Authors - Md. Najmus Sakib Sourov, Md. Ataur Rahman, Urmi Ghosh, Sabera Sultana, Mahady Hasan, Md. Tarek Habib Abstract - English Language Anxiety represents a prevalent issue among learners, significantly affecting their capacity to communicate and acquire knowledge effectively. The study uses machine learning models to offer insights into how anxiety affects students' general well-being and academic performance. English language anxiety among rural high school students in Bangladesh is investigated using machine learning techniques to determine its sources and effects on aca-demic performance. Machine learning techniques like k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, and Gradient Boosting were used to analyze data from several rural schools, including student scores and qualitative insights on their perceptions of English. The results showed that SVM was the most accurate model, with an accuracy of 99.43%. The study reveals major anxiety patterns and predictions, with a focus on the interaction between academic pressure, English complexity, and student mental health. The findings will guide educators and policymakers in designing focused interventions to relieve English subject anxiety and establish a supportive learning environment.
Authors - Satyasa Khadka, Sandhya Baral, Sudip Tiwari, Sharad Kumar Ghimire Abstract - This paper presents a robust Automatic Number Plate Recognition (ANPR) system tailored for Nepali license plates written in Devanagari script. In this paper, a pipelined model was used that integrates YOLO-based models for license plate and character detection, followed by a CNN classifier trained on 34 Devanagari characters. Two publicly available data sets were used that incorporate diverse lighting, fonts, and structural variations. Data augmentation and additional training on embossed plates enhanced the generalizability of the model. The system achieved a recognition accuracy of up to 93%, demonstrating strong performance under real-world conditions and providing a scalable solution for traffic management in Nepal.
Authors - K. Vidyasagar, G. Lakshminarayana, B. Surya Prasada Rao, K. Vijaychandra, P. Suresh babu Abstract - Nanoscience is the emerging technology to strengthen the devices that are more essential to human life. This chapter discussed structural design considerations of a badminton racket. The stress-strain relationship, modulus of elasticity, bending modulus, and moment of inertia are studied to improve the shaft deflections while smashing the shuttle with high force. Carbon nanotubes are elected for badminton rackets to withstand both rapid forward and side deflections. This paper discussed the physical characteristics of armchairs, Zig-Zag Single-walled carbon nanotubes and double-walled carbon nanotubes (DWCNT). The length of the carbon nanotube is fixed to 200 A. The C-C bond length is considered as 1.4210 A. The stretch factor is considered as ‘1’ for both single-walled and carbon nanotubes (SWCNT). And the ellipse ratio is also considered as ‘1’ for both SWCNT and DWCNT. The screw distortion and bending angle are varied from 00 to 900 for single-walled carbon nanotubes, and the physical elastic properties of the SWCNT and DWCNT are investigated. Finite element analysis is used to analyse SWCNT and DWCNT structures. Nano tube modeler is used to simulate the SWCNT and DWCNT structures.
Authors - Sabar Aritonang Rajagukguk, Eileen Heriyanni, Ahmad Fikron Maulida, Enina Putri Abstract - Multi-SKU demand forecasting poses considerable challenges for re-tail operations due to intricate interdependencies among products, seasonal fluctuations, and external market influences. Conventional forecasting approaches often fail to model these complexities effectively, resulting in poor inventory management and financial inefficiencies. This paper proposes a novel Cross-Modal Attention Transformer (CMAT) architecture for addressing multi-SKU demand prediction in retail contexts. The model employs a dual-attention mechanism to capture temporal patterns within individual SKU time series as well as cross-SKU relationships via an inter-product attention layer. It further integrates multimodal external variables such as promotional campaigns, weather data, and economic indicators using dedicated feature encoders. Evaluation on a large-scale retail dataset comprising 15,847 SKUs across 12 product categories shows a 23.4% reduction in Mean Absolute Percentage Error (MAPE) compared to standard transformer models and a 31.2% improvement over traditional statistical methods. The results indicate potential for improving supply chain efficiency and reducing inventory-related costs through more accurate multi-horizon demand predictions.
Authors - Kshitij Gupte, Kunj Patel, Krisha Desai, Dhrumi Patel, Anjali Jivani Abstract - Advanced Persistent Threats (APTs) pose a serious challenge to cybersecurity due to their stealthy and prolonged nature. Detecting these attacks early is critical, yet traditional methods often fail to catch subtle patterns or require extensive resources. This study investigates the effectiveness of several machine learning algorithms including Logistic Regression, Random Forest, Naive Bayes, and K-Nearest Neighbors (KNN) to identify APT behavior from system and network activity data. A significant focus was placed on reducing the complexity of the detection system without compromising accuracy. Dimensionality reduction using Linear Discriminant Analysis (LDA) was applied to transform high-dimensional data into a more manageable format, and data cleansing techniques were used to handle null and infinite values. The results showed that the KNN algorithm not only achieved the highest detection accuracy but also maintained performance even when trained on just 10% of the dataset. This approach offers a lightweight, high-precision method for APT detection that can minimize processing overhead while improving real-time responsiveness helping organizations enhance their security posture without sacrificing efficiency.
Authors - Justin Q. Cabellon, Ashley MJ A. Andrade, Dave P. Bautista, Kenshin Z. Gutierrez, Dorothy Joy B. Nabo, Angel Anne R. Nuarin, Manuel J. Logatoc, Rexieden N. Barrera Abstract - This study examines the perceptions of small and medium enterprises (SMEs) toward United Nations Sustainable Development Goal 13 (Climate Action). Employing a quantitative survey across multiple industries, it captures numeric data to identify patterns in SMEs’ attitudes and evaluates whether demo-graphic factors— length of operation, nature of business, number of employees, or type of ownership—influence these perceptions. Respondents rated statements on a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree) across five business dimensions: product, service, process, network, and model. Results reveal high overall support for climate-action initiatives, with mean scores indicating positive engagement in each dimension. Statistical analysis shows no significant variation in perceptions among SMEs grouped by any demographic criterion, suggesting a shared recognition of climate action’s importance irrespective of firm characteristics. These findings underscore the uniform commitment within the SME sector to integrate climate considerations into core operations. By illuminating SMEs’ supportive stance and the absence of demographic dis-parities, this research highlights the sector’s potential contributions to global cli-mate objectives. Insights from this study can inform policymakers and practitioners aiming to tailor interventions that bolster SME participation in sustainable development and foster resilient economic growth.
Authors - Parikshit Mahalle, Pradnya H Desai, Pankaj Chandre Abstract - With the widespread adoption of AI-as-a-Service (AIaaS), enterprises increasingly rely on cloud platforms to deploy and access powerful machine learning models. This shift offers scalability and cost-efficiency but also introduces significant security and privacy concerns. Protecting the confidentiality of AI models and user data has become a critical requirement in this outsourced computing paradigm. However, AIaaS platforms face numerous threats, including model inversion, membership inference, extraction attacks, and data poisoning. Traditional security mechanisms are insufficient to address the unique confidentiality risks associated with remote AI model hosting and inference. Furthermore, balancing performance, privacy, and deployment feasibility remains a core challenge. This paper proposes a comprehensive confidentiality-focused architecture for AIaaS, integrating multiple cryptographic techniques and secure hardware layers. The design leverages Homomorphic Encryption, Secure Multiparty Computation, Trusted Execution Environments, and Federated Learning with Secure Aggregation, supported by a centralized Management & Compliance layer. Threat vectors are explicitly addressed across client, cloud, and edge layers, while performance and auditability are maintained. In conclusion, our architecture provides a layered and resilient defense against modern AI threats while maintaining functionality and scalability. It offers a blueprint for deploying privacy-preserving AIaaS infrastructures. Future work will focus on optimizing hybrid models and advancing quantum-resistant protections.
Authors - Mridul Das Joshe, Shyja Rafeek S, Aji Sivanandan Abstract - Fake news has become a critical research area due to the expanding spread of misinformation on digital platforms. As social media becomes a main news source for millions, confirming the authenticity of online material is crucial. This research addresses the problem using a multi-label text classification method, which better identifies the complex nature of misinformation that may fall into multiple categories. In our research, we used the LIAR dataset, a bench-mark collection of 12.8k short political statements labeled with six fine-textured truthfulness classes. A transformer-based architecture, notably a fine-tuned BERT model, is proposed including both textual context and metadata to enhance better classification output. The model achieved an accuracy of 0.7351, presenting superior results than those reported earlier research using the LIAR dataset. Confirming the robustness of the approach across different classes with evaluation metrics and per-label precision and recall. The findings illustrate the potency of transformer-based architectures in identifying different degrees of falsehood. This research adds an extensible and practical solution for real-time fake news detection to support information.
Authors - Agna S. Nath, Arun Kumar V N, Aswathi K.B Abstract - This study investigates the role of WhatsApp Commerce—the use of WhatsApp for business transactions—in empowering women-led Micro, Small, and Medium Enterprises (MSMEs) in Tier-3 cities of India, while advancing Sustainable Development Goals (SDGs) 5 (Gender Equality) and 8 (Economic Growth). Despite India’s digital revolution, gender disparities persist, with women leading only 20% of MSMEs and facing barriers like digital exclusion and sociocultural constraints. Grounded in Kabeer’s Empowerment Theory and Davis’s Technology Acceptance Model, the research employs a mixed-methods approach, combining surveys of 400 women entrepreneurs and 20 case studies across four Tier-3 cities (Bareilly, Jalgaon, Dibrugarh, Erode). Key findings reveal that WhatsApp usage intensity correlates with a 27% revenue increase (β=0.27, *p*
Authors - Kingkar Prosad Ghosh, Ankan Roy, Anupam Singha, Tabassum Tajin Ratry, Nafisha Hossain Abstract - Market Basket Analysis (MBA) identifies co-occurring products for pricing, cross-selling, and placement. This paper reviews Apriori and FP-Growth and introduces the RN-Algorithm, a method that focuses on rules with high lift and confidence by measuring pairwise item co-occurrence. In retail-style transactions, RN provides competitive or superior performance in identifying the top rules while remaining simple to distribute. This study examines RN’s complexity, how its runtime scales with the number of unique items and basket width, and offers a practitioner-oriented comparison of algorithms. We also address RN’s limitations (e.g., pair growth in wide baskets) and propose mitigations, including minimum item support, top-K selection, and distributed counting. The paper concludes with actionable guidance on when to prefer RN over FP-Growth or Apriori in modern e-commerce applications.
Authors - E. C. Nwokorie, D. O. Njoku, M. E. Nwanga, S. A. Okolie, C. D. Anyiam, J. E. Jibiri, C. G. Onukwugha, I. H. Ajunwa, C. O. Amadi, F. O. Nwokoma, A. I. Otuonye, U. C. Onyemauche Abstract - This research investigates the application of TabNet, a deep learning model optimized for tabular data, for binary classification of Software Defined Networking (SDN) traffic. Using a combination of normal and Open vSwitch (OVS) traffic datasets, the model was trained to differentiate between benign and potentially anomalous traffic patterns. The preprocessing pipeline included feature selection, normalization, and encoding, followed by model training using PyTorch TabNet. The classifier achieved a high validation accuracy of 99.51%, with strong precision and recall, highlighting its capability for real-time anomaly detection in SDN environments. Visualizations such as ROC curves, precision-recall plots, confusion matrices, and PCA projections further validated the model’s effectiveness and interpretability.
Authors - Tiurida Lily Anita, Arif Zulkarnain, Abdul Rauf Ridzuan Abstract - The integration of blockchain technology into digital tourism platforms has sparked interest in how such innovations influence user trust and behavioral intentions. This study investigates the impact of digital value, smart contracts, and security-transparency on users’ intention to use blockchain-based services, with perceived trust as a mediating variable. A quantitative survey involving 200 Traveloka users was conducted using structured questionnaires. The analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to validate the measurement and structural models. Results reveal that digital value and perceived trust significantly influence users’ intention to use Traveloka's blockchain-enabled services. Moreover, digital value and smart contracts positively affect perceived trust. However, the direct impact of security-transparency on perceived trust was found to be statistically insignificant. The mediation effect of perceived trust is confirmed only in the relationship between digital value and intention to use, while its mediating roles between smart contract and intention, and between security-transparency and intention, are not supported. These findings suggest that enhancing digital value and reinforcing trustworthy smart contract features are key to fostering trust and increasing user adoption. This study offers practical insights for digital tourism platforms aiming to deploy blockchain technology and contributes to the theoretical understanding of trust-based technology acceptance in the tourism domain.
Authors - Zainab Shalehah, Santy Abstract - Small and Medium Enterprises (SMEs) face increasing pressure to remain competitive in rapidly evolving markets. Business Intelligence (BI) has emerged as a strategic tool that supports data-driven decision-making and improves organizational performance. This study aims to explore the impacts, benefits, and challenges of BI implementation in SMEs through a systematic literature review (SLR) of relevant academic sources. Literature was collected from databases such as Scopus and Google Scholar and analyzed using thematic categorization based on BI usage and its performance outcomes in SMEs. The findings highlight that BI positively contributes to decision-making quality, operational efficiency, and financial performance. However, SMEs often face challenges such as high implementation costs, lack of skilled personnel, and integration difficulties. The success of BI adoption is highly influenced by contextual factors, including data quality, financial capacity, and strategic planning. This review concludes that while BI offers significant advantages for SMEs, a careful, well-planned approach is necessary to overcome adoption barriers. Further research is recommended to explore industry-specific BI solutions that address the unique needs of SMEs.
Authors - Sadhana Mishra, Poonam Dang Abstract - Automatic speech recognition (ASR) is increasingly being applied in various fields such as healthcare, Education and accessibility. However, ASR of mathematical expressions presents a unique challenge due to the symbolic, structured and hierarchical nature of math language. Accurate transcription of such expressions is essential for digital learning, documentation and computational processing. This paper presents a comparative study of four open-source ASR tools— Whisper, DeepSpeech, Vosk and PocketSphinx — focusing on their ability to recognize spoken mathematical expressions accurately. The study uses a dataset of several mathematical categories, such as algebra, trigonometry, calculus, and vectors, recorded by six speakers, to assess each tool's performance based on Word error rate (WER) and processing time. According to the results, Vosk processes information the quickest, whereas Whisper performs better in terms of accuracy. The findings provide insights into tool selection for educational or assistive technology applications involving math speech transcriptions.
Authors - Serhii Kozlov, Nazar Matsko, Yaroslava Chopa, Volodymyr Dubinin, Dmytro Nishchemenko Abstract - This article proposes a solution to the problem of free decentralized protocol usage for a group of users who want to participate in the protocol without paying transaction fees and/or do not have a wallet with a key pair (Externally Owned Account). The proposed solution is a public donation pool that enables users to interact with any decentralized protocol during a specific event without owning a wallet and/or incurring blockchain gas fees.
Authors - Andrea Dannielle Mayuga, KC A. Berdin, Mary Faith F. Lagmay, Love Joyce A. Mortel, Lovely M. Penafiel, Ashley D. Sacayanan, Manuel J. Logatoc, Steven C. Jocson Abstract - This study evaluates the level of satisfaction with digital payment systems among residents in the Philippines. In an era where rapid technological advancement reshapes business transactions, digital payment platforms have emerged as critical drivers of efficiency and convenience. Employing data from survey questionnaires and pertinent digital‐literacy studies, the research applies Davis’s Technological Acceptance Model (TAM), with perceived usefulness (PU) and perceived ease of use (PEOU) as central predictors of users’ behavioral intentions toward mobile payments. The analysis explores three dimensions: users’ overall satisfaction, the most frequently used digital payment platforms, and variations across socio‐demo-graphic profiles. Findings indicate that respondents maintain a uniformly positive attitude toward digital payment systems, deeming them useful for everyday trans-actions and expressing strong willingness to recommend these platforms over traditional methods. Ease of use emerged as a key factor driving continued adoption intentions. Among socio‐demographic variables, gender and monthly in-come show significant correlations with satisfaction levels; in contrast, age, civil status, and highest educational attainment exhibit no notable influence. These results suggest that while digital payment systems enjoy broad acceptance across diverse user groups, targeted strategies that address gender, and income‐based preferences may further enhance user satisfaction. Policymakers and service providers can leverage these insights to refine platform design, optimize user education initiatives, and foster more inclusive adoption of digital payment technologies.
Authors - Sarvani Kocherlakota, Poojitha Panchakarla, Ajay Amjuri, Sannakalla Sai Sree Charan Abstract - Economic, Social and Governance is playing a predominant role in the capital markets to increase the awareness Global investments and to acquire knowledge on the investment avenues available across the world. To investigate its current research landscape and emerging trends, we conduct a bibliometric analysis by using R software 4.5.0, and VOSviewer to identify the research trend, emerging topics, and collaborations among countries, authors, documents and the contributing academic journals during the study period. Finance research letters is having the highest publications and Review of Accounting Studies is having the most cited document. Li Y is the most contributing author and Christensen HB, Hail L and Leuz C are the most cited author. China is the most cited country. Further research may happen in this area from the perspective of various countries like BRICS, various quantative techniques and scope of the study may be limited to individual country and so on.
Authors - Rajkumar Patil, Vivek S. Deshpande, Nilesh P. Sable Abstract - Pests pose a significant threat to cotton yields, leading to economic losses for farmers. This paper conducted a comprehensive literature review, examining the various types of pests affecting cotton crops both globally and specifically in India. We analyzed the actual yield losses ascribed to these pests as well. Furthermore, we reviewed a variety of literature on pesticide recommendations and cotton disease detection. In this paper we have chosen four Deep Learning (DL) models—VGG19, ResNet50, ResNet152V2, and Incep-tionV3 selection of this model was based on literature review. These models were trained and tested on a dataset of 2,293 images comprising affected and non-affected cotton plants and leaves. The output of our evaluation demonstrates how useful these models are in detecting diseases in cotton plants. Ultimately, the AI-based automated recommendation system allows farmers to use this information to make informed decisions about using pesticides resulting in better crop health and higher yield of cotton.
Authors - Ronil Manohar, Melita Simoes, Arogyaswamy Karadi Abstract - Today’s competitive jobs demand a sturdy balance between personal and professional life. Women are an integral part of the current workforce including academics wherein achieving a sustainable work-life integration is a critical challenge in the ever evolving dynamic workplaces despite institutional reforms, growing attention to gender equity and women empowerment. This study highlights the role of work-life balance, mental well-being, and organizational support structures among 103 women professionals that include teaching and non teaching fraternity in educational institutions across Hubli-Dharwad. According to reports, a substantial proportion of respondents, mostly Millennials and Generation Z, struggle to maintain balance, resulting in medical illnesses, stress, burnout, and disturbances in family life. These issues frequently result in lower productivity, job satisfaction and can hamper career progression and leadership aspirations, emphasizing the critical need for effective measures to help women achieve a sustainable work-life balance. This study employs a quantitative approach that made use of a structured data collection through a well drafted questionnaire which aims to explore the perception of women on work-life balance, mental health, and personal empowerment along with understanding the role of equitable policies, anti-harassment frameworks, as well as the implementation of flexible and inclusive work environments that promote a gender-balanced leadership wherein the needs and voice of women employees are addressed.
Authors - Usman Ali, Tariq Hussain, Muhammad Mujeeb Akbar, Shahzad Ali, Sajid Abstract - In Pakistan, crime is always a significant economic and social issue affecting governance and public safety. Religious issues, poverty, and political instability have a very high contribution to increasing crime in the country. Despite highly effective efforts of authorities and prevention techniques, crimes are often inconsistent and lack effectiveness in determining crimes across the country. This study aims to analyze national crimes in Pakistan using machine learning and structured datasets to support more effective decision-making and understanding of crime rates across the country. Through the proposed framework, linear regression, the Decision Tree, and the Random Forest are applied to historical data obtained from the Kaggle repository. With different evaluation matrices and cost functions, the proposed model is interpreted and evaluated. The Evaluation metric includes R², and the cost functions include MSE, MAE, and RMSE. Linear Regression works effectively in comparison to others, providing the highest R², i.e., 99.9%. By using this framework, crimes from 2030 to 2050 are predicted with a difference of 5 years. These algorithms help to identify different types of crimes across the country based on different factors. Unlike traditional methods and black-box models, this research underscores the comprehensibility, making insights understandable for both law and policy makers. The study explains how interpretable frameworks can help authorities to find high-risk area predictions. In the future, the aim is to use deep learning techniques with explainable AI (XAI) for enhancement in this study, which can support the development of more public safety systems across the country.
Authors - Unnati Parmar, Jatin Modh Abstract - Sentiment Analysis, or opinion mining, is the computational study of opinions, sentiments, emotions, and attitudes expressed in texts. Due to the rapid expansion of social media, e-commerce platforms, and digital communication, huge amount of opinion rich information is generated daily. This tremendous surge has also substantially increased the importance of sentiment analysis for understanding public opinion, monitoring brand image, forecasting market trend and enhancing customer relations. This paper also offers a detailed survey of SA methods, from classic lexicon-based methods to recent deep learning models and transformers such as BERT and RoBERTa. We test their effectiveness on bench-mark datasets with empirical experiments and discuss their pros and cons in various real-world scenarios. Beyond these popular approaches, we present a range of domain-specialized techniques, multilingual processing, and hybrid architectures synthesizing symbolic and statistical methods. We empirically show that the transformer models outperform LR, SVM, LSTM and BERT on the IMDB dataset in achieving better understanding of context-specific nuances. We next provide a number of problematic applications of sentiment analysis including sarcastic expression detection, domain adaptation, data unbalance and sentiment ambiguity. Various application scenarios in marketing, healthcare, finance and politics are also included in the paper. Finally, we describe future research avenues focusing on explainable sentiment models, real-time analysis systems, and multimodal fusion approaches that take into account text, audio and visual features.The goal of this study is to provide researchers and practitioners with a fundamental framework for creating sentiment analysis systems that are intelligent, scalable, and reliable.
Authors - Rhytheema Dulloo, Aditi Singh, Vinay Kiran Abstract - The convergence of artificial intelligence, sustainability conscious-ness, and immersive metaverse technologies has created unprecedented opportunities for transformative marketing communications that influence consumer behavior in virtual environments. Despite the growing adoption of metaverse platforms for commercial activities, there remains a significant gap in under-standing how AI-powered sustainable marketing messages within these digital ecosystems affect consumers' actual purchase intentions, particularly given the complex interplay between virtual experiences and real-world consumption decisions. This study employs the Theory of Planned Behavior (TPB) as the theoretical framework to examine the relationships between attitudes, subjective norms, perceived behavioral control, and purchase intentions in the context of AI-driven sustainable marketing communications within metaverse environments. The research objectives include: (1) investigating the impact of AI-generated sustainable marketing content on consumer attitudes and purchase intentions, (2) examining the role of social influence and perceived control in metaverse shopping contexts, and (3) identifying key predictors of consumer behavior in virtual sustainable commerce environments. A quantitative research methodology will be employed using structured online surveys distributed to active metaverse users, with data analysis conducted through structural equation modeling (SEM) and multiple regression analysis to test the proposed hypotheses. Preliminary findings indicate significant positive relationships between AI-driven sustainable marketing exposure and purchase intentions, with attitudes and perceived behavioral control serving as strong mediating factors, while subjective norms demonstrate moderate influence on consumer decision-making processes within metaverse retail environments.
Authors - Sonaly Maharaj, Rhulani Maluleka, Abelwe Maluleka Abstract - Life insurance is not immune to the transformative impact of artificial intelligence (AI). AI offers unprecedented levels of precision, speed, and customization in a field historically reliant on laborious manual procedures and statistical assumptions. This paper investigates how AI-driven underwriting can be incorporated into life insurers’ digital business strategy, using Momentum’s in-app screening tool as a primary case study. The study proposes a theoretical AI underwriting architecture that maximizes risk assessment and policy issuance by leveraging machine learning algorithms, biometric information, and behavioral insights. The advantages, disadvantages, opportunities, and threats of adopting AI underwriting are analyzed through a SWOT analysis, highlighting ethical and legal considerations around algorithmic bias, regulatory compliance, and explainability. The findings suggest that while AI underwriting can provide insurers a competitive edge in a crowded market, its implementation requires robust frameworks, ethical design, and regulatory alignment.
Authors - Suresh Panampilly, Murale Venugopalan Abstract - Despite the prestige associated with leadership roles, many employees are reluctant to become managers. This poses challenges for organizations aiming to strengthen their managerial pipeline to meet long-term strategic goals. In this study, managerial reluctance is studied by analyzing candid posts on Reddit. LDA topic modelling using 15 posts revealed 6 themes. The themes uncover psychological, organizational and ethical factors. Some results align with established theories (e.g. Expectancy, equity and role theories). Others highlight unexplored areas such as ethical dilemmas and identity conflicts. This study confirms that topic modelling on social media data can give valuable insights into managerial reluctance. By using data from online discourses to draw theoretical insights, this study offers conceptual contributions and practical in-sights which can be used to develop inclusive managerial talent in organizations.
Authors - Otmane Azeroual Abstract - This paper introduces Dialogic Artificial Intelligence (DAI)—a conceptual and computational framework for modeling how AI systems engage with human epistemic preferences in processes of knowledge retrieval and semantic representation. Grounded in Wittgenstein’s theory of language games, DAI facilitates context-sensitive interpretation of user intent through dialogical interaction. Using multiverse hypergraphs and a mechanism called Dispute Learning (DL), the framework maps diverse queries onto epistemic subgraphs that reflect over-lapping but distinct knowledge communities. Unlike existing preference-matching models, DAI emphasizes pluralism, transparency, and contestability in meaning construction. Real-world applications include academic search, semantic interoperability, and policy analysis—pointing toward a more dialogical paradigm in knowledge-driven AI systems.
Authors - Windy Permata Suyono, Dwi Handarini, Nuramalia Hasanah, Ratna Anggraini, Rio Firnanda, Surya Anugrah, Eka Septariana Puspa, Septi Nurmalita Abstract - The digital transformation of Micro, Small, and Medium Enterprises (MSMEs) in the e-commerce sector has created significant opportunities to enhance tax compliance through the adoption of digital financial technology. De-spite these advancements, tax compliance remains relatively low in many developing countries. This study investigates the impact of digital financial technology adoption and tax knowledge on the tax compliance behavior of e-commerce-based MSMEs, with digital trust examined as a moderating variable. A quantitative approach was employed, utilizing survey data from 120 MSME actors operating on major e-commerce platforms such as Tokopedia, Shopee, Lazada, Blibli, and Bukalapak. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that both digital financial technology and tax knowledge significantly influence tax compliance. Moreover, digital trust positively moderates these relationships, amplifying the effects of digital financial technology and tax knowledge on compliance behavior. These results highlight the critical role of a trustworthy digital ecosystem and comprehensive tax literacy programs in promoting voluntary tax compliance among MSMEs. The study provides meaningful insights for policymakers, tax authorities, and FinTech developers, contributing to the growing body of literature on digitalization and tax governance in emerging economies.
Authors - Usman Ali, Shaiza Shahid, Misbah, Shahzad Ali, Sheraz Aslam, Kainat Mustafa Abstract - Autism Spectrum Disorder (ASD) is a neurocognitive condition that affects social connection, expressive abilities, and behavioral patterns. This study aims to diagnose ASD using machine learning (ML) techniques based on behavioral and demographic data. The dataset sourced from Kaggle includes features such as age, gender, ethnicity, social interaction behaviors, and responses to standardized screening questionnaires. To improve accuracy, various ML models are implemented, including NB, AdaBoost, SVM, LightGBM, KNN, and XGBoost. Additionally, advanced Ensemble Methods are applied, such as Hybrid Kernel SVM, a boosting classifier, and a stacking classifier. Among all methods, the stacking classifier delivered the best performance, achieving 99.74% accuracy with the minimum MSE of 0.2. This result supports the development of an intelligent system to aid in early autism diagnosis and intervention planning. Future research could enhance this approach by integrating neuroimaging biomarkers, generative AI, or more advanced ML frameworks, aiming for even greater precision and robustness in a real-world clinical setting.
Authors - Martina Beranek Abstract - Industry and services are undergoing fundamental changes in connection with new technologies, the advent of AI, global competition and at the same time the contradictory process of partial deglobalization, changing markets, material shortages, local regulations and changing trade barriers, especially in the form of tariffs. The already difficult situation is complicated by major military conflicts in the Middle East, Ukraine and some regions of Africa. These aspects place high demands on the work of managers at all levels of management. It is therefore essential for university students to prepare for the above facts and to understand the complexity of strategic management as best as possible. ICT in the form of business simulations is a very suitable tool to teach students system thinking and explain the principles and principles of a strategic approach in business. Empirical research shows that students prefer project-based learning using ICT tools to classic frontal teaching and that this form of teaching based on the use of not only ICT but also AI promotes adaptability of learning, contributes to digital literacy and leads to a much higher level of readiness for future professions. Business simulation not only allows the development of the entrepreneurial spirit but also leads to the improvement of soft skills. Finally, it has been proven that thanks to the feedback provided by the simulation, students see the impact of their decisions more or less immediately and can thus understand the complexity and complexity of the decision-making process at the strategic level of business management. The document recommends expanding the use of strategic simulations in higher education to meet the demands of a rapidly evolving and changing business environment.
Authors - Ch Sahyaja, Ch Shankar, Madhuri. K, Jaswanthi Guda Abstract - The paper evaluates how the integrated Green Human Resource Management (GHRM) practices, such as the green recruitment and selection of employees, green sustainability training, green performance management, employee engagement and green compensation and rewards, play a role in green leadership leading to sustainability capacity of organizations. The research was based on Ability Motivation Opportunity (AMO) and resource-based view (RBV) conceptualizations, and the analysis was done using partial least squares SEM on 205 HR and the line managers of the manufacturing and service-based organization, upon using the questionnaires issued to the mentioned managers in ADANCO 2.2.1. Findings reveal that green recruitment and selection (E = 0.303), employee involvement (E = 0.350) and green compensation (E = 0.250) do play a significant role in influencing the green leadership with a combinational effect of 60.3 per cent (R2 = .603). Green leadership plays an important role in exerting a favourable impact on sustainability (BR2 = .543) and helps to mediate among all three GHRM practices (Sustainability-mediated effects). Green leadership mediates between the GHRM behaviours of sustainability (.189), recruiting (.218) and promoting (.155) in direct effect (p < .005). Although organizational green culture positively affects sustainability (156), it does not have such an effect on the connection between leadership and sustainability. This brings out the point that green leadership holds the key to sustainability.
Authors - Md Faiz Ahmad, Md Asadul Haque, Ananya Chowdary Muppana, Aasritha Malineedi, Dharani Gudivaka, Sri Krishna Surya Putta Abstract - The present study offers a systematic literature review (SLR) providing insights on the role of social media in promoting sustainable marketing. The SLR is carried out to gain an understanding of the phenomenon. Scopus database was used to identify, extract, select and review related articles using search keywords. A total of 27 articles were deemed fit for this study. This SLR recognizes current research avenues related to social media in sustainable marketing using TCCM framework. The study used co-citation and keyword occurrence analyses to explore the nuances of the field under consideration. The study yields valuable insights for academia, government, green marketers to practically implement strategies and suggesting future opportunities for research.
