Volume 14 | Issue 4 |

 Abstract

Recognising emotions is becoming more and more important for making interactions between people and computers better. This is because emotions are a big part of how people interact with each other and how they feel overall. Many industries need machines that can pick up on and respond to emotional cues like people do. Emotionally responsive agents are useful in many fields, such as education, healthcare, gaming, marketing, customer service, human-robot interaction, and entertainment. This study investigates the potential for improving virtual assistants through multimodal Artificial Intelligence (AI), employing diverse emotion recognition techniques to develop more empathetic and efficient systems. The suggested method uses facial expressions and written cues to make the system more aware of emotions and make users happy by having empathetic conversations. The Facial Emotion Recognition (FER) model was 71% accurate in real time, and the Textual Emotion Recognition (TER) model was 59% accurate in validation, showing that Multimodal Emotion Recognition (MER) works well. Our lightweight architecture makes sure that inference happens in real time and that facial and textual emotion recognition are combined with DialoGPT-based response generation. This shows that it works with large language models for empathetic dialogue, unlike previous multimodal emotion-aware systems.

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Recognising emotions is becoming more and more important for making interactions between people and computers better. This is because emotions are a big part of how people interact with each other and how they feel overall. Many industries need machines that can pick up on and respond to emotional cues like people do. Emotionally responsive agents are useful in many fields, such as education, healthcare, gaming, marketing, customer service, human-robot interaction, and entertainment. This study

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As cyber attacks on industrial control systems become more common, it is more important than ever to use cyber security controls and check security against these attacks. Cyber attacks on nuclear power plants (NPPs) can cause not only economic loss, but also loss of life. So, to protect NPPs and other places from security threats, cyber security controls must be put in place. However, there aren't many resources available right now for protecting information, which is necessary to use all the controls needed to follow cyber security rules. To solve this problem, we need to find good cyber security controls and give each NPP enough resources to protect their information. NPPs use a different security control based on NEI 13-10 (Cyber Security Control Assessments) to protect their systems. However, this is not enough to show that the security controls have really reduced these threats or to show that they have really reduced these threats. To solve this problem, the Electric Power Research Institute (ETRI) came up with the technical assessment methodology (TAM), which can be used to give a quantitative score by looking at how possible cyber attacks could affect an asset and the security controls that go with it. This method lets you use differential security control based on the score to see if the security controls have really reduced the risks. In light of this context, the objective of this paper is to perform a comparative analysis of the outcomes obtained from the implementation of security controls and risk assessment utilising solely NEI 13-10, as well as both NEI 13-10 and TAM, on the plant protection system of the nuclear power reactor APR1400. This paper also talks about the areas for future research by talking about the TAM's limits and things to think about when using it.

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 Keywords

Cybersecurity, Nuclear Power Plants (NPP), Technical Assessment Methodology (TAM), Risk Assessment, Industrial Control Systems (ICS).

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Healthcare professionals now frequently use medical documents due to the expansion of the Internet. To facilitate collaboration while safeguarding private patient data, secure medical image transmission and management are crucial. This study examines several approaches to securely exchanging medical data, emphasising both their benefits and drawbacks. We divide these strategies into two categories: distributed strategies like blockchain and federated learning, and centralised strategies like encryption and watermarking. This study also looks at how medical image watermarking has changed over time, from conventional approaches to sophisticated AI-based systems. Deep learning models are regarded as "black boxes," providing more resilience and flexibility than conventional methods known as "white boxes," which are straightforward and easy to understand. In order to handle the increasing complexity of threats while maintaining the diagnostic integrity of medical images, this analysis highlights the necessity of integrating contemporary technology. Additionally, our work contributes to the ongoing discussion on improving data security in medical imaging by offering a thorough classification of watermarking techniques and outlining future research directions.

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Medical Image Security, Digital Watermarking, Blockchain-Based Traceability, Federated Learning, AI-Driven Tamper Detection

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This study examines the correlation between the Latin American Artificial Intelligence Index (ILIA) and the Web Accessibility Index (WAIN) across 19 Latin American nations to assess the alignment of technological innovation with digital inclusivity. The ILIA gives a broad picture of how ready AI is by looking at things like infrastructure, data capacity, talent development, research and adoption, and governance structures. This index shows that some regions are better at digital transformation than others. For example, Uruguay, Chile, and Brazil are still ahead of the game when it comes to AI adoption because they have strong infrastructure and governance frameworks.

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 Keywords

Artificial Intelligence Readiness (ILIA), Web Accessibility Index (WAIN), WCAG 2.2 Compliance, Digital Inclusivity, Sustainable Digital Transformation

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Creative Commons Attribution 4.0 and The Open Definition

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As more and more autonomous and smart vehicles are used in ground transportation systems, new security problems arise. This change from operations run by people to those run by computers makes it easier for bad people to attack. As the Internet of Things (IoT) becomes more common in cars, they are always making and sharing a lot of data. Attackers can take advantage of this trend's weaknesses in complicated ways, like with Advanced Persistent Threats (APT). It is very important to be able to find APTs in vehicles that have IoT technology. To find these threats, we need better ways to do so. The urgent requirement for vehicle data privacy constrains conventional centralised Machine Learning (ML) methodologies. Also, there aren't many APT datasets available to the public in the vehicle field, which makes it harder to develop and test models. This is a big problem for cybersecurity in this area that is always changing. This study introduces an innovative Federated Deep Neural Network (FDNN) framework incorporating a privacy-preserving technique to address these challenges. The research emphasises the primary obstacles in APT detection and delineates its distinctive contributions to the domain. It talks about the research questions that are guiding the study. The UNSW-NB15, Edge-IIoTset, and CSE-CIC-IDS2018 datasets each represent a different stage of an APT attack. We use these datasets to look at and judge the framework that was made. For these datasets, the framework without the privacy-preserving technique gets APT detection accuracies of 97.32%, 96.81%, and 98.06%, respectively. But when the privacy-preserving technique is used, the framework's accuracies are 95.62%, 96.11%, and 95.63%, respectively. There are tables that show all of the results, as well as other evaluation metrics like Precision, False Positive Rate, and F1 Score. We use "Shapley Additive Explanations (SHAP)" analysis on the framework we made to find the most important features for finding APTs. This study validates the efficacy of a novel framework for identifying APTs in distributed vehicular contexts. The framework works well because it cuts down on the amount of data and the number of features. This was shown by extensive testing with several benchmark datasets. Future work will look into how well the framework can find APTs in different areas.

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 Keywords

Federated Learning, Advanced Persistent Threats (APT), Connected Vehicles Security, Explainable Artificial Intelligence (XAI), Privacy-Preserving Deep Learning.

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Creative Commons Attribution 4.0 and The Open Definition

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Tool condition monitoring (TCM) is very important in today's manufacturing because it makes sure that products are of high quality, cuts down on unplanned downtime, and makes production as efficient as possible. Over time, mechanical stress, heat, and friction cause cutting tools like milling cutters, drills, and lathes to wear down. Tool wear not only affects the size and surface quality of parts made, but it also raises production costs and can put workers' safety at risk. Most traditional TCM systems use manual inspection or supervised machine learning methods, which need a lot of labelled datasets. However, labelling data from industrial sensors takes a lot of time and work, and it is often not possible in real-world manufacturing settings, especially for machines that run on a continuous production schedule.

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 Keywords

Tool Wear Recognition, Unsupervised Learning, Semi-Supervised Learning, Sensor Fusion, Predictive Maintenance.

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A significant amount of web text data, including news articles, blogs, social network status updates, online reports, and more, has been gathered along with the growth of the internet. Accurately classifying these web texts automatically is a crucial task in the fields of artificial intelligence and natural language processing. Numerous applications, including sentiment analysis, web filtering, automatic recommendation, and web information retrieval, are made possible by it. The issue is that it is challenging to accurately classify web text because of its complex semantic structures and distant relationships. Current word representations, like Word2Vec and GloVe, generate fixed word embeddings for each and every word. This paper presents a more advanced deep learning structure called BERT, BGCA, which combines BERT (Bidirectional Encoder Representations from Transformers), BiGRU (Bidirectional Gated Recurrent Unit), CNN (Convolutional Neural Network), and Attention in a web text classification problem to address the aforementioned problems. In order to help the model learn the syntax and semantics between words through bidirectional transformer architecture, BERT is used to obtain the contextual encoding of words in the web text. Contexts can be investigated both forward and backward using the BiGRU layer. The CNN layer is in charge of extracting important details and gram features from the text. and the focus is on capturing the importation of key terms. To confirm the effectiveness of the suggested model, BERT, BGCA, experiments were carried out on a sizable enough news dataset called THUCNews. Conventional embedding techniques (Word2Vec and GloVe) and BERT-based embedding were tested. According to the comparison results, the BERT significantly outperformed the static embedding techniques. On the 20 categories THUCNews news dataset, the high accuracy and F1 score (95.21%, 94.36%) and F, measure of 95.21% and 94.36%, respectively, demonstrate that the BERT, BGCA can achieve better classification results and be favourable to semantic representation when compared to other deep learning models like TextCNN. Therefore, it can be said that the BERT, BGCA method for large-scale web text classification is practical, scalable, and effective.

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 Keywords

Web Text Classification, BERT (Bidirectional Encoder Representations from Transformers), BiGRU (Bidirectional Gated Recurrent Unit), Convolutional Neural Network (CNN), Attention Mechanism, Natural Language Processing,

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Ballari Fort (Ballari Kote), situated on a monolithic granite hill (Ballari Gudda) in the semi-arid Deccan plateau of Karnataka, India, stands as a premier testament to centuries of military engineering, political succession, and cultural continuity. Constructed over two major historical phases--initially under the feudatories of the Vijayanagara Empire (16th century) and subsequently renovated and expanded under Hyder Ali and Tipu Sultan of the Kingdom of Mysore with French engineering influence (late 18th century)--the fortification complex embodies an architectural synthesis of indigenous Deccan and European-Islamic military traditions. This comprehensive research paper explores the historical stratigraphy, architectural morphology, material culture, and socio-religious significance of Ballari Fort. It critically examines the structural features of both the Upper (Balagadde) and Lower (Kelegadde) forts, analyzes the complex's advanced hydraulic systems, and highlights its role in regional memory and identity. Furthermore, the article establishes an objective analytical framework by articulating explicit research limitations, assessing contemporary preservation challenges, and detailing conservation imperatives necessary for sustainable heritage management. Wikipedia

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BALLARI FORT: ARCHITECTURAL SYNTHESIS, HISTORICAL STRATIGRAPHY, AND CULTURAL HERITAGE PRESERVATION

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Information and Communication Technology (ICT) has become an intrinsic part of contemporary higher education that is influencing the planning, delivery and experience of teaching and learning. Educational institutions have developed more technology-enabled ways of teaching and learning due to the emergence of digital resources. In this context the present study investigates the use of ICT tools in higher education institutions in Mysore District. The study is conducted on the basis of primary data, acquired from 180 respondents including 25 Assistant Professors, 25 Associate Professors, 10 Professors and 120 students of six selected colleges in Mysore District. Simple random sampling procedure was used for the selection of respondents. The data collected was analyzed using simple percentage analysis, charts and pie graphs. The objective of the study is to understand the extent to which ICT tools have been used by the students and faculty members and their function in promoting successful teaching and learning in higher education.

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Information and Communication Technology (ICT), Higher Education, ICT Tools, Mysore District, Digital Education, Faculty and Students.

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Creative Commons Attribution 4.0 and The Open Definition

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This article undertakes a critical examination of the construction, deconstruction, and contestation of truth in Gabriel García Márquez's Chronicle of a Death Foretold (1981). Drawing on Mikhail Bakhtin's theory of polyphony and dialogism, Michel Foucault's concept of the régime of truth, and Paul Ricoeur's narrative theory, this study argues that Márquez deploys a fragmented, retrospective narrative structure to expose the radical instability of truth within a patriarchal, honour-bound Latin American community. The novel, far from offering a transparent account of Santiago Nasar's murder, presents truth as a layered, contested, and politically charged construct shaped by collective memory, social silence, and moral evasion. The analysis proceeds through five thematic axes: narrative unreliability and the fragmentation of testimony; collective memory versus individual knowledge; the intersection of fate, free will, and moral truth; honour culture as a suppressor of authentic selfhood; and magical realism as an epistemological vehicle that gestures toward truths inaccessible to empirical narration. The article concludes that Márquez's formal innovations do not merely serve aesthetic ends but constitute a sustained critique of the social mechanisms by which communities manufacture, police, and ultimately silence inconvenient truths.

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 Keywords

Chronicle of a Death Foretold, García Márquez, narrative truth, polyphony, honour culture, magical realism, Foucault, Bakhtin, collective memory, Latin American literature

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Creative Commons Attribution 4.0 and The Open Definition