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Emotion Recognition Through Facial Expressions: A Machine Learning Perspective in Mobile Multimedia
Facial expression-based emotion detection is very attractive because of the possibilities in security systems, mental health monitoring, and human-computer interaction. Even with the progress in accuracy in real-world settings, issues such as the lack of balanced datasets and the inability to differentiate between faint or superimposed emotions continue to plague it. This study aims to bridge these constraints by developing a CNN-based model that would be able to recognize face emotions reliably and be utilized in real-time situations, such as webcam integration. The Affect Net dataset, which is a comprehensive collection of over a million facial photos labeled with the seven major emotions of anger, disgust, fear, happiness, neutrality, sadness, and surprise, was used to train the proposed model. Other pre-processing data techniques used include grayscale conversion, normalization, scaling, and data supplementation to increase the robustness of the model. Using metrics like accuracy and loss trends for evaluation, the model demonstrated efficiency stability at around the 30th training phase. When the model is compared to existing models, this proposed model can attain the competitive level of accuracy up to approximately 60%. It also has the potential to run in real applications through its webcam integration. While the model can differentiate between various clear-cut emotions, it becomes ineffective at identifying subtle emotions, which include Fear and Neutral majorly because of unbalanced data and the subtleness of these expressions. 2025 River Publishers. -
Emotion Detection Using Machine Learning Technique
Face Emotion Recognition (FER) is an emerging and crucial topic today; since much research has been done in this field, there are still many things to explore. In daily life, where people dont have time to fill out feedback, emotion detection plays an important role, which helps to know customer feedback by analyzing expressions and gestures. Analyzing current studies in emotion recognition demonstrates notable advancements made possible by deep learning. A thorough overview of facial emotion recognition (FER) is provided in this publication. The literature cited in this study is taken from various credible research published in the last 10years. This study has built a model for emotion recognition using photos or a camera. The paper is based on the concepts of Machine Learning (ML), Deep Learning (DL), and Neural Networks (NN). A range of publicly available datasets have been used to evaluate evaluation metrics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
EMOTICONS AND THE NON-VERBAL COMMUNICATION: WITH REFERENCE TO FACEBOOK
In the recent years, the use of emoticons in text-based and computer-mediated communications has gained a lot of popularity. Though emoticons (a combination of punctuation marks and letters) first began as a representation of facial expression, they have over the years been transformed to now include graphical representations of a variety of items (both static and animated). The usage of emoticons and their interpretation differ from one person to another, depending on factors such as gender, age and culture. Facebook is a platform where people across the globe communicate, share opinions and connect with each other. The researcher, thus, seeks to understand whether emoticons have the ability to infuse the text-based computer-mediated- communications on Facebook with the richness and authenticity of face-to-face interactions, and to arrive at an understanding of how these different groups use and interpret emoticons. A sample size of 139 was selected using the snowball sampling technique. The methods of primary data collection included surveys in the form of questionnaires that were distributed online. A quantitative analysis of the collected data was conducted using SPSS. The study revealed that age, gender and location do have a bearing on the patterns of usage and interpretation of emoticons. It also showed that emoticons cannot provide the text-based computer-mediated- communications on Facebook with the richness and authenticity of face-to-face interactions. -
EMONET: A Cross Database Progressive Deep Network for Facial Expression Recognition
Recognizing facial features to detect emotions has always been an interesting topic for research in the field of Computer vision and cognitive emotional analysis. In this research a model to detect and classify emotions is explored, using Deep Convolutional Neural Networks (DCNN). This model intends to classify the primary emotions (Anger, Disgust, Fear, Happy, Sad, Surprise and Neutral) using progressive learning model for a Facial Expression Recognition (FER) System. The proposed model (EmoNet) is developed based on a linear growing-shrinking filter method that shows prominent extraction of robust features for learning and interprets emotional classification for an improved accuracy. EmoNet incorporates Progressive- Resizing (PR) of images to accommodate improved learning traits from emotional datasets by adding more image data for training and Validation which helped in improving the model's accuracy by 5%. Cross validations were carried out on the model, this enabled the model to be ready for testing on new data. EmoNet results signifies improved performance with respect to accuracy, precision and recall due to the incorporation of progressive learning Framework, Tuning Hyper parameters of the network, Image Augmentation and moderating generalization and Bias on the images. These parameters are compared with the existing models of Emotional analysis with the various datasets that are prominently available for research. The Methods, Image Data and the Fine-tuned model combinedly contributed in achieving 83.6%, 78.4%, 98.1% and 99.5% on FER2013, IMFDB, CK+ and JAFFE respectively. EmoNet has worked on four different datasets and achieved an overall accuracy of 90%. 2020. All Rights Reserved. -
Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning
Analyzing the sentiment behind emojis can provide valuable insights into the emotional context and user sentiment associated with textual content. To conduct a comparative analysis of diverse supervised machine learning models that can achieve the highest level of accuracy in Emoji Sentiment Analysis is the purpose of this research. Five machine learning models used in this research are K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Logistic Regression, Naive Bayes, and Random Forest. The experimental process resulted in ANN and KNN models giving an accuracy of 92%. The ANN model shows its proficiency in effectively managing large datasets. ANN also supports fault tolerance. The KNN model refrains from conducting calculations during the training phase and only constructs a model when a query is executed on the dataset. This characteristic makes KNN particularly well-suited for data mining. Both ANN and K-NN excelled in the experimental study due to these distinctive attributes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Emission line star catalogues post- Gaia DR3: A validation of Gaia DR3 data using the LAMOST OBA emission catalogue
Aims.Gaia Data Release 3 (DR3) and further releases have the potential to identify and categorise new emission-line stars in the Galaxy. We perform a comprehensive validation of astrophysical parameters from Gaia DR3 with the spectroscopically estimated emission-line star parameters from the LAMOST OBA emission catalogue. Method. We compare different astrophysical parameters provided by Gaia DR3 with those estimated using LAMOST spectra. By using a larger sample of emission-line stars, we performed a global polynomial and piece-wise linear fit to update the empirical relation to convert the Gaia DR3 pseudo-equivalent width to the observed equivalent width, after removing the weak emitters from the analysis. Results. We find that the emission-line source classifications given by DR3 is in reasonable agreement with the classification from the LAMOST OBA emission catalogue. The astrophysical parameters estimated by the esphs module from Gaia DR3 provides a better estimate when compared to gspphot and gspspec. A second degree polynomial relation is provided along with piece-wise linear fit parameters for the equivalent width conversion. We notice that the LAMOST stars with weak H? emission are not identified to be in emission from BP/RP spectra. This suggests that emission-line sources identified by Gaia DR3 are incomplete. In addition, Gaia DR3 provides valuable information about the binary and variable nature of a sample of emission-line stars. 2022 EDP Sciences. All rights reserved. -
Emerging world of the metaverse: An Indian perspective
[No abstract available] -
Emerging trends in usage of drone technology: Exploring the ethico-legal frontiers
As an offshoot of technological advancement, the integration of autonomous Artificial Intelligence (AI) in the usage of drone technology has entirely changed the current landscape. Usage of drones in commercial activities, military services, emergency operations has expanded the horizons of human life. But together with this evolution there arise several critical ethico-legal concerns such as privacy and security issues, accountability, and environment impact making its real-world implication a significant concern in the current times. These issues need to be addressed in purview of policy framework and existing guidelines concerning the ethical usage of drone technology. Keeping in view the above, this chapter explores the emerging trends in drone usage through the lens of ethico-legal perspective across various sectors. It also highlights the complexities of its usage with regard to its rapid advancement. Furthermore, the global governance perspective considering responsible use of drones among policy makers and stakeholders is widely examined. 2025, IGI Global Scientific Publishing. All rights reserved. -
Emerging Trends in the Future of FinTech: The Transformative Role of AI and Blockchain
As the financial technology (FinTech) landscape evolves, two transformative forces are emerging: Artificial Intelligence (AI) and Blockchain. These technologies are reshaping how businesses operate, enhancing transparency, and optimizing customer experiences. AI algorithms analyze vast data sets to predict market trends, streamline operations, and personalize services, enabling firms to make data-driven decisions swiftly. On the other hand, Blockchain technology offers a decentralized and secure method for conducting transactions. By eliminating intermediaries, Blockchain not only increases the speed and security of transactions but also provides an immutable ledger that enhances accountability. Together, these technologies are fostering financial inclusivity, allowing underserved communities access to banking services through decentralized finance (DeFi) platforms. Looking ahead, the integration of AI and Blockchain will enable the creation of a more efficient, secure, and userfriendly financial ecosystem. 2026, IGI Global Scientific Publishing. All rights reserved. -
Emerging Trends in Blue Economy: A Roadmap Through the Lens of Sustainable Development
On Earth, the oceans make up 71% of the land area. Approximately 40% of the worlds population lives in coastal regions with 3 billion people depending on it for livelihood. 80% of the global trade is (en)routed via oceans. A long-term unbalanced usage of marine routes leads to unsettling issues such as ocean-acidification, marine pollution and habitat destruction. This causes disruption towards the attainment of sustainability. To safeguard the oceans and marine life, Blue Economy (BE)" was introduced by the UN Conference on Sustainable Development (Rio+20) in 2012. The SDG-14 (life below water) contributes to focusing attention on BE. Till date there is no universally accepted policy towards implementation of BE. However several regions like Africa, Brazil, China, EU and India formulated guidelines towards the implementation of BE. With this perspective, the chapter analyzes the emerging trends of BE as a way forward towards sustainable development. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Emerging Trends in Blue Economy: A Roadmap Through the Lens of Sustainable Development
On Earth, the oceans make up 71% of the land area. Approximately 40% of the worlds population lives in coastal regions with 3 billion people depending on it for livelihood. 80% of the global trade is (en)routed via oceans. A long-term unbalanced usage of marine routes leads to unsettling issues such as ocean-acidification, marine pollution and habitat destruction. This causes disruption towards the attainment of sustainability. To safeguard the oceans and marine life, Blue Economy (BE)" was introduced by the UN Conference on Sustainable Development (Rio+20) in 2012. The SDG-14 (life below water) contributes to focusing attention on BE. Till date there is no universally accepted policy towards implementation of BE. However several regions like Africa, Brazil, China, EU and India formulated guidelines towards the implementation of BE. With this perspective, the chapter analyzes the emerging trends of BE as a way forward towards sustainable development. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Emerging Trends and the Future of Business Analytics
[No abstract available] -
Emerging trends and security in IoT devices
The Internet of Things has rapidly evolved, connecting billions of devices and transforming various industries. This chapter explores the latest emerging trends in IoT devices, focusing on advancements in connectivity, edge computing, and artificial intelligence (AI) integration. As IoT adoption grows, so does the importance of security, given the increasing vulnerabilities and potential threats. This chapter delves into key security challenges, including data privacy, secure communication protocols, and device authentication. It also discusses cutting-edge security solutions, such as blockchain technology and AI-driven anomaly detection, to safeguard IoT ecosystems. By examining both technological advancements and security considerations, this chapter provides a comprehensive overview of the current landscape and future directions for IoT devices. 2026 Elsevier Inc. All rights reserved.. -
Emerging Trends and Innovations in Cybersecurity Insurance Policy Customization Using Generative AI Fraud Detection
Generative AI is transforming policy customization and fraud detection. Cybersecurity insurance can be both a boon and a bane as the existing models for underwriting and assessment are relatively static in a world which is witnessing a constant escalation between attack and defense mechanisms. By assessing huge datasets, spotting emerging pattern and predicting potential vulnerabilities in real time, generative AI introduces dynamic risk modeling. Help insurers design hyperpersonalized cyber insurance products that are tailored to specific business profiles, operational risks and exposure levels to enhance coverage and premium accuracy. Generative AI helps in anomaly detection and identifying suspicious behaviour in insurance claims in fraud detection domain. Using legitimate claims scenarios to train AI model, fraud detection models can detect anomalies and fraud as never before. The intersection of expertise in cybersecurity, AI development and insurance will be key to creating strong,flexible and transparent insurance structures that can respond to the intricacies of today's cyber threats. 2026, IGI Global Scientific Publishing. All rights reserved. -
Emerging ternary nanocomposite of rGO draped palladium oxide/polypyrrole for high performance supercapacitors
In this work, novel electrodeposited palladium oxide-polypyrrole (PdP) and its ternary composite with reduced graphene oxide (PdPGO) draped over the surface of PdP were synthesised to achieve the excellent electrochemical properties and high stability. An exhaustive study has been carried out to correlate the crystalline structure, chemical bonding, morphological behaviour, redox reactions at the electroactive species, and its promising influences on the electrochemical performance. The electrodeposited PdPGO composite on stainless steel bestows superior electrochemical properties and a specific capacitance of 595 F g?1 at 1 A g?1 in 1 M H2SO4. The incorporation of rGO with the PdP matrix prevents the aggregation of rGO layers and is responsible for the enhanced electrostatic interactions at the electrode-electrolyte interface in PdPGO. Outstanding supercapacitance retention of 88% even after 5000 cycles at 5 A g?1 was accomplished for the ternary composite of Pd. These profound electrochemical characteristics are due to the synergistic effect of the individual components involved, manifest a great potential for Pd based composites toward novel electrode materials for supercapacitors of high efficiency. This method facilitates blueprints for synthesizing a series of advanced electrode materials for enhancing high storage capability. The high electrochemical performance of the PdPGO reveals how synergy plays a very important role to work on the blueprint to create active electrode materials for energy storage solutions. 2020 Elsevier B.V. -
Emerging technology adoption and applications for modern society towards providing smart banking solutions
The rapid advancement of emerging technologies has brought significant transformations to various sectors, including banking and finance. This chapter explores the adoption and application of emerging technologies in modern society, particularly focusing on their role in providing smart banking solutions. Technologies such as artificial intelligence (AI), blockchain, internet of things (IoT), and biometrics are revolutionizing traditional banking practices, enabling enhanced security, efficiency, and personalized services for customers. Through a comprehensive analysis of current trends and case studies, this chapter highlights the impact of these technologies on improving customer experiences, streamlining operations, mitigating fraud risks, and fostering financial inclusion. Additionally, it discusses the challenges and opportunities associated with the integration of these technologies into banking systems, including regulatory concerns, data privacy issues, and the need for skill development among banking professionals. 2024, IGI Global. All rights reserved. -
Emerging Technologies Driving Sustainability in Automotive Supply Networks and Smart Mobility
With environmental, social, and governance (ESG) standards at the center of the international business strategies, there is an ever- growing pressure on the automotive industry to be able to guarantee ethical procurement sound carbon footprints and supply chain transparency. This paper examines the lucrative way in which Artificial Intelligence (AI) and Blockchain are soon to transform the sphere of ESG compliance and become the source of end- to- end supply chain visibility. It explores existing issues, namely, fragmentation of data, the lack of its traceability, and the inappropriateness of regulations. The study states the importance of AI to forecast sustainability threats, and the ability of the Blockchain to deliver immutable records of sourcing, logistics, and emissions. It also assesses regulatory bodies, the scale- related problems, and proposes a road map towards incorporation of such technologies, and finally, gives a model of how digital change can be implemented in the creation of responsible, sustainable, and smart networks of automobiles. 2026, IGI Global Scientific Publishing. -
Emerging Novel Functional Materials from Biomass for Environmental Remediation
The Earth faces complex environmental challenges caused by both human activities and natural processes, affecting all life forms and ecosystems. Biomass-derived materials, sourced from renewable resources, serve as effective adsorbents, catalysts, and ion exchangers, providing sustainable solutions to environmental issues like water and air pollution, soil contamination, and waste management. Their significance lies not only in their biodegradability and sustainability but also in standardized testing and scalability considerations. The field of functional materials from biomass has the potential to transform environmental remediation, leading to a cleaner and more sustainable world. Here, we aimed to portrait the key approaches and recent developments in emerging functional materials from biomass tailored for environmental remediation, delving into their fundamental theories and concepts, various applications, and potential to reshape the remediation landscape. It evaluates the sustainability and biodegradability aspects of these materials, addresses challenges, and peers into the dynamic and rapidly evolving future of this field. Collaborative efforts between researchers, industry, and policymakers are pivotal to establishing guidelines and regulations ensuring the safe and responsible use of these materials. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Emerging Nanoparticle-Based Diagnostics and Therapeutics for Cancer: Innovations and Challenges
Malignant growth is expected to surpass other significant causes of death as one of the top reasons for dismalness and mortality worldwide. According to a World Health Organization (WHO) study, this illness causes approximately between 9 and 10 million instances of deaths annually. Chemotherapy, radiation, and surgery are the three main methods of treating cancer. These methods seek to completely eradicate all cancer cells while having the fewest possible unintended impacts on healthy cell types. Owing to the lack of target selectivity, the majority of medications have substantial side effects. On the other hand, nanomaterials have transformed the identification, diagnosis, and management of cancer. Nanostructures with biomimetic properties have been grown as of late, fully intent on observing and treating the sickness. These nanostructures are expected to be consumed by growth in areas with profound disease. Furthermore, because of their extraordinary physicochemical properties, which incorporate nanoscale aspects, a more prominent surface region, explicit geometrical features, and the ability to embody different substances within or on their outside surfaces, nanostructures are remarkable nano-vehicles for conveying restorative specialists to their designated regions. This review discusses recent developments in nanostructured materials such as graphene, dendrimers, cell-penetrating peptide nanoparticles, nanoliposomes, lipid nanoparticles, magnetic nanoparticles, and nano-omics in the diagnosis and management of cancer. 2025 by the authors. -
Emerging Nanomaterials for Catalysis and Sensor Applications
This book reviews emerging nanomaterials in catalysis and sensors. The catalysis section covers the role of nano-photocatalysts in organic synthesis and health care application, oxidation and sulphoxidation reactions, liquid phase oxidation, hydrogen evolution and environmental remediation. It highlights the correlation of surface properties and catalytic activity of the mesoporous materials. The sensor section discusses the fabrication and development of various electrochemical, chemical, and biosensors. Features: Combines catalysis and sensor applications of nanomaterials, including detailed synthesis techniques of these materials. Explores methods of designing, engineering, and fabricating nanomaterials. Covers material efficiency, their detection limit for sensing different analytes and other properties of the materials. Discusses sustainability of nano materials in the industrial sector. Includes case studies to address the challenges faced by research and development sectors. This book is aimed at researchers and graduate students in Chemical Engineering, Nanochemistry, Water Treatment Engineering and Labs, Industries, Research Labs in Catalysis and Sensors, Environmental Engineering, and Process Engineering. 2023 selection and editorial matter, Anitha Varghese and Gurumurthy Hegde; individual chapters, the contributors.