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Malware detection, targeting the microarchitecture of processors, has recently come to light as a potentially effective way to improve computer system security. Hardware Performance Counter data are used by machine learning algorithms in security mechanisms, such as hardware-based malware detection, to categorize and detect malware. It is crucial to determine whether or not a file contains malware. Many issues have been brought about by the rise in malware, and businesses are losing vital data and dealing with other issues. The second thing to keep in mind is that malware can quickly cause a lot of damage to a system by slowing it down and encrypting a large amount of data on a personal computer. This study provides extensive details on a flexible framework related to machine learning and deep learning techniques using few-shot learning. Malware detection is possible using DT, RF, LR, SVM, and FSL techniques. The logic is that these algorithms make it simple to differentiate between files that are malware-free and those that are not. This indicates that their goal is to reduce the number of false positives in the data. For this, we use two different datasets from an online platform. In this research work, we mainly focus on few-shot learning techniques by using two different datasets. The proposed model has an 97% accuracy rate, which is much greater than that of other techniques.

In the present study, we develop a fractional Zika virus model with mutation, which causes new birth problems in infected pregnant women and further spread in society. As a consequence, we apply this notion to the fractional dynamics of the Zika virus. We also use fractional derivatives to gain a better understanding of biological processes and their crossover behavior. The model simulation is based on realColombia data. Mathematical modeling of an infectious disease helps out us in predicting the future spread. Recently, many mathematical models describe the dynamics of infectious disease based on classical integer-order and non-integer order models. Therefore, we study the dynamics of the Zika virus based on non-integer order derivatives for better understanding seen and deeper insight into several biological models. We also describe the different properties of the model like the positivity of the model solution, equilibrium points, and invariant property of the proposed model. The reproduction number is computed with the next-generation technique. The parameters are estimated from confirmed infected cases in Colombia for the year 2016 seems that the model is appropriate for this data set. Moreover, the sensitivity of R0 on the parameters calculated by the direct differentiation approach. The local and global stability analysis at disease-free and endemic states are investigated. The numerical approach generalized predictor–corrector is implemented for the validation of theoretical results and analyzing the effects of sensitive factors that might be helpful in reducing the infection. From graphical results, it is observed that the fractional model gives more insight and a better understanding of disease dynamics. These findings assist us in controlling the virus through contact precautions and recommended therapy, as well as projecting its future spread.

Distributed Agile Software Development (DASD) is the most important approach for the modern software industry that allows geographically independent software development. In the past, different tools and frameworks were proposed to solve communication and collaboration issues in DASD but they lacked transparency, trust, traceability, and security. These shortcomings resulted in project failure or overdue, customer dissatisfaction, project deal cancellations, and payment clashes between the customers and development teams. This paper addresses and overcomes the major issues of transparency, trust, security, traceability, coordination, and communication in DASD by embedding blockchain technology. We have proposed a novel blockchain-based framework named as AgilePlus which executes the smart contracts on a private ethereum blockchain for acceptance testing, secure payment, verification of developer’s payment requirements, and automatic payment distribution into the digital wallets of development teams. The execution of these smart contracts automatically assign penalties to the customers for late or non-payments and also to the developers for overdue tasks. Moreover, we have also solved the blockchain’s scalability challenge in AgilePlus by utilizing Interplanetary File System (IPFS) as off-chain storage. Lastly, experimental results prove that the proposed framework enhances transparency, communication, coordination, traceability, security and solves trust issues of both customers and developers in DASD.

The Internet of Things (IoT) is an emerging paradigm that is transforming real-world things (objects) into smarter devices. IoT is applicable to a variety of application domains including healthcare, smart grid, and agriculture. This domain has started revolutionizing the agriculture industry by providing smart solutions for precision farming, greenhouse management, and livestock monitoring. This article aims to present a comprehensive survey on the role of IoT in the Livestock field by categorizing and synthesizing existing research work in this area. To this end, a detailed discussion has been provided on IoT network infrastructure, topologies and platforms employed for livestock management. In addition, a list of communication protocols and connections of IoT-based livestock systems with relevant technologies have also been explored. Furthermore, numerous IoT-based livestock monitoring, controlling, and tracking applications have been discussed. Apart from this, it also analyses distinct security issues in IoT-based livestock field and developed a collaborative security model to detect and minimize the security risk. Lastly, pertinent open research challenges in the domain of IoT-based livestock management have been presented with future research directions.

Machine learning and deep learning algorithms are widely used in computer science domains. These algorithms are mostly used for classification and regression problems in almost every field of life. Semantic segmentation is an instantly growing research topic in the last few decades that refers to the association of each pixel in the image to the class it belongs. This paper illustrates the systematic survey of advanced research in the field of semantic segmentation till date. This study provides the brief knowledge about the latest proposed methods in the domain of semantic segmentation. The proposed study comprehends the concepts, techniques, tool, and results of different research frameworks proposed in the context of semantic segmentation. This study discusses the latest research papers in which machine learning and deep learning techniques are exploited for semantic segmentation and published between 2016 and 2021. The systematic literature review collected from seven different article libraries including ACM digital Library, Google Scholar, IEEE Xplore, Science Direct, Google Books, Refseek and Worldwide Science. For assuring the quality of the paper those papers are selected which have several citations on standardized platforms. Most of the studies used COCO, PASCAL, Cityscapes and CamVid dataset for training and validation of the machine learning and deep learning models. The results of the selected research articles are collected in the form of accuracy, mIoU value, F1 score, precision, and recall. In this study, we also conclude that most of the semantic segmentation studies use ResNet as the backbone of the architecture and none of the researchers used ensemble learning methods for semantic segmentation that is the loophole of the selected studies.

The term “cyber threats” refers to the new category of hazards that have emerged with the rapid development and widespread use of computing technologies, as well as our growing reliance on them. This article presents an in-depth study of a variety of security and privacy threats directed at different types of users of social media sites. Furthermore, it focuses on different risks while sharing multimedia content across social networking platforms, and discusses relevant prevention measures and techniques. It also shares methods, tools, and mechanisms for safer usage of online social media platforms, which have been categorized based on their providers including commercial, open source, and academic solutions.

In medical field, accurate decisions are very important as they risk human lives. decision support system (DSS) plays important role in making accurate decisions and used for classification/prediction. In gene expression analysis, genes are not only inflated by the external environmental conditions but also the expression values of certain genes are affected (like cancer, obesity etc). in this study, various traditional (Support Vector Machine, Decision Trees, and Linear Discriminant Analysis, naïve Bayes, logistic regression, and multilayer perceptron) and proposed methods (combination of traditional with ensemble and probabilistic classifiers) are used in order to perform the classification and prediction analysis. In this study we used the publicly available datasets comprised of Lymphoid, Leukemia and Colon Cancer. The classification performance on Colon dataset with traditional methods was obtained with accuracy (56%) and proposed probabilistic ensemble methods with accuracy (88%). For dataset, Leukemia, the accuracy was obtained using traditional methods (78%) and proposed methods (92%). Similarly, on Lymphoid dataset, the traditional methods yielded accuracy (75%) and proposed methods (87%). The results revealed that proposed methods yielded the improved detection performance. The proposed methods can be used as a better predictor for early diagnosis and improved diagnosis to improve the healthcare systems.

An unsteady Casson nanofluid with a stagnation point is considered along a radiated stretching surface depending on the heat source and convective condition. Moreover, the influence of magnetic field and the wall suction is considered with the slip condition. In view of these assumptions, a system of nonlinear partial differential equations (PDEs), have been formulated which are primarily converted to the dimensionless ordinary differential equations (ODEs). The MATLAB function bvp4c has been used to solve the resulting system of the ODEs. The resulting solutions have been determined as expressed by the velocity, friction drag, concentration, temperature, mass transfer and heat transfer of the nanofluids which are obtained under the influence of associated flow parameters. It is observed that the Sherwood number and the fluid concentration respectively enhances and decreases for the escalating values of the Brownian motion parameter and the Schmidt number although a reverse of these cases have been observed for the escalation of Thermophoresis parameter. In the same way, the skin friction coefficient enhances for the escalation of Casson parameter, suction parameter, and the Hartmann number whereas the rate of heat transfer declines for the growing values of Eckert number and enhances for the higher values of suction and thermal radiation. Additionally, the fluid temperature is boosting for the growing values of Biot number, Eckert number and heat generation parameter, whereas the velocity is diminishing for the growing values of Casson parameter and unsteadiness parameter though it is boosting for the growing values of suction parameter.

This review article provides a deep insight into the Brain–Computer Interface (BCI) and the application of Machine Learning (ML) technology in BCIs. It investigates the various types of research undertaken in this realm and discusses the role played by ML in performing different BCI tasks. It also reviews the ML methods used for mental state detection, mental task categorization, emotion classification, electroencephalogram (EEG) signal classification, event-related potential (ERP) signal classification, motor imagery categorization, and limb movement classification. This work explores the various methods employed in BCI mechanisms for feature extraction, selection, and classification and provides a comparative study of reviewed methods. This paper assists the readers to gain information regarding the developments made in BCI and ML domains and future improvements needed for improving and designing better BCI applications.

We ought to correct the misprinted terms in our published paper ‘Unsteady Convective MHD Flow and Heat Transfer of a Viscous Nanofluid across a Porous Stretching/Shrinking Surface: Existence of Multiple Solutions’ [Crystals2021, 11, 1359] [...]

Modeling surface water quality using artificial intelligence-based models is essential in projecting suitable mitigation measures. However, it remains a challenge and requires further research to enhance the modeling accuracy. For this aim, this article presents a methodology to optimize the modeling inputs and reduce the associated complexity. The proposed approach employs Random Forest for modeling surface water salinity in terms of electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River basin, one of the major rivers in Asia. Various water quality parameters measured monthly over a historical 30-year period were utilized in the modeling process. Various statistical indicators were used to evaluate the model performance. Random Forest process is suitable technique to simulate the salinity of surface water bodies, and effective tool in minimizing the modeling complexity and elaborating proper management and mitigation measures.

There has been significant development in the facial recognition technology during past few decades. This technology has been widely used by different organizations and governments for defense, security, and surveillance projects. Furthermore, it has now been incorporated into our daily usages, such as consumer applications, personal data protection, or cyber-security, particularly while using smartphones. Most of these systems work very efficient, however, there are some challenges related to the accuracy of results of facial recognition systems when tested on images of people with dark skin. As a matter of fact, various studies demonstrate higher accuracy when tested on data set with white skin personnel, while exhibit a much lesser accuracy when the same algorithms are tested on dataset of people with dark skin. This article highlights the variation in accuracy of existing facial recognition algorithms when applied to dark-skinned people. Furthermore, as a principal contribution it presents a hybrid algorithm based on Gaussian and Explicit rule model that improves the accuracy for face-detection for dark skinned people. Thorough experimental evaluation has been conducted with a data set of black faces by first identifying skin and non-skin regions and then applying skin segmentation. The results have been compared with existing face detection algorithms with a clear improvement in the accuracy of 89% for dark skin.

Sign languages are used by the deaf and mute community of the world. These are gesture based languages where the subjects use hands and facial expressions to perform different gestures. There are hundreds of different sign languages in the world. Furthermore, like natural languages, there exist different dialects for many sign languages. In order to facilitate the deaf community several different repositories of video gestures are available for many sign languages of the world. These video based repositories do not support the development of an automated language translation systems. This research aims to investigate the idea of engaging the deaf community for the development and validation of a parallel corpus for a sign language and its dialects. As a principal contribution, this research presents a framework for building a parallel corpus for sign languages by harnessing the powers of crowdsourcing with editorial manager, thus it engages a diversified set of stakeholders for building and validating a repository in a quality controlled manner. It further presents processes to develop a word-level parallel corpus for different dialects of a sign language; and a process to develop sentence-level translation corpus comprising of source and translated sentences. The proposed framework has been successfully implemented and involved different stakeholders to build corpus. As a result, a word-level parallel corpus comprising of the gestures of almost 700 words of Pakistan Sign Language (PSL) has been developed. While, a sentence-level translation corpus comprising of more than 8000 sentences for different tenses has also been developed for PSL. This sentence-level corpus can be used in developing and evaluating machine translation models for natural to sign language translation and vice-versa. While the machine-readable word level parallel corpus will help in generating avatar based videos for the translated sentences in different dialects of a sign language.

A typical higher education institute classically has all the five major constituents, i.e. current students, alumni, faculty members, employers and industrial advisory board. Expectations of the rest of the constituents are fully dependent on the faculty members, as they play a pivotal and backbone role in building up an educational institute, to a high repute. Faculty members provide the knowledge and skills necessary to students, to address market needs. Letting go of skilled, professional and enthusiastic faculty members can have a huge impact on any higher education institution’s reputation and in fulfilling the market needs. In this research, we explore the different influencing factors and criteria for retaining teaching staff at schools and higher education institutes. These criteria are then evaluated against Accreditation Board of Engineering and Technology (ABET) accredited institutes criteria data of “faculty” and “Institutional support,” to see the impact. Study shows that only very few faculty members leave the institution because of higher student-to-faculty ratio or denial of tenure. Also, most of institutions concentrate on providing reduced number of lecture hours per week to keep their faculty members happy and perform research at the institution. Out of 737 faculty members of 27 institutes included in this study, only 0.54% faculty members left ABET accredited institutions.

This article investigates the flow properties of two-dimensional steady incompressible Williamson nanofluid moving over a stretched exponential surface with the features of both suction/injections. The coupled boundary conditions introduce the influence of thermophoresis boundary as well as the Brownian motion. The assessment of heat transfer is conducted for the two conditions of prescribed exponential order heat flux (PEHF) and prescribed exponential order surface temperature (PEST). The fundamental laws of heat transfer and motion have been implemented mathematically to model the existing flow situations. The non-dimensional variables have been assigned to put the main equations of the current problem into a dimensionless ordinary differential equation (ODEs). This dimensionless system of nonlinear ODEs has been solved on the basis of Homotopy analysis method (HAM). The results under various flow parameters have been graphically plotted in the form of temperature, velocity, and concentration profiles. The three profiles correspondingly modify for the increase in suction/injection parameter, thermophoresis parameter, and Lewis number both in PEST and PEHF case. Moreover, the escalation in the values of Brownian motion parameter, and the thermophoresis parameter respectively causes to enhance and decline the temperature and concentration profile only in case of PEST whereas a respective drop and development is detected in case of PEHF.

Gliomas are tumors of the central nervous system, which usually start within the glial cells of the brain or the spinal cord. These are extremely migratory and diffusive tumors, which quickly expand to the surrounding regions in the brain. There are different grades of gliomas, hinting about their growth patterns and aggressiveness and potential response to the treatment. As part of routine clinical procedure for gliomas, both radiology images (rad), such as multiparametric MR images, and digital pathology images (path) from tissue samples are acquired. Each of these data streams are used separately for prediction of the survival outcome of gliomas, however, these images provide complimentary information, which can be used in an integrated way for better prediction. There is a need to develop an image-based method that can utilise the information extracted from these imaging sequences in a synergistic way to predict patients’ outcome and to potentially assist in building comprehensive and patient-centric treatment plans. The objective of this study is to improve survival prediction outcomes of gliomas by integrating radiology and pathology imaging. Multiparametric magnetic resonance imaging (MRI), rad images, and path images of glioma patients were acquired from The Cancer Imaging Archive. Quantitative imaging features were extracted from tumor regions in rad and path images. The features were given as input to an ensemble regression machine learning pipeline, including support vector regression, AdaBoost, gradient boost, and random forest. The performance of the model was evaluated in several configurations, including leave-one-out, five-fold cross-validation, and split-train-test. Moreover, the quantitative performance evaluations were conducted separately in the complete cohort (n = 171), high-grade gliomas (HGGs), n = 75, and low-grade gliomas (LGGs), n = 96. The combined rad and path features outperformed individual feature types in all the configurations and datasets. In leave-one-out configuration, the model comprising both rad and path features was successfully validated on the complete dataset comprising HGFs and LGGs (R=0.84 p=2.2×10−16). The Kaplan–Meier curves generated on the predictions of the proposed model yielded a hazard ratio of 3.314 [95%CI:1.718−6.394], log−rank(P)=2×10−4 on combined rad and path features. Conclusion: The proposed approach emphasizes radiology experts and pathology experts’ clinical workflows by creating prognosticators upon ‘rad’ radiology images and digital pathology ‘path’ images independently, as well as combining the power of both, also through delivering integrated analysis, that can contribute to a collaborative attempt between different departments for administration of patients with gliomas.

The suspension of tiny solid particles inside the energy transport liquids could enhance their thermal conductivity as well as provide an efficient and inventive approach to significantly improve their properties of heat transport. Therefore, our aim is to explore the radiative two-dimensional unsteady flow of a viscous nanofluid about an aligned magnetic field that includes the joint effect of suction, velocity slip, and heat source across a porous convective stretching/shrinking surface. Initially, using non-dimensional variables, the nonlinear governing partial differential equations (PDEs) were transformed into ordinary differential equations (ODEs) which were subsequently solved with the help of bvp4c built-in package in MATLAB. The results declare that escalating the values of the unsteadiness parameter escalates the friction drag whereas it reduces with the escalation of the slip parameter. Furthermore, the heat transfer rate escalates with the escalation of radiation and concentration parameter, and the escalation of the heat source parameter causes to reduce the heat transfer rate. Finally, it is found that the rate of heat transfer and friction drag continuously improve and decline against the rising rates of stretching, respectively.