namely: 299 and 804 nm. UV-VIS spectral examination was conducted after a month and was very intense. It also showed two absorbance peaks (300 and 805nm) with increasing intensity. This is evidence of the insolubility of the nanomaterial and its stability over the month. The scanning electron microscopy results showed that the dimensions of the prepared copper nanoparticles ranged between (46.59 and 58.82 nm). The production of copper nanoparticles in this inexpensive and environmentally friendly biological way has given excellent results in inhibiting the growth of bacteria isolated from COVID-19 patients. The effectiveness of copper nanoparticles was tested against cancerous cells isolated from laryngeal carcinoma, called HeP-2, of a 60-year-old man. The concentration of 50% of the copper nanoparticle solution, which is equivalent to 0.5 mM, gave an inhibition rate of 44.081% in cell cultures. Its effect was compared with the sensitivity of the normal cell line of liver cells (WRL-68); the concentration of 50%, which is equivalent to 0.5 mM, gave an inhibition rate of 5.997% in cell cultures, which showed a good affinity for copper nanoparticles. From this, we conclude that the copper nanoparticles were more effective in inhibiting cancerous cell lines than the normal ones. PubDate: Thu, 19 Jan 2023 02:08:59 PST
Authors:Basamma Umesh Patil et al. Abstract: Regular monitoring of physical activities such as walking, jogging, sitting, and standing will help reduce the risk of many diseases like cardiovascular complications, obesity, and diabetes. Recently, much research showed that the effective development of Human Activity Recognition (HAR) will help in monitoring the physical activities of people and aid in human healthcare. In this concern, deep learning models with a novel automated hyperparameter generator are proposed and implemented to predict human activities such as walking, jogging, walking upstairs, walking downstairs, sitting, and standing more precisely and robustly. Conventional HAR systems are unable to manage real-time changes in the surrounding infrastructure. Improved HAR approaches overcome this constraint by integrating multiple sensing modalities. These multiple sensors can produce accurate information, leading to a better perception of activity recognition. The proposed approach uses sensor-level fusion to integrate gyroscope and accelerometer sensors. The analysis is carried out using the widely accepted benchmark UCI-HAR dataset. Based on several performance evaluation experiments, the classification accuracy of long short-term memory (LSTM), convolutional neural network (CNN), and deep neural network (DNN) classifiers is reported to be 96%, 92%, and 93%, respectively. Compared to state-of-the-art deep learning models, the proposed method gives better results. PubDate: Tue, 17 Jan 2023 22:56:31 PST
Authors:Kifayah K. Thbayh et al. Abstract: Glass ionomer cement (GIC) is a common restorative material in dentistry, but it exhibits relatively weak mechanical properties. The present study focuses on incorporating nano-hydroxyapatite (nHAP) with different ratios (1, 3, 5, and 7wt%) in GIC to improve its properties. Mechanical properties, sorption, solubility, and diffusion coefficients after storage in distilled water for 60 days were studied. The highest sorption was measured at 7%wt (46.66 µg/mm3), and the lowest solubility was in the case of the sample containing 5% (29.166 µg/mm3). Moreover, the highest value of diffu-sion coefficient was 8.5 mm/s in the case of the sample with 7%wt nHAP. All in all, an ideal nHAP/GIC composition was prepared, and it can be applied as the basis of underneath dental filling. PubDate: Tue, 17 Jan 2023 22:56:27 PST
Authors:Mustafa S. Tukmachi et al. Abstract: Polyetheretherketone (PEEK) has favorable biomechanical properties to be used as an implant material. Unfortunately, it is hydrophobic and does not promote cellular adhesion, which could result in poor integration with bone tissue. Bio-functionalization of PEEK surface with osteogenic peptides derived from bone extracellular matrix proteins is an excit-ing approach to encourage bone formation around the implant. In the current study, bone-forming peptide-2 was immo-bilized on PEEK surface using two different methods, using dopamine and a diglycidyl ether as conjugate compounds, respectively. Peptide quantification test revealed that the two strategies resulted in the most amount of peptides were attached with 0.5 mM concentration and no further peptides were grafted with a higher peptide concentration. Both methods showed good peptide stability after agitation in aqueous solution. Peptide grafting was confirmed with ATR-FTIR. Surface characterizations with AFM and wettability tests resulted in a significant increase in surface roughness and surface area ratio for the peptide-grafted PEEK compared to unmodified PEEK, which led to a signifi-cant enhancement in the wettability of the modified PEEK surface PubDate: Tue, 17 Jan 2023 22:56:24 PST
Authors:Dhony Hermanto et al. Abstract: The role of an antioxidant in cancer prevention has led to the development of its synthesis, including green tea-capped silver nanoparticles (AgNPs), which are natural antioxidants. The biogenic AgNPs were synthesized electrochemically from pure silver wire using local green tea extract for the first time, with a characteristic peak at 420 nm. The method shortened the reaction time and produced long-term stable and narrow-size distribution nanoparticles. The spherical AgNP with an average size of ~17 nm was confirmed in TEM analysis. Furthermore, FTIR analysis and phytochemical assays revealed that the -OH group of flavonoids and terpenoids was responsible for the process. The DPPH method confirmed the higher antioxidant activity of AgNPs than extract, with an IC50 of 54.99 ppm. Hence, the method can be implemented for the large-scale production of pharmaceutical products in treating harmful diseases. PubDate: Tue, 17 Jan 2023 22:51:44 PST
Authors:Rasha Q. Hassan et al. Abstract: Researchers have recently focused their attention on Content-Based Image Retrieval (CBIR). It has emerged as one of the most fascinating areas in image processing and computer vision. With CBIR, the most comparable pictures that match the query image are pulled from an image database. As a result, it necessitates feature extraction (Local / Global) and similarity calculation. This paper uses a CBIR technique to determine the images that best match the image query by utilizing both global and local image features. A color moment is used for global features to describe the complete image. Local Binary Pattern (LBP) as a local feature, on the other hand, extracts interest points by building a Bag of Visual Words (BoVW). The distance between the query and database image features is computed using the Euclidean distance. Precision and recall are computed on the Corel-1K dataset to assess the retrieval performance. PubDate: Tue, 17 Jan 2023 22:51:40 PST
Authors:Siba Prasad Patil et al. Abstract: In today’s world, the rapid growth of textual data on internet sites & online resources makes it challenging for human being to assimilate essential information. To handle such issues, text summarization (TS) plays an important role. Through the TS process, a shorter version of the original content is generated to preserve the relevant information. This study suggests a quantitative assessment of models for single and multi-document summarization based on the sentence scoring method. Experimentation of the models has been carried out on DUC datasets. A detailed comparative analysis of the models is reported with respect to the performance of algorithms based on various metrics such as Recall Oriented-Understudy for Gisting Evaluation (ROUGE), Range, Co-efficient of Variation (CV) and Readability score. PubDate: Tue, 10 Jan 2023 02:08:44 PST
Authors:Hadeel Qasem Gheni et al. Abstract: Obviously, the increasing threats to network security, which led to devastating network attacks, have taken a heavy toll on enterprises as a simple firewall cannot prevent complex and changing attacks. Therefore, companies should use intrusion detection systems in combination with other security devices to protect against corporate network security issues. In fact, intrusion detection is a system whose primary function is to protect network security by monitoring traffic, collecting and analyzing information, and then issuing an alert in cases where the output of the analysis represents a threat to network security. Intrusion Detection Systems (IDS) can stop unauthorized activity on a network or operating system, react automatically, stop the intrusion's source in time, record it, and alert the network administrator to ensure maximum system security. The process of detecting attacks using a single algorithm has not proven its worth. Therefore, several algorithms were used together by using ensemble learning. To elaborate, ensemble learning is a well-known predictive technique that involves training multiple algorithms to treat the same problem, after which the results are combined to produce a single, potent prediction that can provide performance better than that of a single algorithm. The primary goal of this study is to present an overview of the main ensemble techniques that are used to enhance the effectiveness of the intrusion detection system, as well as the research using these methods as published by Elsevier and Springer from 2018 until the time being. The results prove that the two easiest methods within ensemble learning to implement are majority voting and weighted averaging, which provide good results in terms of accuracy. In cases where the base models have a significant variance, the bagging method would be more beneficial, while the boosting method would be used in cases where the basic models are biased, and in order to lower bias by learning different algorithms, the stacking ensemble methods are used. PubDate: Tue, 10 Jan 2023 02:08:41 PST
Authors:Domenico Prisa Abstract: One of the most anticipated challenges in the field of agriculture is to ensure high production levels while limiting the use of environmentally harmful synthetic chemicals. One of the most interesting strategies to overcome this challenge is the exploitation of the interactions between soil microorganisms and plants which result in stimulating plants’ natural activity. The interactions among mycorrhiza, growth-promoting microorganisms and plants play a crucial role in soil fertility, biocontrol and protection. The use of mixed microbial products can simulate interactions between fungi and bacteria, realising all the benefits that can be obtained from these associations in terms of quantity and quality of agri-cultural production and ensuring a significant reduction in the chemicals usually used in agriculture. PubDate: Tue, 10 Jan 2023 02:08:37 PST
Authors:A.Zh. Sakhabutdinov et al. Abstract: The work is dedicated to the mathematical modeling of the combined fiber-optic structure, which consists of a Fabry-Perot interferometer in the form of a thin polymer film at the end of an optical fiber and a fiber Bragg grating formed near it. The simulation results obtained using the proposed rigorous mathematical model are in good agreement with the reflectance spectrum of the experimental fiber-optic structure. It is shown that the combination of two resonant wave processes in the optical fiber leads to the asymmetric Fano-type resonance. The spectral shape of the resonance depends on the parameters of the structure, in particular, on the permittivity and the permeability of the constituting media, the thickness of the Fabry-Perot resonator, the length of the fiber Bragg grating, its induced refractive index and the period, as well as on temperature and humidity. The prospects of using the asymmetric resonance effect of the combined structure in sensing applications are discussed. PubDate: Tue, 10 Jan 2023 02:08:33 PST
Authors:Sama Hussein Al-Gburi et al. Abstract: Recently, driver inattention has become the leading cause of automobile accidents. As a result, the driver's perception and decision-making abilities are diminished, and the driver can lose control of the car. To prevent accidents caused by driver inattention, it’s vital to continuously monitor the driver and his driving behaviour and inform him if he becomes distracted or sleepy. This topic has been the subject of study for decades. Whenever feasible to recognise unsafe driving in advance, accidents could be avoided. This document presents an overview of the existing driver alertness system and the various techniques for detecting driver attentiveness. PubDate: Tue, 10 Jan 2023 02:08:30 PST
Authors:Ofeliya Kostadinova et al. Abstract: Present work studies the influence of the pretreatment milling media (deionized water (BLTOS-H) and isopropanol (BLTOS-i)) on the surface characteristics, structure, chemical composition and catalytic activity of non-stoichiometric Ba2Sr2La2Ti4O12 perovskites. The IR spectroscopy and XRD analyses shows a difference in the structure and phase composition of the two materials. X-ray photoelectron spectroscopy detects a Ba2+- and La3+-enriched and Sr2+-depleted surface. The BLTOS-i sample appears to exhibit higher specific surface area (SSA) and pore volume in comparison to BLTOS-H. The electrochemical tests showed that BLTOS-H sample have similar behavior to platinum at current densi-ties up to 10 mA cm-2, while BLTOS-i presented highest electrochemical performance among all tested samples over the entire current range. PubDate: Tue, 10 Jan 2023 02:08:26 PST
Authors:Monika Dhiman et al. Abstract: Using five refractive index mixing rules: Lorentz–Lorenz, Gladstone–Dale, Weiner, Heller, and Arago –Biot; refractive indices of six binary polymer mixtures have been determined at 303.15 K, under atmospheric pressure. The binary mixtures investigated here are PEG-200 + 1,3-Dioxolane, PEG-200 + Oxolane, PEG-200 + Oxane, PEG-400 + 1,3-Dioxolane, PEG-400 + Oxolane, and PEG-400 + Oxane. A good agreement has been observed between the obtained results and respective literature data for all these mixtures. The relative merit of refractive index mixing rules is assessed. Deviation in refractive index and reduced free volume values are also calculated using the refractive index data taken from the literature. Furthermore, the molecular radius of these binary polymer mixtures is computed with the help of the refractive index and molar volume data. In addition, an ideal mixing method is also employed to calculate the molecular radius of these systems.The molecular radius of these binary mixtures is found to be additive with respect to the mole fraction of the pure components. Finally, the results are discussed in terms of the intermolecular interactions among the constituent molecules. PubDate: Fri, 11 Nov 2022 09:02:34 PST
Authors:Adambek Tatenov et al. Abstract: Laboratory-based work is necessary for developing the skills of measuring physical quantities, performing physics experiments, and drawing correct conclusions from their observations. Training programs that simulate physics processes and phenomena that cannot always be shown “live”, can significantly help students. The authors developed and implemented the processes of the phenomenon of the physics course “Electromagnetism” – “Study of dynamic hysteresis of ferromagnets” and interactively virtualised using the Java Script computer software environment. The originality of this approach is that it provides a convenient tool for creating a simulation environment for any physics problem. This interactive virtual laboratory development will be introduced into the educational process of the Kazakh National Women’s Pedagogical University. PubDate: Fri, 11 Nov 2022 09:02:29 PST
Authors:Hussein A. Ismael et al. Abstract: Diabetic Foot Ulcers (DFU) are considered to be a common complication of diabetes, usually resulting in the amputation of lower extremities. Therefore, diagnosing this disease at an early stage is necessary to avoid the accompanying treatment approach, and this results in a significant cost reduction for the patient. To achieve an early diagnosis of this disease, we need to classify a patient's skin as normal or abnormal. A classification process relies heavily on the extracted features. So, we proposed a new technique called CNN_GLCMNet for feature extraction. This technique relies on Convolution Neural Network (CNN) and the Gray-Level Co-Occurrence Matrix (GLCM) techniques to mine abstract features and second-order statistical texture features. Also, Singular Value Decomposition (SVD) is applied to reduce the dimensionality of the obtained features that result from CNN, Next, the GLCM method is applied to extract second-order statistical texture features. Then, these two kinds of features (abstract features and statistical features) are combined and used as input for the classifier. Two classification mechanisms have been adopted in the classification of images into normal and abnormal skin. First, the Deep Neural Network (DNN) classifier achieves the following performance evaluation metrics (accuracy 97.43%, recall 97.25%, specificity 97.59%, precision 97.53%, f1-score 97.38%). Second, the Support Vector Machine (SVM) classifier achieves the following performance evaluation metrics (accuracy 96.93%, recall 96.99%, specificity 96.94%, precision 96.76%, f1-score 96.85%). Since both classifiers have been validated against the DFU dataset using 10-fold cross-validation. The DNN classifiers with our new feature extraction technique achieve better results in terms of accuracy, specificity, precision, recall, and f1-score than in previous work. Furthermore, a comparison of DNN and SVM classifiers finds that DNN gives a better result according to performance metrics. PubDate: Fri, 11 Nov 2022 09:02:24 PST
Authors:Chandran Masi et al. Abstract: This research was aimed at identifying a bacterium that can produce alkaline proteases. As a result, bacteria that produce proteases were isolated from Batu tannery effluents, tested for protease synthesis on skim milk agar plates, and validated with a protease assay. Microscopic and molecular phylogenetic analyses identified Paenibacillus dendritiformis (BT7) as the bacterial isolate with the highest alkaline protease production. The isolate's maximum enzyme production was obtained by 2% inoculum size, 40°C temperature, 9.0 pH, and a 48-hour incubation time with production media components such as glucose, casein, MgCl2, and 2% NaCl. The maximal enzyme activity was 270 U/mL under all optimum culture conditions. Concentrated ammonium sulfate precipitation (75%) and dialysis were employed to obtain a cell-free, partially purified protease. The specific activity of the dialysate, which accounts for 3% of the enzyme yield, was discovered to be 134 U/mL. The partially purified protease was used for application in blood stain removal. It was studied and found that the alkaline protease resistance under stringent conditions is very stable with bleach detergent. Also, this enzyme could clean blood-stained fabrics. This study shows that the alkaline protease from Paenibacillus dendritiformis - BT7 could be used in various ways in the detergent industry that are good for the environment. PubDate: Fri, 11 Nov 2022 09:02:19 PST
Authors:Jayanudin Jayanudin et al. Abstract: The aims of this study were to characterize the urea-loaded chitosan microspheres and determine the release kinetic constants and diffusion coefficients. An emulsion cross-linking method was used to prepare the urea-loaded chitosan microspheres. Urea was dissolved in a solution of chitosan then put into vegetable oil and stirred to form an emulsion. Glutaraldehyde saturated toluene (GST) was added into the emulsion dropwise while continuously stirring for the solidification process. Chitosan microspheres filled with urea were washed, dried, and then analyzed. Characterization of the urea-loaded chitosan microspheres was conducted using a scanning electron microscope (SEM), Raman spectroscopy, X-ray diffraction, and particle size distribution. The cumulative release analysis was used to determine the amount of urea released from the chitosan microspheres and determine the release kinetic constants and diffusion coefficients. The chitosan microspheres had a good spherical geometry with a smooth surface and crystallinity of 95.5 - 98.18%. They had a diameter in the range of 125.31 - 153.65 m and a cumulative release value in the range of 38.22 - 48.06%. Based on the kinetic analysis, the best kinetic models were models of Korsmeyer-Peppas, Peppas-Sahlin, and simple power law with the burst effect resulting in the highest R2 of 0.99. The diffusion coefficient obtained was in the range of 5.439 × 10-11 - 7.512 × 10-11 cm2/sec. PubDate: Fri, 11 Nov 2022 09:02:14 PST
Authors:Mohanad Q. Fahem et al. Abstract: One gram of TiO2 nanoparticles, size of 30-50 nm and 20 ml of 3M of NaOH as the suspension were utilized in a hy-drothermal process using three homemade reactors of different surface areas but of the same capacity to synthesise tita-nium sub-oxide Ti6O11. X-ray diffraction, Raman spectroscopy, and field-emission scanning electron microscopy (FE-SEM) were employed to characterise the samples. When the temperature was raised to 363 K (90 °C) for 6 h and the surface area changed, X-ray diffraction revealed the development of sub-oxide titanium (Ti6O11) with a triclinic Magnéli phase from TiO2 nanoparticles. FE-SEM revealed consistent hierarchical structures with grass-like planar ge-ometries. In titanium, a new phase has been discovered. PubDate: Fri, 11 Nov 2022 09:02:09 PST
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