A Bibliometric Survey on the Reliable Software Delivery Using Predictive Analysis

Delivering a reliable software product is a fairly complex process, which involves proper coordination from the various teams in planning, execution, and testing for delivering software. Most of the development time and the software budget\u27s cost is getting spent finding and fixing bugs. Rework and side effect costs are mostly not visible in the planned estimates, caused by inherent bugs in the modified code, which impact the software delivery timeline and increase the cost. Artificial intelligence advancements can predict the probable defects with classification based on the software code changes, helping the software development team make rational decisions. Optimizing the software cost and improving the software quality is the topmost priority of the industry to remain profitable in the competitive market. Hence, there is a great urge to improve software delivery quality by minimizing defects and having reasonable control over predicted defects. This paper presents the bibliometric study for Reliable Software Delivery using Predictive analysis by selecting 450 documents from the Scopus database, choosing keywords like software defect prediction, machine learning, and artificial intelligence. The study is conducted for a year starting from 2010 to 2021. As per the survey, it is observed that Software defect prediction achieved an excellent focus among the researchers. There are great possibilities to predict and improve overall software product quality using artificial intelligence techniques

AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images

A Bibliometric Survey of Smart Wearable in the Health Insurance Industry

Smart wearables help real-time and remote monitoring of health data for effective diagnostic and preventive health care services. Wearable devices have the ability to track and monitor healthcare vitals such as heart rate, physical activities, BMI (Body Mass Index), blood pressure, and keeps an individual notified about the health status. Artificial Intelligence-enabled wearables show an ability to transform the health insurance sector. This would not only enable self-management of individual health but also help them focus from treatments to the preventions of health hazards. With this customer-centric approach to health care, it will enable the insurance companies to track the health behaviour of the individuals. This can perhaps lead to better incentivization models with a lower premium to the health-centric customers. Health insurance companies can have better outreach with these customer-centric products. The area is exceptionally novel and shows potential for the research opportunities. Although the literature shows the presence of few works incepting the application of smart wearables in health insurance, it was found that the works are across sections of the society and extremely limited to regions and boundaries. Thus, a need for Bibliometric survey in the area of Smart Wearables in Health insurance is necessary to track the research trends, progress and scope of the future research. This paper conducts Bibliometric study for “Smart Wearables in Health Insurance Industry” by extracting documents of total 287 from Scopus database using keywords like wearables, health insurance, health care, machine learning and health risk prediction. The study is conducted since the last decade that is 2011-2020 for the research analysis. From the study, it is observed that application of wearables in health insurance are in a nascent stage and there is a scope for researchers, insurance, health care stakeholders to explore the used cases for a better user experience

A Bibliometric Analysis of Online Extremism Detection

The Internet has become an essential part of modern communication. People are sharing ideas, thoughts, and beliefs easily, using social media. This sharing of ideas has raised a big problem like the spread of the radicalized extremist ideas. The various extremist organizations use the social media as a propaganda tool. The extremist organizations actively radicalize and recruit youths by sharing inciting material on social media. Extremist organizations use social media to influence people to carry out lone-wolf attacks. Social media platforms employ various strategies to identify and remove the extremist content. But due to the sheer amount of data and loopholes in detection strategies, extremism remain undetected for a significant time. Thus, there is a need of accurate detection of extremism on social media. This study provides Bibliometric analysis and systematic mappings of existing literature for radicalisation or extremism detection. Bibliometric analysis of Machine Learning and Deep Learning articles in extremism detection are considered. This is performed using SCOPUS database, with the tools like Sciencescape and VOS Viewer. It is observed that the current literature on extremist detection is focused on a particular ideology. Though it is noted that few researchers are working in the extremism detection area, it is preferred among researchers in the recent years

Bibliometric Analysis of Passive Image Forgery Detection and Explainable AI

Due to the arrival of social networking services such as Facebook and Instagram, there has been a vast increase in the volume of image data generated in the last decade. The use of image processing tools like GNU Gimp, Adobe Photoshop to create doctored images and videos is a major concern. These are the main sources of fake news and are often used in malevolent ways such as for mob incitement. Before a move can be taken based on a fake image, we should confirm its realness. This paper shows systematic mappings of existing literature for image forgery detection using deep learning and explainable AI. This uses the Scopus database for data analysis and various tools such as Sciencescape, Gephi, Tableau and VOS Viewer. The study discovered that the largest number of reviews on image forgery detection using deep learning and explainable AI had explored very recently. It was observed that USA universities/institutions are foremost in the research studies focusing on this research topic

A Bibliometric Survey on Cognitive Document Processing

Heterogenous and voluminous unstructured data is produced from various sources like emails, social media tweets, reviews, videos, audio, images, PDFs, scanned documents, etc. Organizations need to store this wide range of unstructured data for more and longer periods so that they can examine information all the more profoundly to make a better decision and extracting useful insights. Manual processing of such unstructured data is always a challenging, time-consuming, and expensive task for any organization. Automating unstructured document processing using Optical Character Recognition (OCR) and Robotics Process Automation (RPA), seems to have limitations, as those techniques are driven by rules or templates. It needs to define the template or rules for every new input, which limits the use of rule or templates based techniques for unstructured document processing. These limitation demands to develop a tool which can be able to process the unstructured documents using Artificial Intelligence techniques. This bibliometric survey on Cognitive Document Processing reveals the mentioned facts about unstructured data processing challenges. This survey is performed on the Scopus database’s scientific documents. Various tools such as Microsoft Excel, Sciencescape, VOSviewer, Leximancer, and Gephi for drawing network data analysis diagrams are used. The study revealed that the largest number of publications on Cognitive Document Processing had been explored very recently. It is observed that universities/institutions in India are leading in the research studies focusing on this research topic

A Bibliometric Survey on the Reliable Software Delivery Using Predictive Analysis

Delivering a reliable software product is a fairly complex process, which involves proper coordination from the various teams in planning, execution, and testing for delivering software. Most of the development time and the software budget\u27s cost is getting spent finding and fixing bugs. Rework and side effect costs are mostly not visible in the planned estimates, caused by inherent bugs in the modified code, which impact the software delivery timeline and increase the cost. Artificial intelligence advancements can predict the probable defects with classification based on the software code changes, helping the software development team make rational decisions. Optimizing the software cost and improving the software quality is the topmost priority of the industry to remain profitable in the competitive market. Hence, there is a great urge to improve software delivery quality by minimizing defects and having reasonable control over predicted defects. This paper presents the bibliometric study for Reliable Software Delivery using Predictive analysis by selecting 450 documents from the Scopus database, choosing keywords like software defect prediction, machine learning, and artificial intelligence. The study is conducted for a year starting from 2010 to 2021. As per the survey, it is observed that Software defect prediction achieved an excellent focus among the researchers. There are great possibilities to predict and improve overall software product quality using artificial intelligence techniques

Bibliometric Analysis of Passive Image Forgery Detection and Explainable AI

Due to the arrival of social networking services such as Facebook and Instagram, there has been a vast increase in the volume of image data generated in the last decade. The use of image processing tools like GNU Gimp, Adobe Photoshop to create doctored images and videos is a major concern. These are the main sources of fake news and are often used in malevolent ways such as for mob incitement. Before a move can be taken based on a fake image, we should confirm its realness. This paper shows systematic mappings of existing literature for image forgery detection using deep learning and explainable AI. This uses the Scopus database for data analysis and various tools such as Sciencescape, Gephi, Tableau and VOS Viewer. The study discovered that the largest number of reviews on image forgery detection using deep learning and explainable AI had explored very recently. It was observed that USA universities/institutions are foremost in the research studies focusing on this research topic

A Bibliometric Survey of Smart Wearable in the Health Insurance Industry

Smart wearables help real-time and remote monitoring of health data for effective diagnostic and preventive health care services. Wearable devices have the ability to track and monitor healthcare vitals such as heart rate, physical activities, BMI (Body Mass Index), blood pressure, and keeps an individual notified about the health status. Artificial Intelligence-enabled wearables show an ability to transform the health insurance sector. This would not only enable self-management of individual health but also help them focus from treatments to the preventions of health hazards. With this customer-centric approach to health care, it will enable the insurance companies to track the health behaviour of the individuals. This can perhaps lead to better incentivization models with a lower premium to the health-centric customers. Health insurance companies can have better outreach with these customer-centric products. The area is exceptionally novel and shows potential for the research opportunities. Although the literature shows the presence of few works incepting the application of smart wearables in health insurance, it was found that the works are across sections of the society and extremely limited to regions and boundaries. Thus, a need for Bibliometric survey in the area of Smart Wearables in Health insurance is necessary to track the research trends, progress and scope of the future research. This paper conducts Bibliometric study for “Smart Wearables in Health Insurance Industry” by extracting documents of total 287 from Scopus database using keywords like wearables, health insurance, health care, machine learning and health risk prediction. The study is conducted since the last decade that is 2011-2020 for the research analysis. From the study, it is observed that application of wearables in health insurance are in a nascent stage and there is a scope for researchers, insurance, health care stakeholders to explore the used cases for a better user experience

A Bibliometric Analysis of Online Extremism Detection

The Internet has become an essential part of modern communication. People are sharing ideas, thoughts, and beliefs easily, using social media. This sharing of ideas has raised a big problem like the spread of the radicalized extremist ideas. The various extremist organizations use the social media as a propaganda tool. The extremist organizations actively radicalize and recruit youths by sharing inciting material on social media. Extremist organizations use social media to influence people to carry out lone-wolf attacks. Social media platforms employ various strategies to identify and remove the extremist content. But due to the sheer amount of data and loopholes in detection strategies, extremism remain undetected for a significant time. Thus, there is a need of accurate detection of extremism on social media. This study provides Bibliometric analysis and systematic mappings of existing literature for radicalisation or extremism detection. Bibliometric analysis of Machine Learning and Deep Learning articles in extremism detection are considered. This is performed using SCOPUS database, with the tools like Sciencescape and VOS Viewer. It is observed that the current literature on extremist detection is focused on a particular ideology. Though it is noted that few researchers are working in the extremism detection area, it is preferred among researchers in the recent years