Authors - Sivakami B U, M. Suresh Abstract - In a world racing toward net-zero emissions, how can MSMEs strategically align their business operations to adopt green finance? Despite MSMEs’ growing recognition of sustainable development, they continue to face persistent challenges in aligning their strategies with green finance adoption due to financial, regulatory, and knowledge-related barriers. The extant literature provides limited insights into the strategic alignment for adopting green finance. To ad-dress this gap, the study develops a Fuzzy Logic-based assessment framework to evaluate alignment levels and identify weaker attributes. The framework specifies five criteria and nineteen attributes. Findings reveal a state of “moderately aligned” with four among nineteen attributes requiring immediate attention. The framework offers MSMEs a structured pathway to assess their alignment and strategically position themselves for green finance adoption and sustainable growth.
Authors - Premchand, Jayant Kalra, Ajoy Kumar, Sahil Sankhyan, Varun Dutt Abstract - Cardiovascular diseases (CVDs) continue to be the globe's leading cause of death, and therefore, there is a need for low-cost, long-term cardiac monitoring technologies especially in low-resource environments. Current wearable ECG technologies are limited by cost, proprietary design, and single functionality. We introduce ECG Go Device, a low-cost, portable, multifunctional, wearable device with real-time ECG monitoring, heart rate variability (HRV) analysis, fall detection, and step counting in one platform. The system utilizes a high-fidelity analog front-end and inertial sensing to provide mobility aware cardiac monitoring. A 44-subject validation trial compared ECG Go Device with a clinical-grade Shimmer3 ECG system and the Fitbit Sense 2 on 12 standard time-domain HRV measures and 10-day step count. The ECG Go Device demonstrated good agreement (all p > .05; r > .93) with Shimmer3, and high correlation in step count measure (r = .94). With clinically robust signal quality, real-time wireless transmission, and safety alert capability, ECG Go Device provides a scalable solution for longitudinal cardiac and mobility monitoring especially for eldercare and underserved communities.
Authors - Sarath M, M Suresh Abstract - Enterprise Resource Planning systems and Supply Chain Management systems shape how organizations plan, coordinate, and deliver goods and services. When they function together, they improve efficiency, visibility, and decision making. Research on this integration, however, is scattered across industries and viewpoints, making it hard to see the full picture. This study clarifies that picture through a Bibliometric Systematic Literature Review (B-SLR). A PRISMA-based review identified and screened relevant studies with clear criteria to ensure transparency. Ninety-nine publications from the Scopus database, covering 2020 to 2025, were retrieved. From these, twenty studies were selected for qualitative synthesis, while the full set underwent bibliometric analysis to map the field through performance metrics, collaboration networks, keyword co-occurrence, and thematic clusters. The results show a rising focus on ERP and SCM integration, driven by digital transformation and the demand for resilient supply chains. The analysis high-lights five recurring dimensions: scope, enabling technologies, outcomes, success factors, and barriers. These insights form a Five-Dimensional ERP and SCM Integration Framework that links theory with practice and provides a foundation for future research and implementation.
Authors - Kunihiko Takamatsu, Sayaka Matsumoto, Katsuhiko Murakami, Hidehiko Kamei, Kazuya Tsuruta, Naoya Honda, Yumi Ishige, Shimpei Matsumoto, Hiroya Kawasaki, Ikuhiro Noda, Kenya Bannaka, Tetsuhiro Gozu, Tomoyuki Sakai, Ryosuke Kozaki, Aoi Kishida, Hibiki Ito, Koichi Akashi, Sayaka Hama, Gerald Prescott, Masahiro Uchida, Akira Nakamura, Yasuhiro Kozaki, Tamotsu Mori, Shintaro Tajiri, Naruhiko Shiratori, Shotaro Imai, Kenichiro Mitsunari, Yasuo Nakata, Sachio Hirokawa,Masao Mori Abstract - This paper extends the Lifelong Sustainable Inquiry-based Community Learning (LSiCL) framework, based on Edu in formatics, to address challenges and opportunities presented by the rise of Large Language Models (LLMs) in education. LLMs like ChatGPT are fundamentally transforming education, with human-LLM and inter-LLM learning relationships opening new frontiers. This research explores whether the traditional human-to-human LSiCL can be meaningfully extended to encompass human-LLM and LLM-LLM interactions while retaining its core principle of collaborative inquiry. Our analysis demonstrates that LSiCL principles are applicable across all these configurations, paving the way for sustainable and scalable Human-LLM Learning ecosystems. This frame-work is crucial for improving resource efficiency, promoting educational equity, and enhancing global educational sustainability.
Authors - Nakhoon Baek Abstract - Since the computers are introduced, there have been many meaningful attempts to use computers as education tools. Additionally, the wide use of web servers on the internet makes video-on-demand services available especially in the entertainment area. They also developed digital video lectures, which record video lectures as digital contents, and provide them also as parts of video-on-demand services. During the COVID-19 pandemic in the year of 2020, almost all schools around the world provided digital video lectures. Since the digital video lectures can provide high-quality lectures to lots of people at relatively-low costs, it is now expected to be much actively used in near future. In this paper, we will present more details of what preparations and choices are possible, what to do, and what to avoid, in the process of providing digital video lectures, especially in the field of computer science. At this time, these kinds of literature surveys are seriously lacking, and thus, our case studies will be used to further improve the overall quality of digital video lectures and expand the e-learning markets. This paper will be a good starting point for providing check points and reference items to lecturers who prepare and provide digital video lectures.
Authors - Ikram Ahamed Mohamed, Hafiz Abdulla, Mohaideen Mohamed Mohabilasha, Fiyaz Ahmed, Pankaj Chandre, Parikshit Mahalle Abstract - With the surge in digital wallet usage across fintech platforms, secure and privacy preserving Know Your Customer (KYC) mechanisms have become increasingly critical. This survey reviews the current state of digital identity verification frameworks, emphasizing the challenges posed by centralized KYC models, including data breaches, lack of user consent, and regulatory fragmentation. We systematically analyze more than 50 recent studies and industry practices addressing privacy-preserving identity protocols, decentralized identity (DID) systems, cryptographic techniques (such as zero-knowledge proofs), and access control architectures. The paper proposes a reference architecture reKYC based on common patterns found in the literature, integrating layered components for identity verification, access governance, trust evaluation, and regulatory compliance. We classify existing approaches by their trust model, cryptographic basis, and consent mechanisms. Key gaps identified include limited regulatory interoperability, weak user-centric control models, and lack of scalable trust frameworks. This survey concludes with open research directions and a future outlook for secure, privacy-aligned digital wallet verification.
Authors - Ujwala Kshirsagar, Asmita Dani Abstract - The National Education Policy 2020 (NEP 2020) encourages the inclusion of an Indian Knowledge System (IKS) in the Higher education curriculum. In traditional Indian philosophy, the search for knowledge wisdom and truth was always seen as the highest goal of human life. The scholars made significant contributions in many fields likes mathematics, medicine, astronomy, engineering, surgery, architecture, navigation, shipbuilding, arts, yoga, and even chess. These valuable traditions and contributions should be preserved, studied, developed, and used in new ways through our education system to benefit future generations This paper focused on strategies for implementing IKS Indian Knowledge System into curriculum of higher education institutes, to roadmap efficient execution of IKS and impact of addition of IKS on modern syllabus.
Authors - Vikas Shah, Aarav Shah, Aarush Shah Abstract - The crowdfunding landscape, positioned at the intersection of entrepreneurship and technological innovation, compels organizations to address rising competitiveness while embedding corporate social responsibility (CSR) and advancing employee empowerment. Traditional crowdfunding approaches often struggle to meet the primary objectives due to inconsistent mechanisms to ac-count for regulatory changes, cultural diversity, and the demand for measurable social impact. A persistent limitation of existing practices is the absence of standardized frameworks and measurable indicators capable of capturing the organizational and societal value of crowdfunding. The paper introduces a Scenario-based Crowdfunding Framework (SCRF) that categorizes scenarios and defines actionable objectives to enhance alignment between CSR and employee empowerment. SCRF is a lightweight and adaptable methodology incorporating elementary mechanisms to evaluate, refine, and strengthen the social and organizational impact of crowdfunding. An empirical case study of crowdfunding for Artificial Intelligence (AI) initiatives and programs is performed, providing evidence of how scenario-based initiatives can serve as a strategic lever for CSR and employee empowerment in the context of Society 5.0. The research identifies key determinants influencing crowdfunding effectiveness and examines their implications for CSR integration and organizational engagement with scenario-driven analysis, governance cycle, and impact evaluations. The study ex-amines and contributes to accelerating CSR initiatives, positioning employees as empowered change agents, and advancing the broader societal vision of So-ciety 5.0.
Authors - Nurianna Thoha, Stephen Wijaya Abstract - The purpose of this research is to explore the motivational factors of Generation Y (Gen Y) to work in Chinese-owned garment companies and to analyze how these factors are linked to Herzberg’s theory of motivation and the Chinese business culture since based on the previous research many companies do not properly motivate Gen Y employees. A qualitative method by utilizing exploratory study was applied for this research. In-depth semi-structured inter-views with 25 Gen Y employees were conducted in two Chinese-owned garment companies in the Greater Jakarta area of Indonesia, to collect the primary data. The interviews were recorded, and the data was used to develop the transcripts, which were then analyzed using thematic data analysis. The research results showed that Herzberg’s theory of motivation and Guanxi are not always aligned with the motivations of Gen Y employees to work in Chinese-owned garment companies. For example, quality of relationship and status are aligned with Guanxi but not aligned with Herzberg’s theory of motivation. However, several Guanxi factors are aligned with Herzberg’s theory of motivation, such as trust and responsibility, and recognition. In conclusion, there is a connection between motivations, Chinese business culture, and generation cohort which is GenY employees who are currently working in both garment companies in the Greater Jakarta area of Indonesia.
Authors - Akash Patel, Hardik kumar Jayswal, Rishi Patel, Prem Trivedi Abstract - This paper provides an in-depth analysis of various methods and concluding things for plant disease detection and classification, utilizing combinations of machine learning and deep learning approaches. The literature review highlights three primary methods: computer vision-based ML models, DL architectures, and spectroscopy-based techniques. While computer vision methods, including segmentation and manual feature extraction, have been extensively used, they are time-consuming and less effective for datasets with minimal visible symptoms. Deep learning, with its capability for automatic feature extraction, demonstrates significant potential, although challenges such as high computational costs and overfitting persist. Spectroscopy methods present alternative strategies, yet they are not universally applicable across all types of plant diseases. Our analysis suggests that optimized methodologies, capable of handling diverse datasets and overcoming the limitations of visual-based techniques, are essential for the timely and accurate classification of plant diseases. Future research must ad-dress these challenges to develop more robust, scalable, and efficient disease detection systems.
Authors - Shital Khaparde, Rakesh Verma Abstract - In the field of Speech Emotion Recognition (SER) the research is gaining more attention, with the ability to augment human-computer interaction by allowing systems to recognize emotional states from speech. The research area has evolved significantly, with most of the advancements being due to deep models of learning which are particularly good at extracting subtle patterns from raw audio data directly. The review discussed the current research, with focus on the use of prevalent deep learning architectures—e.g., Long Short-Term Memory networks (LSTM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), , and hybrid models like CNN-BiLSTM—for SER tasks. The area of research has come a long way, some of the major challenges still persist. The design of dependable SER systems is still beset by issues such as imbalanced datasets, the ongoing difficulty of effective feature selection, noise sensitivity, and computational efficiency problems. Although deep learning has advanced remarkably, the review emphasizes that significant obstacles must still be overcome before the area can provide consistently trustworthy performance in actual situations. This paper proposes several future research including broadening and diversifying datasets, increasing the cross-linguistic strength of the model, and better real-time system performance.
Authors - Sharon Elizuba Koshy, Shyam A.V, Padmadas Sundaram, Sofia Rani Shaik Abstract - The growing complexity of urban ecosystems, fueled by rapid population expansion, is intensifying the challenges cities face in achieving sustainability, circularity, and resilience. This evolving landscape demands innovative management approaches that integrate technological advancements and resource optimization strategies to support resilient urban development. This research proposes a management-oriented framework for Artificial Intelligence enabled Industrial Symbiosis networks within smart cities, focusing on enhancing resource recovery and establishing closed-loop systems. By leveraging AI technologies of predictive analytics, dynamic matchmaking, and optimization algorithms, the framework aims to identify, coordinate, and scale symbiotic partnerships among industries, utilities, and municipal stakeholders. Rather than emphasizing the technical development of AI algorithms, the study addresses strategic governance models, stakeholder engagement mechanisms, and policy interventions that facilitate the effective integration of AI in managing industrial symbiosis networks. Drawing on principles of the circular economy and smart infrastructure governance, this research highlights how AI can drive adaptive and resilient resource flows within urban environments. By bridging technological innovation with strategic management practices, the study contributes to the evolving discourse on sustainable smart city development and offers actionable insights for policymakers, city planners, and infrastructure managers.
Authors - Mohar Banerjee Biswas, Srikant Das Abstract - Individuals display different levels of innovativeness towards technology and this innovativeness is a crucial personality trait to understand adoption behavior. Personal Innovativeness in Information Technology (PIIT) has emerged as a strong personality trait that influences the adoption behavior towards Information and Communication Technologies (ICT) in higher education. This research analyses the influence of PIIT on the perception of utility and simplicity of ICT by higher education students. The study extends the Technology Acceptance Model (TAM) by adding PIIT and analyses how it affects the perceived usefulness (PU), perceived ease of use (PEOU) and subsequent behavior intention (BI) towards ICT adoption. For the data collection, 493 sample data were obtained from university students using an online structured questionnaire and was processed using Structural Equation Modeling (SEM). The findings of the study reveal that PIIT significantly impacts the PU and PEOU, indicating that students who are more innovative will view ICT as beneficial to their academic task and will find it easy to use. PIIT also has an indirect influence on BI via the construct of PU and PEOU, emphasizing its mediating role. The findings propose that developing PIIT amongst learners can improve students' preparedness to adopt ICT in academics. The research has important implications for stakeholders in developing specialized interventions, training, and curriculum designs that nurture creative mindsets, thus enabling stronger integration of technology in learning.
Authors - Santanu Mandal, Srinija M, Sukruthi M Abstract - This study investigates the structural barriers hindering digital well-being among Generation Z (Gen Z) through an integrated lens of Self-Determination Theory (SDT) and Cognitive Load Theory (CLT). Using a cross-sectional survey conducted between June and July 2025, 126 valid responses were collected from Indian Gen Z participants, comprising students and early-career employees. Ten barriers—including lack of awareness, absence of digital literacy, mixed media messages, platform design addiction, and peer pressure—were examined for their interrelationships using Interpretive Structural Modelling (ISM) and MICMAC analysis. Results reveal that foundational barriers such as lack of awareness, absence of digital literacy, and mixed media messages act as high-driving factors, creating cascading effects on dependent barriers like inconsistent sleep patterns, low perceived control, and productivity guilt. From the SDT perspective, these drivers undermine autonomy and competence, while CLT explains how cognitive overload from addictive design features exacerbates maladaptive digital behaviours. The findings highlight the need for multi-stakeholder interventions—spanning policy, education, workplace culture, and ethical platform design—to address root causes and promote sustainable digital wellness. This research advances theoretical integration between SDT and CLT, offering a diagnostic framework for targeted, systemic interventions aligned with SDG 3 and SDG 12.
Authors - Rupsa Sarkar, Jaydev Mishra Abstract - Natural language processing (NLP) has several key applications, including sentiment analysis (SA). SA can bed escribed as a procedure that identifies the polarity of a sentence as well as its goal through analysis. SA is now the most active NLP research area. Aspect-based sentiment analysis, which is a subset of SA, is the process of examining a sentence's structure and determining its polarity. This field has been grown because now people feel free to share their thoughts, opinions, expressions. Internet, social media are now the massive resource of opinion assuming. In this paper, we have used Sem Eval 2014 task 4 dataset to focus on Aspect-based Sentiment Analysis (ABSA) problem. We have focused on previous work Based on Aspect-based Sentiment Analysis and Some work of Sentiment Analysis based on conversational data. We have used GPT2 for this task.
Authors - Mark Anthony C. Ochoa, Aloysius J. Aurelio, Stephan Kupsch Abstract - This sub-study evaluates the application of descriptive and inferential statistics in graduate research at DMMMSU-CGS. Findings show that while nominal and interval/ratio scales are often treated with appropriate measures (frequency counts and means), ordinal data is frequently misrepresented using the mean instead of median or mode. Such lapses compromise validity and highlight gaps in statistical literacy. This misuse underscores the need for statistical diagnostics prior to test selection and the inclusion of assumption checks in research guidelines. Moreover, analysis of appropriateness and accuracy revealed that while proper methods generally produce accurate results, some accuracy still arises from misapplied tools due to compensating factors. These findings stress the importance of enhanced statistical training and methodological rigor. . . .
Authors - Jordan Cardenas, Fabian Cardenas, Marcos Levano, Billy Peralta Abstract - In today’s digital economy, where personalization has become a cornerstone of effective marketing strategies, companies face the dual challenge of increasing advertising impact while safeguarding sensitive customer information. Despite the rapid progress of large language models (LLMs), existing commercial solutions often neglect the integration of synthetic data to reduce privacy risks and enhance adaptability, leaving organizations dependent on external providers. To address this gap, our work fine-tunes open-source LLMs (LlaMa2, Mistral, and Zephyr) with synthetic datasets generated via GPT, aiming to produce customized marketing emails tailored to demographic and behavioral features. This thesis demonstrates not only the feasibility but also the competitiveness of such models by evaluating outputs with standard metrics (BLEU, ROUGE) and human-like scoring through GPT-4, showing that open-source models can approximate the performance of proprietary alternatives at significantly lower cost. The results confirm that fine-tuned LLMs with synthetic data represent a viable solution for enterprises seeking efficiency, personalization, and internal control of data.
Authors - Rricky Tim Solomon Sison, Romary Reyes Lincod Abstract - Diversity management has become a strategic priority in the hospitality industry, particularly in regions marked by increasing demographic variation. This study investigates the perceived effects of diversity management on the work environment in Department of Tourism (DOT) accredited hotels in Western Pangasinan, Philippines. It also examines the relationship between administrative diversity management application and employees’ experiential perceptions. This quantitative-descriptive research employed an adapted and expert validated instrument, refined through pilot testing. Using purposive and stratified sampling, data were collected from 53 participants (10 supervisors/managers and 43 employees) across five DOT-accredited hotels. Four sub-variables were examined: employee engagement, creativity and innovation, stereotyping and bias, and employee retention. Analytical tools included mean, average weighted mean (AWM), one-way ANOVA, and Pearson correlation. The study revealed that diversity management was perceived to be highly effective, particularly in fostering stronger employee engagement and enhancing creativity in the work-place. Overall, its impact on the organizational environment was regarded as very positive. Nevertheless, the findings also indicated that administrative strategies showed a weak and statistically non-significant relationship with employee perceptions, suggesting that structural approaches alone may not be sufficient to shape employees’ views. Among the demographic factors considered, marital status emerged as the only variable with a meaningful influence, highlighting its relevance in understanding how diversity initiatives are experienced within the hospitality context. The study suggests a disconnect between diversity policies and their perceived impact, highlighting the need for inclusive implementation. Limitations include the small, localized sample. The findings emphasize the im-portance of participatory diversity planning, inclusive leadership training, and institutional mechanisms to ensure meaningful and sustainable diversity out-comes in hotel organizations.
Authors - Sebastian Carrasco, Pablo Schwarzenberg, Marcos Levano, Carla Taramasco, Billy Peralta Abstract - In an era where rapid, accurate keyboarding underpins virtually every academic, professional and everyday digital interaction, conventional drill-based tutors still struggle to sustain user engagement and to adapt difficulty in real time. Addressing this gap, we present Terra INVicta, a browser-native serious game that defends Earth from procedurally generated “cosmic” threats only when the player types their associated words correctly, thereby combining cognitive processing and motor skills practice in an engaging and accesible way. This paper details the game’s design workflow and a first-round evaluation of its gameplay mechanics and adaptive-difficulty engine, which modulates challenge through a mixed time–score progression factor. Initial tests indicate that Terra INVicta delivers fluid animation, near-instant keystroke-to-action responsiveness, and sustains a “flow” state across diverse player abilities, confirming its viability as a cognitive training game. Beyond validating its educational value, we highlight lessons learned and propose future work—including their application as a tool for cognitive assessment and improvement of cognitive and motor-skills in older adults. An interactive prototype is available at https://inverosimilitudes.github.io/ TerraINVicta/.
Authors - Sanjeeb Prasad Panday, Ravi Gautam, Basanta Joshi, Aman Shakya, Anunaya Pandey Abstract - The proposed approach focuses on autonomous UAV (Unmanned Aerial Vehicle) navigation and obstacle detection using only visual sensors specifically, the on-board front camera. Unlike traditional methods that rely on multiple sensors (e.g., LiDAR, radar, or GPS), this vision-based system aims to reduce hardware complexity and cost while maintaining robust performance in diverse environments. By leveraging computer vision and deep learning techniques, the UAV processes real-time camera feed data to detect obstacles, map surroundings, and plan collision-free paths. Key challenges include handling dynamic environments, varying lighting conditions, and real-time processing constraints. The system employs feature extraction, depth estimation, and semantic segmentation to interpret visual data, enabling the UAV to navigate autonomously without external aids. Advantages of this approach include reduced sensor dependency, lower power consumption, and improved adaptability in GPS denied or cluttered spaces (e.g., indoor settings or dense urban areas). However, limitations may arise in low-visibility conditions (e.g., fog or darkness) or with texture-less surfaces that complicate depth perception. The method aligns with advancements in lightweight AI models optimized for edge computing, ensuring efficient onboard processing. Future enhancements could integrate multi-camera setups or hybrid sensor fusion for increased reliability. Overall, this vision only navigation strategy offers a scalable and cost-effective solution for UAV autonomy, particularly in applications like surveillance, inspection, and disaster response where simplicity and agility are critical.
Authors - Tushaar Yenduri, S S R Subramanya Hemant Konduri, Kalyan Netti Abstract - This study examines three inference strategies—serial, multi-core parallel (Joblib), and distributed memory (MPI)—to address scalability challenges in applying machine learning models to large tabular datasets. Using the HIGGS dataset as a representative benchmark of high-volume scientific data, a Random Forest classifier was trained and validated with performance metrics of 73.3% accuracy and 81.25% ROC-AUC. The results offer concrete guidance on selecting appropriate inference backends based on system architecture and dataset size, particularly for practitioners in high-performance computing and scientific machine learning domains.
Authors - Kevin De la Cruz, Alberto Taza, Edgar Ramos, Donovan Fuqua Abstract - The textile industry is facing growing pressure to transition toward production systems that are sustainable, efficient, and aligned with circular economy principles. This study develops a structured multi-criteria decision-making framework designed to integrate lean practices, circularity, and sustainability into strategic planning for textile manufacturing. The methodology applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to evaluate and rank key operational practices considering their environmental, operational, and economic impacts. Results indicate that prioritizing lean initiatives with embedded circularity practices provides a dual advantage: improving short-term efficiency while simultaneously supporting broader sustainability goals. The proposed model offers decision-makers in the textile sector a practical and evidence-based tool to address challenges of competitiveness, environmental regulation, and sustainable value creation. Ultimately, this research contributes to advancing integrated planning approaches that strengthen the ongoing transition toward sustainable manufacturing systems.
Authors - Matana Charoenwongsa, Sangkama Sarawat, Laknara Chandraramya, Waralak Vongdoiwang Siricharoen, Thirachit Saenphon Abstract - The global media landscape is experiencing a profound shift, shaped by the rise of artificial intelligence (AI), growing cross-cultural content demands, and evolving patterns of work. This study investigates how active stakeholder participation can guide the development of an AI-integrated curriculum within global media education. Emphasis is placed on nurturing technological fluency, interdisciplinary perspectives, and ethically grounded innovation. Drawing on a curriculum design initiative from Thailand’s Global Media Industry and Innovation Master’s Programme, the research explores how AI-driven tools, media production technologies, and entrepreneurial approaches are brought together in educational practice. Insights were collected from over 50 stakeholders—including media entrepreneurs, policymakers, educators, and students—and examined through thematic content analysis. The research highlights three core priorities that shaped the curriculum design: strengthening technological fluency, deepening global media literacy, and encouraging social innovation through entrepreneurship. It proposes a collaborative model that addresses technological change while centring human agency, with practical implications for curriculum reform and the future of sustainable media ventures in an AI-influenced era.
Authors - Chethana N S, Anithadevi M D Abstract - We introduce a groundbreaking steganographic technique that merges non-linear cost evaluationmethods with binary triangular matrix structures to achieve superior data concealment in color images.Our approach tackles the inherent weaknesses of conventional linear cost models by implementing amultiplicative framework that more effectively maintains the statistical characteristics of images whileoptimizing embedding performance. The integration of structured binary matrices enables intelligentposition selection through organized randomization, preserving both visual fidelity and cryptographicsecurity. Our methodology incorporates sophisticated texture evaluation, self-adjusting parameter tuning, andmulti-channel analysis to deliver outstanding results across various image categories. Through rigoroustesting, we demonstrate remarkable improvements over current leading methods, achieving averagePSNR values of 52.34 dB and SSIM scores of 0.9847 across standardized test collections from multipleimage databases. The system exhibits substantially enhanced resilience against contemporarysteganalysis tools while supporting high embedding rates up to 0.8 bits per pixel with strengthenedsecurity assurances.
Authors - YURY MIKHEEV, URSULA PODOSENIN, SERGEY V. SYCHOV Abstract - This study investigates the reliability and validity of the 360-degree feedback method by focusing on three primary sources of bias: respondent roles, interpersonal relationships, and individual evaluative styles. Drawing on data from over 15,000 respondents across multiple organizations, it applies a Python-based statistical algorithm to quantify the impact of these biases on competency ratings. Findings reveal that such factors can account for up to half of the variance in assessments. Mitigation approaches include evaluator anonymity, structured training, and correction coefficients. With these measures in place, 360-degree feedback continues to serve as an effective tool for fair and accurate performance appraisal, supporting personnel decisions and sustainable talent management.
Authors - Zarif Wasif Bhuiyan, Zaed Bin Monir Atib, Mahady Hasan, Farhad Alam, Md. Tarek Habib Abstract - Early and accurate identification of common skin conditions is essential for appropriate therapy and better clinical outcomes. However, diagnostic expertise is often limited in resource-constrained healthcare or remote settings. We present a robust deep learning approach based on a custom convolutional neural network (CNN) to automatically classify five prevalent skin diseases (acne, contact dermatitis, nail fungus, scabies, and urticaria) from clinical images. A curated dataset of 8,900 clinical images (1,780 per class) was compiled, with standardized resizing, normalization, and extensive augmentation applied to simulate real-world variability and enhance generalization. The CNN architecture consists of five sequential convolutional blocks with progressively increasing filters to extract high-level features, accompanied by dropout layers to mitigate overfitting, and includes a fully connected layer prior to the SoftMax output. On evaluation, the model achieved an overall classification accuracy of 99%, demonstrating robust performance and high diagnostic precision across all categories. It notably outperformed conventional machine learning classifiers as well as state-of-the-art pretrained deep networks on the same task. This exceptional performance, combined with strong generalization capabilities, underscores the model’s reliability and suitability for deployment in real-world clinical practice and tele dermatology settings. In such scenarios, rapid and accurate screening for multiple skin conditions can significantly improve patient care and enable earlier interventions.
Authors - Amruta Khanaj, Sourabh Khanaj, Smita S. Sangewar Abstract - This research investigates the security implications of reusing code snippets of Stack Overflow in open-source software projects. We present a novel framework that proactively identifies and mitigates security risks associated with outdated Stack Overflow code snippets. Our methodology combines static code analysis techniques, continuous monitoring of Stack Overflow post changes, and an automated developer notification system. We analyzed 1.5 million code snippets from Stack Overflow approximately across 5000 popular open-source GitHub projects, focusing on programming codes. Our observations reveal that there are a significant number of reused snippets becoming outdated over time, with developers often missing critical security updates made to the original Stack Overflow posts. We showcase the effectiveness of our framework in detecting potential vulnerabilities and alerting developers to security relevant updates in reused code. The research emphasizes the need for continuous monitoring of community-sourced code and suggests solutions to ensure its secure integration in software development practices. Our work contributes to the broader understanding of software supply chain security and the evolving nature of code reuse in the open-source ecosystem.
Authors - Mst.Sadia Siddika, Sherin Sultana, Md. Tarek Habib, Sabrina Alam, Md. Mahbub Alam, Mahady Hasan Abstract - Agricultural products are graded based on their quality and adherence to standards. Managing the grading, packaging, and transportation of products, especially perishable items like food, is crucial for maintaining freshness and safety. Packaged goods are transferred to storage facilities and prepared for transportation. To maintain product freshness, a well-organized and traceable supply chain is essential. This study demonstrates how technologies such as the Internet of Things (IoT) can facilitate an agricultural supply chain system that uses maps, computer vision, smart packaging, and smart barcodes. These technologies help track products in real-time, ensure data security via databases, and streamline supply chain operations through web-based platforms. Using an app-based system, the solution highlights traceability, automation, and user-friendly interfaces to optimize efficiency from farm to market. The app also provides automated alerts and real-time inventory updates for both customers and sellers. Additionally, the system should integrate IoT-based temperature and humidity monitoring to maintain the quality of perishable goods. Overall, this study suggests that combining these advanced technologies into a unified system can address challenges in traditional methods, such as delays and product quality loss.
Authors - Kostaryev D.B., Tevyashev A.D., Sizova N.D., Tkachenko V.P. Abstract - An intelligent information system for proactive management of a printing enterprise based on artificial intelligence is proposed. Approaches to proactive enterprise management grounded in Industry 4.0/5.0 are substantiated, including the use of relevant scientific methods and mathematical models. The architecture of the information system, based on mathematical models and scientific principles, has been developed. The results of evaluating the effective-ness of implementing and using this intelligent information system at a printing enterprise are presented.
Authors - Zarif Wasif Bhuiyan, MD. Humayun Kabir, Paramita Saha, Md Mahbub Alam, Mahady Hasan Abstract - Urban–rural evidence in Bangladesh is often fragmented or short-lived, limiting its value for tracking change. This study leverages the Life in the Field Experience at Independent University, Bangladesh to build a pre-COVID baseline from eight annual waves of student-collected surveys spanning 2013–2020 across six districts: Jessore, Bogra, Mymensingh, Sylhet, Barishal, and Dinajpur. Heterogeneous spreadsheets are harmonized into standardized sector indices for education, medications and health service use, employment, marital history, acute morbidity, and chronic morbidity. A transparent pipeline handles header inconsistencies, variable shifts, and missingness, producing comparable annual series. ARIMA models are estimated for Jessore to generate five-year projections and are interpreted alongside cross-district benchmarking using z-scores. Results show steady gains in education that level off, an employment dip followed by recovery, rising medication use, low and stable acute morbidity, a gradual increase in chronic morbidity, and largely stable marital patterns. Forecasts are framed as extensions of pre-COVID dynamics with explicit uncertainty. The pipeline provides a reusable template for short annual series and supports policy diagnosis at district level. Future work will extend forecasting to all districts, incorporate structural-break tests and hierarchical models, and enrich indices with external covariates and administrative validation.
