In this manuscript, imbalanced and small sample space (IB-SSS) dataset problems for pedestrian gender classification using fusion of selected deep and traditional features (PGC-FSDTF) are considered. In this regard, data preparation is first done through data augmentation and preprocessing steps to handle imbalanced classification problem and environmental effects, respectively. The proposed approach follows different types of feature extraction schemes, for instance, pyramid histogram of oriented gradients, hue saturation value histogram, deep visual features of DenseNet201 and InceptionResNetV2-based convolutional neural network architectures. The parallel fusion method computes the maximum and average values-based features from the learned features of both deep networks. Features are selected through features selection methods such as entropy and principal component analysis (PCA). The subsets of features are serially fused and provided to multiple classifiers to perform gender classification on IB-SSS datasets. Resultantly, the proposed PGC-FSDTF method shows better results in terms of different accuracies (overall, mean, and balanced), and area under curve on selected datasets. Further, improved results are achieved on applied datasets using PCA-based selected features and medium Gaussian support vector machine (M-SVM) classifier. These results on different datasets confirm that the selected feature combination provides a way to handle IB-SSS issues for PGC effectively.

The objective of IEEE 802.15.4 standard is to establish the foundation for a low-rate wireless personal area network that focuses on ubiquitous communication between devices while maintaining a reasonable data rate. Its popularity has increased significantly as a result of its implementation at low power and cheap cost, and the need to improve its performance has become a necessity. The most persistent issues are throughput, packet delivery ratio (PDR), packet loss ratio (PLR), and packet delay (PD). The advances in wireless technology place a strong emphasis on overcoming these problems. To accomplish stated goals, GFCO: A genetic fuzzy-logic approach to optimize channel of IEEE 802.15.4 LR-WPAN is proposed. It employs the $Fuzzy Logic Controller$ (FLC), and the $Genetic Algorithm$ (GA), by doing so, GA optimally modifies the FLC. For this, five algorithms are presented, Algorithm-1: GFCO for LR-WPAN, Algorithm-2: $GA_{1}$ for GFCO, Algorithm-3: $FLC_{1}$ for GFCO, Algorithm-4: $GA_{2}$ for GFCO, and Algorithm-5: $FLC_{2}$ for GFCO. The suggested GFCO approach is assessed for $Random Exponential Backoff$ (REB) algorithm, which was chosen as a fundamental algorithm, along with the $Survivability~Aware~Channel~Allocation$ (SACA) algorithm, taken as a benchmark study. Two scenarios are implemented in NS-3.20 in conjunction with fuzzylite in a hospital environment. First scenario is implemented in randomly deployed 10 sensors on a person’s body ( $2\times 2\,\,m^{2}$ area), whereas second scenario is implemented in $20\times 20\,\,m^{2}$ area of a ward in hospital having 10 to 50 persons. The simulated outcomes of both scenarios were recorded for REB, SACA, and GFCO. Simulated testing demonstrated that the proposed GFCO greatly enhanced performance of throughput 15.11%, SR 3.11%, PLR 3.11%, and PD 5.52% on average for scenario-I, whereas throughput 12.06%, SR 9.0%, PLR 9.0%, and PD 2.23% on average for scenario-II, as compared to SACA. Following that, these results are used to calculate the throughput, PDR, PLR, and PD and to draw a graphical representation. The proposed GFCO technique significantly improved efficiency, according to the results of the simulated testing.

The advancement in automation and medical care technologies in recent decade has changed the traditional medical treatment of patients. Although, these technologies have increased the treatment’s precision but the growing number and the complexity of IoT healthcare devices are impacting accuracy along with several other challenges. Moreover, the use of different programming languages, operating platforms and data management methodologies are creating restrictions in safe exchange, integration and reuse of information across different applications. However, with the advent of the semantic web, the semantic technologies are growing in healthcare systems due to the capability of machine interpretation and processing by overcoming the restriction of languages and data heterogeneity. The most common shortcoming in the existing systems are the context-awareness and quality of services and the absence of rich patient ontology; leading towards low accuracy of results. To this aim, this paper provides a smart health framework, consisting on the collection and processing of IoT data (related to patient conditions and context). The framework is supported by the patient ontology along with SWRL rules for better decision making that consider different features (context-awareness and quality of services) differently that results in the improved accuracy. In the evaluation process the proposed work has achieved an accuracy of 89.81%. This work will help the practitioners to treat the patients in a better way.

Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development.The use of test cases makes it easier to test the ripple effect of changed requirements.Rigorous testing may help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders.However, a minimized and prioritized set of test cases may reduce the efforts and time required for testing while focusing on the timely delivery of the software application.In this research, a technique named TestReduce has been presented to get a minimal set of test cases based on high priority to ensure that the web application meets the required quality criteria.A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases.The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements.In this research, the 100-Dollar prioritization approach is used to define the priority of the new requirements.

One of the most essential operational difficulties that water companies face today is the capacity to manage their water treatment process daily. Companies are looking for long-term solutions to predict how their treatment methods may be enhanced as they face growing competition. Many models for biological growth rate control, such as the Monod and Contois models, have been suggested in the literature. This review further emphasized that the Contois model is the best and is more suited to predicting the performance of biological growth rate than the other applicable models with a high correlation coefficient. Furthermore, the most well-known models for optimizing and predicting the wastewater treatment process are response surface methodology (RSM) and artificial neural networks (ANN). Based on this review, the ANN is the best model for wastewater treatment with high accuracy in biological wastewater treatment. Furthermore, the present paper conducts a bibliometric analysis using VOSviewer to assess research performance and perform a scientific mapping of the most relevant literature in the field. A bibliometric study of the most recent publications in the SCOPUS database between 2018 and 2022 is performed to assess the top ten countries around the world in the publishing of employing these four models for wastewater treatment. Therefore, major contributors in the field include India, France, Iran, and China. Consequently, in this research, we propose a sustainable wastewater treatment model that uses the Contois model and the ANN model to save time and effort. This approach may be helpful in the design and operation of clean water treatment operations, as well as a tool for improving day-to-day performance management.

In recent years, Software Defined Networking (SDN) has become an important candidate for communication infrastructure in smart cities. It produces a drastic increase in the need for delivery of video services that are of high resolution, multiview, and large-scale in nature. However, this entity gets easily influenced by heterogeneous behaviour of the user's wireless link features that might reduce the quality of video stream for few or all clients. The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences. Besides, multicast routing protocol with multiple constraints in terms of Quality of Service (QoS) is a Nondeterministic Polynomial time (NP) hard problem which can be solved only with the help of metaheuristic optimization algorithms. With this motivation, the current research paper presents a new Improved Black Widow Optimization with Levy Distribution model (IBWO-LD)-based multicast routing protocol for smart cities. The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive. Besides, a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints. A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects. The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.

Wireless Body Area Network (WBAN) is a special purpose wireless sensors network designed to connect various self-autonomous medical sensors and appliances located inside and outside of human body. Interests in human Healthcare Monitoring System (HMS) are based on WBAN due to the increasing aging population and chronically ill patients at home. HMS is expected to reduce healthcare expenses by enabling the continuous monitoring of patient's health remotely in daily life activities. This research focuses on routing protocols in WBAN. The major problems in routing protocols are maximum energy consumption, path loss ratio, packet delivery ratio and maintaining stable signal to noise ratio. Real time analysis is required in HMS to support the patients through doctors, caregivers and hospital systems. Collected data is relayed by using existing wireless communication schemes towards the access point for further retransmission and processing. In this research, an Improved Quality of Service aware Routing Protocol (IM-QRP) is proposed for WBAN based HMS to remotely monitor the elderly people or chronically ill patients in hospitals and residential environments. The proposed protocol is capable to improve 10% residual energy, 30% reduction in path loss ratio, 10% improvement in packet transmission (link reliability) and 7% improvement in SNR as compared to existing CO-LEEBA and QPRD routing protocols. Convolutional Neural Network is used outside the WBAN environment to analyze the medical health records for healthcare diagnosis and intelligent decision-making.

This research presents an Urdu handwriting database named as LIKHAI database, which is designed to facilitate Urdu handwritten text recognition. A standard Urdu database ‘LIKHAI’ was developed consisting of 456 forms. The database is then divided in seven different categories to get the variety of words. These forms are written by different writers of different ages, education level etc. Sampling strategy was used for realistic representation of the Urdu handwriting, two different samples, that is writer sample and text sample, were selected. The writer sample helps to find balanced writer samples and the text sample helps to find a compact collection of text that covers all the characters of Urdu language. Then the handwritten forms are scanned on 300 dpi and saved as in .XML format. This database covers all the shapes of the Urdu Language characters and it contains all natural and real Urdu handwritten words. Then the filters are applied on the first page of each form in order to extract the information of writers. This database can be used for multiple tasks like text recognition, Ground Truth, and Line segmentation. Statistics of underlying research showed that database provides an excellent representation of Urdu handwriting.

Requirements engineering (RE) is an important phase of software engineering. During this phase, an important set of activities are carried out to manage requirements elicitation, verification, prioritization and validation. Dimension and dynamics of software development are changing with the passage of time. Economic growth in different sectors is increasing the demand for software development. This enhancement has introduced the concept of Value Base Software (VBS) development. Requirements prioritization is playing a vital role in ordering requirements to support the release planning of the software. A prioritization process is considered as highly complex process and depends on the nature and size of requirements. VBS systems are entirely different from typical software development, and prioritization process for VBS is also very challenging. A need arises from the provision of prioritization techniques to support the technical and business aspects-based prioritization. Existing techniques are not qualified to meet the expectation of the industry for VBS development. Therefore, this research contribution is an effort made, based on an intelligent decision support system for requirements prioritization in the domain of VBS system. Aspects based requirements prioritization is applied to many requirements and results are produced in two clusters. Results are claimed as a prioritized list of requirements for traditional as well as value-based system.

the visualization of the 3D models is a scorching topic in computer vision and human-computer interaction. The demands for 3D models have been increased due to high involvement in animated characters, virtual reality and augmented reality. To interact with 3D models with the help of mouse and keyboard is a very hectic, less efficient and complex process because of multiple types of operations required by the models to view properly in all sides. So it is essential to improve the user interaction with the 3D system. In this paper, a new method is introduced by using the Microsoft Kinect v2 to detect the human body and joints. First, we trained the Kinect to understand the specific gestures, and then recognize to perform the specific task on an object in the proposed environment.

The paper presents a 3D Android-based classical object viewer system that improves user experience when viewing 3D datasets. The system is well suited to 3D simulated images and facilitates a more accurate list of images in response to search-based quarries using Android’s SearchView widget. Existing 2D object viewer systems are unable to display the simulated images accurately and older 3D object viewing systems face performance-related challenges. The developed Android system reduces the issues of performance and improves the flexibility in viewing rotation for 3D environments. It also provides better user experience and versatility to 3D object viewers through different paths and channels. The effectiveness of the developed system has been demonstrated using standard datasets.

To simulate the concurrent users' workload, this paper proposes a performance testing approach and novel average performance measuring (APM) metric for assessing the web services performance. It extracts the response time, throughput, and response code metrics information to build an efficient performance-related issues identification approach that detects performance faults earlier with less effort or expense. Multiple open-source and commercial performance testing tools are used in the information technology (IT) industry to analyze the performance of web services. Practitioners rely only on either response time or throughput metric to evaluate the performance of web services, which is not sufficient for a comprehensible performance testing of web services. Our proposed performance testing approach is more convenient and straightforward to perform performance testing of web services. Moreover, experiment results show that the proposed APM metric is efficient in the evaluation of performance testing approaches.

Due to increasing interest in distributed agile software development, there is a need to systematically review the literature on challenges encountered in the agile software development environment. Using the Systematic Literature Review (SLR) approach, 32 relevant publications, dated between 2013 and 2018 were selected from four electronic databases. Data from these publications were extracted to identify the key challenges across the system development life cycle (SDLC) phases, which essentially are short phases in each agile-based iteration. 5 types of key challenges were identified as impacting the SDLC phases; these challenges are Communication, Coordination, Cooperation, Collaboration and Control. In the context of the SLDC phases, the Communication challenge was discussed the most often (79 times, 33%). The least discussed challenges were Cooperation and Collaboration (26 times, 11% each). The 5 challenges occur because of distances which occur in distributed environment. This SLR identified 4 types of distances which contribute to the occurrence of these key challenges - physical, temporal, social-cultural and knowledge/experience. Of the 32 publications, only 4 included research which proposed new solutions to address challenges in agile distributed software development. The authors of this article believe that the findings in this SLR are a resource for future research work to deepen the understanding of and to develop additional solutions to address the challenges in distributed agile software development.

This research deals with the industrial financial forecasting in order to calculate the yearly expenditure of the organization. Forecasting helps in estimation of the future trends and provides a valuable information to make the industrial decisions. With growing economies, the financial world spends billions in terms of expenses. These expenditures are also defined as budgets or operational resources for a functional workplace. These expenses carry a fluctuating property as opposed to a linear or constant growth and this information if extracted can reshape the future in terms of effective spending of finances and will give an insight for the future budgeting reforms. It is a challenge to grasp over the changing trends with an effective accuracy and for this purpose machine learning approaches can be utilized. In this study Long Short-Term Memory (LSTM), which is a variant of Recurrent Neural Network (RNN) from the family of Artificial Neural Networks (ANN), is used for forecasting purposes along with a statistical tool IBM SPSS for comparative analysis. In this study, the experiments are performed on the data set of Pakistan GDP by type of expenditure at current prices - national currency (1970-2016) produced by Economic Statistics Branch of the United Nations Statistics Division (UNSD). Results of this study demonstrate that the proposed model predicted the expenses with better accuracy than that of the classical statistical tools.

In globalization of information, internet has played a vital role by providing an easy and fast access of information and systems to remote users. However, with ease for authentic users, it has made information resources accessible to unauthorized users too. To authorize legitimate user for the access of information and systems, authentication mechanisms are applied. Many users use their credentials or private information at public places to access their accounts that are protected by passwords. These passwords are usually text-based passwords and their security and effectiveness can be compromised. An attacker can steal text-based passwords using different techniques like shoulder surfing and various key logger software, that are freely available over internet. To improve the security, numerous sophisticated and secure authentication systems have been proposed that employ various biometric authentication systems, token-based authentication system etc. But these solutions providing such high-level security, require special modification in the design and hence, imply additional cost. Textual passwords that are easy to use but vulnerable to attacks like shoulder surfing, various image based, and textual graphical password schemes are proposed. However, none of the existing textual graphical passwords are resistant to shoulder surfing and more importantly to mobile key-logging. In this paper, an improved and robust textual graphical password scheme is proposed that uses sectors and colors and introducing randomization as the primary function for the character display and selection. This property makes the proposed scheme resistant to shoulder surfing and more importantly to mobile key-logging. It can be useful for authentication process of any smart held device application.

Facial Recognition is a commonly used technology in security-related applications. It has been thoroughly studied and scrutinized for its number of practical real-world applications. On the road ahead of understanding this technology, there remain several obstacles. In this paper, methods of 3D face recognition are examined by measuring quantifiable applications and results. In facial recognition, three Dimensional Morphable Model (3DMM) techniques have attracted more and more attention as effectiveness in use increases over time. 3DMM provides automation and more accurate image rendering when compared to other traditional techniques. The accuracy in image rendering comes at a cost; as 3DMM requires more focus on texture estimation, shape-controlling limits, and extrinsic variations, accurately matching fitting models, feature tracking and precision identification. We have underlined different issues in comparison based on these methods.

Agile Software Development techniques are worldwide accepted, regardless of the definition of agile we all must agree with the fact that agile is maturing day by day, suppliers of software systems are moving away from traditional waterfall techniques and other development practices in favor of agile methods. There are numerous types of methodologies, domains/ methods in agile for which are to be selected according to the current situation and demand of the current project. As a case scenario in the following research will discuss scrum as a development technique in which we will focus on the effort estimation(s) and their effects by discussing distinct metrics. Mainly estimation refers directly to cost, time and complexity during the life cycle of project. Metrics will help the teams to better understand the development progress and building releasing (releases) of software easier in a fluent and robust way. The following paper thus identifies aspects mainly ignored by the development team(s) during estimation.

Use of mobile applications are trending these days due to adoption of handheld mobile devices with operating systems such as Android, iOS and Windows. Delivering quality mobile apps is as important as in any other web or desktop application. Simplification and ease of quality assurance or evaluation in mobile devices is achieved by using automated testing tools. These tools have been evaluated for their features, platforms, code coverage, and efficiency. However, they have not been evaluated and compared to each other for different quality attributes they can enhance in the apps under test. This research study aims to evaluate different testing tools focusing on identifying quality factors they aid to achieve in the apps under test. Furthermore, it aims to measure overall trends of essential quality factors achieved using automated testing tools. The findings of this study are beneficial to the practitioners and researchers. The practitioners need to look up for specific tools which aid them to assure the desired quality factors in the apps under test. The researchers may base their studies on the findings of this study to propose solutions or revise existing tools in order to achieve maximum number of critical quality attributes in the app under test. This study revealed that the trend of automated testing is high on usability, correctness and robustness. Moreover, the trend is average on testability and performance. However, for assurance of extensibility, maintainability, scalability, and platform compatibility, only a few tools are available.

Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction.

[This corrects the article DOI: 10.1371/journal.pone.0163346.].