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Abstract Intuitionistic fuzzy sets (IFSs) represent a significant advancement in classical fuzzy set (FS) theory. This study advances IFS theory to generalized intuitionistic fuzzy sets (GIFSBs) and introduces novel operators GIFWAA, GIFWGA, GIFOWAA, and GIFOWGA, tailored for GIFSBs. The primary aim is to enhance decision-making capabilities by introducing aggregation operators within the GIFSB framework that align with preferences for optimal outcomes. The article introduces new operators for GIFSBs characterized by attributes like idempotency, boundedness, monotonicity and commutativity, resulting in aggregated values aligned with GIFNs. A comprehensive analysis of the relationships among these operations is conducted, offering a thorough understanding of their applicability. These operators are practically demonstrated in a multiple-criteria decision-making process for evaluating startup success in the Tech Industry.

During the current COVID-19 pandemic, many digital solutions around the world have been proposed to cope with the deadly virus but the role of mobile-based applications is dominant one. In Pakistan, during the current COVID-19 pandemic, an array of mobile health applications (apps) and platforms have been launched to grapple with the impacts of the COVID-19 situation. In this survey, our major focus is to explore and analyze the starring role of mobile apps based on the features and functionalities to tackle the COVID-19 disease, particularly in Pakistan. In this study, over fifty (50) mobile apps have been scrapped from the well-known three different sources i.e. Google Play Store, iOS Play Store, and web source. We developed our own data set after searching through the different play stores. We have designed two criteria such that the first criteria are known as eligibility criteria, while the second one is known as assessment criteria. The features and functions of each mobile app are pinpointed and discussed against the parameters of the assessment criteria. The major parameters of assessment criteria are: (i) Home monitoring; (ii) COVID-19 awareness; (iii) contact tracing; (iv) telemedicine; (v) health education; (vi) COVID-19 surveillance; (vii) self-assessment; (viii) security; and (ix) accessibility. This study conducted exploratory analysis and quantitative meta-data analysis by adopting PRISMA guidelines. This survey article is not only discussing the function and features of each COVID-19-centered app in Pakistan, but it also sheds light on the limitations of every mobile app as well. The results of this survey might be helpful for the mobile developers to review the current app products and enhance the existing mobile platforms targeted towards the COVID-19 pandemic. This is the first attempt of its kind to present a state-of-the-art survey of the COVID-19-centered mobile health apps in Pakistan.

This paper introduces a precision agriculture application aimed at mitigating the excessive utilization of agricultural chemicals, including pesticides and fungicides during crop spraying. The prevailing spraying techniques face two principle challenges: first, the indiscriminate dispensation of chemicals irrespective of plant size and requirements and second, the farmer's exposure to health hazards. To tackle these issues, a detection and segmentation model employing both YOLOv5 and YOLOv6 architectures is proposed and a comparative assessment of their accuracies within the same model category is conducted. The training dataset originates from a subset of the TobSet dataset, while the evaluation of the trained models is executed using publicly accessible aerial videos/images from available repository. The best detection accuracy achieved for the tobacco plant model size is observed with YOLOv6s and the YOLOv5-segmentation model, yielding accuracies of 95% and 94.8%, respectively. Additional performance metrics such as precision, recall, area under the PR-curve, inference time, and NMS per image are also compared between the two models. The YOLOv5-segmentation model excels by outperforming the YOLOv6s model in precision, recall score, and area under the PR-curve whereas slightly extended inference time and NMS per image duration are noted for the YOLOv5-segmentation model and the speed performance is comparable for the two models. Subsequently, the evaluation of these two models is conducted on the drone videos, which were recorded during drone traversal at a speed of 2 km/hr. The results demonstrate superiority of YOLOv5-segmentation model over the YOLOv6s model, with detection accuracies of 98.1% and 97.3%, respectively. These findings indicate the potential of integrating YOLOv5 segmentation models in precision spraying applications and contribute in improving the overall agricultural practices.

The integration of radiology reports for healthcare treatment using AI presents a transformative opportunity to enhance patient care and optimize healthcare delivery. Generating accurate radiology reports is crucial for guiding patient treatment in the clinical traditional applications or machines. However, the task of writing these reports can be a significant burden on medical practitioners. To address this challenge, we propose an adaptive, multi-modal technique for generating reports from chest X-rays. Our technique is based on the measurement that the content of clinical reports is highly accompanied by particular information present in the X-ray images. It comprises two key stages: Firstly, we introduce wisdom learning that leverages textual embedding to automatically extract and preserve medical wisdom from radiology reports. This wisdom base eliminates the need for manual labor in distilling valuable insights. Secondly, we employ a Cross-modal coherence technique to enhance the semantic alignment between reports, disease labels, and images. By utilizing clinical embedding, we channel the learning process of the visual feature attribute, ensuring meaningful associations between the different modalities. We conducted comprehensive evaluations of our model using both natural language generation metrics and clinical efficacy assessments on publicly available IU-Xray datasets. Through ablation analyses, we affirm the individual contributions of each module in improving the quality of generated reports. Moreover, when both modules are employed together, our approach surpasses existing state-of-the-art techniques across a wide range of evaluation metrics. This highlights the effectiveness of our technique in generating high-quality radiology reports and alleviating the burden on radiologists using intelligent medical applications or machines.

In this study, an internet of things (IoT)-enabled fuzzy intelligent system is introduced for the remote monitoring, diagnosis, and prescription of treatment for patients with COVID-19. The main objective of the present study is to develop an integrated tool that combines IoT and fuzzy logic to provide timely healthcare and diagnosis within a smart framework. This system tracks patients' health by utilizing an Arduino microcontroller, a small and affordable computer that reads data from various sensors, to gather data. Once collected, the data are processed, analyzed, and transmitted to a web page for remote access via an IoT-compatible Wi-Fi module. In cases of emergencies, such as abnormal blood pressure, cardiac issues, glucose levels, or temperature, immediate action can be taken to monitor the health of critical COVID-19 patients in isolation. The system employs fuzzy logic to recommend medical treatments for patients. Sudden changes in these medical conditions are remotely reported through a web page to healthcare providers, relatives, or friends. This intelligent system assists healthcare professionals in making informed decisions based on the patient's condition.

By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.

Rehabilitation engineering is playing a more vital role in the field of healthcare for humanity. It is providing many assistive devices to diplegia patients (The patients whose conditions are weak in terms of muscle mobility on both sides of the body and their paralyzing effects are high either in the arms or in the legs). Therefore, in order to rehabilitate such types of patients, an intelligent healthcare system is proposed in this research. The electric sticks and chairs are also a type of this system which was used previously to facilitate the diplegia patients. It is worth noting that a voice recognition system along with wireless control feature has been integrated intelligently in the proposed healthcare system in order to replace the common and conventional assistive tools for diplegia patients. These features will make the proposed system more user friendly, convenient and comfortable. The voice recognition system has been used for movements of system in any desired direction along with the ultrasonic sensor and light detecting technology. These sensors detect the obstacles and low light environment intelligently during the movement of the wheelchair and then take the necessary actions accordingly.

DevOps (development and operations) is a set of collaborative practices that automate delivery of new software updates with the aim to reduce the development life cycle and produce quality software products. Software organizations face several barriers while adopting DevOps practices as the integration of development and operation teams requires merger of different processes, tools, and skill sets. This study aims to develop a prioritization-based framework of the DevOps best practices based on evidence collected from industry experts. To attain the study aims, firstly, a systematic literature review was conducted to identify DevOps best practices reported in the literature. Next, a questionnaire survey study was conducted to receive insight from industry practitioners for the identified best practice. Finally, the fuzzy-AHP technique was applied to prioritize the best practices concerning to the significance for DevOps process. We believe that the identified best practices, their categorization and fuzzy-AHP based framework will help industry experts to revise and improve their strategies to make the DevOps process sustainable.

A speech impairment limits a person’s capacity for oral and auditory communication. A great improvement in communication between the deaf and the general public would be represented by a real-time sign language detector. This work proposes a deep learning-based algorithm that can identify words from a person’s gestures and detect them. There have been many studies on this topic, but the development of static and dynamic sign language recognition models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers’ speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. For the accurate and effective recognition of signs, this study uses two different deep learning-based approaches. We create a real-time American Sign Language detector using the skeleton model, which reliably categorizes continuous signs in sign language in most cases using a deep learning approach. In the second deep learning approach, we create a sign language detector for static signs using YOLOv6. This application is very helpful for sign language users and learners to practice sign language in real time. After training both algorithms separately for static and continuous signs, we create a single algorithm using a hybrid approach. The proposed model, consisting of LSTM with MediaPipe holistic landmarks, achieves around 92% accuracy for different continuous signs, and the YOLOv6 model achieves 96% accuracy over different static signs. Throughout this study, we determine which approach is best for sequential movement detection and for the classification of different signs according to sign language and shows remarkable accuracy in real time.

Abstract Software industry is adopting global software development (GSD) due to its potential to produce quality products at a lower cost. However, the GSD firms face many challenges that make development activities more complicated, especially related to the requirements engineering (RE) process. The objectives of this article are to investigate and prioritize the barriers faced by the GSD organizations during the RE process. First, we identified 17 barriers related to the RE process in the GSD projects. Next, the identified barriers were further validated with real‐world GSD practitioners using a questionnaire survey. Finally, we applied the analytical hierarchy process to prioritize the investigated barriers with respect to their significance for the RE process in the GSD domain. The results show that coordination is the most significant barrier category for the RE process in GSD projects. Lack of standard and procedure for RE in GSD, lack of synchronized communication infrastructure, and lack of mutual understanding between the overseas RE teams are also high‐ranked barriers for the RE process in GSD. The authors believe that the findings of this study will assist practitioners and researchers in developing effective strategies and plans for the successful implementation of the RE process in the GSD context.

Internet of Things (IoT) is made up of various smart devices for the exchange of sensed data through online services. Direct contact with people through smart devices to define parameters for healthcare and send them to a centralized repository. At the time of data exchange, messages need to be secure between a source (sender) and target (receiver) in order to confront human malicious attacks. Various signature-based schemes are presented in the literature to provide secure communication. Smart apps, however, require lightweight activities by maintaining critical security strengths. The key challenge in signature-based methods is more incurred computational expense for signing and checking process involving large numbers. In this article, a new lightweight provably secure partial discrete logarithm (DL) based subtree-based short signature with fuzzy user data sharing for human-centered IoT systems is introduced and it's security analysis is demonstrated on random oracle (RO) model. The presented scheme provides assurance of better security than other standing short-signature schemes. For low-storage, low-computation environments and low-bandwidth communication, the presented new provably secure and lightweight subtree-based short-signature scheme is needed. The results demonstrate the strength of proposed scheme, as opposed to existing works.

Internet of things (IoT) is leading a new digital age. IoT is regarded as the significant frontier that can improve almost all aspect of our lives. Currently, the IoT technology faces several challenges to academic researchers and industry practitioners, mainly that related with security of data. The objective of this study is to develop a prioritization-based taxonomy of the challenging factors that could hinders the security of IoT. By conducting the literature review and questionnaire survey studies 21 challenging factors were identified that are reported in existing literature and in real-world practices. Moreover, the identified challenging factors are mapped in the core domain of IoT (i.e. smart city, smart home, smart wearable’s and smart health care); and apply the fuzzy- AHP approach to rank the identified challenging factors with respect to their criticality for security of IoT technology. The application of fuzzy-AHP is novel in this research area as it is successfully applied in other domains of information technology to address the multi-criterion decision making problems. This study is contributing by providing a prioritization-based taxonomy of the IoT security challenging factors that could help the practitioners and research community to revise and develop the new strategies for the secure IoT.

Global Software Development (GSD) continues to receive interest from software industry due to potential economic benefits. Management of GSD projects is not straightforward due to involvement of different geographically distributed teams who collaborate to produce a software. The objective of this study is to prioritize the challenges faced by practitioners during management of a GSD project. A questionnaire survey was developed to collect feedback from GSD practitioners about relative importance of 20 challenges reported in literature. Next, the Fuzzy Analytical Hierarchy Process (FAHP) was used to rank the challenges associated with management of GSD projects. The study provides a prioritization-based taxonomy of challenges associated with management of GSD projects. We believe software organizations can use the taxonomy to better plan and manage GSD projects.

Trust and reputation are important terms whether the communication is Humans-to-Human (H2H), Human-Machine-Interaction (HMI) or Machine-to-Machine (M2M). As Cloud computing and the internet of things (IoT) bring new innovations, they also cause various security and privacy issues. As numerous devices are continuously integrating as a core part of IoT, it is necessarily important to consider various security issues such as the trustworthiness of a user or detection of a malicious user. Moreover, fog computing also known as edge computing is revolutionizing the Cloud-based IoT by providing the Cloud services at the edge of the network, which can provide aid in overcoming security, privacy and trust issues. In this work, we propose a context-aware trust evaluation model to evaluate the trustworthiness of a user in a Fog based IoT (FIoT). The proposed approach uses a context-aware multi-source trust and reputation based evaluation system which helps in evaluating the trustworthiness of a user effectively. Further, we use context-aware feedback and feedback crawler system which helps in making trust evaluation unbiased, effective and reliable. Furthermore, we introduce monitor mode for malicious/untrustworthy users, which helps in monitoring the behavior and trustworthiness of a user. The proposed approach uses several tunable factors, which can be tuned based on the system's requirements. The simulations and results indicate that our approach is effective and reliable to evaluate the trustworthiness of a user.

The software organizations are outsourcing their development activities across the geographical border due to huge business gains. However, the adoption of the global software development (GSD) paradigm is not straightforward; various challenges are associated with it, particularly related to the requirements engineering (RE) process. The objective of this study is to identify the barriers to the RE process faced during GSD. To achieve this, we have conducted a systematic mapping study and questionnaire survey to identify and validate the barriers of the RE process with industry practitioners. A total of 20 barriers were identified and validated with the experts. Moreover, we have performed organization types (client and vendor), organization size (small, medium, and large) and experts' levels (junior, intermediate, and senior) based analysis to provide a clear understanding of the RE barriers in the three different context. Besides, we have also developed a theoretical framework by mapping the investigated barriers into six core knowledge areas of software process improvement. The mapping results indicated that project administration is the most significant knowledge area of investigated barriers. We believe that the findings of this study will provide a framework that assists the GSD practitioners in developing an effective plan and strategies to improve the RE process in the GSD context.

DevOps is a combination of collaborative and multidisciplinary efforts of an organization to control continuous delivery and updates of new software while guaranteeing their reliability and correctness. In the software industry, the implementation of DevOps (development and operations units) faces many challenges that are specifically associated with the security. The objective of this study is to identify and develop a prioritization based taxonomy of DevOps security challenges. The total of eighteen DevOps security challenges were extracted using systematic literature review approach and were further evaluated with experts using questionnaire survey study. Finally, the multi criteria decision making PROMETHEE-II approach was used to prioritize and develop the taxonomy of identified factors and their categories. The implications of PROMETHEE-II approach are novel in this research domain as it has been used successfully in various other domains e.g. medical, banking, internet techniques and management etc. The contribution of this study is not limited to develop the taxonomy based structure of DevOps security challenges, but also the proper prioritization of these challenges by introducing PROMETHEE-II approach in the research field of DevOps. The study results will assist the practitioners to remove the uncertainty and vagueness in the opinion of DevOps experts to secure DevOps implementation for better and continuous software development process.

In current era, DevOps gain much interaction in software industry as it provides the flexible development environment. To meet the continuous development and operations, DevOps mainly focus, to integrate the data from heterogeneous source. While DevOps adoption, the quality assessment of data integrated from heterogeneous environment, is important and challenging at the same time. This study aims to identify the critical factors that could negatively impact the data quality assessment process in DevOps. We have used the systematic literature review (SLR) approach and identify a total of 13 critical challenging factors. The finding of SLR are further validated with industry experts via questionnaire survey. Finally, we have applied the Fuzzy TOPSIS approach to prioritize the investigated challenging factors with respect to their significance of DevOps data quality assessment process. The results show that analyzing data in real time, visualization of data and missing information and other invalid data are the highest ranked challenging factors which need to be addressed on priority basis, to successfully measure the quality of heterogeneous data in DevOps. We believe that the finding of this study will assist the practitioner to consider the most significant factors for measuring the quality of heterogeneous data in DevOps.

The economic and strategic gains motivated the majority of software firms to adopt global software development (GSD). The adoption of GSD is not straightforward. The geographical distance between the GSD teams causes the various problems for the implementation of agile software development activities, particularly, that related to requirements change management in agile (ARCM). The objective of this study is to explore the success factors of ARCM in GSD context and prioritized them based on their significance. The study comprised of two steps: (1) identify the success factors of ARCM using systematic mapping study (SMS) and validate them with industry practitioners using questionnaire survey approach; (2) prioritize the identified success factors by applying the analytical hierarchy process (AHP) approach. A total of 21 success factors that are significant for ARCM process were identified using SMS and questionnaire survey study. The results of AHP shows that allocation of resources at overseas sites, 3C's (communication, coordination and control), geographically distributed change control board (CCB), RCM process improvement expertise and continuous top management support are the highest priority success factors of ARCM process in the context of GSD. The findings assist the practitioners to address the most priority success factors in order to successfully implement the ARCM activities in the domain of GSD.

The DevOps (development and operations) is a collaborative software development environment which offers the continues development and deployment of quality software project within short time. The DevOps practices are not yet mature enough, and the software organizations hesitate to adopt it. This study aims: 1) to explore the DevOps challenges by conducting systematic literature review (SLR) and to get the insight of industry experts via questionnaire survey study and 2) to prioritize the investigated challenges using fuzzy analytical hierarchy process (FAHP). The study findings provide the set of critical challenges faced by the software organizations while adopting DevOps and a prioritization-based taxonomy of the DevOps challenges. The application of FAHP is novel in this research area as it assists in addressing the vagueness of practitioners concerning the influencing factors of DevOps. We believe that the finding of this study will serve as a body of knowledge for real world practitioners and researchers to revise and develop the new strategies for the successful implementation of DevOps practices in the software industry.

The cloud based global software development (CGSD) is the most widely adopted development paradigm in software industry. The CGSD offers significant economic and strategic benefits; besides, various complexities are faced by the practitioners while deploying CGSD. Hence, this study aims to identify and prioritize the best practices that are important for the success and progression of CGSD paradigm. Using the systematic literature review a total of 30 best practices were identified and were further verified with industry experts using questionnaire survey study. The identified best practices were further prioritize using fuzzy-AHP approach. The fuzzy-AHP is novel in this domain as it successfully applied in other engineering domain to address the multicriteria decision making problems. The findings of this study will provide a prioritization-based taxonomy of the investigated best practices which assists the academic researchers and industry experts to develop and revise the strategies of CGSD.

In this research work, a multifunctional metamaterial inspired surface is designed for two different applications, namely, electromagnetic (EM) absorber and radiator with pattern agility. The designed surface consists of periodic arrays of metallic loops and circular patches printed on a thin grounded dielectric slab. Lumped resistors are fixed in the outer ring where the inner patch is connected to the feeding network through metallic vias. Firstly, the basic unit cell of the surface is utilized as a dual-band EM absorber. Secondly, the same surface is utilized as a beam switching radiator. Each unit cell has two switchable feeding points which are connected to a single-pole double-throw (SPDT) switch designed on the bottom layer. The surface is symmetrical therefore the excitation of each unit cell at two feeding ports can produce a phase shift of 180°. By properly selecting the feeding point of the four elements, the surface can generate sum-beams and difference-beams. The surface exhibit low radar cross section (RCS), high gain, and high efficiency. A prototype of 4 × 4 elements array is manufactured and experimentally verified in an anechoic chamber.

The software organizations rapidly adopting global software development (GSD) to gain the economic and strategic benefits. Besides, GSD faces many challenges that mainly concerned with the requirements change management (RCM). This study aims to identify and empirically validate the factors that can negatively influence the RCM process in GSD. To this end, literature review and questionnaire survey were conducted for the investigation and validation of RCM challenges. A total of 31 RCM challenges were identified. We have further classified the identified challenges in organization types, organization size and based on experts' opinions with the aim to provide a clear understanding of the RCM process and its challenges to the practitioners. Based on these identified challenges, we believe that this study can provide a framework for tackling problems associated with RCM activities in GSD environment, which is significant to success and progression of GSD organizations.

The software organizations widely consider the cloud based global software development (CGSD) as it offer the quality projects with low cast. The adoption of CGSD is challenging due to the geographical distance between practitioners. This study aims to identify and analyses the motivators that could positively impact the implementation of CGSD paradigm. The systematic literature review approach was applied to identify the CGSD motivators reported in the literature, and were further validated with industry experts using questionnaire survey study. Moreover, the fuzzy-AHP approach was applied to prioritize the investigated motivators concerning their significance for the successful adoption of CGSD. The findings of the study provide the prioritization-based taxonomy of the investigated motivators that assists the software organizations to develop and revise their strategies for the successful implementation of CGSD.

Global Software Development (GSD) is adopted by organizations to develop quality software at relatively low cost. Requirement Change Management (RCM) plays a key role in overall success of a GSD project. The objective of this study is to identify the challenges of RCM process by adopting systematic literature review (SLR) and validate them by employing questionnaire survey approach in real world practices. A total of 25 challenges were identified through SLR and empirical study. We have further classified the identified challenges into client and vendor organizations with an aim to understand RCM challenges in the context of both types of GSD organizations. The identified challenges were also categorized into three core types of the organization size (small, medium, large), that highlights the significance of each challenge for specific organizational size. The results indicate that there is a moderate correlation between the ranking of these challenges in the literature and the survey results. The finding of this study has the potential to help the GSD organizations in addressing the problems related with RCM in GSD projects.

The main objective of this unmanned ground vehicle is to deal with the security issues like terrorist activities across the border and in various remote combat missions by reducing the involvement of soldiers. This unmanned ground robot comprises a wireless high-definition camera that can transfer live streams from the robot to headquarters using Wi-Fi. The robot’s movement can be controlled with two modes; one of them is a radio controller working on 2.4 GHz frequency with seven independent channels. Secondly, its movement can also be controlled using a Python-based GUI application. Nowadays, different techniques have been used for face recognition; in our remotely piloted robot, we have used Haar-cascade classifiers in combination with the LBPH algorithm to implement real-time facial recognition. The robot uses a rack and pinion driving mechanism and an ATMEL Cortex-M3 CPU as a controller with 32-bit/s processing speed. In addition, a laser is installed on the turret to shoot the targets down, which can be used in an autonomous mode based on facial recognition results, or it can be used manually either through an RF controller or Python-based GUI. The turret moves in 2-DOF with the help of metallic geared servo motors. Both servo motors can rotate up to 180°. The driving mechanism of the robotic tank is just like DDR, with one difference, the two DC gear motors of the robot are connected diagonally.

Currently, being deployed by organizations to develop high-quality software at a low cost, global software development (GSD) faces many challenges that make development activities more complex. These GSD challenges are mainly concerned with requirements to change management (RCM). RCM plays a key role in the successful execution of software projects. The objective of this paper is to identify the best practices of the RCM process by adopting a systematic literature review (SLR) and validate them using questionnaire survey with industry experts. A total of 46 best practices were identified through SLR and validated with industry experts. We have further classified the identified practices in the domain of client and vendor GSD organizations with the aim to provide a clear understanding of the RCM best practices in the context of both types of GSD organizations (client, vendor). Moreover, we have conducted a comparison analysis between SLR and questionnaire survey data and found a moderate positive correlation in the ranks of both data sets (rs = 0.522, p=0.003). In addition, the criticality of the identified best practices was assessed using the criteria of a practice having frequency ≥50%. The findings of this paper provide a framework that could help the GSD organizations to address the problems related to RCM in GSD environment.

Context modeling is often used to relate the context in which a system will operate to the entities of interest in the problem domain. It remains the case that context models are inadequate in emerging computing paradigms (e.g., smart spaces and the Internet of Things), in which the relevance of context is shaped dynamically by the changing needs of users. Formal models are required to fuse and interpret contextual information obtained from the heterogeneous sources. In this paper, we propose an integrated and formal context modeling approach for intelligent systems operating in the context-sensitive environments. We introduce a goal-driven, entity-centered identification method for determining which context elements are influential in adapting the system behavior. We then describe a four-layered framework for metamodeling the identification and management of context. First, the framework presents a formal metamodel of context. A formalization of context using the first-order logic with relational operators is then presented to specify formally the context information at different abstraction levels. The metamodel, therefore, prepares the ground for building a formal modeling language and automated support tool (https://github.com/metamodeler/CIM-CSS/). The proposed model is then evaluated using an application scenario in the smart meeting rooms domain, and the results are analyzed qualitatively.

The strenuous mining and arduous discovery of the concealed community structure in complex networks has received tremendous attention by the research community and is a trending domain in the multifaceted network as it not only reveals details about the hierarchical structure of multifaceted network but also assists in better understanding of the core functions of the network and subsequently information recommendation. The bipartite networks belong to the multifaceted network whose nodes can be divided into a dissimilar node-set so that no edges assist between the vertices. Even though the discovery of communities in one-mode network is briefly studied, community detection in bipartite networks is not studied. In this paper, we propose a novel Rider-Harris Hawks Optimization (RHHO) algorithm for community detection in a bipartite network through node similarity. The proposed RHHO is developed by the integration of the Rider Optimization (RO) algorithm with the Harris Hawks Optimization (HHO) algorithm. Moreover, a new evaluation metric, i.e., h-Tversky Index (h-TI), is also proposed for computing node similarity and fitness is newly devised considering modularity. The goal of modularity is to quantify the goodness of a specific division of network to evaluate the accuracy of the proposed community detection. The quantitative assessment of the proposed approach, as well as thorough comparative evaluation, was meticulously conducted in terms of fitness and modularity over the citation networks datasets (cit-HepPh and cit-HepTh) and bipartite network datasets (Movie Lens 100 K and American Revolution datasets). The performance was analyzed for 250 iterations of the simulation experiments. Experimental results have shown that the proposed method demonstrated a maximal fitness of 0.74353 and maximal modularity of 0.77433, outperforming the state-of-the-art approaches, including h-index-based link prediction, such as Multiagent Genetic Algorithm (MAGA), Genetic Algorithm (GA), Memetic Algorithm for Community Detection in Bipartite Networks (MATMCD-BN), and HHO.

Precise vertebrae segmentation is essential for the image-related analysis of spine pathologies such as vertebral compression fractures and other abnormalities, as well as for clinical diagnostic treatment and surgical planning. An automatic and objective system for vertebra segmentation is required, but its development is likely to run into difficulties such as low segmentation accuracy and the requirement of prior knowledge or human intervention. Recently, vertebral segmentation methods have focused on deep learning-based techniques. To mitigate the challenges involved, we propose deep learning primitives and stacked Sparse autoencoder-based patch classification modeling for Vertebrae segmentation (SVseg) from Computed Tomography (CT) images. After data preprocessing, we extract overlapping patches from CT images as input to train the model. The stacked sparse autoencoder learns high-level features from unlabeled image patches in an unsupervised way. Furthermore, we employ supervised learning to refine the feature representation to improve the discriminability of learned features. These high-level features are fed into a logistic regression classifier to fine-tune the model. A sigmoid classifier is added to the network to discriminate the vertebrae patches from non-vertebrae patches by selecting the class with the highest probabilities. We validated our proposed SVseg model on the publicly available MICCAI Computational Spine Imaging (CSI) dataset. After configuration optimization, our proposed SVseg model achieved impressive performance, with 87.39% in Dice Similarity Coefficient (DSC), 77.60% in Jaccard Similarity Coefficient (JSC), 91.53% in precision (PRE), and 90.88% in sensitivity (SEN). The experimental results demonstrated the method’s efficiency and significant potential for diagnosing and treating clinical spinal diseases.

Quality is the most important factor for software development as it mainly defines customer satisfaction that is directly related to the success of a software project. The software process model is used to ensure software quality, represent a variety of task settings, manage project duration, improve the process and range to execute the process understanding, and to appropriate implicit conjecture for all task settings. Several software processes models exist in software albeit with limited scope. Given this viewpoint, this paper presents a new software development life cycle model, “AZ-Model,” for software development by introducing new activities during software development life cycle. It overcomes the limitations of traditional models and significantly impacts the production of a quality product in a time-box. This paper also presents a comprehensive comparative study and statistical analyses to examine the significance of AZ–Model for software development.

Traditionally, software development organizations relied on heavyweight development methodologies, such as waterfall, V-model, and others. Later, agile development methodologies known as lightweight methodologies were introduced. Many considered these to be more flexible and more effective than heavyweight methodologies. Both methodologies are equally important for a software development life cycle. The purpose of adopting software development methodologies is to optimize the process model to achieve milestones while concurrently and effectively managing time, budget, and quality. The literature review reveals that there is a lack of statistical evidence for determining the effect of both methodologies on the six-pointed star model (schedule, scope, budget, risk, resource, and quality). In this paper, statistical comparisons were performed for the effects of both methodologies on each factor of the six-pointed star model and the interdependency among factors. Numerical analyses were conducted based on survey responses collected from the experienced users of both methodologies. After examining the results of all the factors of both methodologies, it was determined that lightweight methodologies are suitable for small-scale projects and that heavyweight methodologies perform better for mediumand large-scale projects.

Presently, global software development (GSD) is growing very rapidly. However, it is not an easy and straightforward process. Requirements engineering (RE) and requirements change management (RCM) both are considered as very challenging activities due to demanding rich communications. Because it is necessary to address geographical and cultural differences in GSD, this requirement makes RE and RCM challenging. This paper investigates the importance of project management in RE and RCM processes. First, the frameworks with the phenomena of specialized project management are proposed for RE and RCM. Then, a survey and blind interviews of the experts are conducted to validate the proposed frameworks. Finally, statistical tools are applied to analyze the collected data. By utilizing the analyzed data, our results show the significant impacts of both frameworks (i.e., RE and RCM) in the GSD environment.

Confidentiality and efficient bandwidth utilization require a combination of compression and encryption of digital images. In this paper, a new method for joint image compression and encryption based on set partitioning in hierarchical trees (SPIHT) with optimized Kd-tree and multiple chaotic maps was proposed. First, the lossless compression and encryption of the original images were performed based on integer wavelet transform (IWT) with SPIHT. Wavelet coefficients undergo diffusions and permutations before encoded through SPIHT. Second, maximum confusion, diffusion and compression of the SPIHT output were performed via the modified Kd-tree, wavelet tree and Huffman coding. Finally, the compressed output was further encrypted with varying parameter logistic maps and modified quadratic chaotic maps. The performance of the proposed technique was evaluated through compression ratio (CR) and peak-signal-to-noise ratio (PSNR), key space and histogram analyses. Moreover, this scheme passes several security tests, such as sensitivity, entropy and differential analysis tests. According to the theoretical analysis and experimental results, the proposed method is more secure and decreases the redundant information of the image more than the existing techniques for hybrid compression and encryption.