Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed to a late stage, making it a leading cause of cancer-related mortality. Lung cancer is fatal if not treated early, making this a significant issue. Initial diagnosis of malignant nodules is often made using chest radiography (X-ray) and computed tomography (CT) scans; nevertheless, the possibility of benign nodules leads to wrong choices. In their first phases, benign and malignant nodules seem very similar. Additionally, radiologists have a hard time viewing and categorizing lung abnormalities. Lung cancer screenings performed by radiologists are often performed with the use of computer-aided diagnostic technologies. Computer scientists have presented many methods for identifying lung cancer in recent years. Low-quality images compromise the segmentation process, rendering traditional lung cancer prediction algorithms inaccurate. This article suggests a highly effective strategy for identifying and categorizing lung cancer. Noise in the pictures was reduced using a weighted filter, and the improved Gray Wolf Optimization method was performed before segmentation with watershed modification and dilation operations. We used InceptionNet-V3 to classify lung cancer into three groups, and it performed well compared to prior studies: 98.96% accuracy, 94.74% specificity, as well as 100% sensitivity.

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 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.

ChatGPT, an advanced Artificial Intelligence tool, is getting considerable attention in higher education. ChatGPT significantly changes the student learning experience through its AI-aided support, personalized study assistance and effective educational experiences, and it has become an object of particular interest in this context. This research aimed to build a technology acceptance and usage model that encapsulates the elements influencing students’ adoption and utilization of ChatGPT, drawing on constructs from the ‘Unified Theory of Acceptance and Use of Technology’ and ‘Flow Theory’. The proposed model was found valid and prolific, with the credibility of the results relying on the self-reported surveys of 505 students from three universities in Pakistan. Structural Equation Modelling (SEM) was used to analyze data that confirmed the robustness and validity of the proposed model of the study. The study findings supported nine out of the ten proposed hypotheses. Perceived playfulness was declared the paramount predictor of behavioral intention, while perceived values and performance expectancy were the next-level predictors. Additionally, behavioral attention was a high and inspiring determinant of ChatGPT usage behavior, followed by attention focus. This analysis demonstrates a need for a thorough investigation of AI tools like ChatGPT in higher education.

School management is responsible and accountable for implementing educational policies into practice effectively and efficiently to provide quality education. Simultaneously, school management can grasp the core features of the whole school process and identify the relationship among three variables: quality management practices, school culture, and student performance. The current study aims to explore the school principals' perception about quality management practices and its relationship with school culture and student performance in the public secondary schools of Punjab province, Pakistan. In order to achieve the objectives of the study, the study adopted an exploratory sequential mix-methods research design. The researcher conducted a systematic literature review of sixty-three previous studies and interviews with eleven school principals for the qualitative data. Based on results obtained from the qualitative phase, a questionnaire was prepared and dispatched to 150 school principals to get quantitative data. Successively 120 valid responses were received. SEM analysis was performed to get quantitative results. The study's preliminary conclusion demonstrated a positive connection between quality management and student performance in public secondary schools, and quality management was also a significant predictor of school culture. Further, school culture served as a complete mediator between quality management and student performance.

According to the World Health Organization, heart disease is the biggest cause of death worldwide. It may be possible to bring down the overall death rate of individuals if cardiovascular disease can be detected in its earlier stages. If the cardiac disease is detected at an earlier stage, there is a greater possibility that it may be successfully treated and managed under the guidance of a physician. Recent advances in areas such as the Internet of Things, cloud storage, and machine learning have given rise to renewed optimism over the capacity of technology to bring about a paradigm change on a global scale. At the bedside, the use of sensors to capture vital signs has grown increasingly commonplace in recent years. Patients are manually monitored using a monitor located at the patient’s bedside; there is no automatic data processing taking place. These results, which came from an investigation of cardiovascular disease carried out across a large number of hospitals, have been used in the development of a protocol for the early, automated, and intelligent identification of heart disorders. The PASCAL data set is prepared by collecting data from different hospitals using the digital stethoscope. This data set is publicly available, and it is used by many researchers around the world in experimental work. The proposed strategy for doing research includes three steps. The first stage is known as the data collection phase, the data is collected using biosensors and IoT devices through wireless sensor networks. In the second step, all of the information pertaining to healthcare is uploaded to the cloud so that it may be analyzed. The last step in the process is training the model using data taken from already‐existing medical records. Deep learning strategies are used in order to classify the sound that is produced by the heart. The deep CNN algorithm is used for sound feature extraction and classification. The PASCAL data set is essential to the functioning of the experimental environment. The deep CNN model is performing most accurately.