Authors:Vinita Abhishek Gupta et al. Abstract: Automatic identification of insects and diseases has attracted researchers for the last few years. Researchers have suggested several algorithms to get around the problems of manually identifying insects and pests. Image processing techniques and deep convolution neural networks can overcome the challenges of manual insect identification and classification. This work focused on optimizing and assessing deep convolutional neural networks for insect identification. AlexNet, MobileNetv2, ResNet-50, ResNet-101, GoogleNet, InceptionV3, SqueezeNet, ShuffleNet, DenseNet201, VGG-16 and VGG-19 are the architectures evaluated on three different datasets. In our experiments, DenseNet 201 performed well with the highest test accuracy. Regarding training time, AlexNet performed well, but ShuffleNet, SqueezeNet, and MobileNet are better alternatives for small architecture. PubDate: Mon, 22 May 2023 08:37:08 PDT
Authors:Iman S. Razaq et al. Abstract: Face recognition is the most extensively utilized security and public safety verification method. In many nations, the Automatic Border Control system uses face recognition to confirm the identification of travelers The ABC system is vulnerable to face morphing attacks; the face recognition systems give acceptance for the traveller, even though the passport photo does not represent the actual image of the person but is a result of the merger of two images. Therefore, it is vital to determine whether the passport image is altering (morph) or actual. This research proposes an improved method to extract features from facial images. The proposed method consists of four phases: In the first stage, morph images were generated using a set of databases of images of real people, used every two images that were similar in general shape or landmarks in producing the morphed image using three types of techniques used in this field (Automatic selection landmark, StyleGAN, and Manual selection landmark). StyleGAN has been relied upon to achieve the best results in producing artefact-free images. In the second phase, a Faster Region Convolution neural network is utilizing for determining and cutting important landmarks area (eyes, nose, mouth, and skin) in the face, where we leave the hair, ears, and image background for every image in the database. In the third phase, the features are extracted using three techniques Principal component analysis, eigenvalue, and eigenvector; a matrix of two-dimensional features is generated with one layer for each technique. Then merge the extracted features (with out s) from each image into one image with three layers. The first layer represents the principal component analysis features, the second the eigenvalue features, and the third the eigenvector features. Finally, the features are introduced into the convolutional neural networks to obtain optimal features. The fourth phase represents the classification process using the Deep Neural Network (DNN) classifier and Support Vector Machine (SVM) second classifier. The DNN classifier achieved an average accuracy of 99.02% compared with SVM, with an accuracy of 98.64%. The power of the proposed work is evident through the FRA and RFF evaluation. Which achieved values as low as possible for DNN FAR 0.018, indicating the error rate in calculating morphed images is actual, and FRR 0.003, meaning the error rate in calculating the actual images is morphed, FAR 0.023, FRR 0.06 for SVM whenever these ratios are less than one, the higher system's accuracy in detection. The AMSL dataset (Accuracy 95.8%, FAR 0.039, FRR 0%) (Accuracy 95.2%, FAR 0.047, FRR 0.98) for DNN and SVM, respectively. It turned out that the training of the proposed network optimized for the features extracted for the landmarks area significantly affects finding the difference and discovering the modified images, even in the case of minor modifications as in the AMSL dataset. PubDate: Tue, 09 May 2023 05:32:17 PDT
Authors:Safa S. Abdul-Jabbar et al. Abstract: QR codes have become ubiquitous across several industries, including e-commerce, education, and healthcare. In the healthcare sector, QR codes are increasingly used to relay essential information regarding medical products, patient history, and healthcare education. In addition, QR codes have proven to help secure and preserve patient records during transmissions. This paper aims to develop and analyze the implementation of QR code technology for healthcare appli-cations. The proposed approach involves generating a unique QR code for each patient's information, facilitating data transmission between nodes such as hospitals. With its small size, the QR code provides a simple solution to transfer patient records, reducing internet capacity requirements and minimizing latency. The proposed system utilizes advanced techniques such as Diffie Hellman and logistic map for key generation and distribution, hash algorithm and Lorenz equation for key mask generation, the salting algorithm for data integrity, DNA encoding, and Huffman Algorithm for data coding. The proposed system's usability was tested with 366 patient records, and the results indicate that 99.76% of data was saved with a compression ratio of 416.8 for each patient record. Moreover, the comparative analysis demon-strated that the proposed strategy outperforms other data capacity, security, and integrity methods. Overall, the proposed system can revolutionize data transmission and security in the healthcare sector, ensuring the safety and confidentiality of patient records. PubDate: Tue, 09 May 2023 05:32:14 PDT
Authors:Suriati Eka Putri et al. Abstract: A green chemistry method was used for the first time to synthesize copper nanoparticles (Cu-NPs) using CuSO4 as a precursor and red dragon fruit (Hylocereus costaricensis) peel wasted extract as a bio-reductor. Cu-NPs produced were then used as a photocatalysts for acid orange 7 (AO7) dyes degradation. The results showed that the smallest average crystallite size of the products ranged from 8.84 - 8.86 nm, and the FCC crystal structure had a surface area of 244.38-278.85 m2g-1. Furthermore, the optimum degradation of AO7 dye occurred at a ratio of 1:3 with a percentage of 81.07% for four cycles. These findings indicated that Cu-NPs can be used for the treatment of textile wastewater under sunlight in the future. PubDate: Thu, 04 May 2023 07:57:46 PDT
Authors:Ari Sulistyo Rini et al. Abstract: Hazardous waste from mercury is a critical problem that requires serious attention. Herein, we report bio-colloidal silver nanoparticles (AgNPs) synthesized via green synthesis route from AgNO3 aqueous solution and Sandoricum koetjape (SK) peel extract and demonstrate their sensitivity at detecting mercury ions (Hg2+) using colorimetry. AgNPs were synthesized by varying the volume ratio of AgNO3 solution to SK peel extract, namely AgNPs(4:1), AgNPs(3:2), AgNPs(1:1), AgNPs(2:3), and AgNPs(1:4). Bio-colloidal AgNPs were tested for their ability to detect toxic heavy metal mercury ion by adding different concentrations of Hg2+. All samples have successfully detected Hg2+ at varying levels of sensitivity. This investigation revealed that AgNPs(4:1) is the most sensitive colloid for detecting Hg2+ up to a concentration of 1 ppm. PubDate: Thu, 04 May 2023 07:57:42 PDT
Authors:Mohammed Basil Albayati et al. Abstract: COVID-19 vaccination helps protect people from getting the virus. Some people show up normal signs from the vaccine, which indicates that their body is building protection. However, adverse effects on people could cause long-term health problems. Severe allergic reactions, Myocarditis, and Pericarditis appeared in the vaccinated people that have been reported to the (FDA/CDC) Vaccine Adverse Event Reporting System (VAERS). In fact, other possible effects are still being studied in clinical trials. In the present work, the adverse reactions caused by Covid-19 vaccines of Pfizer/BioNTech, Moderna, and JJ Johnson & Johnson manufacturers are studied. Specifically, the supervised machine learning approach is utilized to discriminate body reactions against the vaccine and provide a decision-making model for the vaccine recipients. The model study and analyze the recipients’ reactions whether they showed mild, moderate, or severe acute syndromes to reduce the fatality rates. To validate our model, a dataset of more than 52k records with 18 informative attributes provided by VAERS has been utilized, and three supervised learning algorithms have been implemented in Python which are Decision Tree, Support Vector Machine, and Naïve Bayes to conduct two experiments. A simple splitting percentage method was performed in the first one, while a k-Folds Cross-validation technique was used in the second experiment with k=5. The model showed a promising result with stable performance in both experiments, the Decision Tree outperformed other algorithms with a predictive rate of 0.91999 in the first experiment, and 0.91369 in the second one. PubDate: Tue, 02 May 2023 06:27:21 PDT
Authors:Wira Eka Putra et al. Abstract: Breast cancer is the most prevalent malignancy in the world and the leading cause of female cancer-related mortality. Several ways for combating this entity have been proposed, but a significant and effective cure has yet to be established. Natural compounds originating from plants, including Citrus limon, are employed as alternative cancer treatments. In this study, we aimed to evaluate the immunomodulation activity of Citrus limon’s extract against breast cancer inci-dence in the mouse model. An in vivo study using female BALB/c mice served as the basis for this investigation. Four groups of eight-week-old mice, each weighing 20 grams, were randomized. The carcinogenic substance called 7-12 Dimethylbenz(a)anthracene (DMBA) was used to generate cancer in the experimental animal model. Several antibodies combination were used for the extracellular and intracellular staining experiments, including FITC-conjugated rat an-ti-mouse CD11b, PE/Cy5-conjugated rat anti-mouse IL-6, FITC-conjugated rat-anti mouse CD4, PE-conjugated rat anti-mouse CD8, PE-conjugated rat anti-mouse CD62L, PE-conjugated rat-anti-mouse TNF-α, and PE Cy5-conjugated rat-anti mouse IFN-ɣ. A one-way analysis of variance test was used to examine the relationship between flow cytometry data and the relative cell number. In this present study, we found that the induction of DMBA decreased the relative number of both naïve CD4 and CD8 T cells. On the other hand, the induction of DMBA increased pro-inflammatory cytokines such as TNF-α, IFN-ɣ, and IL-6. Interestingly, Citrus limon’s extract administration can directly change the immune system condition into the normal level in carcinogenic mouse model. Thus, this study suggested that Citrus limon’s extract ameliorative activity against breast cancer incidence warrants further investigation. PubDate: Fri, 28 Apr 2023 05:21:59 PDT
Authors:Gurmeet Kaur et al. Abstract: The study aims to identify soft-computing-based software fault prediction models that assist in resolving issues related to the quality, reliability, and cost of the software projects. It proposes models for implementation of software fault prediction using decision-tree regression and the K-nearest neighbor technique of machine learning. The proposed models have been designed and implemented in Python using designed metric suites as input, and the predicted-faults as output, for the real-time, wider dataset from the Promise repository. By comparing the prediction and validation results of the proposed models for the same dataset, it has been concluded that the decision-tree regression-based fault prediction model has the best performance with values of MMRE, RMSE, and accuracy of 0.0000204, 3.54, and 99.37, respectively. PubDate: Thu, 20 Apr 2023 04:06:53 PDT
Authors:Hussain J. Oudah et al. Abstract: Recently, social trust information has become a significant additional factor in obtaining high-quality recommendations. It has also helped to alleviate the problems of collaborative filtering. In this paper, we exploit explicit and implicit trust relations and incorporate them to take advantage of more ratings (as they exist) of trusted neighbors to mitigate the sparsity issue. We further apply the idea of weighted voting of the ensemble classifier for the election of the most appropriate trust neighbors’ ratings. Additionally, the certainty of these elected rating values was confirmed by calculating their reliability using a modified version of Pearson’s Correlation Coefficient. Finally, we applied the K-Nearest Neighbors method with a linear combination of original and trust-elected ratings using a contribution weight to obtain the best prediction value. Extensive experiments were conducted on two real-world datasets to show that our proposed approach outperformed all comparable algorithms in terms of both coverage and accuracy. Specifically, the improvement ratio ranged approximately from 4% as a minimum to 20% on FilmTrust, and to 10% on Epinions as a maximum in terms of Fmeasure between the inverse of Mean Absolute Error (accuracy) and coverage. PubDate: Thu, 20 Apr 2023 04:06:51 PDT
Authors:Aliru O. Mustapha et al. Abstract: The underutilization of sesame (Sesamum indicum L.), sweet almond (Prunus amygdalus), and jatropha (Jatropha curcas) seeds indicates a significant resource waste. Nine different varieties of 25% (short), 40% (medium), and 60% (long) alkyd resins were produced from the cross-section of drying, semi-drying, and non-drying oils. A large amount of conversion occurred, with the extent of the reaction (Pav) decreasing as time progressed and the corresponding increase in alkyds' average degree of polymerization (Dp) showing synthesized alkyds. Surface drying from the set-to-dry and dry-through periods climaxed at 2 hours for all the alkyds. Except for alkali, synthesized alkyd resins demonstrated outstanding resistance to service media as surface coatings, but the sesame resins gave excellent film qualities equivalent to those of a commercial and standard oil-based paint. PubDate: Mon, 17 Apr 2023 10:27:56 PDT
Authors:Domenico Prisa Abstract: The growing interest in mycorrhizal fungi in agriculture is related to their symbiotic relationships with cultivated plants. Thanks to functional genomics approaches, mycorrhizae and symbioses with host plants have emerged for their fea-tures. Besides improving nutritional supply, plant-fungal interactions increase plants' tolerance to abiotic stresses such as drought, salinity and cold, as well as their resistance to diseases. Recent studies have investigated the interactions between plants and mycorrhizae, however the mechanisms often remain unclear. Indeed, plants in the field are affected by various stresses and results often appear contradictory. This review is aimed at presenting the most relevant studies in this field in order to highlight the possible benefits of mycorrhizal interactions and their application in agriculture. PubDate: Sun, 16 Apr 2023 09:37:11 PDT
Authors:Thanida Charoensuk et al. Abstract: Combining various types of ferrites brings about magnetic properties desirable for different applications. This study aims to modify barium hexaferrite (BaFe12O19) by physically mixing it with cobalt ferrite (CoFe2O4). BaFe12O19/CoFe2O4 magnets were produced by ball-milling and pressing sol-gel-derived ferrite powders. The ferrite composites showed variations in magnetic properties from BaFe12O19 magnets with a saturation magnetization of 69.46 emu/g and a maximum energy product of 0.4529 MGOe. For the BaFe12O19:CoFe2O4 weight ratio of 4:1, both satura-tion and remanent magnetizations were increased due to the addition of CoFe2O4 with high magnetizations. However, the magnetizations were reduced when the BaFe12O19:CoFe2O4 ratio was reduced to 2:1. On the other hand, the coercivity was monotonously decreased with increasing CoFe2O4. Interestingly, the maximum energy product in this study was linearly decreased with the bulk density of the magnets from 3.59 to 3.15 g/cm3. It is concluded that magnetic properties could be modified from a facile physical mixing of ferrites. PubDate: Sun, 16 Apr 2023 09:37:07 PDT
Authors:Chonticha Saisawang et al. Abstract: In this long-term storage study, we optimized the lyophilization conditions of each reaction stage of a nucleic acid-based assay for SARS-CoV-2 detection. The stability testing demonstrated that the dried reactions from all 3 steps (cDNA synthesis, isothermal amplification and detection) can be kept at -20°C or 4°C for up to 6 or 3 months, respectively, whereas, if stored at 25°C or 37°C, the reagents only could be stored for a few days without quality loss. This suggests that we can have the dried reactions at -20°C for long-term storage until needed. Moreover, this assay is now simpler to perform as each of the 3 steps now proceeds with pre-mixed regents lyophilized in a single tube for each step. PubDate: Tue, 11 Apr 2023 05:51:55 PDT
Authors:Raaid Nawfee Hassan et al. Abstract: Random turbulence fluctuations cause continuous fluctuations in the refractive index, which will eliminate the coherence of light waves. Research into atmospheric turbulence is in fact an investigation of the atmospheric refractive index. The constant of atmospheric structure represents a significant parameter that is indicative of the strength of the atmospheric turbulence. In the present study, the performance of some standard models, such as Hufnagel-Valley 5/7, Sub-mission Laser Communication, Critical Laser Enhancing Atmospheric Research, and the Air Force Geophysics Laboratory and Air Force Maui Optical Station, for estimating the, was evaluated with a focus on location assessment. The bias and RMSE were found to be significantly less than 1x10-14 m^-2/3 for Holloman and Hefei cities, and σ is basically less than 0.25x10-14 m^-2/3 for Holloman and Hefei cities. During day and night transitions, Rxy, is well for Holloman, Louisiana, and Hefei cities. Using standard models, we discovered a satisfactory match between the measured (observed) and estimated values for tropical locations. PubDate: Mon, 03 Apr 2023 04:01:58 PDT
Authors:Damilare Emmanuel ROTIMI et al. Abstract: Plantains make a substantial contribution to food security and malnutrition eradication, thus making them a staple food in several tropical and subtropical climates. This research determined the mineral contents, antioxidant capacities and phytochemical constituents of plantain whole fruit (pulp and peel) and pulp extracts. To this end, the plantain samples were evaluated for their phytochemicals, mineral element content, and in vitro antioxidant properties. The phytochemical screening showed that the plantain whole fruit and pulp extracts contained phenols, terpenoids, flavonoids, cardiac glycosides, reducing sugars, alkaloids, and steroids. Tannins, on the other hand, were detected only in the plantain whole fruit extract. The mineral elements in the whole fruit were higher than those in the pulp, especially for iron, sodium, calcium, phosphorus, zinc, and copper. Among the mineral contents, sodium was predominant. In comparison to the plantain whole fruit extract, the pulp showed better scavenging activity for nitric oxide, DPPH, and hydroxyl free radicals. In conclusion, the findings revealed that unripe plantain whole fruit and pulp extracts may be a promising source of critical nutrients and bioactive substances. PubDate: Mon, 03 Apr 2023 04:01:55 PDT
Authors:Domenico Prisa Abstract: Microbial population in the rhizosphere establishes a number of important interactions with plants, whose study is crucial in perspective of sustainable agricultural production. Studies on various plant crops have revealed that, despite the complex microbial biodiversity of the soil, the bacterial microbiome is characterised by multiple functionalities. A better understanding of the molecular mechanisms, underlying the interactions between plants and the microbiome, could enable better development of plants, related to the beneficial action of microorganisms. Therefore, this review aims to describe the characteristics of the rhizosphere microbiome with the interactions that occur between soil and roots, as well as the signals that influence bacterial activities, and the importance of molecular techniques for analysing microbial activities. PubDate: Mon, 03 Apr 2023 04:01:51 PDT
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
Open Access journal
