Authors - Ankush Soni, Sanjay K. Sahay Abstract - The integration of unmanned aerial vehicles (UAVs) into remote and beyond-visual-line-of-sight missions has increased the demand for secure, efficient, and low-latency communication systems, particularly in scenarios where satellite connectivity is the only viable option. However, high-latency satellite links and constrained onboard resources pose significant challenges to traditional cryptographic protocols. In this paper, we present a lightweight, symmetric key-based authentication and secure communication protocol designed specifically for satellite-driven UAV applications. Our protocol replaces resource-intensive public key operations with a pre-shared key approach and incorporates AESHA3, a variant of AES that leverages SHA-3 for key scheduling, along with SHA2-512 for hashing. We implement this design within the TLS and QUIC frameworks, adapting their internal packet structures to accommodate constrained environments. Experimental evaluations on Raspberry Pi testbeds reveal that our proposed QUIC implementation significantly outperforms TLS in encryption speed, handshake latency, and overall protocol execution time. The results demonstrate the suitability of the proposed system for real-time mission-critical UAV operations over satellite links, highlighting the benefits of integrating optimized symmetric cryptography into modern transport protocols.
Authors - Reena (Mahapatra) Lenka, Jaya Chitranshi, Vanishree Pabalkar Abstract - Marketing automation with the use of data-driven insights will help in anticipating customer-behavior. It will enable personalized campaigns, improve the process of decision-making, and ensure market-accountability by measuring effectiveness of the system and help in optimizing strategies This will ultimately drive customer engagement, loyalty, and higher-returns on investment in the competitive business environments. The present invention relates to a method and a system for customer predictive analysis for marketing automation and market accountability. The present invention attempts to develop a layout for the job of marketing-computerization (MA) in estimating the return on displaying exercises and the difficulties related with arriving at responsibility in marketing. To explore the goal of the assessment, the creators took on a subjective methodology, leading an exploratory review among ten key witnesses. Based on the aftereffects of the subjective investigation, an applied system was proposed, which incorporates both key and functional level elements fully intent on making a worth based plan. In this plan, leaders, for example the Chief Marketing Officer. arise as worth makers, encouraging business versatility, what further contentions can be given to legitimize spending plan portion to MA exercises. Through cautious examination of the components that describe the peculiarity under study, the present invention eventually adds to a superior comprehension of MA and responsibility inside the current business worldview. Focusing on the marketing setting, an organized conversation of how AI can recognize the objective clients exactly in spite of their various practices was introduced in this contemporary invention. The uses of AI in client focusing and the extended viability all through the unique periods of client lifecycle were similarly inspected.
Authors - Reena (Mahapatra) Lenka, Jaya Chitranshi, Vanishree Pabalkar Abstract - The systems and regulations that control human behaviour are the main emphasis of HRM. Finding the greatest talent from around the world, training them, evaluating their performance, rewarding them, and creating a positive work atmosphere are all part of the HRM field's magnificent duty. Developing a framework that accurately forecasts the need for talent and workforce skills becomes crucial because every organization's strategy depends, in part or in full, on its skill sets. When the human resource data is accessed for study of the higher cognitive processes involved, a variety of techniques may be used to extract the most useful information from the dataset. A common strategy for using data to inform decisions is data processing. On the other side, "state-of-the-art accuracy" in decisions is what predictive analytics is known for. The goal of this study is to provide a strategic decision-making model for "human resource management (HRM)." The study develops a decision-making framework based on data processing and predictive analytics for decisions pertaining to human resources. Given that HRM has a significant impact on an organization's longevity and efficacy, the model was created for global corporations. The suggested approach will be useful in enhancing HR systems' effectiveness, which could have a favourable impact on business results.
Authors - Bryna Meivitawanli, Liu Fen Phaw, Daniella Maria Natalia Abstract - The increasing influence of social media and user generated con-tent has significantly transformed consumer behavior, with negative publicity emerging as a critical factor affecting brand perception and purchase intention. This study aims to investigate the impact of negative publicity on the purchase intentions of Generation Z consumers in Indonesia, focusing on the mediating roles of brand advocacy and brand betrayal. A quantitative research approach was employed, utilizing a structured questionnaire distributed to 158 respond-ents. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test four hypotheses regarding the relationships be-tween negative publicity, brand advocacy, brand betrayal, and purchase intention. The findings indicate that negative publicity significantly reduces purchase intention, with brand advocacy mitigating its adverse effects, while brand betrayal amplifies the negative impact. These results underscore the importance of managing brand reputation in the digital age, highlighting the need for brands to adopt ethical marketing strategies and proactive reputation management to counter the detrimental effects of negative publicity. This research contributes to the understanding of how digital media influences consumer behavior, offering valuable insights for marketers seeking to enhance brand loyalty and mitigate the risks associated with negative publicity. The findings are particularly relevant for businesses targeting Generation Z in emerging markets like Indonesia, where social media plays a pivotal role in shaping consumer decisions.
Authors - Sateesh Kumar TK, Vishnu Achutha Menon, Juby Thomas, Lijo P Thomas Abstract - This study examines emotion classification in news headlines related to Operation Sindoor, a precision military response initiated by India in May 2025 following the Pahalgam terrorist attack. The operation’s symbolic naming, reflecting cultural mourning and national resolve, resulted in emotionally charged media coverage. To analyze the emotional framing within these head-lines, a dataset from multiple news outlets was compiled and annotated with six emotion categories: anger, fear, joy, sadness, surprise, and neutral. Preprocessing involved TF‑IDF vectorization with unigram and bigram features, followed by classification using Logistic Regression and Support Vector Machines (SVM). Model performance was evaluated using accuracy, precision, recall, and F1-score, with macro-averaging to address class imbalance. Both models achieved an overall accuracy of 54%, with SVM yielding a higher macro‑F1 score (0.40) compared to Logistic Regression (0.22). The results indicate that SVM performed better in identifying dominant emotions such as fear and sur-prise, while both models struggled with underrepresented categories like joy and disgust. The findings suggest the need for advanced approaches such as deep learning architectures or data augmentation methods to improve classification of minority emotions in crisis reporting contexts.
Authors - Ananth Chiravuri Abstract - Prior studies have indicated a positive effect of a student’s digital competence on their academic performance in higher education institutes. However, the relationship between variables such as digital competence and academic performance may not be direct. There are other variables such as student engagement and self-efficacy that could mediate the role between digital competence and academic performance, warranting a further investigation Specifically, this research investigates whether students' digital skills/literacy influence their academic success through the mediating factors of self-efficacy and engagement. Findings from the study aim to emphasize the importance of empowering students with digital literacy skills to enhance their learning experiences and achievements.
Authors - Cu Quoc Le, Huy Khanh Hua, Nguyen Viet Ngo, Tien Trung Bach, Hoang Ngoc Tran Abstract - This paper presents an improved multi-camera people tracking pipeline with a focus on enhancing object detection to improve overall tracking performance. The system integrates object detection, singlecamera tracking, re-identification, and multi-camera matching. We evaluate five versions of the YOLO model, from YOLOv8 to YOLOv12 in the same tracking pipeline to ensure a fair comparison. Part of Track 1 data from AIC2024 dataset is used for both training and inference purposes. For each frame, detected objects are tracked using a Kalman filter and matched via the Hungarian algorithm based on both spatial and appearance features. Inter-camera identity association is achieved through cluster-based matching, followed by refinement steps to improve consistency. Tracking performance is assessed using the MOTA metric. Experimental results demonstrate that stronger detection models significantly improve tracking accuracy, with YOLOv12 achieving the highest MOTA score.
Authors - T. A. Akshaya, M. Suresh Abstract - This study is a journey to identify the opportunities, challenges, and future research scope in the metaverse entrepreneurship based on a systematic literature review. The Scopus database is employed for the selection of documents. For review, 25 documents are selected through the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The study identified the opportunities in metaverse entrepreneurship connected with the technological enablers of entrepreneurship, innovative business and market opportunities, transformation of education and entrepreneurial learning, economic growth and social inclusion, and further development in metaverse entrepreneurship. The major challenges of the metaverse in entrepreneurship encompass economic, technological, regulatory, security, and skill-based aspects. Metaverse entrepreneurship is a galaxy waiting to be explored. This study represents the first attempt to synthesize the existing research through a systematic literature review methodology. To policymakers, this study guides to creation regulatory framework in social, economic, and legal dimensions. To entrepreneurs, this study helps to identify opportunities in metaverse entrepreneurship not just as a marketplace but as an ocean for innovation and ideation. To educators, this study is a light into the rethinking in redesigning of the curriculum and the pedagogical approach to integrating metaverse tools and entrepreneurship education to create a generation with immense knowledge in digital entrepreneurship. The limitation of this in-depth study is, it only included the documents from the Scopus database till August 2025. The novelty of the study is to the theory by informing future research possibilities in this evolving domain by highlighting current research trends.
Authors - Chirani Perera, Uvini Ranaweera, Indra Mahakalanda Abstract - This study investigates tourist perceptions of seven southern beaches in Sri Lanka using Google Reviews. With the increasing influence of online plat-forms in travel decision-making, analyzing review content provides valuable in-sights into tourist experiences and preferences. The research employs trans-former-based models from Hugging Face for sentiment analysis and topic modeling, offering a modern, data-driven approach to textual review interpretation. Word clouds and bigram visualizations are used to highlight common positive and negative expressions associated with each beach. The findings reveal themes such as cleanliness, natural beauty, surfing opportunities, crowd, and local ser-vice quality as key themes associated with the southern coastline of Sri Lanka. Sentiment patterns vary across beaches, with some consistently rated positively while others receive mixed feedback. This analysis offers practical insights for stakeholders in the field of tourism to improve destination management and marketing strategies. The study demonstrates the effectiveness of modern-day NLP techniques in understanding tourist experiences and provides a scalable frame-work for future such analysis that centres around the user responses.
Authors - Mayanka Gupta, Ayman Amjad, Arjun Prabhakaran, Bhanoday Kurma, Bhanu Prakash M, Kiran Agarwal Gupta, Chaitra Ravi, Sindhoor N Abstract - Despite significant progress in autonomous driving, detecting and segmenting obstacles under poor conditions remains a major challenge. This paper reviews deep learning models that tackle real-world difficulties like occlusion, fog, motion blur, and uneven road surfaces such as potholes and broken speed bumps—factors that heavily impact safety and detection accuracy. Over ten recent models are analyzed, including prompt-based approaches like Semantic-SAM and EPCFormer, memory-augmented ones like OOSIS and XMem, and task-specific detectors such as DR-YOLO, D-YOLO, and motion-aware YOLO variants. Each model type comes with trade-offs: prompt-based systems are flexible but depend on large vision-language datasets, while memory-based methods offer temporal consistency at the cost of increased computation. A key focus is on handling unstructured and uncertain road conditions, especially common in countries like India, where irregular infrastructure and unpredictable traffic are everyday challenges. Models trained solely on structured data often fail in these environments. To address this, the survey includes detailed comparisons and benchmark tests under difficult traffic and weather conditions. These insights inform the design of XenSense-V.1, a real-time deep learning framework using optical f low, temporal reasoning, and efficient segmentation to handle occlusion, weather issues, and complex road scenarios—particularly suited to Indian driving conditions.
Authors - Ramesh Chandra Poonia, Vishal Singh Rathore, Ajay Kumar Abstract - In contemporary network infrastructures, ensuring the fidelity of data transmission is paramount for robust communication and security. The intrusion of corrupted data packets can severely degrade network efficiency, resulting in critical data loss, exploitable security gaps, and suboptimal resource allocation. This paper indicates the significantly increase detection accuracy and system resilience by synergistically using the predictive capability of many machine learning paradigms especially. This paper employs sophisticated feature engineering to extract discriminative attributes from network packet headers and payloads, followed by a refined ensemble learning strategy that leverages both stacking and boosting techniques for optimal classification performance. Compared to conventional single-model techniques, evaluated on real-world network traffic datasets our model shows a significant increase in key performance measures. Here a pioneering hybrid machine learning ensemble framework designed for the precise identification and mitigation of corrupted data packets. Notably, the ensemble framework excels in minimizing false positives, enabling real-time packet analysis and bolstering network security. This study contributes to the evolution of intelligent, adaptive network defense mechanisms, providing a scalable and high-performance solution for safeguarding data integrity and mitigating the deleterious effects of corrupted data packets in modern, high-throughput communication environments.
Authors - Smit Patel, Priyanka Patel Abstract - Speech Emotion Recognition (SER) plays a vital role in enhancing human-computer interaction by enabling machines to interpret and respond to human emotions. This study focuses on SER using the RAVDESS dataset, emphasizing speech-only modalities. A comprehensive set of audio features including MFCCs, chroma, spectral contrast, tonnetz, and wavelet transforms is extracted, and the performance of four deep learning models— CNN, LSTM, BiLSTM, and CNN-LSTM—is evaluated. Among them, CNN achieves the highest accuracy (68.37%), with strong F1-scores across several emotion classes. The results underscore the effectiveness of spatial feature extraction in emotion classification and suggest further enhancements using attention mechanisms and transformer-based models.
Authors - Sundar S, Prathilothamai M Abstract - Accurate prediction of monsoon rainfall remains a persistent challenge due to the intricate interplay among meteorological conditions, oceanic influences, and broader climatic patterns. While significant effort has been devoted to improving model architectures, the effect of input feature composition on prediction accuracy has received relatively less attention. This study addresses that gap by conducting an extensive empirical evaluation of 2,047 feature group combi-nations, systematically derived from eleven curated sets of climate-related variables. Using a hyperparameter-tuned XGBoost model, each configuration was evaluated independently to assess the predictive contribution of domains such as lagged climate indices, cyclical temporal encodings, and event-based indicators. The results show that model performance improved significantly from an R² of 0.5801 (RMSE: 10.8999 mm, MAE: 4.9009 mm) using only meteorological features to an R² of 0.7606 (RMSE: 8.2297 mm, MAE: 3.8752 mm) when combined with oceanic and climatic inputs, particularly lagged MJO and ENSO indices and temporal signals. These insights reinforce the value of domain-informed feature fusion and provide a replicable approach to enhancing monsoon prediction models through thoughtful feature group design and empirical validation.
Authors - Atharva Godkhindi, Anjali Naik Abstract - Fraud detection in financial transactions presents a persistent challenge due to extreme class imbalance and evolving attack patterns. While several machine learning (ML) and deep learning (DL) methods have shown promise, these solutions are fragmented and use traditional methods to address the severe class imbalance, leading to models with inflated metrics and poor generalization. In this study, we propose a unified ML-DL-XAI pipeline that integrates Variational Autoencoders (VAE) not only for data augmentation but also for feature engineering. Unlike traditional resampling, the VAE enables representation learning that preserves underlying data distributions while mitigating overfitting. Our pipeline incorporates interpretable machine learning models alongside neural networks to ensure both high performance and explainability. Empirical evaluations on a large-scale financial dataset demonstrate superior and reliable performance, that achieves an accuracy of 99.6%, a precision of 92%, and a recall of 85%, outperforming several recent benchmarks. By combining augmentation, feature engineering, and explainability in a single pipe-line, this work offers a robust and practical answer for real-world fraud detection applications.
Authors - Gitanjali S. Poothuvallil, Dhanya Manayath Abstract - This paper explores the use of generative AI in social entrepreneurship education through the REFLECT model—a justice-informed ethics framework emphasizing participatory, empathic, and power-sensitive reasoning. Using ChatGPT, we simulated context-rich dialogues based on real-life renewable energy cases to examine ethical dilemmas, systemic failures, and power dynamics. Through iterative refinement, we developed a prompt template capable of producing realistic, grounded dialogues. These were evaluated on three criteria: depth of empathy, systems complexity, and presence of ethical tension. Findings suggest that integrating the REFLECT framework enhanced the pedagogical quality of AI-generated cases, aligning them more closely with the aims of values-based social entrepreneurship education. The approach demonstrates how generative AI can foster critical thinking and ethical reflection, helping students engage with complex social justice and sustainability issues. Our study presents a novel application of AI in the classroom, showing its potential to ad-vance pedagogy in socially responsible entrepreneurship. The paper concludes by exploring the future potential of integrating AI into educational pedagogy, highlighting both its ethical implications and the ongoing need for refinement in its application to ensure responsible and effective use in teaching and learning environments.
Authors - Bharateesha lvn, J Vignesh, Jabez Lawrence G, Bhaskarjyoti Das Abstract - The emergence of sophisticated cyber threats calls for the evolution of sophisticated Network Intrusion Detection Systems (NIDS). Even though graph-based approaches have been promising, they have mostly was concerned with node classification to determine the bad actors. This paper provides a new framework that recontextualizes the NIDS challenge as a marginal concern classification problem on a heterogeneous graph. We assume that classifying the boundaries (relations) between network objects as harmful or benign offers a more timely and efficient means of intrusion detection. In order to achieve this, we build a heterogeneous graph from network flow data and employs a Heterogeneous Graph Transformer (HGT) model, which is designed to maintain the integrity of instructional and semantic detail formation present in such graphs. The model is trained and tested on a large dataset from the UNSW-NB15 dataset. Our experiments show that the edge classification method greatly outperforms a conventional node classification baseline, achieving superior accuracy, precision, and recall. These results illustrate the potential of edge-centric GNN models for constructing more efficient and complete network intrusion detection systems.
Authors - Poojitha Panchakarla, Sarvani Kocherlakota Abstract - Cryptocurrency has emerged as a prominent research topic in recent decades, with numerous findings published in leading international journals. 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. Lecture Notes in Networks and Systems is having the highest publications and IEEE Access is having the most cited document. Kumar A is the most contributing author and Song H is the most cited author. India is the most cited country and contributing country. By research gap analysis, the future direction in the domain of cryptocurrency can be machine language forecasts, sustainable energy, and regulatory framework.
Authors - Jiri David, Jan Fabry, Josef Bradac Abstract - With the growing importance of electromobility, the efficient planning in the production of lithium-ion batteries has become a critical factor in maintaining competitiveness. This article focuses on the optimisation of batch scheduling on parallel processors – an essential challenge in a complex manufacturing environment characterised by hybrid (sequential-parallel) processes, technological dependencies, and high variability. Based on a formal mathematical model of the P|batch, rj , sj | Cmax type, a method is proposed that integrates batch planning, nonlinear setup times, multi-objective optimisation, and robust scenariobased control. The model was implemented in the AMPL (A Mathematical Programming Language) environment and tested using real production data from battery manufacturing at the famous production company. The results demonstrate significant improvements over conventional methods (e.g., FCFS, LPT), including a reduction in production time of up to 10%, a 6% decrease in setup operations, and an increase in capacity utilisation (OEE) by more than 10%. Moreover, reductions in energy consumption and enhanced schedule predictability were observed. The model is designed to be integrable with MES/ERP systems and offers both scalability and adaptability across varying production scenarios. The findings confirm that the combination of a rigorously defined optimisation model and operationally validated data can significantly enhance planning efficiency in battery system manufacturing for electric vehicles.
Authors - Etty Gurendrawati, Hera Khairunnisa, Aji Ahmadi Sasmi, Andrew Saw Tek Wei, Nayla Nandhita Nuril Hadi, Rohadatul Aisy, Shoofiyah Nur Aliifah Abstract - This study examines how Information Quality (IQ), System Quality (SQ), and Service Quality (SeQ) influence Use (U) and User Satisfaction (US). It also investigates the impact of U on US, and how both US and U affect Net Benefit (NB) among Public Sector Accounting students at an Indonesian State University. Data from 114 student questionnaires were analysed using SEM PLS by SMART PLS 4. Seven of nine hypotheses were supported. SQ significantly impacted U and US, while SeQ significantly affected U. Both U and US positively influenced NB, with US having the strongest effect. The insignificant role of IQ suggests students prioritize system functionality and support over data output. These findings emphasize that enhancing system and service quality drives user satisfaction and use, ultimately boosting the perceived net benefits of nonprofit accounting information systems.
Authors - Rhytheema Dulloo, Kirti Biradar, Srijaa M Abstract - The aviation industry faces mounting pressure to achieve net-zero carbon emissions by 2050, yet passenger adoption of sustainable aviation practices remains inconsistent, highlighting the urgent need to understand the psychological and behavioral factors influencing passenger decision-making in sustainable aviation contexts. This study develops and tests an integrated theoretical framework combining Theory of Planned Behavior (TPB), Value-Belief-Norm (VBN) theory, and Technology Acceptance Model (TAM) to explore how environmental values, technology perceptions, and travel context shape attitudes, intentions, and behaviors. A quantitative cross-sectional study of 847 airline passengers across seven metropolitan hubs in India was conducted. Environmental values emerged as the strongest predictor of attitudes toward sustainable aviation behavior (β = 0.42, p < 0.001), while the integrated model explained 62% of variance in behavioral intentions and 34% in actual behavior. Perceived usefulness and ease of use were found to significantly affect sustainable aviation technology acceptance and TPB constructs effectively predicted behavioral intention and actual behavior towards sustainable aviation, with per-sonal norms adding further explanatory power. Significant differences were found between business and leisure travelers, with leisure travelers showing stronger relationships between environmental values and attitudes (p = 0.032) and between intentions and behavior (p = 0.007), while the intention-behavior gap was more pronounced among business travelers, highlighting structural barriers to sustainable aviation adoption. The findings suggest that airlines should adopt differentiated strategies, with sustainability messaging emphasizing personal environmental responsibility for leisure travelers, while structural interventions such as corporate partnerships and policy changes are needed for business travelers to address organizational barriers. This study provides the first comprehensive integration of TPB, VBN, and TAM theories in sustainable aviation contexts, offering novel insights into travel context moderation effects and actionable guidance for industry stakeholders seeking to enhance passenger adoption of sustainable aviation practices.
Authors - GGS Pradeep, Thrilok. Kolla, Rajesh Sharma R, Akey Sungheetha, N Vijayalakshmi, Pellakuri Vidyullatha Abstract - Ambient Intelligence (AmI) systems are intended to support responsive and adaptive services by understanding the context of their users. The present paper proposes a machine learning framework for context prediction in ubiquitous environments on the basis of multi-sensor ambient data. The method classifies user activity into four high-level contexts: Sleeping, Working, Cooking, and Outdoors, exploiting features like Temperature, Light, Sound, Motion, and Time. The model described here consists of three steps: Preprocessing, Normalization, and Classification based on Random Forests. Visualization techniques, such as KDE plots, correlation matrices, and 3D scatter analysis, are used for the interpretation of contextual separability and the influence of features. Experimental results show that outdoor activity strongly dominates the dataset, while other contexts are almost neglected. The proposed framework shows high predictive accuracy with interpretable insights into context-dependent behavioral patterns. This work thus contributes to the design of such adaptive, context-aware systems that can find a home in intelligent environments, especially in the realms of smart homes and assistive technologies. The abstract makes it abundantly clear how crucial Ambient Intelligence systems are to contemporary ubiquitous surroundings, particularly for assistive technology and smart houses.
Authors - Adrian Diepeveen, Siphesihle Sithungu Abstract - Traditional electricity grid networks have approached critical resilience thresholds, with archaic grid architectures demonstrating inadequate capacity to accommodate volatile demand fluctuations. South Africa's current energy crisis, characterised by unpredictable power outages and extensive economic losses from electricity outages, emphasises the need for novel optimisation frameworks. This re-search proposes a framework named AMPERE (Autonomous Multi-Agent Predictive Electricity Reinforcement Learning Engine), a dual-phase solution which com-bines hierarchical multi-agent deep reinforcement learning (MADRL) and evolutionary algorithms (EAs) in order to address electricity distribution challenges through decentralised multi-agent coordination. A significant gap exists in research integrating distributed hierarchical knowledge-sharing MADRL with EAs at both regional and national levels, since current approaches focus on singular artificial intelligence (AI) methodologies or non-hierarchical frameworks. Ultimately, AMPERE introduces a novel three-tier hierarchical agent architecture consisting of smart grid national agents, smart home regional agents, and smart home battery agents, utilising Pecan Street's real-world Internet of Things (IoT) time-series datasets. The Centralised Training with Decentralised Execution (CTDE) paradigm enables effective multi-agent coordination across regional and national scales, subsequently enhancing smart grid stability and scalability while minimising electricity lost in distribution.
Authors - Jayan V, Sreejith Alathur Abstract - The COVID-19 pandemic not only strained global healthcare systems but also exposed healthcare professionals to unprecedented psychological challenges. Among the various stressors, the rapid spread of misinformation on social media emerged as a significant contributor to emotional distress, confusion, and burnout among frontline workers. This study explores the impact of social media misinformation on the mental health of healthcare professionals in India during the pandemic, using qualitative interviews with 41 public sector healthcare workers. Guided by the honeycomb social media framework and stress coping models, the re-search examines how misinformation intensifies psychological stress and how individuals and institutions respond to it. Findings reveal that misinformation created confusion in clinical protocols, fueled public panic, and intensified professional anxiety. Participants emphasized the need for verified communication channels, stronger digital literacy, and mental health support systems. The study underscores the critical role of individual responsibility, institutional action, and policy reform in combating the "infodemic" and protecting the well-being of healthcare workers. Recommendations include the establishment of centralized information platforms, community-driven myth-busting initiatives, and proactive involvement of enforcement agencies. This research calls for a multi-stakeholder approach to address misinformation as a public health risk in its own right.
Authors - John Paul C. Masuhay, Reynaldo R. Corpuz Abstract - This piece of work, setting up a new stage, introduces a novel optimization approach for Torus Fully Homomorphic Encryption (TFHE) using Genetic Algorithms (GA). TFHE protects data privacy by facilitating calculations on encrypted data. The study integrates GA with TFHE to optimize key parameters, such as LWE dimension, decomposition level, and security level, aiming to achieve an optimal trade-off between encryption accuracy, bootstrapping time, ciphertext size, and security. The GA optimization uses a multi-objective fitness function, in which performance metrics are derived from synthetic datasets such as text, alphanumeric, and special characters for simulation. The results indicate that the GA framework using DEAP was effectively integrated with TFHE. After five generations, the optimization process attained a best fitness value of 0.9691, marking significant enhancements in system performance. Specifically, the GA optimization improved TFHE parameters, such as increasing the LWE dimension from 1024 to 1514.02 by reducing the noise standard deviation from 3.2 to 2.53 and improving the security level from 128 bits to 242.57 bits. The decomposition level was adjusted from 4 to 2, optimizing computational performance while slightly reducing security. Variation in bootstrapping times across datasets reflects how efficiently the optimization balances efficiency and performance. These remarkable findings suggest that GA could potentially be applied to im-prove TFHE parameters, making it a more efficient and secure method to perform privacy-preserving computations, particularly within edge computing environments.
Authors - Alpa R. Barad, Ankit R. Bhavsar Abstract - A good quality of forage is essential to feed cattle to improve its health and productivity. Quality of grass has been affected by various factors such as weather condition, leaf disease, and diverse species with texture similarity. Over multiple species of grass and variation over sessions leads quality recognition more complex and difficulty. Proposed study uses unsupervised approach to reduce dependence of annotation over multiple species of grass. Proposed work uses median and fast denoising algorithm to enhance input image. Article present novel approach with integration of Gabor filter with k-means++ model. Gabor filters helps to identify optimized features over texture similarity problem of grass. Simulation of proposed study also measures denoising input grass image with 0.72 SSIM. Simulation of proposed study also finds remarkable performance with 79.86% accuracy with median and Gabor filter. Simulation of study also explore possible variations with hybrid unsupervised approach to enhance performance of the model.
Authors - GGS Pradeep, Thrilok. Kolla, N Vijayalakshmi, U Ananthanagu, Rajesh Sharma R Abstract - The booming volume of genomic data requires computational techniques that will allow the efficient extraction of biologically meaningful patterns. This work investigates advanced data mining algorithms for extracting frequent patterns and motifs in DNA and RNA sequences based on k-mer analysis. The nucleotide sequences are divided into shorter pieces called k-mers, where frequent pattern mining, positional frequency mapping, and dimensionality reduction techniques are applied to expose conserved motifs and structural characteristics. The proposed methodology employs Apriori for mining, followed by Principal Component Analysis (PCA) and a variation of a sequence logo style for visualization, which exposes latent relationships and biologically relevant sequence patterns. The experimental data show uniformly distributed occurrences of dominant k-mers and positionally clustered functional motifs like the “ATG” codon. The PCA projections in 2D and 3D space further highlight the structural diversity and possible clustering of sequences according to k-mer profiles. This research extends the frontier of bioinformatics by aiding in the interpretation of genomic sequences, with applications in genome annotation, disease-associated gene prediction, and regulatory motif identification. It serves the purpose of informing about the methods being brought together under k-mer analysis, frequent pattern mining, PCA, and motif visualization.
Authors - Zlatinka Kovacheva, Kalinka Kaloyanova, Ina Naydenova, Mariana Trifonova Abstract - This paper concerns project-based learning. The aim of the study is to compare the results of group and individual projects of undergraduate students studying Information systems. The research is based on statistical inference and hypotheses tests. A hypothesis test is presented regarding a difference between means of two dependent samples for quantitative indicators that have a normal distribution. The result analysis is based on 3-years experiments with student pro-jects. For all experiments, the null hypothesis is rejected, and in both methods of assessment (group and individual projects), different results are obtained. For the majority of the groups involved in the experiments, the marks of the group project are higher than the average marks of the individual project but there are also exceptions. The results show that students who performed better on the individual project often received lower grades on the group project. Also, students with low individual grades received higher scores on the group assessment. As a conclusion, we can recommend applying both methods – group and individual projects for students’ assessment, paying attention at their advantages and disadvantages.
Authors - Nguyen Hoang Kha, Su Truong Phuc, Huynh Tu Anh, Ha Cao Vi, Tran Quoc Hao, Vinh Dinh Nguyen Abstract - Face recognition tasks often suffer from poor performance when limited real training data is available per class. To address this, we propose a novel pipeline that combines LoRA-based fine-tuning of a generative model with synthetic image generation and YOLOv8 classifier training. Our method fine-tunes Stable Diffusion using just a few real face images per identity, then generates realistic, identity-preserving synthetic images to augment the training dataset. The augmented dataset is then used to train a YOLOv8n classifier for face recognition. We evaluate our pipeline on a 31-class face dataset and achieve a Top-1 classification accuracy of 80.05%, significantly outperforming the baseline YOLOv8n model trained only on resized real images (60.94%). These results demonstrate the effectiveness of our method in low-resource settings for face recognition.
Authors - Keshav Dudani, Nischay Jain, Rishi Raj, Nitesh Patnaik, Vishal Meena Abstract - Stock market volatility and the effect of financial trends and external news perspectives are reasons why predicting the direction of the stock market is so intrinsically tough. Conventional models may fail to provide accurate forecasts when they fail to react to the quick changes in the marketplace. Our proposed method boosts the success of predicting by employing StockVisionX, which constructs classifications by combining past stock movements and sentiment analysis of financial news sources. The sentiment polarity that is integrated into StockVisionX delivers a more thorough market movement projection, thereby forecasting whether a stock will gain or drop in price. StockVisionX adds even more strength in its predictability with the usage of vital technical indicators such as SMA, EMA, RSI, MACD, ATR, and OBV. Supplementing these criteria with sentiment analysis ensures a better and evidence-based manner of categorizing stock movement. The model is trained and tested on diverse datasets to analyze sentiment and forecast prices, and task-specific optimization is seen. Our Naive Bayes technique is 54.5% more accurate when compared to Vader sentiment categorization. Our Prophet + XGBoost model, which has been trained on non-correlated real-world data, outsmarts ARIMA and LSTM by 49.0% and 29.6%, respectively, when predicting price; however, it is hard to determine if these models would work better with highly correlated data. This is evidence of the power and generalizability of our process. StockVisionX is efficient and precise in its classification model on predicting stock movement through the meticulous integrating of sentiment-related information with the trend history.
Authors - GGS Pradeep, Thrilok. Kolla, Rajesh Sharma R, Akey Sungheetha, N Vijayalakshmi, Pellakuri Vidyullatha Abstract - The ever-increasing implementation of IoT sensor networks in smart environments has created a multitude of multivariate spatiotemporal data streams. The effective identification of temporal patterns and anomalies in those streams becomes necessary to ensure environmental awareness and operational integrity. This paper proposes an elaborate framework for temporal pattern recognition in IoT sensor data through the integration of spatiotemporal reasoning, statistical modeling, and anomaly detection. The approach uses statistical descriptors, kernel density estimation, correlation matrices, and spatiotemporal visualization to capture local anomalies as well as trends prevalent in aggregation over distributed sensors in order to illustrate its strengths in analyzing the data. One of the case studies depicts the environmental awareness based on monitoring temperature: deviation detection, inter-sensor coherence, and a conceptual view of anomaly propagation. The results indicate the potential of establishing a system capable of real-time and interpretable monitoring in dynamically evolving IoT settings. This work contributes toward more adaptive and transparent sensing architectures capable of operating under uncertainty and environmental variability. The abstract is coherently structured, flowing from motivation to case study, methodology, results, and contribution. It also accurately frames the rationale for the study and successfully defines the fundamental difficulty in IoT sensor networks, which is the recognition of temporal patterns and abnormalities.
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.
