Volume 14 | Issue 4 | April-2026

 Abstract

Because digital content is growing so quickly, sentiment analysis (SA) is now an important tool for figuring out how people feel and sorting through text data. Natural language processing (NLP) has come a long way, but low-resource languages, especially Sindhi, still haven't been studied enough because there aren't enough computational tools and annotated datasets. This study fills this gap by presenting the Sindhi News Headlines Dataset (SNHD), a new collection of data that has been labelled for both SA and category classification in eight areas: Crime, Economy, Entertainment, Health, Politics, Science & Technology, Social, and Sports. We compare different machine learning (ML), deep learning (DL), and transformer-based methods on SA and category classification tasks to see how well they work. We also use Explainable Artificial Intelligence (XAI) methods like Local Interpretable Model-Agnostic Explanations (LIME) to learn more about how models make decisions. The SNHD dataset shows that traditional ML models work better than DL and transformer-based models in experiments. Support Vector Machines with Radial Basis Function (SVM-RBF) is the best for SA (0.74 accuracy and weighted F-score), and the Ridge Classifier (RC) is the best for category classification (0.84 accuracy and weighted F-score). XLM-RoBERTa is one of the best transformer models for category classification, with an accuracy of 0.82 and a weighted F-score. These results set a standard for future research in Sindhi NLP and show how hybrid methods could help with problems that come up with low-resource languages. This work is a basic resource for NLP researchers who want to improve computational methods for Sindhi and other lesser-known languages.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sentiment Analysis, Sindhi News Headlines Dataset (SNHD), Category-Based Classification, Machine Learning, Transformer Models, Explainable Artificial Intelligence, Low-Resource Language Processing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

ecognising 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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Multimodal Emotion Recognition; Facial Emotion Recognition; Textual Emotion Analysis; Affective Computing; Human-Computer Interaction; Empathetic Virtual Assistants;

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Because higher education institutions are changing so quickly to digital, students and university administration need to be able to talk to each other quickly and easily. Students often want to know about things like how to sign up for classes, how to pay for them, deadlines, academic rules, and other administrative tasks. But traditional manual systems often slow things down, make things less consistent, and give administrative staff more work to do. This project suggests using a chatbot to help students with questions about paying for and enrolling in college (CSM). The chatbot uses Machine Learning (ML) and Natural Language Processing (NLP) to understand and answer student questions quickly and accurately. The system offers automated help around the clock, making sure that students always get accurate, timely answers without having to rely on administrative staff. There are many parts to the chatbot, such as user interaction, natural language processing, machine learning adaptation, database management, response generation, and analytics tracking. These modules work together to understand what users are asking, get the right information from institutional databases, and give answers that are relevant to the situation. To check how well the system worked, we looked at usability metrics like response time, task completion time, and user satisfaction levels. Students said they had good experiences with the chatbot because it was easy to use, fast, clear, and they felt confident using it again. The solution cuts down on repetitive work for staff by a lot, and it also makes it easier for students to access and enjoy their work. The proposed chatbot system makes administration more efficient, makes sure that information is consistent, and makes the student experience better through smart automation.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Chatbot System, Higher Education, Natural Language Processing (NLP), Machine Learning (ML),

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Contribution: This study examines the advantages and obstacles associated with the development, implementation, and assessment of a large language model (LLM) chatbot, MoodleBot, within computer science educational environments. It shows how LLMs could be used in LMSs like Moodle to help with self-regulated learning (SRL) and help-seeking behaviour. Computer science teachers have a lot of trouble adding new tools to LMSs to make the learning environment more supportive and interesting. MoodleBot solves this problem by giving students and teachers a place to interact with each other. Questions for Research: This study examines two questions, notwithstanding challenges such as bias, hallucinations, and the reluctance of teachers and educators to adopt new AI technologies. (RQ1) How much do students think MoodleBot is a useful tool for helping them learn? (RQ2) How accurate are the answers that MoodleBot gives, and how well do they fit with the course content that has already been set? Methodology: This study examines pedagogical literature regarding AI-driven chatbots and employs the retrieval-augmented generation (RAG) methodology for the design and data processing of MoodleBot. The technology acceptance model (TAM) looks at how much users accept something by looking at things like how useful they think it is and how easy it is to use. Forty-six students took part, and thirty of them filled out the TAM questionnaire. Results: Chatbots that use LLM, like MoodleBot, can make teaching and learning a lot better. This study found that there was a high accuracy rate (88%) in helping with course-related tasks. Students' positive feedback shows that AI-powered educational tools work and can be used in real life. These results show that educational chatbots can be used in courses to make learning more personalised and make teachers' jobs easier, but automated fact-checking needs to get better.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Large Language Models, Retrieval-Augmented Generation, Learning Management Systems, AI Chatbot, Personalized Learning, Higher Education

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

People all over the world are now paying attention to the deaths of mothers and children. In low- and middle-income countries, maternal mortality is high, especially among teens and young adults. Healthcare professionals can use CTGs to keep an eye on the mother's heartbeat during pregnancy to make sure the baby is still alive and avoid these deaths. This study utilised machine learning techniques to conduct a risk factor analysis aimed at decreasing child and maternal mortality. This study assessed seven machine learning algorithms. Accuracy, precision, and recall were used to compare how well different categorisation algorithms worked. The random forest is the most accurate of the other algorithms, with an accuracy rate of 99.98%. At first, the dataset was not balanced. After using under sampling and oversampling methods, all of the algorithms worked very well. A primary objective of the current study was to forecast the risk factors associated with child and maternal mortality utilising clinical data. Ultrasound devices work by sending out a pulse and reading the response. This analysis is a good and cost-effective choice for healthcare professionals who want to keep mothers and children from dying.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Maternal Mortality, Child Mortality Prediction, Machine Learning, Random Forest, Cardiotocography (CTG), Risk Factor Analysis, Clinical Data.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Wi-Fi-Based Human Activity Recognition (HAR), Channel State Information (CSI), Deep learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The modernisation of the traditional energy grid into an integrated platform is made easier by constant communication and advances in information technology. The Internet of Things (IoT) includes power systems, especially smart grid features and the ability for utilities to send new services to end users over a two-way communication channel. But relying too much on IoT-based communication systems has made security holes very serious. Also, cybercriminals are always interested in stealing important information from two people or devices, especially if they can do so by damaging the integrity, confidentiality, and authenticity of a communication channel for financial gain. Maintaining data security and preserving privacy in between two entities during the transmission or any data distribution are essential. To build a strong cyber security system, we need to look into the possible attacks and their effects. A lot of researchers have focused on finding and stopping these weak cyber attacks using advanced computing tools. This review article thoroughly investigated possible ways to address cyber security challenges such as smart meter security, end-users privacy, electricity theft cyber-attacks using blockchain and cryptography against communication attacks in smart grid. A lot of research has been done on how cyberattacks affect the security of power systems and how they affect the economy of deregulated energy markets. The resilience of security features and cryptographic techniques against diverse cyber-attacks is examined to propose uncharted cyber-attack avenues for future exploration. Specially, the study of real-world cyber security events, case studies, new findings and new scopes in diverse power industries are carried out. This review article has looked at more than 135 research papers. This paper primarily focuses on distribution-side cyberattacks, encompassing impact analysis, detection, and protection techniques.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cybersecurity, Machine Learning, Deep Learning, Intrusion Detection Systems, Network Security

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Creative Commons Attribution 4.0 and The Open Definition

 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.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition