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.
