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Real-Time Application with Data Mining and Machine Learning
Data mining and machine learning are the most expressive research and application domain. All real-time application directly or indirectly depends on data mining and machine learning. There are manyrelevantfields, like data analysis in finance,retail, telecommunications sector, analyzing biological data, otherscientific uses, and intrusiondetection.The most expressive research and application domain is data mining and machine learning. Data mining and machine learning are used in all real-time applications, whether directly or indirectly. Data analysis in finance, retail, telecommunications, biological data analysis, extra scientific applications, and intrusion detection are just a few exampleswhere it can be used. Because it captures a lot of data from sales, client purchase histories, product transportation, consumption, and services, DM has a lot of applications in the retail industry. It's only logical that the amount of data collected will continue to climb as the Internet's accessibility, cost, and popularity increase. In the retail industry, DM assists in the detection of customer buying behaviors and trends, resulting in improved customer service and increased customer retention and satisfaction. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Real-time video segmentation using a vague adaptive threshold
For the last two decades, video shot segmentation has been a widely researched topic in the field of content-based video analysis (CBVA). However, over the course of time, researchers have aimed to improve upon the existing methods of shot segmentation in order to gain accuracy. Video shot segmentation or shot boundary analysis is a basic and vital step in CBVA, since any error incurred in this step reduces the precision of the other steps. The shot segmentation problem assumes greater proportions when detection is preferred in real time. A spatiotemporal fuzzy hostility index (STFHI) is proposed in this work which is used for edge detection of objects occurring in the frames of a video. The edges present in the frames are treated as features. Correlation between these edge-detected frames is used as a similarity measure. In a real-time scenario, the incoming images are processed and the similarities are computed for successive frames of the video. These values are assumed to be normally distributed. The gradients of these correlation values are taken to be members of a vague set. In order to obtain a threshold after defuzzification, the true and false memberships of the elements are computed using a novel approach. The threshold is updated as new frames are buffered in and is referred to as the vague adaptive threshold (VAT). The shot boundaries are then detected based on the VAT. The VAT for detecting the shot boundaries is determined by using the three-sigma rule on the defuzzified membership values. The effectiveness of the real-time video segmentation method is established by an experimental evaluation on a heterogeneous test set, comprising videos with diverse characteristics. The test set consists of videos from sports, movie songs, music albums, and documentaries. The proposed method is seen to achieve an average F1 score of 0.992 over the test set consisting of 15 videos. Videos from the benchmark TRECVID 2001 are selected for comparison with other state-of-the-art-methods. The proposed method achieves very high precision and recall, with an average F1 score of 0.939 on the videos chosen from the TRECVID 2001 dataset. This is a substantial improvement over the other existing methods. 2020 Elsevier Inc. -
Recent advances in cancer nanotheranostics
The innovative synthetic approaches coupled with bioengineering aptitude created multiple functional materials in the nanoscale dimension aiming for a combination of therapeutic and diagnostic capacities, often referred to as nanotheranostics. The diverse role played by nanomaterials has been broadly examined in biomedicine, especially in the disciplines of imaging and drug delivery. In this view, cancer is an intimidating foe to the entire human species by adopting various survival skills. Conventional therapies remain to be a failure in meeting the anticipations of the entire medical community. Stepping to the emphasis on cancer nanotheranostics, which requires more advancement to amalgamate and fine-tune diagnosis and therapy, has already attracted significant research interest among researchers in chemistry, material science, life science, and clinicians. Monitoring the therapeutic response in a real-time manner with the intelligent fabrication of nanotheranostic agents could strike down the daunting claws of cancer by facilitating personalized treatment approaches. Here, we aimed to portrait the key approaches and recent developments in nanotheranostics with a focus on its clinical impact in oncology. 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Recent advances in lightweight epoxy-based composites for X-Ray and y-Ray shielding applications
Rapidly advancing technologies in the nuclear industry have led to the increased use of X-rays and ?-rays in our day-to-day life. They have emerged to be an integral part of several industries including medical diagnostics and imaging, nuclear medicine, reactor research facilities, industrial gauging, agricultural irradiation, geological exploration and security purposes. However, considering the adverse effects of prolonged exposure to these radiations on human health, this is also a cause of concern for mankind and radiation shielding and protection have become issues of paramount importance. In the search for alternatives to conventional shielding materials such as lead, metals, glass composites, ceramics and concretes, epoxy-based composites have emerged as promising X-ray and ?-ray shields. Material properties like high mechanical and bonding strength, high temperature resistance, low electrical conductivity and thermal expansion coefficients, dielectric constant with minimal shrinking stress and lightweight structure render epoxy composites to be particularly suitable for structural applications. Epoxy composites incorporated with fillers/additives such as inorganic metal oxides, carbon fibers, clay and carbon nanotubes are an emerging class of high-performance materials. The primary focus of this article is to present a detailed review on the recent research directed towards developing epoxy-based materials for radiation shielding applications. Influence of filler loading, filler size and interfacial adhesion on microstructural, thermo-mechanical and radioprotective efficacy of epoxy composites are discussed. We present a general overview and propose new possibilities for further research in this direction. 2022 Nova Science Publishers, Inc. -
Recent development on self-powered and portable electrochemical sensors: 2D materials perspective
Electrochemical sensors have attracted tremendous research interest due to their simplicity and compatibility to be integrated with standard electronic technologies and capability to produce electrical signals that can be effectively acquired, processed, stored, and analyzed. Due to the incredible electronic and physical properties derived from the 2D structure, two dimensional (2D) nanomaterials such as graphene, phosphorene black phosphorus, transition metal dichalcogenides (TMDCs), and others have proven to be attractive for the fabrication of high-performance electrochemical sensors. The book chapter is focused in the unique characteristics of 2D materials leading toward excellent sensing performance, the structural and molecular designing of various 2D materials, structure-property relationships, various sensing applications employing disparate 2D nanostructures with an emphasis on highlighting various prototypical and prominent research paths. 2023 Elsevier Inc. All rights reserved. -
Recent Progress on the Development of Chemosensors
Chemosensors are the chemical structures which convert chemical stimuli into responsive form that can be easily detected, such as change of colour, fluorescence, and other electronic signal. Recently, chemosensors development for detection and monitoring of gases has been growing interest due to the significant importance in environmental and biological systems. Subsequently, the development of chemosensors for detection of various gases is considered to be a significant goal in science and among the all gases, carbon dioxide (CO2) is a major public concern due to its role in global greenhouse warming with environmental pollution. Moreover, quite critical level of CO2 in the modern agricultural, food, environmental, oil and chemical industries is dangerous for living beings to survive such high concentration levels of CO2. Therefore, rapid and selective detection and monitoring of CO2 in the gaseous as well as in the liquid phases provides an incentive for development of new methods. The coverage of this book chapter is divided into different sections according to the use of different types of molecular backbones and the detection pathways. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Recommendation Framework for Diet and Exercise Based on Clinical Data: A Systematic Review
Nowadays, diet and exercise recommender frameworks have gaining expanding consideration because of their importance for living healthy lifestyle. Due of the expanded utilization of the web, people obtain the applicable wellbeing data with respect to their medicinal problem and available medications. Since diseases have a strong relationship with food and exercise, it is especially essential for the patients to focus on adopting good food habits and normal exercise routine. Most existing systems on the diet concentrate on proposals that recommend legitimate food items by considering their food choices or medical issues. These frameworks provide functionalities to monitor nutritional requirement and additionally suggest the clients to change their eating conduct in an interactive way. We present a review of diet and physical activity recommendation frameworks for people suffering from specific diseases in this paper. We demonstrate the advancement made towards recommendation frameworks helping clients to find customized, complex medical facilities or make them available some preventive services measures. We recognize few challenges for diet and exercise recommendation frameworks which are required to be addressed in sensitive areas like health care. 2019, Springer Nature Singapore Pte Ltd. -
Recommendation of diet using hybrid collaborative filtering learning methods
These days, various recommender systems exist for online advertisement services which recommend the products considering users interests. Similarly, health recommendation systems are becoming most important component in individuals life. Due to the modernization and busy schedule, people give less concern to their eating patterns. This leads to various health issues like obesity, thyroid disorder, diabetes and others. Every individual has different health issues and food habits. Therefore, diet recommendations should be suggested by considering their personal health profile and food preferences. So, it becomes essential to analyze individuals health concerns before recommending the diet with required nutrient values. Thus, it helps people to minimize the further risks associated with the current health conditions. The proposed diet and exercise recommender framework suggests a balanced diet for thyroid patients. It takes care of the food intake with necessary nutrients requirement based on thyroid disorders. This paper applies K-nearest neighbor collaborative filtering models using various similarity measures. The paper assessed two-hybrid learning methods, KNN with alternating least squares: KNN-ALS and KNN with stochastic gradient decent: KNN-SGD. The experimental setup analyzed and evaluated the performances of all algorithms using mean absolute error (MAE) and root mean squared error (RMSE) values. Springer Nature Singapore Pte Ltd 2020. -
Recommendation of food items for thyroid patients using content-based knn method
Food recommendation system has become a recent topic of research due to increase use of web services. A balanced food intake is significant to maintain individuals physical health. Due to unhealthy eating patterns, it results in various diseases like diabetes, thyroid disorder, and even cancer. The choice of food items with proper nutritional values depends on individuals health conditions and food preferences. Therefore, personalized food recommendations are provided based on personal requirements. People can easily access a huge amount of food details from online sources like healthcare forums, dietitian blogs, and social media websites. Personal food preferences, health conditions, and reviews or ratings of food items are required to recommend diet for thyroid patients. We propose a unified food recommendation framework to identify food items by incorporating various content-based features. The framework uses the domain knowledge to build the private model to analyze unique food characteristics. The proposed recommender model generates diet recommendation list for thyroid patients using food items rating patterns and similarity scores. The experimental setup validated the proposed food recommender system with various evaluation criteria, and the proposed framework provides better results than conventional food recommender systems. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Recommendations from teachers on schools' roles in identifying problems and building awareness among students
Students develop skills, gain knowledge, and achieve greater wellbeing by creating a positive school environment. Through the years, schools have realized the importance of mental health services for adolescents. Research on the role of schools in mental health awareness building and preventing mental health problems is meager, and focuses on students in the western context. This chapter focuses on the recommendations given by teachers on what role schools can play in identifying, preventing, and building awareness among adolescents. These recommendations are based on the themes obtained through semi-structured interviews with 24 teachers teaching 10th, 11th, and 12th graders in private high schools and colleges in Bangalore. Consequently, it aims to provide an overview of incorporating techniques and strategies to enhance mental health among school students in the Indian Scenario. 2024, IGI Global. All rights reserved. -
Recruitment Analytics: Hiring in the Era of Artificial Intelligence
Introduction: Traditional recruitment system relied heavily on the applicants curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be misfits. CVs were the only source of candidates data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process. Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages. Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes. Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles. Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidates perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees queries. Resume screening techniques can save the recruiters time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team. 2023 by V. R. Uma, Ilango Velchamy and Deepika Upadhyay. -
Redefining learning: Harnessing the power of flipped classroom pedagogy
This chapter examined the ever-changing educational environment by utilizing flexible classroom pedagogy as a framework. The authors anticipate thoroughly examining how this novel methodology revolutionizes conventional learning paradigms by focusing on active and individualized learning encounters. This chapter illuminates how instructors can proficiently implement flipped classroom methodologies to augment student engagement, critical thinking, and final learning results by examining foundational principles, exemplary approaches, and case studies. By examining many instructional strategies and technologies, this chapter imparted insightful perspectives on the future of education. 2024, IGI Global. All rights reserved. -
Redefining Organizational Sustainability Through Revamping Digital Capital
[No abstract available] -
Redefining traditional education using augmented reality and virtual reality
[No abstract available] -
Reduce Overfitting and Improve Deep Learning Models Performance in Medical Image Classification
A significant role in clinical treatment and educational tasks is played by clinical image classification. However, the traditional approach has reached its peak in terms of implementation. Additionally, using traditional approaches requires a lot of time and effort to remove and choose arrangement features. The deep learning (DL) model is a new machine learning (ML) technique that has proven effective for various classification problems. To alter image classification problems, the convolutional neural network performs well, with the best results. This chapter discusses the importance and challenges of deep learning models in medical image classification and explains some techniques for reducing overfitting and leveraging model performance during model training. 2024 Taylor & Francis Group, LLC. -
Regression Approach for Predictive Analysis in Cognitive Decline
Cognitive decline refers to the deterioration of cognitive abilities, including memory, thinking, and reasoning, often associated with aging or neurological disorders like Alzheimer's disease. Machine learning (ML) methods can be used for predicting cognitive decline. Techniques such as Generative Adversarial Networks (GANs), feed-forward neural networks, supervised, and unsupervised learning process and analyse data patterns to forecast cognitive changes. By analyzing large datasets, ML algorithms can identify subtle cognitive shifts and predict future decline, enabling early intervention and personalized healthcare strategies. These diverse ML methods provide valuable tools for understanding, detecting, and potentially mitigating cognitive decline, advancing our ability to address cognitive health challenges. Some of these methods have been discussed later. In this research paper, a model to predict cognitive decline using principles of logical regression is proposed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Reinventing the business model to navigate the evolving business landscape
In today's rapidly evolving business landscape, technological advancements, shifting consumer behaviours, and global economic fluctuations increasingly challenge traditional business models. Organisations must reinvent their business models to remain competitive, embracing innovation, agility, and sustainability. This chapter explores the critical components of reinventing business models, including leveraging digital transformation, adopting customer-centric approaches, and integrating sustainable practices through a comprehensive view of their development. This study thoroughly understands the existing literature on business models, focusing on the features. Integrating sustainability into a business model is also a challenge for many practitioners. The business model innovation agenda is the topic of discussion among most companies. This chapter will explore adopting a business model towards sustainability by integrating environmental, social, and governance factors. The findings underscore the necessity for continuous adaptation and strategic foresight to drive long-term success. 2024, IGI Global. All rights reserved. -
Relevance of backcasting as a strategic tool towards organisations' sustainable future ' A key to thrive in the VUCA world
Purpose: The business world has become more turbulent than ever. Organisations must be proactive to meet the challenges of the increasingly disruptive, dynamic, and unpredictable world. One technique that has supported leaders and organisations under challenging circumstances is 'backcasting', which works by envisioning a desirable future state and then working backwards to attain it. The current study aims to extend an understanding of the backcasting practices and strategic approaches that can be used by leadership teams in different industries in order to survive in turbulent times and can be adapted within and beyond any pandemic. Methodology: The research employs a desktop research method to review and compare the most commonly used strategies that leaders from different sectors can use for their respective organisations to thrive in the VUCA world. Findings: There needs to be more research on the applicability and relevance of backcasting that the leaders of different sectors can employ. The study would provide insights that would bridge the existing research gap and come up with suitable strategies that can be employed for dealing with VUCA challenges in the Indian context. Significance: The outcome of the study will be helpful to the leaders in designing and aligning 'out of the box' backcasting strategies to survive in the highly disruptive world. 2024 The authors. Published under exclusive licence by Emerald Publishing Limited. All rights reserved. -
Remote work realities: A comprehensive study on individual choices and task accomplishments
The global work landscape has undergone a paradigm shift with the widespread adoption of telecommuting, a transformation further accelerated by the COVID- 19 pandemic. This research delves into the intricate dynamics of telecommuting, focusing on the impact of individual characteristics on the choice to work remotely in the post- pandemic era. It reveals that gender significantly influences telecommuting preferences, while age and years of experience do not show a discernible impact. Beyond individual factors, the study examines how telecommuting attitudes affect task accomplishment, highlighting substantial effects on goal attainment and underscoring the need to understand work arrangement's complexities. Additionally, the research explores the diverse strategies employed by employers and employees, illustrating successful approaches to remote work. Ultimately, this study navigates the evolving telecommuting landscape, offering insights into challenges and achieve-ments, and provides a foundation for future research on telecommuting's impact on work practices and individual well- being. 2024, IGI Global. All rights reserved. -
Removal of Occlusion in Face Images Using PIX2PIX Technique for Face Recognition
Occlusion of face images is a serious problem encountered by the researchers working in different areas. Occluded face creates a hindrance in extracting the features thereby exploits the face recognition systems. Level of complexity increases with changing gestures, different poses, and expression. Occlusion of the face is one of the seldom touched areas. In this paper, an attempt is made to recover face images from occlusion using deep learning techniques. Pix2pix a condition generative adversarial network is used for image recovery. This method is used for the translation of one image to another by converting an occluded image to a non-occluded image. Webface-OCC dataset is used for experimentation, and the efficacy of the proposed method is demonstrated. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.