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Leaf Disease Identification in Rice Plants Using CNN Model
Rice is a staple food crop for more than 10 countries. High consumption of rice demands better yield of crop. Fungal, bacterial and viral are different classes of diseases damaging rice crops which results in low and bad yield as per quality and quantity of the crop. Some of the most common diseases affecting plants are fungal blast, fungal brown spot, fungal sheath blight, bacterial blight and viral tungro. The deep learning CNN model with ResNet50V2 architecture was used in this paper to identify disease on the paddy leaves. Mobile application proposed in this paper will help farmers to detect disease on the leaves during their regular visit. Images were captured using this application. The captured images were tested using the trained deep learning model embedded with mobile application. This model predicts and displays input images along with the probabilities compared to each disease. The mobile application also provides necessary remedies for the identified disease with the help of hyperlink available in mobile application. The achieved probability that the model can truly classify the input image in this project was 97.67%, and the obtained validation accuracy was 98.86%. A solution with which farmers can identify diseases in rice leaves and take necessary actions for better crop yield has been demonstrated in this paper. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Misuse of Internet Among School Children: Risk Factors and Preventative Measures
The Internet has been one of the most transformative and rapidly growing technologies. In recent years, it has improved the quality of life in areas such as communication, education, recreation. On the contrary, there are growing concerns about the use of the Internet that have created adverse consequences in the areas of social life, interpersonal relationships, family environment, and school activities. School-going children were vulnerable to such unhealthy outcomes due to readily available high-speed Internet and ease of access to different Internet platforms, which resulted in risky behaviours, decreased academic performance, poor nutrition, decreased sleep quality, and a high incidence of inter-social conflicts. While the majority of the research has focused on the adolescent population in terms of problematic Internet use, only a few studies have identified the vulnerabilities of school-going children in the same context. The research also confirmed that the risk factors for problematic Internet use start as early as middle childhood. Heightened risky use of the Internet was observed in children with neurodevelopmental concerns. This study explores risk factors associated with problematic Internet use among school-going children, identifying relevant warning signs followed with preventative measures. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Psychogenic Non-epileptic Seizures in Children: Prevention and Intervention Strategies
Psychogenic non-epileptic seizures (PNES) often get misdiagnosed to be epileptic seizures, and the price that is paid for the same by the patient and the family is huge. It is called by multiple names such as pseudoseizures non-epileptic attack disorder, dissociative seizures, and functional motor disorder. Not only is it difficult to identify the disorder, but it also poses an added difficulty with comorbid epilepsy. Adding on to these, it becomes difficult for both the healthcare provider to offer psychoeducation and the family to accept since no tangible evidence such as scans portrays any abnormality. Though in a simple manner it can be said this disorder presents itself like epileptic seizures but has no neurological base, explaining the same to the patients and their family is not as simple. However, surprisingly the prognosis for PNES is better for children. This chapter thus focuses on aspects that are essential for the treatment of the disorder and prevention. In particular, the manifestation of PNES in children is discussed by introducing the disorder with epidemiological information. Further clinical picture, etiology, diagnosis and prevention and intervention are discussed. Although there are limited studies that exist on the treatment of the disorder in the pediatric population, their outcomes to reduce PNES symptoms are significant. Hence, the chapter makes an attempt to review these studies in detail and mention highlights of these studies that contribute to a reduction in the symptoms. Finally, the chapter concludes with a biopsychosocial model that explains the relationship between these factors and PNES and how this can be used in prevention. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Importance of brain-based learning in effective teaching process
Neuroscience plays an important role to inform about brain functioning and corresponding behaviors. While scientists have continuously studied about brain in its entirety, several other fields in science, arts, and humanities are drawing applications from these neuroscientific studies. Similarly, in terms of education too, there are several applications. Once such is brain-based learning. This is to learn and teach with an understanding about brain's capability and functioning to increase one's learning potential. In order to do so, effective teaching is required. This particular chapter thus focuses on different teaching strategies which adopt brain-based learning and are evidence-based. The chapter initially informs about neuroscience and cognitive science interaction and its impact on effective teaching-learning process. After which evidence-based teaching strategies in classrooms, their importance via evidence-based studies, and techniques to incorporate them in classroom settings are explained. Finally, the chapter concludes with challenges and benefits. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Brain-based learning method: Opportunities and challenges
The chapter examines the concept of brain-based learning to bridge the gap between neuropsychology and education while understanding the best way to use our brain for meaningful learning. It suggests learning as a developmental process that enhances in a challenging but less threatening environment. Brain plasticity suggests that repeated exposure to a stimulus in a conducive environment helps in better recall and retrieval, as the repeated exposure allows the formation of new neural connections and strengthening the old ones while engaging in the task. As the application of brain-based learning moves away from the traditional style of learning, it focuses on a more holistic understanding of the process of learning. The chapter talks about applying a brain-based learning model to enhance learning in a stimulating surrounding to explore and stimulate various sense organs and further enhance neural connections. It discusses strategies to incorporate to allow engagement of sensory organs and problem-solve to enhance learning. Another perspective suggests attaching emotion to a situation which leads to forming a meaningful association in our brain. Depending on the strength of this connection, it becomes easier to recall and retrieve the memory. When compared to the traditional style of teaching, brain-based learning has shown to better academic accomplishment. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Child Mental Health in the Milieu of Online Education
The aim of this chapter is to examine the impact of online education on mental health of children, and explore methods to improve the same. With the advent of COVID-19 pandemic, major overhauls were made in day-to-day life including work, home, and education. Shift to online mode of instruction became the primary, if not the only, channel of education. This drastic shift has led to issues like limited social interaction, learning gaps due to insufficient in-person interaction, excessive screen time on devices, and decreased physical activity, which can impact mental health of children. This chapter will explore the impact of online learning on the mental health of children from both mental ill-health and well-being perspectives, the role of parents, teachers, and educational systems, and challenges and opportunities presented by the situation. Further to this, the ways to safeguard and improve mental health of children in the milieu of online education will be discussed. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Integration of blockchain to IoT: Possibilities and pitfalls
[No abstract available] -
Advances in the use of ceramic catalysts in fine chemical synthesis
Ceramics are versatile materials that have been put to many different uses. Catalysis is one such area where they have been used, both as catalyst and as a robust support material for catalysts. Properties like porosity and thermal and mechanical stability make ceramics attractive in these applications. Oxidation, esterification, hydrogenation, reduction, condensation reaction, and FriedelCrafts reaction are important reactions, which have uses spanning a wide range of applications, most notably in energy and environment. This chapter gives the recent advancements in ceramic materials used in the synthetic applications of the abovementioned reactions. The type and class of the ceramic material used and its role have been mentioned for these reactions. 2023 Elsevier Ltd. All rights reserved. -
An Empirical Study of Blockchain Technology, Innovation, Service Quality and Firm Performance in the Banking Industry
Despite the potential promises that blockchain technology (BT) offers to the financial services sector, its large-scale implementations are still in a nascent stage. There is no consensus on what benefits BT may bring, and there is always a possibility of difference between expected benefits and experienced real-world impact. Since the actual impact can be assessed only after large-scale implementations by financial institutions, there is little empirical evidence available in the literature. In this context, this research seeks to explore the potential impact of BT by developing and empirically testing a model. For this purpose, we have identified four dimensions of BT, namely, Decentralization, Transparency, Trustlessness, and Security. The impact of BT on innovation, service quality, and firm performance is assessed based on the extent to which these dimensions are present in the organization. The linkages of the latent constructs are estimated by analyzing the primary data collected from senior managers of various banks in India. The findings of this study provide several important considerations regarding the implementation of BT. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. -
An Analysis of Sentiment Using Aspect-Based Perspective
Opinions play a major role in almost every human practice. Finding product and service reviews is made easy online. Product reviews are readily available in huge quantities. Considering each review and making a concise decision about a product is not feasible or even possible. Aspect-based sentiment analysis (ABSA) is one of the best solutions to this problem. Summary and online reviews analysis is delivered in this paper. ABSA has made extensive use of machine learning techniques. Recent years have seen deep learning take off due to the growth of computer processing power and digitalization. When applied to various deep learning techniques, numerous NLP tasks produced futuristic results. An overview of various deep learning models used in the field of ABSA is presented in this chapter after an introduction to ABSA. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Implementation of AI in manufacturing industries a case study
Artificial intelligence (AI) is getting progressively integrated into nearly every facet of our existence. Its applications are ubiquitous and ever-evolving, spanning fields such as autonomous vehicles, geology, medicine, and art. AI has, however, posed as many questions as it has answered. These include the definition and application of the technology (viz., assisted, augmented, or independent intellect), the question of whether computers are thinking machines similarly to humans, the wider implications of the impact of automation on society, and the unexpected moral and principled quandaries. This chapter provides an overview of artificial intelligence in manufacturing intended for executives in manufacturing and industrial companies who want to integrate AI into their business. Its main objective is to apply AI to the engineering, testing, and production stages of the manufacturing value chain. The goal is to discuss business applications that technology, data, and automated processes can support, and how the appropriate personnel, organizational structure, and culture can support them. This article discusses current advancements, poses problems, asks questions, and attempts to bring cutting-edge concepts and research closer to business. 2025 Mohamed Arezki Mellal. All rights reserved. -
An Efficient Comparison on Machine Learning and Deep Neural Networks in Epileptic Seizure Prediction
Electroencephalography signals have been widely used in cognitive neuroscience to identify the brains activity and behavior. These signals retrieved from the brain are most commonly used in detecting neurological disorders. Epilepsy is a neurological impairment in which the brains activity becomes abnormal, causing seizures or unusual behavior. Methods: The benchmark BONN dataset is used to compare and assess the models. The investigations were conducted using the traditional algorithms in machine learning algorithms such as KNN, naive Bayes, decision tree, random forest, and deep neural networks to exhibit the DNN models efficiency in epileptic seizure detection. Findings: Experiments and results prove that deep neural network model performs more than traditional machine learning algorithms, especially with the accuracy value of 97% and area under curve value of 0.994. Novelty: This research aims to focus on the efficiency of deep neural network techniques compared with traditional machine learning algorithms to make intelligent decisions by the clinicians to predict if the patient is affected by epileptic seizures or not. So, the focus of this paper helps the research community dive into the opportunities of innovations in deep neural networks. This research work compares the machine learning and deep neural network model, which supports the clinical practitioners in diagnosis and early treatment in epileptic seizure patients. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Unlocking Happiness: The Power of Spiritual Intelligence for Emerging Adults
This study investigated the relationship between spiritual intelligence (SI) and happiness among emerging adults. 163 undergraduate and postgraduate psychology students from private universities completed standardized measures of SI and subjective happiness. Results showed positive correlations between SI and happiness (r = 0.26 to 0.59, p <.01). Two SI domains - transcendental awareness and conscious state expansion - were found to be significant predictors of happiness. The findings suggest that SI plays a crucial role in promoting happiness among emerging adults, supporting the hypothesis that SI can be used as an aid in the process of achieving happiness through independent decision-making and responsibility. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Machine Learning-Based Driver Assistance System Ensuring Road Safety for Smart Cities
Technologies around smart city and green computing are gaining more and more interest from diversified workforce areas. The transportation system is one of them. The transportation vehicles are operating day and night to provide proper support for the need. This is really tiring for the transportation workers, especially the drivers who are driving the vehicle. A slight negligence of a driver may cause a huge loss. The increasing number of road accidents is therefore a big concern. Research works are going on to comfort the drivers and increase the security features of vehicle to avoid accidents. In this chapter, a model is proposed, which can efficiently detect drivers drowsiness. The discussion mainly focuses on building the learning model. A modified convolution neural network is built to solve the purpose. The model is trained with a dataset of 7000 images of open and closed eyes. For testing purpose, some real-time experiments are done by some volunteer drivers in different conditions, like gender, day, and night. The model is really good for daytime and if the driver is not wearing any glass. But with a glass in the eyes and in night condition, the system needs improvements. 2025 selection and editorial matter, Yousef Farhaoui, Bharat Bhushan, Nidhi Sindhwani, Rohit Anand, Agbotiname Lucky Imoize and Anshul Verma; individual chapters, the contributors. -
Pattern of Carbon Dioxide Emission, Economic Growth and Energy Consumption in South-Asian Countries: An Empirical Analysis
The main aim of this chapter is to analyse the pattern of environmental pollution as represented by per capita carbon dioxide emission (PCCO2), per capita gross domestic product (PCGDP) and per capita energy consumption (PCEC) and their nexus in case of South-Asian countries for the time period 19912014. Econometric tools such as panel co-integration and fully modified ordinary least squares have been used to study the relations. A positive significant relationship has been observed between PCGDP and PCCO2 emission. In addition, an increase in PCEC also has a positively significant impact on PCCO2 emission. Therefore, the governments of all the countries need to come together and take steps to curb the rising carbon emission since neither the problem nor the responsibility is restricted to one country alone. There is a need for countries to increase the consumption of renewable energy and explore alternate options that are fewer dependents on coal or any other fossil fuel. On priority, economies in South-Asian region should focus on sustainable economic activities by balancing growth of economy with clean environment. 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Analytical Study of Security Enhancement Methods on Diverse Cloud Computing Platforms
Cloud storage is a convenient and virtually limitless storage option for the bulk of data technology is producing in recent times. Data security in cloud is not so robust as data owners need to depend upon the service providers for the safe storage. In this paper, we have identified few broadly used cloud computing paradigms: mobile cloud, cloud-based IoT and multi-tenant cloud. Mobile cloud helps reduce the data storage overhead on the mobile device and give users access to their personal data as and when required through cloud access. Cloud-based IoT helps the network of IoT devices, which is growing exponentially, to create on-demand cloud repositories. Multi-tenant cloud platforms are cloud environment accessed by more than one user. Few recent and related research work which aims at enhanced security from all these three paradigms is discussed and analysed. Encryption and similar network securing methods are used for mobile cloud and cloud-based IoT. For multi-tenant cloud, the objective is to keep the user spaces separate to keep their resources confidential. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Enhanced Stock Market Prediction Using Hybrid LSTM Ensemble
Stock market value prediction is the activity of predicting future market values so as to increase gain and profit. It aids in forming important financial decisions which help make smart and informed investments. The challenges in stock market predictions come due to the high volatility of the market due to current and past performances. The slightest variation in current news, trend or performance will impact the market drastically. Existing models fall short in computation cost and time, thereby making them less reliable for large datasets on a real-time basis. Studies have shown that a hybrid model performs better than a stand-alone model. Ensemble models tend to give improved results in terms of accuracy and computational efficiency. This study is focused on creating a better yielding model in terms of stock market value prediction using technical analysis, and it is done by creating an ensemble of long short-term memory (LSTM) model. It analyzes the results of individual LSTM models in predicting stock prices and creates an ensemble model in an effort to improve the overall performance of the prediction. The proposed model is evaluated on real-world data of 4 companies from Yahoo Finance. The study has shown that the ensemble has performed better than the stacked LSTM model by the following percentages: 21.86% for the Tesla dataset, 22.87% for the Amazon dataset, 4.09% for Nifty Bank and 20.94% for the Tata dataset. The models implementation has been justified by the above results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Effect of psychological pricing on consumer buying behaviour: A study on indian consumers
Consumer behaviour is a topic most sought after when it comes to creating successful marketing practices that affect consumers' psychology, acting as a stimulus and inducing them to make purchases. Evidence explains that the psychological pricing strategy communicates with the subconscious mind of consumers, creating a perceptual illusion. This makes the deal seem more appealing to them. This chapter entails a practical study examining the impact of psychological pricing strategies on consumers' buying behaviour. This study has used authentic primary data that has been collected directly from consumers in India based on their buying experiences when encountering psychological pricing. The findings of this research show how socio-demographic factors like age, income, education, gender and family size influence consumers' buying behaviour when encountered with psychological pricing and if psychological patterns such as the anchoring heuristics, recency bias, scarcity effect and halo effect can overpower the influence of psychological pricing strategies in consumer buying behaviour. 2024, IGI Global. -
Fostering student engagement and empathy: The role of service learning in promoting mental health awareness and human rights advocacy
The chapter highlights the potential of integrating service learning into mental health and human rights curricula. It emphasizes the transformative impact of service learning in fostering student engagement, empathy, and social responsibility by blending theoretical frameworks, historical context, and practical models. Service learning emerges as a powerful tool for promoting positive change within communities and encouraging student activism on mental health and human rights issues. Advocating for ongoing exploration and implementation of service learning initiatives is crucial despite India's delayed implementation of the Mental Healthcare Act of 2017. Harnessing service learning's potential is essential for effectively addressing these critical issues. 2024, IGI Global. All rights reserved. -
Leading and learning in inhospitable terrain
This chapter explores the obstacles that minority women in K-12 education leadership must overcome, emphasizing the critical importance of acknowledging barriers and prejudices. Notwithstanding its underrepresentation, their leadership demonstrates a steadfast dedication to diversity and offers distinctive viewpoints. Mentorship programs, educational institutions, and policymakers all play a crucial role in promoting diversity via inclusive practices and supportive policies. The recommendations include fostering an environment of inclusiveness, providing training on diversity, implementing precise career trajectories, and acknowledging and commemorating the accomplishments of a wide range of individuals. Collaborative endeavours and inclusive approaches aim to establish educational leadership that is fair, diverse, and student-focused. Addressing inequalities is critical to establishing inclusive and resilient educational environments where mental health should be regarded as a fundamental right, highlighting the convergence of mental health and human rights. 2024, IGI Global. All rights reserved.