Volume 14 | Issue 4

 Abstract

This study investigates the critical gap between academic curricula and industrial expectations in India. Compiled during the "Nurturing Future Leadership Development Program" at IIM Nagpur (Jan 2026), this paper presents a synthesized framework titled "Corporate Connection." The research draws upon long-term data from three distinct initiatives: (i) An Industry-Powered Curriculum implemented over five years with Mahindra & Mahindra and Ador Welding, (ii) Joint Industrial Research for placement enhancement (2013-2018), and (iii) A copyrighted model for facilitating live industry projects. The findings suggest that structured, dual-mentorship ecosystems significantly reduce the graduate "training gestation period" and enhance institutional strategic competitiveness.

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 Keywords

Corporate Connect, Industry-Academia Gap, NEP 2020, Skill Development, Live Projects, Leadership

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Modern learning environments have altered due to the quick adoption of cutting-edge educational technologies, which have also altered student participation, assessment process and teaching strategies. Although technology-enabled learning provides students with increased academic resources, flexibility, and accessibility, it has also given rise to new forms of academic stress. This study motivation at the types, causes, and levels of academic stress that students face in the age of cutting-edge educational technology. A structured questionnaire were used to gather primary data from students using a descriptive study methodology.

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students, time management, digital learning, academic stress, and cutting-edge educational technologies

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

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

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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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 Keywords

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

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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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 Keywords

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

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

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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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 Keywords

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

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

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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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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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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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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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The introduction of the Goods and Services Tax (GST) in India in 2017 marked a major structural reform aimed at creating a unified indirect tax system. India's GST structure is a unified, multi-tiered indirect tax system designed to simplify and harmonize taxation across the country. This article examines the impact of GST on the Ease of Doing Business (EoDB) in Rajasthan, focusing on key dimensions such as tax simplification, compliance burden, digitalization, and business competitiveness. While GST has contributed to formalization, transparency, and efficiency, challenges remain, particularly for micro, small, and medium enterprises (MSMEs). The study concludes that GST has had a mixed but progressively positive impact on EoDB in Rajasthan.

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GST, one nation one tax, category, compliance, EoDB

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The development of advanced materials for high-temperature and high-load applications has become increasingly critical in nuclear reactor inspection systems. Robotic vehicles operating inside reactor vessels require superior traction, thermal stability, and wear resistance under extreme environmental conditions. This study investigates the fabrication and characterization of elastomer-based polymer nanocomposites designed for enhancing frictional performance in robotic wheel linings. Fluorocarbon rubber (FKM) was selected as the base elastomer due to its excellent thermal and chemical resistance and was reinforced with Multiwalled Carbon Nanotubes (MWCNTs) and Nano Graphene Platelets (NGPs). The nanocomposites were fabricated using a two-roll mill blending process followed by compression molding and vulcanization. Characterization was conducted using X-ray Diffraction, Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy, and Energy Dispersive Spectroscopy. Tribological properties were evaluated using a pin-on-disc apparatus. The results demonstrate a significant enhancement in friction and wear resistance, with graphene-reinforced composites exhibiting superior performance. The study confirms that nanoparticle reinforcement is an effective approach for improving elastomer performance in extreme environments.

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Fluorocarbon elastomer; Polymer nanocomposites; Carbon nanotubes; Graphene nanoplatelets; Tribological performance

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Abstract: India has become one of the world's fastest-growing renewable energy markets, fueled by ambitious national targets, supportive policy measures, and abundant solar, wind, and bioenergy resources. India's dedication to a low emission future is demonstrated through key initiatives such as the National Solar Mission. However, range of techno-economic, market, and institutional Challenges continue to impede the development and large scale adoption of renewable technologies. Despite their current limited contribution to electricity generation, renewable hold strong potential to adapt to emerging economic, environmental, and sustainability challenges. This paper analyzes the evolution, present status, and future outlook of renewable energy in India. It analyses trends in installed capacity, regional adoption patterns, policy frameworks and sectoral challenges during 2014 to 2024. Through data-driven insights and visualizations, the study highlights India's ongoing transformation toward a more sustainable and resilient energy mix.

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Keywords: Renewable Energy, Sustainable Development, Policy Framework.

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