Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

Advances and challenges of methanol-tolerant oxygen reduction reaction electrocatalysts for the direct methanol fuel cell

Methanol cross-over effects from the anode to the cathode are important parameters for reducing catalytic performance in direct methanol fuel cells.A promising candidate catalyst for the cathode in direct methanol fuel cells must have excellent activity toward oxygen reduction reaction and resistance to methanol oxidation reaction.This review focuses on the methanol tolerant noble metal-based electrocatalysts,including platinum and palladium-based alloys,noble metal–carbon based composites,transition metal-based catalysts,carbon-based metal catalysts,and metal-free catalysts.The understanding of the correlation between the activity and the synthesis method,electrolyte environment and stability issues are highlighted.For the transition metal-based catalyst,their activity,stability and methanol tolerance in direct methanol fuel cells and comparisons with those of platinum are particularly discussed.Finally,strategies to enhance the methanol tolerance and hinder the generation of mixed potential in direct methanol fuel cells are also presented.This review provides a perspective for future developments for the scientist in selecting suitable methanol tolerate catalyst for oxygen reduction reaction and designing high-performance practical direct methanol fuel cells.

Evidence-based consensus on the diagnosis, prevention and management of hepatitis C virus disease

Hepatitis C virus(HCV) is a potent human pathogen and is one of the main causes of chronic hepatitis round the world. The present review describes the evidencebased consensus on the diagnosis, prevention and management of HCV disease. Various techniques, for the detection of anti-HCV immunoglobulin G immunoassays, detection of HCV RNA by identifying virus-specific molecules nucleic acid testings, recognition of core antigen for diagnosis of HCV, quantitative antigenassay, have been used to detect HCV RNA and core antigen. Advanced technologies such as nanoparticlebased diagnostic assays, loop-mediated isothermal amplification and aptamers and Ortho trak-C assay have also come to the front that provides best detection results with greater ease and specificity for detection of HCV. It is of immense importance to prevent this infection especially among the sexual partners, injecting drug users, mother-to-infant transmission of HCV, household contact, healthcare workers and people who get tattoos and piercing on their skin. Management of this infection is intended to eradicate it out of the body of patients. Management includes examining the treatment(efficacy and protection), assessment of hepatic condition before commencing therapy, controlling the parameters upon which dual and triple therapies work, monitoring the body after treatment and adjusting the co-factors. Examining the treatment in some special groups of people(HIV/HCV co-infected, hemodialysis patients, renal transplanted patients).

Prediction of Time Series Empowered with a Novel SREKRLS Algorithm

For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems.With the help of an ortho-normal triangularization method,which relies on numerically stable givens rotation,matrix inversion causes a computational burden,is reduced.Matrix computation possesses many excellent numerical properties such as singularity,symmetry,skew symmetry,and triangularity is achieved by using this algorithm.The proposed method is validated for the prediction of stationary and non-stationary Mackey–Glass Time Series,along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness.By the learning curves regarding mean square error(MSE)are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS.This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays,which is efficient in developing very-large-scale integration(VLSI)applications with non-linear input data.Multiple experiments are carried out to validate the reliability,effectiveness,and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares(EKRLS)algorithm.

Zika: As an emergent epidemic

Zika virus is a new global threat for 2016 that has been swept to almost all Americas and is now posing serious threats to the entire globe.This deadly virus is playing havoc to unborn lives because of its reported association with upsurge of fetal deformation called microcephaly and neuropathic disorders including Guillain-Barrésyndrome.Till today,there is no vaccine prospect,antiviral therapy or licensed medical countermeasures to curb the teratogenic outcomes of this destructive viral infection.Diagnosis,treatment,chronicity and pathogenesis are still vague and unsettled.Therefore,this review article addresses all the aspects related to this disease to mitigate the explosive rise in Zika virus infection.

Hepatitis C virus genotype 3a infection and hepatocellular carcinoma: Pakistan experience

AIM: To assess the association between chronic hepatitis C virus (HCV) infection and hepatocellular carcinoma (HCC) in Pakistan, and the genotype distribution among these HCC patients.METHODS: One hundred and sixty-one subjects with HCC were included in this study. Liver biopsy was performed on 145 of the patients; sixteen were excluded because they failed to fulfill the inclusion criteria. Qualitative polymerase chain reaction (PCR) was performed for hepatitis B virus and HCV. Samples positive for HCV RNA were genotyped using genotype-specif ic PCR and conf irmed by HCV 5' noncoding region sequencing analysis. RESULTS: Chronic HCV infection was identified a major risk factor (63.44% of tested HCC patients) forthe development of HCC. The time from HCV infection to appearance of cancer was 10-50 years. In the HCC patient population, broader distributions of genotypes were present with genotype 3a as the predominant genotype. Using the type-specific genotyping method, we found HCV genotype 3a in 40.96%, 3b in 15.66%, 1a in 9.63%, and 1b in 2.40% of HCC tissue samples. About 28% of cases were found with mixed genotypes. Two cases were unable to be genotyped because of low viral load. Sixty-six percent of treated patients with cirrhosis had an end of treatment response, but unfortunately they relapsed quickly when the treatment was discontin-ued, and HCC developed during a median 3.8 years. CONCLUSION: There was a strong association between chronic HCV infection and HCC in Pakistan, and between HCV genotype 3a and HCC.

Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively.Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment.The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand.Elasticity in cloud computing is one of the fundamental properties,and elastic load balancing automatically distributes incoming load to multiple virtual machines.This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing.In this article,a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources.A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model.The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.

Regulation and Improvement of Cellulase Production: Recent Advances

Cellulases and hemicellulases are the main industrial sources from different microorganisms used to depolymerise plant biomass to simple sugars that are converted to chemical intermediates and biofuels, such as ethanol. Cellulases are formed adaptively, and several positive (xyr1, Ace2, HAP2/3/5) and negative (Ace1, Cre1) components involved in this regulation are now known. In this review, we summarise current knowledge about how cellulase biosynthesis is regulated, and outline recent approaches and suitable strategies for facilitating the targeted improvement of cellulase production by genetic engineering. Trichoderma reesei is the preferred organism for producing industrial cellulases. However, a more efficient heterologous expression system for enzymes from different organism is needed to further improve its cellulase mixture. In addition those optimizations of the promoter and linker for hybrid genes can dramatically improve the efficiency of heterologous expression of cellulase genes.

An updated systematic review of Zika virus-linked complications

Objective: To determine the breadth of Zika virus(ZIKV)-associated brain anomalies in neonates and adults. Methods: Systematic review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) statement using electronic databases ScienceDirect, Pubmed, Medline, Scopus, and Global Health Library.Only those research articles, case studies, case-control studies, case-cohort studies, crosssectional studies, and organizational survey reports were included in the study that reported any fetal outcomes for pregnant women who had infected with ZIKV during the gestational period and ZIKV-related neurological complications in adults as well. Results: Out of total 72 retrieved articles, 50 met the inclusion criteria. We estimated a significant increase in incidence of neural abnormalities such as Guillain-Barre syndrome and microcephaly in the regions that are experiencing ZIKV outbreaks. Other neurological malformations found in ZIKV patients include hydrancephaly/hydrops fetalis, myasthenia gravis,meningoencephalitis and myelitis. Conclusion: Our systematic analysis provides the broad spectrum of neurological malformations in ZIKV infected patients and these data further support the causal link of ZIKV with neurological disorders.

Advances in research on Zika virus

Zika virus(ZIKV) is rapidly spreading across the America and its devastating outcomes for pregnant women and infants have driven this previously ignored pathogen into the limelight.Clinical manifestations are fever,joint pain or rash and conjunctivitis.Emergence of ZIKV started with a first outbrcak in the Pacific arca in 2007.a second large outbreak occurred in the Pacific in 2013/2014 and subsequently the virus spread in other Pacific islands.Threat of explosive global pandemic and severe clinical complications linked with the more immediate and recurrent epidemics necessitate the development of an effective vaccine.Several vaccine platforms such as DNA vaccine,recombinant subunit vaccine.ZIKV purified inactivated vaccine,and chimeric vaccines have shown potent efficacy in ritro and in rim trials.Moreover,number of drugs such as Sofosbuvir.BCX4450.NITD008 and 7-DMA are ready to enter phase I clinical trial because of proven anti-ZIKV activity.Monoclonal based antibodies offer promise as an intervention effective for use in pregnant women.In this review,we describe the advances in research on ZIKV such as research strategies for the development of antiviral drugs & vaccines,molecular evolution,epidemiology emergence,neurological complications and other teratogenic outcomes as well as pathogenesis.

Zika virus: Is Pakistan next?

Dear editor,Zika virus(ZIKV)crisis,which caught Brazil in 2015,heralded the rapid spread throughout Americas and has imparted catastrophic devastation to human population in the form of severe

Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

Disease diagnosis is a challenging task due to a large number of associated factors.Uncertainty in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables.This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things(IoT)empowered by the fuzzy inference system(FIS)to diagnose various diseases.The Fuzzy Systemis one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties,and fuzzy logic is the best way to handle uncertainties.Our proposed system differentiates new cases provided symptoms of the disease.Generally,it becomes a time-sensitive task to discriminate symptomatic diseases.The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently.Different coefficients have been employed to predict and compute the identified disease’s severity for each sign of disease.This study aims to differentiate and diagnose COVID-19,Typhoid,Malaria,and Pneumonia.This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms.MATLAB tool is utilised for the implementation of FIS.Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms.The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases.This study may assist doctors,patients,medical practitioners,and other healthcare professionals in early diagnosis and better treat diseases.

Exact Solution for a Class of Stiff Systems by Differential Transform Method

In this paper, Differential Transform Method (DTM) is proposed for the closed form solution of linear and non-linear stiff systems. First, we apply DTM to find the series solution which can be easily converted into exact solution. The method is described and illustrated with different examples and figures are plotted accordingly. The obtained result confirm that DTM is very easy, effective and convenient.

Stable stripe and vortex solitons in two-dimensional spin-orbit coupled Bose-Einstein condensates

We present a flexible manipulation and control of solitons via Bose-Einstein condensates.In the presence of Rashba spin-orbit coupling and repulsive interactions within a harmonic potential,our investigation reveals the numerical local solutions within the system.By manipulating the strength of repulsive interactions and adjusting spin-orbit coupling while maintaining a zero-frequency rotation,diverse soliton structures emerge within the system.These include plane-wave solitons,two distinct types of stripe solitons,and odd petal solitons with both single and double layers.The stability of these solitons is intricately dependent on the varying strength of spin-orbit coupling.Specifically,stripe solitons can maintain a stable existence within regions characterized by enhanced spin-orbit coupling while petal solitons are unable to sustain a stable existence under similar conditions.When rotational frequency is introduced to the system,solitons undergo a transition from stripe solitons to a vortex array characterized by a sustained rotation.The rotational directions of clockwise and counterclockwise are non-equivalent owing to spin-orbit coupling.As a result,the properties of vortex solitons exhibit significant variation and are capable of maintaining a stable existence in the presence of repulsive interactions.

Hybrid DFT study of structural,electronic,magnetic and elastic properties of laves phase binary intermetallics RFe_(2)(R=La,Ce,Pr and Nd)

Hybrid density functional theory was used to investigate the structural,electronic,magnetic and elastic properties of the Laves phase binary intermetallics RFe_(2)(R=La,Ce,Pr and Nd) in C_(15) crystal structure.The calculated lattice constants of these materials are found in good agreement with the experiments.The band structures and density of states distribution confirm the metallic nature of all these intermetallics.The optimized energies in different magnetic phases and magnetic susceptibilities by postDFT treatments confirm that all the understudy compounds are ferromagnetic in nature.Elastic parameters were calculated from the cubic elastic coefficients C_(11),C_(12) and C_(44).The elastic properties reveal that these intermetallics are incompressible,ductile,elastically anisotropic and mechanically stable.Based on the metallic nature and ferromagnetic properties,it is expected that these intermetallics are suitable materials for spintronic technology.

Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive data.Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats.This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack detection.Our approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in performance.It demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant loads.Furthermore,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time applications.Designed for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current defenses.This innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

Photopolymerization-based additive manufacturing of ceramics:A systematic review

Conversion of inorganic-organic frameworks (ceramic precursors and ceramic-polymer mixtures) into solid mass ceramic structures based on photopolymerization process is currently receiving plentiful attention in the field of additive manufacturing (3D printing).Various techniques(e.g.,stereolithography,digital light processing,and two-photon polymerization) that are compatible with this strategy have so far been widely investigated.This is due to their cost-viability,flexibility,and ability to design and manufacture complex geometric structures.Different platforms related to these techniques have been developed too,in order to meet up with modem technology demand.Most relevant to this review are the challenges faced by the researchers in using these 3D printing techniques for the fabrication of ceramic structures.These challenges often range from shape shrinkage,mass loss,poor densification,cracking,weak mechanical performance to undesirable surface roughness of the final ceramic structures.This is due to the brittle nature of ceramic materials.Based on the summary and discussion on the current progress of material-technique correlation available,here we show the significance of material composition and printing processes in addressing these challenges.The use of appropriate solid loading,solvent,and preceramic polymers in forming slurries is suggested as steps in the right direction.Techniques are indicated as another factor playing vital roles and their selection and development are suggested as plausible ways to remove these barriers.

Spatial-dependent probe transmission based high-precision two-dimensional atomic localization

Herein,we propose a scheme for the realization of two-dimensional atomic localization in aλ-type three-level atomic medium such that the atom interacts with the two orthogonal standing-wave fields and a probe field.Because of the spatially dependent atom-field interaction,the information about the position of the atom can be obtained by monitoring the probe transmission spectra of the weak probe field for the first time.A single and double sharp localized peaks are observed in the one-wavelength domain.We have theoretically archived high-resolution and high-precision atomic localization within a region smaller thanλ/25×λ/25.The results may have potential applications in the field of nano-lithography and advance laser cooling technology.