Phytochemical Screening of Some Medicinal Plants in Al Jouf, KSA

The utilization of ethnobotanical and phytochemical investigations in the discovery of novel medications is beneficial. Screening for phytochemicals is an important step in detecting the bioactive ingredients of medicinal plants which are used in conventional therapy. For the first time, 23 medicinal plants utilized in Saudi Arabian traditional therapy were examined. From August 2020 to July 2021, ethnobotanical fieldwork was conducted. There was some plant species identified, divided into pertinent families. Standard procedures were used to screen these medicinal plants for the occurrence of glycosides, alkaloids, saponins, resins, saponins, tannins, and flavonoids. Among the medicinal plants used, the most common phytochemicals were alkaloids (95.65%), glycosides (86.96%), saponin (82.61%), tannins (73.91%), flavonoids (56.52%), and resin (52.17%). The least widely distributed chemicals, on the other side, were resins. Trigonella foenum-graecum L., Pimpinella anisum L., and Cuminum cyminum L. seeds were shown to contain all six categories of secondary metabolites. The ethnographic importance of these medicinal plants is consistent with the content of secondary metabolites.

起始联合降脂策略对心内科门诊“极高危ASCVD”患者疗效及安全性观察——真实世界的前瞻性队列研究

目的:在真实世界的前瞻性队列研究中观察心内科门诊“极高危ASCVD”患者在治疗起始阶段采用联合降脂策略的疗效与安全性。方法:连续入组2021年1月至6月在湘雅医院心内科高脂血症专病门诊就诊的“极高危ASCVD”患者,根据其实际降脂策略分为三组:阿托伐他汀单药组;阿托伐他汀联合依折麦布组;阿托伐他汀联合依洛尤单抗组。主要观察终点为治疗一个月后低密度脂蛋白胆固醇(LDL-C)、脂蛋白a[Lp(a)]、非-HDL-C变化,次要终点为总胆固醇(TC)、甘油三酯(TG)、LDL-C、超敏C反应蛋白(hs-CRP)变化及安全性指标。结果:无论哪种联合方式,在“极高危ASCVD”患者降脂起始阶段联合降脂治疗的疗效均显著优于阿托伐他汀单药组:LDL-C、Log[Lp(a)]、非-HDL-C、TC的降幅更大,差异均有统计学意义(均P<0.05)。在两种联合降脂方案中,阿托伐他汀联合依洛尤单抗与联合依折麦布相比,LDL-C、Log[Lp(a)]的降幅更大,差异均有统计学意义(P<0.05),TC、TG有一定的降幅,差异无统计学意义(P>0.05)。以“LDL-C<1.4 mmol/L或<1.8 mmol/L”作为降脂达标的标准时,两组联合降脂治疗的LDL-C达标率均高于阿托伐他汀单药组,差异均有统计学意义(均P<0.05);阿托伐他汀联合依洛尤单抗组的LDL-C达标率高于阿托伐他汀联合依折麦布组,差异均有统计学意义(均P<0.05)。单独或联合“LDL下降超过50%”的降幅为达标标准,三组均无人能达标。治疗后三组间的肝脏转氨酶水平变化差异均无统计学意义(均P<0.05);三组间心肌酶同工酶(CK-MB)均下降,但差异无统计学意义(P<0.05)。与阿托伐他汀单药组相比,两联合降脂组治疗组的血糖均下降,差异具有统计学意义(P<0.05);阿托伐他汀联合依折麦布组的血糖比阿托伐他汀联合依洛尤单抗组的降幅更大,差异有统计学意义(P<0.05)。结论:起始联合降脂治疗1个月对心内科门诊“极高危ASCVD”患者的降脂效果及LDL-C的达标率优于阿托伐他汀单药组。以LDL-C<1.4 mmol/L或<1.8 mmol/L为降脂达标目标时,阿托伐他汀联合依洛尤单抗组治疗1个月后LDL-C达标率高于联合依折麦布组;以“LDL下降超过50%”的降幅作为降脂达标标准时,1个月内很难达标。门诊“极高危ASCVD”患者采用起始联合的降脂治疗1个月无不良反应。起始联合降脂策略可用于心内科门诊“极高危ASCVD”患者中LDL-C数值需早期达标的人群,阿托伐他汀联合依洛尤单抗可用于需在1个月内LDL-C<1.4 mmol/L或<1.8 mmol/L的人群。

An Attention-Based Approach to Enhance the Detection and Classification of Android Malware

The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware.These malicious applications have become a serious concern to the security of Android systems.To address this problem,researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples.However,most existing studies have focused on the classification task and overlooked the feature selection process,which is crucial to reduce the training time and maintain or improve the classification results.The current paper proposes a new Android malware detection and classification approach that identifies the most important features to improve classification performance and reduce training time.The proposed approach consists of two main steps.First,a feature selection method based on the Attention mechanism is used to select the most important features.Then,an optimized Light Gradient Boosting Machine(LightGBM)classifier is applied to classify the Android samples and identify the malware.The feature selection method proposed in this paper is to integrate an Attention layer into a multilayer perceptron neural network.The role of the Attention layer is to compute the weighted values of each feature based on its importance for the classification process.Experimental evaluation of the approach has shown that combining the Attention-based technique with an optimized classification algorithm for Android malware detection has improved the accuracy from 98.64%to 98.71%while reducing the training time from 80 to 28 s.

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software devel-opment.However,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity measures.This paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of SPL.In the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and business.Specifically,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient ecosystems.Our experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in SPLs.As digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults.To protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT networks.This study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data complexity.Following rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning models.Augmentation using the BAT algorithm enhances dataset variability.Three deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data aspects.Their predictions form a meta-feature set for a subsequent meta-learner,which combines model strengths.Conventional classifiers validate meta-learner features for broad algorithm suitability.This comprehensive method demonstrates high accuracy and robustness in IoMT intrusion detection.Evaluations were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen categories.Experimental results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf diseases.The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images.The model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning model.In the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

Pyronaridine combined with diminazene aceturate inhibits Babesia in vitro and in vivo

Objective:To evaluate the combination therapy of pyronaridine tetraphosphate and diminazene aceturate against Babesia in vitro and in vivo.Methods:Bioinformatic analysis was performed using atom pair fingerprints.An in vitro combination test was performed against Babesia bovis and Theileria equi.Moreover,the in vivo chemotherapeutic efficacy of pyronaridine tetraphosphate in combination with diminazene aceturate was investigated against the growth of Babesia microti in mice using a fluorescence inhibitory assay.Results:Pyronaridine tetraphosphate and diminazene aceturate exhibited nearly similar molecular weights.The in vitro combination of pyronaridine tetraphosphate and diminazene aceturate was synergistic on Babesia bovis and additive on Theileria equi.In addition,5 mg/kg pyronaridine tetraphosphate combined with 10 mg/kg diminazene aceturate inhibited Babesia microti growth significantly compared with those observed after treatment with 25 mg/kg diminazene aceturate alone from day 6 post treatment to day 12 post treatment.The combination therapy also normalized the hematological parameters of infected mice.Conclusions:An oral dose of pyronaridine tetraphosphate combined with a subcutaneous dose of diminazene aceturate inhibits Babesia in vitro and in mice,suggesting it might be a new paradigm for the treatment of babesiosis.

SwinVid:Enhancing Video Object Detection Using Swin Transformer

What causes object detection in video to be less accurate than it is in still images?Because some video frames have degraded in appearance from fast movement,out-of-focus camera shots,and changes in posture.These reasons have made video object detection(VID)a growing area of research in recent years.Video object detection can be used for various healthcare applications,such as detecting and tracking tumors in medical imaging,monitoring the movement of patients in hospitals and long-term care facilities,and analyzing videos of surgeries to improve technique and training.Additionally,it can be used in telemedicine to help diagnose and monitor patients remotely.Existing VID techniques are based on recurrent neural networks or optical flow for feature aggregation to produce reliable features which can be used for detection.Some of those methods aggregate features on the full-sequence level or from nearby frames.To create feature maps,existing VID techniques frequently use Convolutional Neural Networks(CNNs)as the backbone network.On the other hand,Vision Transformers have outperformed CNNs in various vision tasks,including object detection in still images and image classification.We propose in this research to use Swin-Transformer,a state-of-the-art Vision Transformer,as an alternative to CNN-based backbone networks for object detection in videos.The proposed architecture enhances the accuracy of existing VID methods.The ImageNet VID and EPIC KITCHENS datasets are used to evaluate the suggested methodology.We have demonstrated that our proposed method is efficient by achieving 84.3%mean average precision(mAP)on ImageNet VID using less memory in comparison to other leading VID techniques.The source code is available on the website https://github.com/amaharek/SwinVid.

Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data

This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)distribution.The SGLE distribution utilizes the sine transformation to enhance its capabilities.The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues.The suggested model incorporates a hazard rate function(HRF)that may display a rising,J-shaped,or bathtub form,depending on its unique characteristics.This model includes many well-known lifespan distributions as separate sub-models.The suggested model is accompanied with a range of statistical features.The model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored data.In order to evaluate the effectiveness of these techniques,we provide a set of simulated data for testing purposes.The relevance of the newly presented model is shown via two real-world dataset applications,highlighting its superiority over other respected similar models.

The impact of foot reflexology on fatigue and sleep quality in school-aged children undergoing hemodialysis

Objective:This study aimed to evaluate the effects of foot reflexology on fatigue and sleep quality in school-aged children receiving hemodialysis.Methods:A quasi-experimental pretest-posttest design was utilized.Thirty children of ages 6-12 undergoing in-center hemodialysis were recruited.The Pittsburgh Sleep Quality Index(PSQI)and Inventory of Fatigue Symptom(IFS)scales were administered at baseline.Participants then received 30 min of foot reflexology massage before hemodialysis sessions 3 days per week for 12 weeks.Posttest administration of the sleep and fatigue scales occurred after the intervention period.Results:Reflexology massage led to significant improvements in sleep quality components,including duration(0%-30%normal sleepers),efficiency(0%-50%>85%),latency(50%-0%>60 min),disturbances,and daytime dysfunction.The mean PSQI score decreased from 18.2 to 9.7(P<0.05).Fatigue severity substantially decreased,with the mean IFS score improving from 105.7 to 64.1(P<0.05).Conclusion:Foot reflexology is an effective nursing intervention for reducing fatigue and improving sleep quality in children on hemodialysis,warranting integration into routine care.

Evaluating the influence of a structured nursing protocol on targeted outcomes in rheumatoid arthritis patients

Objective:Rheumatoid arthritis(RA)requires comprehensive management.Structured nursing protocols may enhance outcomes,but evidence is limited.This study evaluated the effect of a structured nursing protocol on RA outcomes.Materials and Methods:In this one-group pre-post study,30 Egyptian RA patients completed assessments before and after a 12-week nursing protocol comprising education,psychosocial support,and self-management promotion.Assessments included clinical evaluation of joint counts,erythrocyte sedimentation rate(ESR),and C-reactive protein(CRP)and patient-reported Arthritis Self-Efficacy Scale(ASES),Health Assessment Questionnaire(HAQ),Visual Analog Scale(VAS)for pain,and Hospital Anxiety and Depression Scale(HADS).Results:The study demonstrated significant improvements in both clinical-and patient-reported outcomes.Joint count decreased from 18.4±4.2 to 14.2±3.8(P<0.001),ESR from 30.1±6.8 mm/h to 25.5±6.8 mm/h(P<0.01),and CRP levels from 15.2±3.6 mg/L to 11.8±2.9 mg/L(P<0.01)postintervention.Patient-reported outcomes showed a marked increase in ASES score from 140±25 to 170±30(P<0.001)and reductions in HAQ from 1.6±0.4 to 1.3±0.3(P<0.01),VAS pain score from 7.8±1.7 to 6.2±1.2(P<0.001),and HADS anxiety and depression scores from 11±3 to 8±2(P<0.05)and 10±2 to 7±1(P<0.05),respectively.Conclusion:A structured nursing protocol significantly improved clinical disease activity,physical functioning,pain,self-efficacy,and emotional well-being in RA patients.A multifaceted nursing intervention appears beneficial for optimizing RA outcomes.

Integrated Use of Organic and Bio-fertilizers to Improve Yield and Fruit Quality of Olives Grown in Low Fertility Sandy Soil in an Arid Environment

Olive productivity should be improved through stimulating nutrition,particularly under poor fertility soils.Consequently,the objective of this study was to assess the efficacy of applying organic and bio-fertilizers on the physiological growth,yield and fruit quality of olive trees under newly reclaimed poor-fertility sandy soil in an arid environment.During a field experiment carried out at El-Qantara,North Sinai,Egypt over two consecutive seasons(2019-2020 and 2020-2021),olive Kalamata trees were evaluated under three organic fertilizer treatments alone or in combination with three bio-fertilizers treatments.Organic fertilizer was applied as goat manure(16.8 kg/tree/year),or olive pomace(8.5 kg/tree/year)in mid-December of each season vs.untreated trees.The bio-fertilizers were applied as N-fixing bacteria(150 g/tree)was inculated in early March of each season,or amino acid mixture(1.5%)was applied three times,at 70%of full bloom,21 days after full bloom,and a month later in comparison to a non-fertilized trees(control).The cultivar used was Kalamata,a dual-purpose cultivar for oil and table olives whose value increases when processed as table olives.The results indicated that the goat manure followed by olive pomace significantly enhanced photosynthetic pigments(chlorophyll a,b,and carotenoids),leaf mineral contents(N,P,K,Ca,Mg and Fe),tree canopy volume,number of flowers per inflorescence,number of inflorescences per shoot,initial fruit set,fruit retention.For fruit quality,fruit length and width,fruit weight,and total fruit yield was increased compared to the non-fertilized control.Likewise,The bio-fertilizer N-fixing bacteria followed by the amino acid mixture significantly improved all of the aforementioned parameters.Accordingly,it is recommended,both environmentally and economically to utilize organic and bio-fertizers,particularly goat manure combined with N-fixing bacteria,in low-fertility soil to sustain olive production as well as reducing mineral fertilization.

An Efficient Indoor Localization Based on Deep Attention Learning Model

Indoor localization methods can help many sectors,such as healthcare centers,smart homes,museums,warehouses,and retail malls,improve their service areas.As a result,it is crucial to look for low-cost methods that can provide exact localization in indoor locations.In this context,imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object.Image-based localization faces many issues,such as image scale and rotation variance.Also,image-based localization’s accuracy and speed(latency)are two critical factors.This paper proposes an efficient 6-DoF deep-learning model for image-based localization.This model incorporates the channel attention module and the Scale PyramidModule(SPM).It not only enhances accuracy but also ensures the model’s real-time performance.In complex scenes,a channel attention module is employed to distinguish between the textures of the foregrounds and backgrounds.Our model adapted an SPM,a feature pyramid module for dealing with image scale and rotation variance issues.Furthermore,the proposed model employs two regressions(two fully connected layers),one for position and the other for orientation,which increases outcome accuracy.Experiments on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error(MSE)for both position and orientation.On the indoor 7-Scenes dataset,the MSE for the position is reduced to 0.19 m and 6.25°for the orientation.Furthermore,on the outdoor Cambridge landmarks dataset,the MSE for the position is reduced to 0.63 m and 2.03°for the orientation.According to the findings,the proposed approach is superior and more successful than the baseline methods.

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

Improved Video Steganography with Dual Cover Medium,DNA and Complex Frames

The most valuable resource on the planet is no longer oil,but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic approaches.The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility.In the previous approach,DNA was used only for frame selection.If this DNA is compromised,then our frames with the hidden and unencrypted data will be exposed.Moreover the frame selected in this way were random frames,and no consideration was made to the contents of frames.Hiding data in this way introduces visible artifacts in video.In the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames.Using chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random locations.Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance.Further,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved results.The proposed methodology has been implemented in Matlab.

Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance applications.This trend promotes the development of precise and efficient control schemes for individual motors.This research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)inverters.The framework under consideration is developed in four stages.To begin,MATLAB Simulink was used to develop a detailed mathematical and electromechanical dynamicmodel.The research presents a modified SVPWM inverter control scheme.By tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are derived.All the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control scheme.The reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data sets.For the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed range.The speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 s.The maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust ripple.Due to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

A Novel Machine Learning-Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction method.This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns.The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition.Potential applications of hand gesture recognition research span from online gaming to surgical robotics.The location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.In this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling.When a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation.We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes.The findings from the simulation support the accuracy,precision,gesture recognition,sensitivity,and efficiency rates.The Machine Learning-based Gesture Recognition Framework(ML-GRF)had an accuracy rate of 98.97%,a precision rate of 97.65%,a gesture recognition rate of 98.04%,a sensitivity rate of 96.99%,and an efficiency rate of 95.12%.