Browse Items (1422 total)
Sort by:
-
A Review on Preprocessing Techniques for Noise Reduction in PET-CT Images for Lung Cancer
Cancer is one of the leading causes of death. According to World Health Organization, lung cancer is the most common cause of cancer deaths in 2020, with over 1.8 million deaths. Therefore, lung cancer mortality can be reduced with early detection and treatment. The components of early detection require screening and accurate detection of the tumor for staging and treatment planning. Due to the advances in medicine, nuclear medicine has become the forefront of precise lung cancer diagnosis. Currently, PET/CT is the most preferred diagnostic modality for lung cancer detection. However, variable results and noise in the imaging modalities and the lung's complexity as an organ have made it challenging to identify lung tumors from the clinical images. In addition, the factors such as respiration can cause blurry images and introduce other artifacts in the images. Although nuclear medicine is at the forefront of diagnosing, evaluating, and treating various diseases, it is highly dependent on image quality, which has led to many approaches, such as the fusion of modalities to evaluate the disease. In addition, the fusion of diagnostic modalities can be accurate when well-processed images are acquired, which is challenging due to different diagnostic machines and external and internal factors associated with lung cancer patients. The current works focus on single imaging modalities for lung cancer detection, and there are no specific techniques identified individually for PET and CT images, respectively, for attaining effective and noise-free hybrid imaging for lung cancer detection. Based on the survey, it has been identified that several image preprocessing filters are used for different noise types. However, for successful preprocessing, it is essential to identify the types of noise present in PET and CT images and the appropriate techniques that perform well for these modalities. Therefore, the primary aim of the review is to identify efficient preprocessing techniques for noise and artifact removal in the PET/CT images that can preserve the critical features of the tumor for accurate lung cancer diagnosis. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
The importance of strategic agility and resilience in work-life balance
This chapter's objective is to analyze agility and resilience which are essential qualities of work-life balance. Similarly, both enable individuals to play the important role of both professional and personal responsibilities effectively. In this chapter, the author has mentioned the importance and the role of strategic agility which describes the ability to predict and respond quickly to changes and challenges in the work environment. Also, all these involve being acceptable, adaptable, flexible, and open to new ideas and approaches. In the work-life balance framework, strategic agility supports individuals to be proactive, positive, and efficient enough to manage their time and energy. It helps individuals prioritize their tasks, allocate resources properly, and enhance their understanding of where to invest their efforts. 2024, IGI Global. 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. -
Body mass index implications using data analysis in the soccer sports
Soccer is considered among the most popular sports in the world among the last few years. At the same time, it has become a prime target in developing countries like India and other Asian countries. As science and technology grow, we can see that sports also grow with science, and hence technology being used to determine the results sometime or sometimes it is used to grow the overall effect. This paper presents the attributes and the qualities which are necessary to develop in a player in order to play for the big-time leagues called Premier League, La Liga, Serie A, German Leagues and so on. Simple correlation and dependence techniques have been used in this paper in order to get proper relationship among the attributes. This paper also examines how the body mass index plays an effect on the presentation of soccer players with respect to their speed, increasing speed, work rate, aptitude moves and stamina. The point is likewise to discover the connection of the above credits concerning body mass index. As in universal exchange, football clubs can profit more in the event that they have practical experience in what they have or can make a similar bit of room to maneuver. In a universe of rare assets, clubs need to recognize what makes them effective and contribute in like manner. Springer Nature Singapore Pte Ltd 2021. -
Role of social media in the digital transformation of business
A digital transformation endeavor is the use of technology and digital processes to enhance business operations and consumer experiences. These projects frequently include the use of new technology like social media platforms, artificial intelligence (AI), and analytics, as well as the execution of digital processes like cloud computing, omnichannel commerce, data analytics, and automation. An organization needs to integrate digital transformation initiatives into its current systems if it wants to stay current with the rapidly evolving technology landscape of today. Social media is now an essential part of contemporary life, and businesses are increasingly using it to connect with their clients and other stakeholders. To take advantage of social media's huge potential, businesses are incorporating it into their digital transformation initiatives. Copyright 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 2023 by IGI Global. All rights reserved. -
Fostering civic engagement and community development through service-learning capstone courses in higher education: An in-depth analysis of department models in Indian universities
A nation's strength is intricately linked to the education of its citizens, and the evolving landscape of higher education is evident in the changing role it plays. The National Education Policy (NEP) established on July 29, 2020, provides a framework that opens avenues for experiential learning, emphasizing hands-on experiences to complement theoretical knowledge. Keeping this in mind, this chapter introduces the Service-Learning Capstone Course Model designed to cultivate civic responsibility, empathy, and risk management while fostering the holistic growth of learners as responsible citizens in this century. The study demonstrates how the inclusion of a service-learning component in the syllabus can develop personal, professional, and academic growth in a learner through the knowledge gained. 2024, IGI Global. All rights reserved. -
Data Analytics and ML for Optimized Performance in Industry 4.0
Industry 4.0, the fourth industrial revolution, has revolutionized manufacturing and production systems by integrating Data Analytics (DA) and Machine Learning (ML) techniques. Predictive maintenance, which predicts equipment malfunctions and schedules maintenance in advance, is a crucial application of DA and ML within Industry 4.0. It reduces downtime, improves productivity, and lowers costs. Demand forecasting, which uses historical data and ML algorithms to predict future product demand, and anomaly detection, which identifies abnormal patterns or events within large datasets, are also critical applications of DA and ML in Industry 4.0. They enhance operational efficiency and reduce costs. However, the adoption of DA and ML presents several challenges for organizations, including infrastructure, personnel, ethical, and privacy concerns. To realize the benefits of DA and ML, companies must invest in appropriate hardware and software and develop the necessary expertise. They must also handle data responsibly and transparently to ensure privacy and ethical standards. Despite these challenges, the integration of DA and ML in Industry 4.0 is critical for optimized performance, improved productivity, and cost savings. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
COVID-19 and Mental Health of Indian Youth: Association with Background Variables and Stress
The coronavirus has become a public health concern of the decade, affecting the economic, social, and psychological stability of the whole world. Having understood the detrimental impact of the pandemic to the mental health of people of all age groups, youth is understood to be the most vulnerable population who receives its direct impact. The broad objective was to study the mental health status of Indian youth and its association with various demographic variables. Psychological stress and mental health was another relationship that was explored. A group of 317 participants between the age group of 19 to 29 voluntarily took part in the online survey. Gender was found to be associated with overall mental health status (p < 0.01) as well its correlates, namely anxiety (p < 0.05), depression (p < 0.05), and loss of behavioral control (p < 0.01). Association between age and loss of positive affect (p < 0.05), number of siblings and loss of behavioral control (p < 0.01), and family environment and overall mental health scores (p < 0.001) were found. Similarly, feeling of restlessness during lockdown (p < 0.001), availability of support (p < 0.001), and feeling the need to consult a mental health professional were associated with the overall mental health score as well as all its sub-scales. Further, there were strong negative correlations between psychological stress and overall mental health scores, as well as that of anxiety, depression, and loss of behavioral control and positive affect sub-scales. The study highlighted the need for psychological support services for the youth population of the country to cope with and adapt to the new situation. The Editor(s) (if applicable) and The Author(s), under exclusive license to Taylor and Francis Pte Ltd. 2022. -
Doctoral Research by Youth: Analyzing the Role of Socio-Demographic Variables on Flourishing and Grit
The study examines the importance of socio-demographic variables like age, gender, family environment, and relationship with parents and friends in deter-mining non-cognitive traits such as flourishing and grit, during the tenure of doctoral research. The cross-sectional correlational study comprises 400 Ph.D. scholars from a Central University in India, who were given a personal data sheet, the Flourishing Scale and the Grit Scale, for assessment. The results of the F-test showed that flourishing was significantly related to age, family environment and relationship with friends, and grit was significantly related to family environment and relationship with friends. Analysis using Pearson correlation found a weak correlation between flourishing and the three subscales of grit, namely ambition, consistency of interest, and perseverance of effort. Findings suggest that the socio-demographic variables are important contributors in the long-term goal-oriented behaviors and that flourishing and grit are two related but not correlated variables that influence completion and attrition of the doctoral research. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Comparative Analysis of Different Machine Learning Prediction Models for Seasonal Rainfall and Crop Production in Cultivation
Agriculture is one of the strengths of India, from the last few years, gradually the agriculture growth is going downwards in other side the population growth is upwards. Reason for agricultural downward growth depends on so many parameters. The rainfall is one of the main parameters which affects the crop yield. Because of this, the farmers are also facing the loss. If they know this information in prior, the farmers can plan accordingly the type of crop suited for the particular season and it helps the farmer to get good profit out of it. Machine learning scientific and statistical methods are used for predicting the rain fall and crop yield. Kharif and Rabi are two seasons taken for analysis. The regressor predicting models are constructed to predict the seasonal rainfall and crop yield. This study primarily focuses on seasonal crop production prediction, which is dependent on rainfall. The different types of machine learning regression method are used to achieve better results. The performance of comparison models is evaluated using different metrics. Finally, the linear regression and Bayesian linear regression models comparatively produce the best result in terms of accuracy for rainfall prediction. The boosted decision tree regression model is achieving the better result for crop prediction. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Hereditary factor-based multi-featured algorithm for early diabetes detection using machine learning
Today's advent in the medical industry have given numerous chances to improve the quality of detection and reporting the diseases at the early stages for a better diagnosis. Modern day datasets generate fruitful information for timely and periodic monitoring of patients' health conditions. Such information is hidden to a naked eye or hidden in multiple track records of highly affected population. Diabetes mellitus is one such disease which is predominant among a global population which ultimately leads to blindness and death in some cases. The model proposed in this system attempts to design and deliver an intelligent solution for predicting diabetes in the early stages and address the problem of late detection and diagnosis. Intensive research is carried out in many tropical countries for automating this process through a machine learning model. The accuracy of machine learning algorithms is more than satisfactory in the detection of Type 2 diabetes from the dataset of PIMA Indians Diabetes Dataset. An additional feature of hereditary factor is implemented to the existing multiple objective fuzzy classifiers. The proposed model has improved the accuracy to 83% in the training and tested datasets when compared to NGSA model of prediction. 2022 Scrivener Publishing LLC. -
A hybrid semantic algorithm for web image retrieval incorporating ontology classification and user-driven query expansion
There is always a need to increase the overall relevance of results in Web search systems. Most existing web search systems are query-driven and give the least preferences to the users needs. Specifically, mining images from the Web are a highly cumbersome task as there are so many homonyms and canonically synonymous terms. An ideal Web image recommendation system must understand the needs of the user. A system that facilitates modeling of homonymous and synonymous ontologies that understands the users need for images is proposed. A Hybrid Semantic Algorithm that computes the semantic similarity using APMI is proposed. The system also classifies the ontologies using SVM and facilitates a homonym lookup directory for classifying the semantically related homonymous ontologies. The users intentions are dynamically captured by presenting images based on the initial OntoPath and recording the user click. Strategic expansion of OntoPath based on the users choice increases the recommendation relevance. An overall accuracy of 95.09% is achieved by the proposed system. 2018, Springer Nature Singapore Pte Ltd. -
Longitudinal study on noncommunicable diseases using machine learning
This longitudinal case study thoroughly explores the intricate connection between body mass index (BMI) and four key factors: physical health, psychological well-being, lifestyle choices, and the impact of diet on health. Through the analysis of longitudinal data, notable trends emerge, revealing an increase in risk factors for noncommunicable diseases (NCDs) and unhealthy behaviors over time. This highlights the combined impact of these interconnected factors on health outcomes and the risk of developing NCDs like heart disease, diabetes, and cancer. Leveraging machine learning, the study effectively identifies individuals at elevated risk for NCDs and dispels common health misconceptions, underscoring the significance of holistic wellness approaches. Serving as a beacon for the next generation, this study provides insights that contribute to shaping a healthier future. 2025 selection and editorial matter, Arun Kumar Rana, Vishnu Sharma, Sanjeev Kumar Rana, and Vijay Shanker Chaudhary; individual chapters, the contributors. All rights reserved. -
Polymer-Based Nanomaterial as a Bacteriostatic Agent on Gram-Positive Bacteria
The colonization of surfaces by bacteria is a widespread phenomenon that affects environmental processes and human health. Bacterial growth can also be found in materials used in the textile industries, food packaging, and wearable electronics. Moreover, the necessity for replacing traditional antibiotics is relevant due to the increased health risks of antimicrobial resistance from the excessive use of antibiotics. Recently, research is focused more on developing polymer-based antibacterial materials critical to preventing bacterial proliferation. The use of some nanomaterials appears to be very promising in this regard. This work reports the synthesis of a polymer-based nanomaterial derived from polyvinyl alcohol (PVA) via the hydrothermal method and studies its structural and optical properties. It is also observed that these nanoparticles (NPs) display the highest antibacterial potency against gram-positive (Bacillus subtilis) bacteria than their bulk counterpart. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deep Learning Decision Support Model for Police Investigation
A police investigation is an exciting task with many complicated processes that may or may not succeed. However, it is the sole duty of a police officer to understand the crime scene, reconstruct the event and predict the criminal with accuracy. There are various methods for interrogations, predictions, and confirmation after identifying a person as a criminal or upon concluding their actions as a criminal act. However, we can see massive growth in crime rates every day. This massive growth rate makes conventional prediction or analysis very strenuous. In such times we can use or take the help of deep learning and machine learning methods for crime analysis and suspect prediction by identifying the data points in a set. This prediction methodology is known as intelligence analysis which simulates the dataset to draw a connection or pattern collectively from millions of data points to identify the instigator and linkman. This chapter will summarize the uses of deep learning and artificial intelligence in a decision support model for police investigation. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Applications of artificial intelligence techniques in modern banking sectors
AI-powered decision-making instruments are cutting-edge technology that has the potential to displace conventional banking procedures. This chapter emphasizes the critical role artificial intelligence (AI) has played in guiding the banking industry toward expansion. AI techniques including robotics, deep learning, facial recognition, natural language processing, and more are used to achieve this goal. This chapter provides an overview of the use of AI approaches in several banking functional domains, such as loan approval, customer lifecycle management, customer services, alarm systems, and so on. It also highlights the benefits and difficulties that AI-driven financial apps provide. In summary, artificial intelligence (AI) has enormous promise in banking, but it also confronts several obstacles that, if correctly recognized and overcome, might broaden its use. This chapter is an invaluable tool for researchers, lawmakers, and bank officials who want to learn more about the unrealized potential of artificial intelligence in banking. 2024, IGI Global. All rights reserved. -
Incorporating the metaverse into the green banking revolution: Spearheading the implementation of eco-friendly financial practices
This study aims to explore the awareness and perceptions of green banking among bankers and customers in rural and semi-urban areas of India. A structured questionnaire was employed to gather information from 807 customers and 200 officials of selected commercial banks, utilizing the snowball sampling method. The study utilized chi-square and factor analysis techniques. The chi-square test results revealed an association between educational status and the customer's opinion regarding green banks. Factor analysis derived three key factors influencing the adoption of green banking: convenience and environmental sustainability, financial and technological advantages, and customer retention and prestige. The findings indicate that green banking services provide more benefits to its customers than traditional banking. 2024, IGI Global. -
Water Purification Using Subnanostructured Photocatalysts
Visible light is an abundant resource, and photocatalysts absorb this light and use it to energize chemical reactions. Of the many types of reactions that are catalyzed by photocatalysts, wastewater purification is an important area. Photocatalysis is an economical, eco-friendly, and sustainable method of purifying water, a precious resource for which need is increasing while availability is shrinking. Of the several types of photocatalytic materials available, atomically dispersed metals and metal oxides appear to be the most promising. In conventional materials, the efficiency of utilization of active photocatalytic material is rather poor because only a small fraction of those present on the surface can serve as active materials. As the particle size decreases, this efficiency increases. In this respect, subnanometric catalysts such as single-site heterogeneous catalysts, atomically dispersed catalysts, and single-atom catalysts have distinct advantages when compared with their bulk and nanometric counterparts. The challenges in preparing stable single-atom catalysts have largely been overcome, and several methods are now available for their preparation. Many atomically dispersed photocatalytic materials have been synthesized, and many new insights have been gained, unlocking the tremendous potential in purifying wastewater by utilizing solar radiation. The aspects of higher activity, improved selectivity, economical use of materials, and a better understanding of the structure-activity relationship offered by subnanometric photocatalysts have been explored in this chapter. 2020 American Chemical Society. -
Challenges and Issues in Health Care and Clinical Studies Using Deep Learning
Deep learning is a subset of machine learning, which has more than three layers of neural networks. Neural networks resemble the functioning of human behavior in nature. These neural networks are capable of producing results with single layers, but multiple layers help in producing accurate results with increased precision rate. Deep learning supports a number of artificial intelligence (AI)-based applications and services, which helps in increased automated devices, data analysis, and many more physical tasks in various fields. Deep learning technology has become part of human day-to-day life. It is involved in every aspect of daily routine like voice-based searches, operating a device, baking transactions, and many more. Deep learning allows the healthcare industry to examine data quickly without compromising accuracy. Deep learning uses mathematical models designed to work almost like the human brain. Multiple layers of networking and technology enable unmatched computing capability and the ability to traverse and analyze through vast sets of data that would have previously been lost, forgotten, or missed. 2024 Taylor & Francis Group, LLC. -
Quantum inspired automatic clustering algorithms: A comparative study of genetic algorithm and bat algorithm
This article is intendant to present two automatic clustering techniques of image datasets, based on quantum inspired framework with two different metaheuristic algorithms, viz., Genetic Algorithm (GA) and Bat Algorithm (BA). This work provides two novel techniques to automatically find out the optimum clusters present in images and also provides a comparative study between the Quantum Inspired Genetic Algorithm (QIGA) and Quantum Inspired Bat Algorithm (QIBA). A comparison is also presented between these quantum inspired algorithms with their analogous classical counterparts. During the experiment, it was perceived that the quantum inspired techniques beat their classical techniques. The comparison was prepared based on the mean values of the fitness, standard deviation, standard error of the computed fitness of the cluster validity index and the optimal computational time. Finally, the supremacy of the algorithms was verified in terms of the p-value which was computed by t-test (statistical superiority test) and ranking of the proposed procedures was produced by the Friedman test. During the computation, the betterment of the fitness was judge by a well-known cluster validity index, named, DB index. The experiments were carried out on four Berkeley image and two real life grey scale images. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.