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The rapid development in the field of information technology has provided significant benefits to both society and the business world. There are numerous ethical challenges that computer users must deal with. These include violation of intellectual property, hacking, invasion of privacy, honesty, secrecy, loyalty, accountability, and responsibility. Situational and individual characteristics influence information systems employees' behavioral intentions to act ethically. The situational variables that affect employee ethical behavior and financial losses can be controlled and influenced by organizations to determine and control employee ethical conduct and financial losses. Ethics-related losses caused by information systems employees can be successfully resolved by incorporating the code of ethics into the code of professional conduct and employee contract, incorporating ethics into organizational culture, designing rewards for ethical behavior and severe punishments for unethical behavior, and incorporating ethics into the code of professional conduct and employee contract.

Soft lifting is a type of software piracy that is prevalent in today's computer-dependent world. Soft lifting is a type of software piracy in which software is installed or duplicated in a system in violation of its license agreement. In software development, ethical decisions are critical, as they have a significant impact on end users, organizations, and the environment. As the problem of copying affects soft lifting behaviour, the research lends some credence to the postulates model. Most notably, the study found that one's ethical perspective of soft lifting has no bearing on one's softlifting activity. These findings have significant implications for both software companies and academics who are aiming to prevent piracy through ethics education.

The signal analysis helps us derive useful knowledge from biological processes to analyze, describe, and understand their origin mechanisms. However, biomedical signals are not immune and have time-consuming statistics. The major challenges of signal analysis of sportsperson are reliability and accuracy. Sports psychology uses psychological skills to discuss the optimum success and well-being of sports athletes, the developmental and social dimensions of the sport and sports facilities, and structural problems. The signal detection tool is used to detect the best combination of long-term practice predictors for active, sedentary adults’ signal. This paper proposed the wearable assisted signal detection method (WASDM) to find the sportspersons’ behavior signal analysis. This method performs an IoT based heart rate monitoring using a wearable device named intelligent bracelet mounted on the sportsperson to track the variations in his/her human heart rate. The wearable signal detector method analysis the heart rate abnormality and predicts health status, followed by an alarm to the physician and the respective personnel while performing activity session. In this research, various machine learning algorithms have been tried to perform signal analysis and prediction and compared their results to suggest the best in this application scenario. Finally, the experimental analysis shows better outcomes for the sportspersons’ psychological behavior signal analysis than the conventional methods.

Conventional educational systems have been uplifted for their efficiency using information and communication technologies in a pervasive manner. The information accumulated from the students, environments, and observations increases data exchanged in the smart education platform. The challenging aspect is the data correlation and its correctness in delivering interactive educational services. Because of addressing the correctness issue, this article proposes a Flexible Observation Data Analytics Model (FODAM). The proposed model relies on the session requirement for extracting useful information. The correctness of the information is verified at the interaction level without losing any tiny observation data. In this model, regressive learning is used for the progressive identification of required data. This learning relies on session requirements and the discreteness of the observed data. The progressive training thwarts the discreteness to provide reliable and non-interrupting educational data for the interacting members. The proposed model's performance is verified using the metrics delay, efficiency, interrupts, and information rate.

Education is a dynamic system by which students perceive the factors necessary to fit them into the society. Education is mainly intentional learning that grooms individuals to achieve success in their adult lives. Evaluation of teaching techniques, course management (CM), communication, and student monitoring are the main characteristics of today’s education system. The aim to plan the curriculum of education management in both schools and colleges leads to the implementation of an MS-BDA. The development process for evaluation of teaching techniques and CM includes the use of the sentiment analysis method, which assesses the emotional feelings of students studying the course by managing curriculum quality. The big data analysis with MNN is developed by considering the communication and student monitoring system. This system evaluates the monitoring model provided in MS-BDA for assessing student communication on merging the voice-over with the communication language processing system. The simulation analysis is performed based on accessibility, adaptability, and efficiency, proving the proposed framework’s reliability. Therefore, the system outputs an accuracy of 99.1% when compared to the existing methods.

Students, faculties, and other members of the higher education (HEd) system are increasingly reliant on various information technologies. Such a reliance results in a plethora of data that can be explored to obtain relevant statistics or insights. Another reason to explore the data is to acquire valuable insight regarding the novel unstructured forms of data that are discovered and often found to have a connection with elements of social media such as pictures, videos, Web pages, audio files, etc. Moreover, the data can bring additional valuable benefits when processed in the context of HEd. When used strategically, Big Data (BD) provides educational institutions with the chance to improve the quality of education from all the perspectives and steer students of HEd toward higher rates of completion. Further, this will improve student persistence and results, all of which are facilitated by technology. With this aim, the current research proposes a framework that analyzes the data collected from heterogeneous sources and analyzes using BD analytics tools to do various types of analysis that will be beneficial for different learners, faculties and other members of HEd system. Moreover, current research also focuses on the challenges of acquiring BD from various sources.

Violation of Academic integrity has become one of the major problems in education today. Academic Integrity is an ethical practice that means students are achieving academic success fairly. It suggests that all grades that are achieved are earned honestly. Cheating is one of the most common ways to disobey academic integrity. Statistics exist on various facets of cheating in high schools, colleges and universities and thus violating the academic integrity. The current study is conducted in United Arab Emirates - UAE to check the prevalence of academic integrity (cheating) among the students pursuing undergraduate and postgraduate courses in various universities in UAE. Questionnaire is used to collect the data for the current research. It is divided into three sections. Section A is used to get the demographic information of students. Section B is used to check the attitude of university students towards cheating. Section C consists of questions to find out the factor which influences the student to do cheating in exam. The survey instrument is distributed to 300 students with the approximate returns of 80%. The data is analyzed using SPSS 20 and AMOS 21 to analyze the factors and other results of the study. The results shows that high job position, higher grades, less time to study, cheated in past, difficult course material, cheating environment are factors that encourage students to do cheating. This research is step towards finding the factors that force or motivates the students to do cheating. The current study further helps the management of universities and colleges to formulate the policies. It also gives insights to faculties to formulate strategies that will demotivate the students against cheating. The current research is an attempt to provide insights that can be used to develop a set of academic and administrative strategies to manage academic integrity effectively and efficiently.

Digital techniques have been used to record cultural heritage with high-quality imagery and documentation. However, some historical properties are completely or partially labeled, and some are externally impaired, which reduces their attraction and causes loss of value. Classification of images is one of the most significant digital-era tasks. As for cultural heritage, classification processes must be developed well and less computer-intensive because image classification typically requires huge data. In this article, Deep Learning assisted Intangible Cultural Heritage Management (DLICHM) has been proposed based on digital tools. A deep learning model automatically analyzes damaged images at the computing terminal based on the gathered data. This approach focuses on the automated annotation and completion of metadata by new deep learning and annotation approaches. It tackles visually damaged objects through a novel approach to image reconstruction focused on supervised and unsupervised learning. The findings suggest that deep learning provides an adequate solution to the semantic annotation of shared cultural heritage data.

In today's data-driven panorama, the ability to analyze information to drive decision-making and resolve problems is fundamental for success. This requires a robust, effective, flexible, data analytics that assists to build accurate predictive versions quickly and intuitively. Data analysis is a common technique used to analyze data in various fields of modern scientific research, which includes different divisions of Data analytics include many techniques. Clustering is considered as one of the unsupervised learning technique for analyzing the data. With the increase in number of disciplines, the amount of data is also increased. This results in the development of various tools and algorithms for applying cluster analysis. Each of the clustering algorithm has its own advantages and limitations and it completely depends on the complexity of available information. The current research is an attempt to analyze the data using clustering techniques. The researcher use python language to compile a program to collect the data from an enterprise's information management system. Python is used to analyze and clusters are interpreted accordingly. The results of clustering data based on different dimensions will lead to improve knowledge about the data accordingly.