Free and open-source software has rapidly grown and garnered considerable attention in recent years. However, the design complexities and guidelines inherent to Free and Open Source Software (FOSS) pose a challenging task in enhancing the User Experience (UX) of FOSS. Several pre-existing models have significantly contributed to the existing body of knowledge, yet they often overlook FOSS-specific attributes that establish a direct correlation with the efficiency of the UX in FOSS. To address this gap, the study proposes a FOSS UX enhancement model (FUEM) designed to align with current FOSS practices and guidelines, aiming to improve user experience in free and open-source software. The proposed model consists of six levels, with each level encompassing UX maturity influencing factors (UMXIF) identified in prior research. These factors are re-evaluated through a Likert scale questionnaire to gather feedback from developers, project managers, and UI/UX designers, as the previous study solely included the perspectives of UX experts. Our findings reveal that these factors positively impact FOSS projects, and integrating them into FUEM will enable the FOSS community to ascertain their level of UX maturity and identify which missing UMXIF they must adopt to progress to the next stage of UX maturity in FUEM. Furthermore, the tailored model developed for FOSS is validated through feedback analysis of expert reviews and quantitative analysis employing a one-way ANOVA test. The ANOVA test indicates that the p-value exceeds the significant threshold of. 05, suggesting that the FOSS UX model can considerably enhance UX in FOSS projects by providing a structured pathway for improving UX maturity, serving as a valuable resource for the FOSS community, guiding the development of more user-centric open-source software, and fostering a deeper understanding of UX maturity practices explicitly tailored for FOSS environments.

With various benefits of Information Systems (IS), there are increasing apprehensions regarding their safety implications. Despite researchers’ investigation and publication of the safety advantages of IS, several case studies have revealed distinct, potentially fatal issues and safety risks even with the use of IS. Accidents occur due to differences between the process models (mental models) employed by users and the actual characteristics of the operation. This is particularly true for accidents consisting of interactions between users and safety-critical IS. These users’ process models and Situational Awareness (SA) generate incidents that pose safety hazards to human lives. This ambiguity in the process model and SA may be due to the user’s perspective of what the system does and the actual characteristics of the system. The literature on the user’s process model and SA has been extensively employed in technological systems, but its application to socio-technical systems has been limited. We have identified problems that constitute a potential safety risk with the IS. The issues correspond to the lack of alignment between the process model and situation awareness about the user when interacting with the IS. This lack of alignment and SA can result from various circumstances, including interruptions, multitasking, and cognitive overload. These disruptions make it more challenging to understand the circumstances, which may result in mistakes, inefficiencies, and even safety hazards. Knowing about these interruptions, an information system (IS) could adjust its usage procedure in real time to lessen the disruption’s impact. Such a system could aid in restoring SA by identifying, reacting and adapting to these disturbances. This self-adaptive system (SAS) is used in our study to investigate the impact of an adaptive IS on SA, usability and safety performance outcomes. The findings of this study and the controlled experiment shed light on how system adaptation can mitigate the negative impacts of interruptions, improving safety, effectiveness, efficiency, SA, usability, frustration, and decision-making.

Determining a suitable software developer to match project needs within the Global software development (GSD) context requires detailed information. The complexity of this problem arises from the required combination of the developer’s level of technical expertise, domain knowledge, and the extent to which they possess the collaborative skills necessary for a successful project. Typical developer recommendation systems do not consider the dynamics of expertise and cooperative nature of the tasks for assessing their correctness, often restricting themselves to extracting review comments only to measure their usefulness and suggest reviewers. This research intends to create a recommendation system, using pull request review comments and selected data from developers’ profiles to recommend better experts based on their dynamic expertise. Using advanced algorithm techniques, the proposed model Global Developer Expertise Recommendation System (GDERS) aims to improve the quality of captured data and substantially increase the accuracy of developer recommendations. Impressively, the proposed model significantly outperformed all other text-based classifiers TextCNN, TextRCNN, and Bilstm in this study, showing an accuracy of 91.85%. This research provides a significant achievement of recommendation systems in the global software development context that support more effective collaboration and increase the probability of project completion on time by allowing project managers to find easily accessible developers in the field with the right expertise.

Software Process Improvement (SPI) aims to achieve quality in software products for software organizations, as it helps to manage and improve the development processes. The success of software products highly depends on the right execution of software processes. The current pandemic (COVID-19) has highly affected the workflow of software organizations around the distributed geographical locations, resulting in difficulties in process execution whichis a threat to software process improvement activity. The primary objective of this research is to provide a process improvement model for software development organizations for better management and improvement of the software development processes during the COVID-19 pandemic. Our proposed model is based on the objectives of the ‘Team Software Process’ (TSP)and ‘Personal Software Process’ (PSP) models to effectively manage the software development processes for both the teams and individuals involved in the remote development during the COVID-19 pandemic. The proposed model can also be applied in any uncertain situation other than COVID- 19 to assist software organizations during remote work.

This research examines the integration of sustainability principles within Agile Software Development Life Cycle (SDLC) methodologies. While Agile frameworks such as GLUX emphasize user experience and adaptability, they often prioritize software stability primarily during design and implementation phases, potentially neglecting long-term sustainability considerations. Our study posits that incorporating sustainability as a core element in Agile SDLC is essential for developing software that demonstrates optimal performance, environmental consciousness, and long-term cost-effectiveness. Through a comprehensive systematic literature review, we analyzed 67 papers selected from an initial pool of 1900 studies, employing qualitative research methods to extract and evaluate hypotheses regarding sustainable practices in agile development. The findings reveal a significant gap in current Agile methodologies concerning long-term software viability. This deficiency impedes the software's ability to adapt to evolving requirements, thus limiting its full potential. Our research proposes novel frameworks and approaches to address this limitation, aiming to ensure sustainability throughout the Agile development process. By identifying and analyzing relevant procedures and methods that contribute to sustainability within the Agile framework, this study provides valuable insights for both researchers and practitioners. The proposed integrative approach extends beyond immediate usability concerns, focusing on software products' long-term adaptability and viability. This research contributes to the growing knowledge of sustainable software development and offers practical guidelines for implementing sustainability measures within Agile frameworks. The findings highlight the importance of improving software longevity and adaptability in a rapidly evolving technological landscape.

In an era dominated by information dissemination through various channels like newspapers, social media, radio, and television, the surge in content production, especially on social platforms, has amplified the challenge of distinguishing between truthful and deceptive information. Fake news, a prevalent issue, particularly on social media, complicates the assessment of news credibility. The pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources, creating confusion and polarizing opinions. As the volume of information grows, individuals increasingly struggle to discern credible content from false narratives, leading to widespread misinformation and potentially harmful consequences. Despite numerous methodologies proposed for fake news detection, including knowledge-based, language-based, and machine-learning approaches, their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or inconsistencies. Our study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news detection. We employ multiple feature extraction methods, including Count Vectorizer, Bag of Words, Global Vectors for Word Representation (GloVe), Word to Vector (Word2Vec), and Term Frequency-Inverse Document Frequency (TF-IDF), alongside feature selection techniques such as Information Gain, Chi-Square, Principal Component Analysis (PCA), and Document Frequency. This comprehensive approach enhances the model’s ability to identify and analyze relevant features, leading to more accurate and effective fake news detection. Our findings highlight the importance of a multi-faceted approach, offering a significant improvement in model accuracy and reliability. Moreover, the study emphasizes the adaptability of the proposed ensemble model across diverse datasets, reinforcing its potential for broader application in real-world scenarios. We introduce a pioneering ensemble technique that leverages both machine-learning and deep-learning classifiers. To identify the optimal ensemble configuration, we systematically tested various combinations. Experimental evaluations conducted on three diverse datasets related to fake news demonstrate the exceptional performance of our proposed ensemble model. Achieving remarkable accuracy levels of 97%, 99%, and 98% on Dataset 1, Dataset 2, and Dataset 3, respectively, our approach showcases robustness and effectiveness in discerning fake news amidst the complexities of contemporary information landscapes. This research contributes to the advancement of fake news detection methodologies and underscores the significance of integrating feature extraction and feature selection strategies for enhanced performance, especially in the context of intricate, high-dimensional datasets.

Business process modeling is used to model business processes using Business Process Modeling Notation (BPMN), which is a widely accepted standard for process modeling. BPMN elements are visually represented by the existing model, but the expressiveness of elements in terms of communication between the participants of the business process is a problem reported in modeling literature. Business processes use collaboration models to gain increasing importance in software development, describing their behavior and interaction. Recent years have seen the presentation of various approaches to ensure communication between business process pools. Despite the widespread adoption of BPMN for business process modeling, existing collaboration models often suffer from significant limitations in accurately capturing complex collaborative business processes. The existing approaches do not ensure proper structure and syntax for collaboration elements. The flow of information among multiple pools causes ambiguity in the developed business process. A Collaborative Business Process Model (CBPM) is proposed to address this issue, based on modeling rules that ensure proper syntax and structure of the models. The proposed CBPM also guarantees that the model is a better approach for participant interaction. This approach contributes to improving the communication mechanism between the participants of collaborative business processes. Moreover, we formally analyze and verify the working of CBPM by specifying the model in Z specification language. Performance evaluation regarding the flow of messages through test case coverage criteria indicates that the model is capable of ensuring successful communication among the multiple participants of business processes.

Advancements in the Internet of Things and Information Communication and Technologies have increased the demand for real-time services. Thus computing resources are migrated from the cloud to fog networks to give the users near real-time experiences. Fog computing resources containing storage and networks that are shifted from core to edge to minimize the latency while using the applications-application implementation nearest to fog nodes that reduce the latency however burden leans on the density of users. The fog network performance degrades because of the over-subscription of fog nodes. In this work, we have proposed a method that depends on alliance establishment for resource management while using the model that will charge according to the usage of network resources. We assume that the cluster contains one prime node with several fog nodes. Firstly, the customer needs information regarding the application requirement for the clusters then the prime node evaluates the capacity of the fog node, which fulfills desired demands of the end user. According to end-user requirements, the prime node among the cluster selects the specific symmetry batch nodes. Additionally, in this work, we have suggested an extension of resource handling regarding fog networks, combining end-user demands for the service and allocating individual nodes against the batch application.

Autonomous driving systems are among the exceptional technological developments of recent times. Such systems gather live information about the vehicle and respond with skilled human drivers’ skills. The pervasiveness of computing technologies has also resulted in serious threats to the security and safety of autonomous driving systems. Adversarial attacks are among one the most serious threats to autonomous driving models (ADMs). The purpose of the paper is to determine the behavior of the driving models when confronted with a physical adversarial attack against end-to-end ADMs. We analyze some adversarial attacks and their defense mechanisms for certain autonomous driving models. Five adversarial attacks were applied to three ADMs, and subsequently analyzed the functionality and the effects of these attacks on those ADMs. Afterward, we propose four defense strategies against five adversarial attacks and identify the most resilient defense mechanism against all types of attacks. Support Vector Machine and neural regression were the two machine learning models that were utilized to categorize the challenges for the model’s training. The results show that we have achieved 95

Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been proposed to detect fall events. To overcome and mitigate elderly fall issues, such as being left to lie for a long time after a fall, this project presents a low-cost, motion-based technique for detecting all events. In this study, we used IRA-E700ST0 pyroelectric infrared sensors (PIR) that are mounted on walls around or near the patient bed in a horizontal field of view to detect regular motions and patient fall events; we used PIR sensors along with Arduino Uno to detect patient falls and save the collected data in Arduino SD for classification. For data collection, 20 persons contributed as patients performing fall events. When a patient or elderly person falls, a signal of different intensity (high) is produced, which certainly differs from the signals generated due to normal motion. A set of parameters was extracted from the signals generated by the PIR sensors during falling and regular motions to build the dataset. When the system detects a fall event and turns on the green signal, an alarm is generated, and a message is sent to inform the family members or caregivers of the individual. Furthermore, we classified the elderly fall event dataset using five machine learning (ML) classifiers, namely: random forest (RF), decision tree (DT), support vector machine (SVM), naïve Bayes (NB), and AdaBoost (AB). Our result reveals that the RF and AB algorithms achieved almost 99% accuracy in elderly fall-d\detection.

Software organizations are increasingly embracing the advantages of Global Software Development (GSD), such as access to highly skilled developers and reduced development costs. However, the implementation of Requirement Change Management (RCM) activities in GSD is often hindered by a lack of communication and coordination among project stakeholders, as well as an insufficient focus on traceability and monitoring of RCM activities. To overcome these issues, we have enhanced and improved an existing RCM framework to mitigate the identified challenges and to develop a quality product while achieving customer satisfaction and business objectives. To evaluate the effectiveness of the proposed Enhanced AZ-Model, we sought feedback from the industrial experts and performed a statistical analysis of the collected data. The Enhanced AZ-Model was further validated by performing simulations of the model. The empirical and simulation results indicate that the Enhanced AZ-model efficiently and effectively manage the demanded changes according to the budget and time constraints.

The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information. Primarily, the proposed approach minimizes the network size and eliminates undesirable connections. For that, the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer. Therefore, the proposed approach discards the nodes having a rank (alpha) lesser than 0.5 to reduce the network complexity. alpha is the variable value represents the rank of each node that varies between 0 to 1. Node with the higher value of alpha gets the higher priority and vice versa. The threshold value alpha = 0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems. Finally, the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information. The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks. Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network. Moreover, the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network. Furthermore, the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.

Information and technology have witnessed significant improvement with the introduction of Internet of things (IoT) applications, and most of the IoT applications are dependent on the cloud. Cloud computing is assisting IoT applications by providing storage, analysis, and processing services on the cloud. However, Fog computing is the new paradigm that supports the cloud by providing scheduling, resources optimization, and energy optimization services. Scheduling tasks based on MIPs size and prioritizing the tasks with smaller MIPs size first make critical tasks with larger MIPs wait, which ultimately increases the delay and may result in some serious problems. This paper proposes a methodology for critical tasks having large MIPs size by scheduling and prioritizing the tasks based on the nature of the task. The proposed methodology for latency-critical applications reduces latency, energy consumption, and network utilization. This paper proposed a scheduler “Critical task First Scheduler” (CTFS), which schedules tasks depending on the nature of the requests, which are classified as either critical or noncritical. The proposed methodology is implemented in a healthcare scenario, and the simulations are performed in iFogSim simulator. Critical requests, such as emergency notifications, are prioritized and designated as critical, requiring immediate processing. The environment was kept the same for all the approaches that are implemented to demonstrate the effectiveness of the proposed approach. The results of the proposed approach were compared with First Come First Served (FCFS), Shortest Job First (SJF), and cloud-only approaches to demonstrate the effectiveness of the proposed approach in terms of latency, energy consumption, and network utilization. Simulation results show that the proposed CTFS approach outperformed the compared techniques for all three comparison parameters.

Fog computing has emerged as an extension of cloud computing that provides cloud-like services at the edge of the to internet of things applications. However, the limited storage and processing capability of fog nodes along with inefficient resource scheduling creates a performance bottleneck that can result in high latency and network bandwidth. Therefore, dynamic load balancing is necessary to achieve the true benefits of fog computing. This paper proposes dynamic load balancing mechanism (DLBM) to schedule the number of service requests on fog nodes effectively. Furthermore, three algorithms to dynamically balance the load of fog nodes named are appropriate node selection, effective task distribution and global task execution and resource allocation. The performance of our proposed mechanism is compared with cloud only technique, fog-cloud-placement algorithm, and self-similarity-based load balancing technique. Comparative performance analysis validates the efficiency of the proposed approach. DLBM demonstrates considerable reduction in latency and network bandwidth utilisation.

Cotton is one of the world’s most economically significant agricultural products; however, it is susceptible to numerous pest and virus attacks during the growing season. Pests (whitefly) can significantly affect a cotton crop, but timely disease detection can help pest control. Deep learning models are best suited for plant disease classification. However, data scarcity remains a critical bottleneck for rapidly growing computer vision applications. Several deep learning models have demonstrated remarkable results in disease classification. However, these models have been trained on small datasets that are not reliable due to model generalization issues. In this study, we first developed a dataset on whitefly attacked leaves containing 5135 images that are divided into two main classes, namely, (i) healthy and (ii) unhealthy. Subsequently, we proposed a Compact Convolutional Transformer (CCT)-based approach to classify the image dataset. Experimental results demonstrate the proposed CCT-based approach’s effectiveness compared to the state-of-the-art approaches. Our proposed model achieved an accuracy of 97.2%, whereas Mobile Net, ResNet152v2, and VGG-16 achieved accuracies of 95%, 92%, and 90%, respectively.

BackgroundThe SARS-Cov-2 virus (commonly known as COVID-19) has resulted in substantial casualties in many countries. The first case of COVID-19 was reported in China towards the end of 2019. Cases started to appear in several other countries (including Pakistan) by February 2020. To analyze the spreading pattern of the disease, several researchers used the Susceptible-Infectious-Recovered (SIR) model. However, the classical SIR model cannot predict the death rate.ObjectiveIn this article, we present a Death-Infection-Recovery (DIR) model to forecast the virus spread over a window of one (minimum) to fourteen (maximum) days. Our model captures the dynamic behavior of the virus and can assist authorities in making decisions on non-pharmaceutical interventions (NPI), like travel restrictions, lockdowns, etc.MethodThe size of training dataset used was 134 days. The Auto Regressive Integrated Moving Average (ARIMA) model was implemented using XLSTAT (add-in for Microsoft Excel), whereas the SIR and the proposed DIR model was implemented using python programming language. We compared the performance of DIR model with the SIR model and the ARIMA model by computing the Percentage Error and Mean Absolute Percentage Error (MAPE).ResultsExperimental results demonstrate that the maximum% error in predicting the number of deaths, infections, and recoveries for a period of fourteen days using the DIR model is only 2.33%, using ARIMA model is 10.03% and using SIR model is 53.07%.ConclusionThis percentage of error obtained in forecasting using DIR model is significantly less than the% error of the compared models. Moreover, the MAPE of the DIR model is sufficiently below the two compared models that indicates its effectiveness.

With a high rise in the popularity of Internet of Things (IoT), mobile computing, and wearable devices, a huge amount of data is being generated. Running complex tasks such as that are machine learning-based with minimum energy consumption is a challenge. It requires complex algorithms to run locally such as on middleware fog within the proximity of the devices generating data, or globally in a cloud to analyze the acquired data and create robust and smart applications. However, it depends on the type of task execution policy applied at each level; local or global, to decide on energy and performance efficiency, since certain tasks are high in complexity. Hence, task execution will be hierarchically distributed among the IoT nodes, fog, and cloud. Given that, we present in this paper a three-tier IoT-fog-cloud model. We argue that with distributed task execution, we can achieve high scalability of IoT services, and manage the global energy consumption as well. As a proof-of-concept, we evaluate our three-tier architecture by taking into account computational tasks for various applications in IoT related to medical, multimedia, location-based, and text. We evaluate using real datasets, based on three scenarios: fog-only, cloud-only, and fog-cloud collaborative. Task execution policy (at fog/cloud) play a key role in efficiently processing a task (especially large tasks, such as in deep learning). Therefore, we take that into account and elaborate what types of policies suit what type of offloading environment (fog-only, cloud-only, or fog-cloud collaborative).

Finding a vacant parking slot in densely populated areas leads to excessive emission of Carbon Dioxide, fuel, and time wastage. Recently, the Industrial Internet of Things (IIoT) has shown significant potential to strengthen the notion of smart cities equipped with smart parking. In this paper, we propose a Deep Reinforcement Learning (DRL)-based framework for IIoT enabled smart parking system to solve the parking issues. The proposed framework is consist of smart cameras, fog nodes, and a cloud server. The DRL is used in fog devices to classify the vehicles and intelligently allocate the vacant parking slots to vehicles. The smart cameras are deployed at the entry point of the parking space and in the parking lanes as well. The ground cameras capture the image, detect the vehicle and transmit the information to the fog node. On the fog node, the online deep Q-learning algorithm updates the reward score. The proposed framework helps to recognize the vehicle, identify the vacant parking slot for the vehicle in minimum time with high accuracy. We compare the performance of the proposed DRL based technique with the state-of-the-art techniques in terms of accuracy and processing time. Experimental results demonstrate that the proposed DRL based approach not only has high detection accuracy but also minimizes the processing time than the compared techniques.

The widespread acceptance of cloud based services in the healthcare sector has resulted in cost effective and convenient exchange of Personal Health Records (PHRs) among several participating entities of the e-Health systems. Nevertheless, storing the confidential health information to cloud servers is susceptible to revelation or theft and calls for the development of methodologies that ensure the privacy of the PHRs. Therefore, we propose a methodology called SeSPHR for secure sharing of the PHRs in the cloud. The SeSPHR scheme ensures patient-centric control on the PHRs and preserves the confidentiality of the PHRs. The patients store the encrypted PHRs on the un-trusted cloud servers and selectively grant access to different types of users on different portions of the PHRs. A semi-trusted proxy called Setup and Re-encryption Server (SRS) is introduced to set up the public/private key pairs and to produce the re-encryption keys. Moreover, the methodology is secure against insider threats and also enforces a forward and backward access control. Furthermore, we formally analyze and verify the working of SeSPHR methodology through the High Level Petri Nets (HLPN). Performance evaluation regarding time consumption indicates that the SeSPHR methodology has potential to be employed for securely shar-ing the PHRs in the cloud.