Authors - Siqabukile Ndlovu, Ernest Mkandla Abstract - This paper presents a novel approach to improve test case prioritisation in continuous integration environments by integrating semantic features derived from test case descriptions. The approach uses a Convolutional Neural Network (CNN) model that considers both structured and semantic features. The target variable for prioritisation is defined using a threshold on calculated priority values, helping to identify the most critical test cases. We further engineer interaction and polynomial terms with the semantic feature to capture complex relationships. These polynomial terms allows the model to capture non-linear relationships. For example, a small change in the semantic feature might not matter much in the middle range, but a large implicit_prob (e.g., above 0.9) might have a disproportionately strong effect on the priority. Through a 5-fold crossvalidation, our results demonstrate that while the semantic features do not statistically significantly improve overall F1-score, they yield a statistically significant enhancement in Average Percentage of Fault Detection (APFD) (pvalue = 0.0055). This indicates that incorporating semantic understanding enables the model to effectively reorder test cases, leading to earlier detection of critical faults and increased efficiency in testing efforts within live CI/CD environments.
Authors - Juhi Patel, Tejaskumar Bhatt Abstract - Growing realization of the need for sustainable agriculture has seen Artificial Intelligence (AI) applications implemented in precision agriculture, advocating for sustainable use of resources and enhanced yields in crops. The authors present an AI-driven model that employs multi-modal data of soil attributes, weather, and vegetation indices to predict crop yield. It develops a model with satisfactory test prediction accuracy based on starting from a Linear Regression model (MSE of 0.0835 and R² score of 0.9714). This shows that the model can potentially capture linear trends for big meteorological change influence on crop yields. Feature correlation analysis determines NDVI and soil moisture as the most significant predictors, with the important roles of monitoring vegetation health and effective water management yielding better agricultural results. Although Linear Regression is a robust starting point, this research also paves the way for integrating sophisticated AI techniques like Neural Networks and Ensemble Learning models to tackle non-linear relationships and interactions. In comparative analysis, we have confirmed the utility of interpretable models for scalability, reliability and applicability in smallholder farming. Implications: The result fills the void between the abstract AI structures and the usable application, providing a widely replicable artifact to improve decision-making in precision agriculture. In addition to prediction accuracy, the study also emphasizes on scalability and ethical consideration, the latter coupled with the area of application of IOT based AI empowered systems and data privacy. Further studies will seek to evaluate the model in different agricultural regions, explore new machine learning methods, and implement real time decision support systems. This study contributes to global efforts towards achieving sustainable agriculture, ensuring food security, and limiting the environmental impact of agriculture by providing knowledge that can be used directly.
Authors - Bhumi Patel, Mann Patel, Aum Mehta, Nishat Shaikh, Priteshkumar Prajapati Abstract - Deepfake generation techniques have advanced rapidly in recent years, posing significant risks for misinformation and privacy. In this paper, we propose an ensemble-based deepfake detection framework that leverages EfficientNet-B4 as the backbone architecture for both image and video analysis. By incorporating attention mechanisms and siamese training strategies, our system enhances feature discrimination and improves robustness against subtle manipulation artifacts. The framework is trained and evaluated on two widely adopted benchmarks—the Deep- Fake Detection Challenge (DFDC) and FaceForensics++ datasets. Experimental results demonstrate that the ensemble approach outperforms individual models, achieving higher accuracy and improved log-loss metrics, while also providing interpretability via attention maps. We further discuss the integration of temporal consistency analysis to better handle video data, and outline future directions for real-time deepfake forensic systems.
Authors - Putri Haryani, Sinatria Arka Daniswara, Randany Zevanya Sihombing, Gavikal Hanif Pasopati, Ridho Rambu Bassae, Andhika Hendra Adi Wibisono Abstract - This study investigates the implementation of the Institutional Financial Application System (SAKTI) in digitalizing public sector accounting within Indonesia’s National Research and Innovation Agency (BRIN). Recently, BRIN is a newly formed government institution resulting from the merger of several institutions. It manages budgets from various sources and operates within a complex organizational structure. BRIN faces significant challenges in achieving accurate and timely financial reporting. Using a qualitative case study method, data were collected through semi-structured interviews and observations at BRIN’s Bureau of Planning and Finance. The findings reveal that SAKTI, alongside the MONSAKTI monitoring system, plays a pivotal role in supporting accrual-based financial reporting in compliance with Government Regulation No. 71/2010. Despite progress in automation and integration, several challenges persist, including limited system performance, partial manual processing, and constrained operational budgets. The study highlights the strategic importance of aligning digital infrastructure, internal controls, and human resource capacity to sustain accountability and reporting quality. These insights are expected to inform similar public institutions undergoing digital financial transformation amid structural complexity.
Authors - Dina Sekar Vusparatih, Handy Martinus, Eshaby Mustafa, Ahmad Hidayat Ahmad Ridzuan Abstract - Malaysia has become the primary destination for Indonesian patients over the past two decades. However, international medical tourism carries significant risks due to limited information, restricted communication access caused by distance and language barriers, while the industry heavily relies on trust for critical health-related decision-making. This study focuses on how Island Hospital implements technology strategies to reduce uncertainty in making informed decisions. A qualitative approach using a case study method was employed to gather the necessary data. Interviews were conducted with Indonesian patients and their family members at the hospital, as well as with Indonesian government representatives in Penang, Malaysia. To enrich the findings, participant and documentation observations were also applied. The results indicate that the hospital integrates Human-Computer Interaction (HCI) using four approaches in designing its web-site/portal and mobile application. This enables patients to communicate with healthcare providers despite existing barriers, and supports them in planning both pre- and post-treatment procedures.
Authors - Gweneaella Lyrika R. Aguinaldo, Nellisa F. Cortez, Jocelle Marie S. Dador, Kathleen Kaye L. Getonzo, Loran Ann G. Gonzaga, Angela B. Navarro, Remelyn J. Vicente, Manuel J. Logatoc, John Kenneth M. Arcayos Abstract - This study examines how key store attributes influence shoppers’ purchase intentions and, ultimately, customer satisfaction in five selected supermarkets in Cavite, Philippines. Store attributes—defined as accessibility and cleanliness, product assortment, promotion, price, and customer relations—are hypothesized to drive the likelihood that a customer will buy (purchase intention) and feel satisfied. Employing a quantitative correlational design, the researchers ad-ministered an adaptive, 45-item Likert-scale questionnaire (4-point) both face-to-face and online to 380 supermarket patrons. Instrument reliability was con-firmed via Cronbach’s alpha coefficients between 0.8740 and 0.9379 (good–excellent). Respondent demographics indicated a predominantly young (18–28 years, 75.5 %) and female (62.4 %) sample, with over half (51.6 %) reporting a monthly income below ₱10,000. Descriptive analysis (frequency, percentage, weighted mean, and standard deviation) revealed that accessibility and cleanliness received the highest effectiveness rating (mean = 3.44, SD = 0.43), while customer relations scored lowest. Inferential analysis using Spearman’s rank-order correlation showed a moderate positive relationship between purchase intention and customer satisfaction (ρ = 0.549, p < 0.001). Among individual attributes, promotion correlated most strongly with purchase intention (ρ = 0.507), and price exhibited the highest link to customer satisfaction (ρ = 0.567). These findings suggest that supermarkets seeking to boost sales, and loyalty should prioritize promotional strategies to drive purchase intent and calibrate pricing structures to enhance satisfaction. Optimizing accessibility, cleanliness, and other in-store factors can further improve customer experience, yielding competitive advantage in the retail sector.
Authors - Pradnya H Desai, Parikshit Mahalle, Pankaj Chandre Abstract - The exponential growth of the Internet of Everything (IoE) demands secure, intelligent access control mechanisms to manage data flow and device interaction efficiently. This paper proposes a novel architecture that integrates post-quantum cryptography, federated learning, and explainable AI to ensure privacy- preserving, real-time decision-making for resource-constrained IoE environments. The edge nodes, equipped with lightweight cryptographic engines and context-aware training modules, perform preliminary data processing and secure communication via GG-ULL and PQCrypto protocols. The cloud leverages federated learning to train distributed models and uses a multi-agent policy engine for dynamic access decisions. Blockchain-backed audit trails ensure accountability, while the explainable AI module enhances transparency in access control. The system supports mission-critical services like SCADA and health data lakes, guaranteeing secure and interpretable decision flows. This architecture paves the way for resilient, scalable, and intelligent access control frameworks suitable for next-generation IoE ecosystems.
Authors - Deepika K M, Rohith H P, Srinivas D B, Lakshmi H Abstract - The need for real-time object recognition is growing in a few applications, including robotics, surveillance, and autonomous vehicles. Modern object recognition technique YOLOv5 achieves high accuracy while maintaining real-time performance. This paper proposes a real-time, highly accurate object recognition method using YOLOv5. The system, which was created using PyTorch and Python, is trained and evaluated using the COCO dataset. The proposed system enables fast object detection and achieves outstanding precision and recall rates using single-shot detector architecture. Additionally, the detection accuracy is greatly improving with the introduction of YOLO and its architectural descendants. YOLOs are frequently employed in a variety of contexts, mostly because of their speedy conclusion rather than due to the accuracy of their detection. The YOLO detection accuracy, for instance, ranges between 63.4 and 70. The suggested system is ideal for real-time object detection applications since experimental findings demonstrate that it performs better than current object detection systems in terms of accuracy and speed.
Authors - Asra Fatema Zakir Baig, Amol Mashankar Abstract - Hospital leadership styles are crucial determinants of employee performance and patient satisfaction. In this research, secondary research findings are integrated to explore the influence of transformational, transactional, and laissez-faire leadership styles on healthcare delivery. Transformational leadership is always linked with greater staff engagement, job satisfaction, and better patient care outcomes. Transactional leadership is less consistent in its findings, typically enhancing short-term efficiency but with little developmental impact over the long term. Laissez-faire leadership has typically been associated with negative organizational performance because of the lack of direction and responsibility. The literature highlights the requirement for healthcare managers to embrace transformational styles as a means to create a safe work environment as well as an improved patient experience. Emotional intelligence, communication skills, and encouraging teamwork should feature prominently in any future leadership programs aimed at enabling sustainable healthcare greatness. (Alilyyani, B., Wong, C. A., & Cummings, G. G., 2018), Effective hospital leadership has a profound impact on the performance of staff as well as patient satisfaction. The current paper integrates evidence from research studies to explore how the transformational, transactional, and laissez-faire styles of leadership influence healthcare. The review emphasizes the significance of leadership in creating an optimistic workplace culture, improving staff participation, and finally enhancing patient care.(Wong, C. A., & Cummings, G. G. (2013))
Authors - Najah Najmia Halim, Okta Bayu Prihatma Putra Abstract - This paper uncovers the system-level challenges confronting freelancers in emerging markets, framing freelancing as a form of digital entrepreneurship with significant social implications. Through a systematic literature review of 23 peer‑reviewed studies (2020–2024) using the PRISMA protocol, we identified five critical challenge domains: opaque algorithmic management that limits freelancer autonomy, financial instability due to irregular income and weak social protection; regulatory ambiguity that erodes trust in digital platforms, technical barriers including limited infrastructure and digital literacy, and career development gaps marked by burnout and isolation. These challenges disproportionately affect freelancers in developing regions, highlighting structural inequalities within the global digital labor market. By synthesizing literature across geography and sector, this study positions freelancing not just as a labor trend, but as a pressing issue of digital inclusion and social sustainability. The findings inform policymakers, platform designers, and support institutions about intervention points to foster equitable, resilient freelance ecosystems. Future research must explore how technology, policy, and cross-sector partnerships can create inclusive innovation frameworks that sustain independent digital work.
Authors - David Livingstone T. Ramos, And Julie D. Rosal, Randy Joy M. Ventayen, Timothy Joshua M. Ventayen Abstract - This study look into the practical acceptance and compliance of AI-generated music on major digital distribution platforms, including Spotify, Apple Music, and other popular online music stores. Leveraging advanced generative AI tools such as Udio and Suno, the researchers created original musical tracks and submitted them for distribution. Although many platforms have explicit policies against the use or upload of AI-generated content, all tracks produced through these tools were accepted without issue and made publicly avail-able through the distribution systems. This outcome reveals critical gaps in the enforcement of platform policies, raising serious questions about the current ability of digital platforms to detect and regulate AI-generated content. The findings bring to light the ethical, legal, and economic challenges posed by the growing presence of generative AI in the music industry. In response, the study recommends the implementation of more robust detection technologies, trans-parent labeling of AI-generated works, and the development of consistent regulatory frameworks to address the complexities of AI-driven creativity in the evolving digital music ecosystem.
Authors - Masood Ahmed Shariff, Sushma R Abstract - In today’s complex financial environment, identifying optimal stock investments is challenging. This study applies Multi-Criteria Decision-Making (MCDM) techniques to evaluate NIFTY 50 stocks using nine financial indica-tors, including returns, P/E ratio, dividend yield, and market capitalization. Daily data from April 2020 to March 2025, sourced from the Prowess database, was analyzed. The Entropy Weighting Method ensured objective assignment of metric importance. Four MCDM models—TOPSIS, VIKOR, SAW, and COPRAS—were used to rank stocks. Ranking discrepancies led to the development of a hybrid model combining normalized ranks with entropy-based weights, creating a composite ranking. Spearman’s rank correlation helped compare the models. The hybrid approach enhances ranking consistency and supports data-driven investment decisions. This methodology can also be ap-plied to sectoral or global equity markets, offering a flexible tool for portfolio evaluation and financial analysis.
Authors - Tawsif Hasan Kirkire, Smita Mehendale, Ashish Thatte Abstract - This paper is bibliometric analysis of the academic literature related to e-commerce platforms within the chemical supply chain. From the 17 documents published between 2000 and 2025, this study identifies inconsistent, but off late accelerating, research journey, with a notable rise in publications and citations post-2020, most likely influenced by the global imperative for digital transformation. Co-word analysis reveals "electronic commerce" as a central thematic node, connecting traditional "sales" and "chemical industry" applications with emergent areas like "learning systems" and "chemical activation." The analysis highlights China's dominant intellectual contribution, suggesting a concentrated epistemic center. Inspite of recent contributions from diverse sources, the field displays significant temporal and thematic gaps, particularly regarding sustainability and resilient practices, alongside a uneven collaborative network. This study outlines critical future research directions, promoting for the integration of AI/ML applications, exploration of chemical manufacturing, diversification into Global South economies, and the adoption of enhanced methodological rigor to foster a more mature, interconnected, and impactful interdisciplinary research domain.
Authors - Juanito P. Maneclang Jr. Abstract - The Philippine salt industry, particularly in culturally significant regions like Pangasinan, has faced a steep decline due to a reliance on antiquated, laborintensive production methods. This study addresses this challenge through a three-part objective-driven approach: (1) identifying the technological gaps in artisanal salt production through a comprehensive literature review and needs analysis; (2) developing a prototype salt vibrating screen classifier with an integrated grinder to address these gaps; and (3) evaluating the prototype's acceptability among experts and end-users. The methodology involved a systematic re-view of academic and industry literature, which pinpointed a lack of appropriate-scale post-harvest machinery. A prototype, fabricated from stainless steel and powered by a 5-horsepower motor, was subsequently developed. The machine's acceptability was determined through a mixed-method evaluation with technical experts and salt farmers. The results from the evaluation demonstrated high acceptability, with participants confirming the machine's robust performance, efficiency in grinding and classifying salt, and significant practical advantages over traditional methods. The study concludes that the developed prototype is a highly acceptable and viable technological solution that directly addresses the needs identified in the literature, offering a means to enhance productivity and quality for Filipino salt farmers.
Authors - R. Komala, B. R. Arun Kumar, A. Shreyas Abstract - Service Level Agreements (SLAs) are essential for defining performance and compliance expectations in cloud computing ecosystems. However, traditional SLA models often lack adaptability, failing to address dynamic workloads, evolving regulatory demands, and rising cybersecurity threats. This study presents an adaptive SLA governance architecture driven by Machine Learning (ML), enabling predictive violation detection, real-time compliance monitoring, and dynamic policy adjustments. The system integrates legal frameworks such as GDPR, ISO/IEC 27001, NIST, and India's DPDPA, embedding enforceable standards into SLA logic. Our framework employs ML techniques including violation forecasting, unsupervised anomaly detection, privacy risk quantification, and NLP-based clause synthesis, creating self-adjusting, legally resilient SLAs. In simulations with cloud workload datasets, the system achieved 94% prediction accuracy for SLA breaches, 98% anomaly detection, and over 91% success in automated clause alignment. While promising, broader testing in real-world deployments is needed to validate generalizability. This framework bridges regulatory obligations and dynamic service delivery through explainable, transparent SLA orchestration.
Authors - Akshay Kumar, Sudhir Agarmore, Kumar Gaurav, Edidiong Akpabio, Akshat Jha Abstract - Cyber threats now come in an advanced form and, therefore, call for a proactive rather than a passive cybersecurity strategy. Threat intelligence helps to complement security defences through methodical acquisition, analysis, and utilization of information regarding competitors and new risks. This article gives an insightful review of the approaches to threat intelligence, covering open-source intelligence (OSINT), closed-source intelligence, monitoring of the dark web, and live feeds of intelligence. We classify threat intelligence into strategic, tactical, operational, and technical levels, explaining their functions in cyber security architectures. Particular emphasis is given to the incorporation of artificial intelligence (AI) and machine learning (ML) in the automation of threat identification and intelligence analysis. In addition, we address challenges of implementation, including data glut, verification, and ethics. To provide real-world practicality, we examine actual case studies demonstrating practical threat intelligence implementations in reducing ransomware, blocking phishing, and countering supply chain attacks. We also recommend enhancing intelligence sharing, predictive analytics, and automation to strengthen future threat intelligence systems. Through the integration of dynamic threat intelligence into security operations, organizations are able to effectively detect, analyze, and block cyber threats beforehand, ultimately fortifying their cybersecurity resilience as a whole.
Authors - Romary Reyes Lincod Abstract - This paper provides an review of the pedagogical practices and governance perspectives related to technology integration in Philippine State Universities and Colleges (SUCs). The global pandemic served as a catalyst, accelerating a system-wide shift from emergency remote teaching to a strategic push for sustained flexible learning, underpinned by national policies from the Commission on Higher Education (CHED) and cross-agency collaborations. However, a significant gap persists between ambition and institutional reality. This review examines four key domains: institutional governance and leadership, pedagogical practices in the digital classroom, the human element of faculty and student experiences, and the foundational pillars of infrastructure and finance. Findings reveal a governance paradox where strategic planning is robust, but leadership needs to translate vision into faculty competency. Pedagogically, technology adoption outpaces deep integration, with tools often used to replicate traditional methods. Faculty and students are caught in a "Cycle of Unpreparedness," marked by skill gaps, anxiety, and infrastructural barriers. Foundational weaknesses, including a pervasive digital divide and unstable funding models, create a mismatch between the vision for digital transformation and the capacity for implementation. The paper concludes with multi-level strategic recommendations for policymakers, SUC administrators, and future research, aimed at creating a more resilient, equitable, and innovative digital education ecosystem.
Authors - Caren C. Orlanda-Ventayen, Renato E. Salcedo Abstract - The tangible heritage of a region, particularly its textile traditions, of-ten serves as a primary marker of its cultural identity. This literature review studies the role of fabric arts in the cultural expression of Pangasinan province in the Philippines. A systematic review of historical backgrounds, cultural publications, and recent news archives reveals a notable absence of a deeply rooted, continuous, and widespread indigenous textile weaving tradition for clothing in the province. Historically, Pangasinan's material culture was characterized by non-textile crafts and a reliance on imported fabrics obtained through extensive trade networks. This historical context makes the contemporary situation particularly compelling. As of mid-2025, Pangasinan is the site of new, institution-ally-driven initiatives aimed at establishing a local textile industry. These projects are twofold: a technology-focused hub for developing bamboo-based textile fibers and a culturally-focused center for reviving the transplanted hand-loom weaving traditions of Cordilleran migrant communities. This paper argues that Pangasinan is not merely preserving a dormant craft but is actively designing a new and evolving cultural identity in fabric, one that is uniquely shaped by the confluence of technological innovation, cultural migration, and institutional support. The findings suggest that Pangasinan offers a unique case study in the contemporary construction of cultural heritage.
Authors - Claro S. Sison Jr. Abstract - The rapid shift to remote education has compelled a significant trans-formation in the teaching methodologies for technical subjects like AutoCAD. This paper investigates the technological interventions implemented by Auto-CAD instructors in response to the challenges of online and modular learning environments. The study adopts a mixed-methods approach, beginning with a systematic literature review to identify prevalent challenges and technological solutions documented in academic and technical sources. This is followed by a local survey of 30 AutoCAD teachers in the Schools Division of Pangasinan I, Philippines, to assess the available technological resources, their utilization rates, and the specific challenges faced in a developing country context. Key findings from the literature indicate that while a plethora of advanced software, platforms, and tools are available, their effective implementation is often hindered by issues such as the digital divide, lack of institutional support, and the inherent difficulty of teaching hands-on skills remotely. The local survey corroborates these findings, revealing a heavy reliance on personal resources, unstable internet connectivity, and underutilization of advanced CAD-specific tools. The study reveals that successful interventions often involve a blend of synchronous and asynchronous technologies, including video tutorials, cloud-based CAD solutions, and virtual reality simulations. A strong positive correlation (r = 0.785) was found be-tween the level of technological intervention and teachers' perceived instructional effectiveness. This research concludes by synthesizing the findings to provide a holistic view of the current state of AutoCAD education, offering recommendations for educators, institutions, and policymakers to enhance the quality of technical instruction in remote settings.
Authors - Corlito P. Cruz Abstract - The proliferation of portable computing devices has historically been accompanied by an equally diverse and incompatible array of proprietary power adapters, creating significant issues for consumers and contributing to a growing electronic waste crisis. This scientific review charts the rise of the USB integrated laptop charger adapter as a pivotal solution to this long-standing issue. It presents an in-depth analysis of the technological underpinnings, centered on the USB Power De-livery (USB-PD) specification, that enable a universal charging standard. Key enabling technologies, most notably the material science advancements of Gallium Nitride (GaN) wide-bandgap semiconductors, are analyzed for their role in creating a new generation of compact, highly efficient, and intelligent power adapters. To ground this technological analysis in practical application, this paper further presents a targeted case study involving a survey of Bachelor of Industrial Technology (BIT) students to identify tangible user needs. The results of this survey, which indicated a strong demand for integrated USB ports on laptop chargers, led to the development of a functional proof-of concept prototype, validating the broader market trends dis-cussed. The paper examines the technical evolution of the USB-IF standards, the current technoeconomic landscape, and the persistent challenges in engineering and usability, including protocol fragmentation and safety compliance. Finally, this re-view looks to the future, assessing the impact of the 240W Extended Power Range (EPR) of USB-PD 3.1 and the trajectory toward a truly universal and intelligent power ecosystem. It concludes that the USB-integrated adapter represents a critical, user-driven inflection point in consumer electronics, moving the industry from a fragmented past to a standardized, efficient, and interconnected future.
Authors - Vedansh Verdia, Upesh Patel, Trushit Upadhyaya Abstract - In this paper, we take a look into the world of communication protocols from the ones we've relied on for years like the Ethernet, to newer technologies made just for the Internet of Things (IoT), and even those that are just starting to emerge. As our need for faster, more reliable, secure, and energy-efficient communication keeps growing, these protocols have continued to evolve. We look at how they work, what they're built on, and how well they perform in real-world situations. Along the way, we break down their strengths, their drawbacks, and where they’re best used. Our review spans a broad spectrum—from classic wired systems to wireless options like Wi-Fi and cellular networks, as well as industrial communication setups and cutting-edge technologies like Time-Sensitive Networking. We break down how these protocols compare in terms of speed, ease of implementation, and security, helping readers understand which ones are best suited for different needs and situations. Finally, we talk about the big questions researchers are still trying to answer, and where things might be headed especially when it comes to making systems smarter, more unified, and ready for future challenges like quantum-safe communication. This paper is meant to be a helpful guide for anyone researchers, engineers, or tech enthusiasts—trying to make sense of today’s complex communication landscape.
Authors - Vikas Sihag, Kanika Panwar Abstract - Predictive analytics is reshaping crime mapping and policing by leveraging tools like GIS, machine learning, and big data. This paper reviews current techniques and real-world applications, highlighting their potential to identify crime hotspots and inform proactive strategies. It also examines key challenges, including biased data, ethical concerns, and privacy risks. Through case studies analysis and literature review, the research outlines the limitations of existing systems. The study stresses the importance of fairness, transparency, and accountability. Furthermore, the paper presents a current state of challenges and solutions advocated in research, thus supporting responsible and effective crime prevention practices.
Authors - Moses Moyo, Hanifa Abdullah Abstract - This scoping review was conducted to have an overview of how Artificial Intelligence (AI) influenced cybersecurity posture in South African small and medium enterprises (SMES) utilizing cloud computing technologies. The study extract 57 articles from six electronic databases. After rigorous screen, only eighteen studies were eligible for analysis. Of the eighteen remaining studies, eight were empirical and ten desktops. A qualitative thematic approach was used to extract relevant data from the study to understand the current state of cyberse-curity in SMEs. The study found that South African SMEs were using AI but did not harness opportunities it can offer in cybersecurity. The level of cybersecurity among the SMEs was very low due to lack of proper knowledge about threats that AI technologies could bring about if used by criminals. The study concluded SMEs were poorly prepared for AI use in general and in cybersecurity. The study was limited due to lack of literature related to the research questions. Therefore, it is recommended that a longitudinal empirical study be conducted with South African SMEs to have deeper understanding standing of how the adoption of AI is influencing their cybersecurity posture.
Authors - Aswathi Mary Varghese, Jisha K Abstract - Digital public infrastructure (DPI) denotes foundational digital systems that serve as essential enablers for delivering digital services at scale across society. It has emerged as a key enabler of innovation in developing countries by reducing systemic entry barriers and fostering an inclusive digital ecosystem. DPI serves as a vital connective tissue within the digital ecosystem. Bridging the gap between physical infrastructure—such as internet connectivity, devices, data centers, and the cloud—and a diverse landscape of applications, DPI empowers societies to deploy innovative solutions across sectors like e-commerce, social protection, re-mote education, and telehealth. Real-world illustrations abound: India’s Aadhaar system has revolutionized digital identification, Brazil’s Pix has made fast, real-time payments accessible nationwide, and Australia’s Consumer Data Right is pioneering user-centric data sharing. By enabling dynamic, cross-sectoral innovation, DPI stands as the engine powering the next generation of digital transformation. This paper analyses the evolution and impact of India Stack, focusing on how its layers enable innovation across sectors such as fintech, health, and e-commerce. By mapping the structural features of India Stack against the outcomes in financial inclusion, digital health, and market decentralization, the paper offers a conceptual framework to assess the role of public policy in driving innovation. The findings indicate that when government platforms are built as public goods with interoperable standards and strong institution-al backing, they can create an enabling environment for grassroots and enterprise-level innovation. The India Stack model, while context-specific, holds valuable lessons for other nations seeking to integrate public policy with digital transformation goals.
Authors - Mohammed Hashim Kalam, Guhanathan Poravi Abstract - Time-series data has been increasingly utilized in critical domains making it vulnerable to stealthy adversarial attacks. Explainable AI (XAI) has made major enhancements to model transparency specifically in non-sequential domains (Image, NLP and tabular) but is less researched in time series data due to complexity of temporal dependency in sequential data. This paper systematically reviews around 60 studies published up to June 2025, examining XAI-based approaches for time series, as well as recent XAI extensions tailored for time series data. Various attack types are discussed in terms of their effectiveness, and the performance of recent detection frameworks against these attacks is analyzed, with particular focus on XAI-integrated systems, including SHAP, LIME, LRP, and formal verifications. Despite their usage, most XAI-enhanced approaches, especially those relying on SHAP, face challenges in efficiency and risk explanation instability under noise, with limited evaluation against subtle partial adversarial attacks. While LRP offers computational efficiency vital for high stakes domain real-time detection, its architectural dependency restricts its adaptability across diverse models. This review highlights major unsolved limitations and concludes with prioritizing the need for efficient, robust, temporally aware, and architecture-agnostic frameworks, possibly extending LRP, tailored specifically for time series data.
Authors - Shalini Singh, Amala Siby Abstract - In the last twenty years, efforts have been made to address how financial literacy and inclusion can empower rural women entrepreneurs. However, one essential factor tends to be left behind—the contribution of incubation centers to enabling these women to succeed. This research examines the academic landscape in closer detail through a bibliometric analysis of 2,012 peer-reviewed articles between 2005 and mid-2025 based on the Scopus database. Using tools such as Biblioshiny and VOSviewer, we delve into how the discourse on financial capability, women entrepreneurship, and economic empowerment has progressed throughout time. The research indicates a meteoric increase in interest following 2020, fueled primarily by vulnerabilities uncovered with the COVID-19 pandemic. Despite expansion, however, something is certain: there remains relatively little research linking incubation assistance to rural women directly or financial literacy programs. Our analyses of keywords and co-authorships indicate a splintered discipline with ample space for richer, more holistic research. This article not only charts what is known but also reveals what is lacking, presenting new directions for researchers and policymakers dedicated to building stronger safety nets for rural women entrepreneurs.
Authors - Manupriya P, Rajeshwari.Mattimani, Haripriya G, Hema B, Saroja.V.Siddamal, Vijay H M, Suhas Shirol, Ganga I Abstract - At present, encryption is key to safeguard confidential information from unauthorized access. It means using special algorithms and encryption keys to change readable data into unreadable data. AES is symmetric encryption algorithm adopted by the U.S.Government as a replacement of DES algorithm. AES supports 3-key sizes: 128 bit, 192 bit and 256 bit, with AES -128 being the most commonly used due to its balance of security and performance. Because it is fast and simple, symmetric key encryption which uses a single key for both encryption and decryption, is used in many cases. Advanced Encryption Standard (AES) is a symmetric block cipher that is used extensively in the encryption of electronic data. Out of its various implementations, AES-128 is the most commonly used, with a 128-bit key size and encrypting 128-bit-sized blocks of data and to get 128-bit cipher text. The encryption process goes through 10 rounds of processes like SubBytes, ShiftRows, MixColumns, and AddRoundKey, which are computed to have high confusion and diffusion properties. AES-128 is optimally balanced in terms of security, efficiency, and performance and thus finding use in hardware and software in various industries. Due to its strength and immunity against known attacks on electronic data, passwords etc. AES-128 has been the standard for encrypting data in commercial and government systems worldwide. AES algorithm has many applications such as online banking, ATM machines and military and government communication.
Authors - Anirban Chowdhury, Parul Sachdeva Abstract - This study explores the product-market response to breastfeeding equipment by leveraging user data analytics to evaluate consumer behavior, preferences, and satisfaction. As maternal health awareness grows and parenting practices evolve, breastfeeding products have seen significant innovation in design, functionality, and accessibility. However, market success relies not only on product features but also on understanding real-world user experience and response. The research employs a quantitative method, from user ratings sales metrics to get insights from data collected from amazon.in. Findings reveal key determinants influencing user acceptance, including ergonomic design, portability, ease of use, and aesthetics. The study also identifies gaps between user expectations and product performance that affect repeat purchases despite of product brand. By contextualizing user experience data within broader market trends, the research highlights the role of user-centered design and feedback loops in enhancing product relevance and competitiveness. The outcomes of this study can inform product developers, marketers, and health professionals in creating more effective, user-aligned breastfeeding solutions that support ma- ternal well-being and infant health outcomes.
Authors - I Gusti Ketut Agung Ulupui, Gentiga Muhammad Zairin, Septi Nurmalita, Hera Khairunnia, Dwi Kismayanti Respati, Eka Septariana Puspa Abstract - This study examines the relationship between voluntary risk disclosure and Environmental, Social, and Governance (ESG) performance among Indonesian publicly listed companies. While ESG metrics have become central to investment evaluations, the influence of narrative risk reporting on ESG scores remains unclear, particularly in emerging markets where such disclosures are not standardized. Utilizing content analysis of 2022 annual reports and ESG ratings provided by Sustainalytics through the Indonesia Stock Exchange, this study investigates whether risk disclosure serves as a credible signal of corporate sustainability. The findings reveal that risk disclosure does not significantly influence ESG performance, suggesting that investors and ESG rating agencies may discount voluntary disclosures when they lack precision or strategic alignment. In contrast, firm-specific characteristics, namely profitability (ROA), leverage, and firm size—are positively associated with ESG scores. These results indicate that internal financial and structural factors may offer more reliable indicators of ESG commitment than unregulated disclosure narratives. The study contributes to the ESG literature by questioning the signaling power of voluntary disclosures and highlights the need for stronger disclosure standards in emerging markets to enhance ESG credibility and comparability.
Authors - Devanjan Khuntia, Anirban Chowdhury, Subhra Chakraborty Abstract - Cybercrime poses a significant threat to India’s business environment, disrupting economic ecosystems, revenue growth, innovation, and employee well-being. As digital adoption accelerates, sectors like banking, e-commerce, and manufacturing face escalating vulnerabilities. This study analyses cyber crime’s multifaceted impact, revealing projected losses of ₹20,000 crore in 2025, with banking (₹8,200 crore) and retail (₹5,800 crore) hardest hit. Beyond financial damage, attacks erode trust, delay deliveries (30% of manufacturers), and slow AI/cloud adoption (40%). Intellectual property theft affects 22% of firms, while SMEs struggle with weak defences (73% prioritize cybersecurity). Employees face data breaches (32% via PUPs) and stress (45%). The paper advocates for stronger policies (e.g., enhancing I4C), AI-driven security, and workforce training to build resilience. A holistic approach—combining regulation, public-private collaboration, and awareness—is critical to safeguarding India’s digital economy.
Authors - XIAOLIN HE, CHUANCHEN BI Abstract - With the rapid development of the global health management industry and the growing awareness of personal well-being, traditional wellness practices are provided with significant opportunities for modern transformation. As an important component of traditional Chinese health culture, Health Qigong—featuring the integration of body movement, breath regulation, and mental focus—has demonstrated notable advantages in chronic disease prevention, psychological regulation, and overall health promotion. However, integration of Health Qigong into the modern health management industry continues to encounter multiple obstacles, such as insufficient standardization, limited market recognition, and a lack of diversified business models. These challenges constrain its commercialization and sustainable development. This study adopts a qualitative research approach based on secondary data and case analysis to explore emerging trends, practical models, and key barriers in the commercialization of Health Qigong. By applying Business Model Canvas (BMC) and Creating Shared Value (CSV) framework, the study proposes sustainable business pathways for Health Qigong, including course standardization, digital dissemination, membership subscriptions, corporate health partnerships, and integration with health tourism. The findings indicate that establishing standardized curricula, utilizing digital platforms, and fostering cross-sectoral cooperation can support the formation of viable commercialization models that align with contemporary health industry demands. This, in turn, may enable the global expansion of Health Qigong as a sustainable health intervention. The results not only offer theoretical support for the modern promotion of Health Qigong but also provide practical insights for the commercialization of traditional cultural products within the health sector.
Authors - Aditya Dere, Bharati Dixit Abstract - The world of AI is has gone through tremendous advancements in over last decade. AI journey started from Algorithmic Intelligence and Linguistic Intelligence, and is moving towards Imaginative intelligence. This generative AI’s historical journey is focused on generating realistic texts and images through the means of early models like autoencoders and variational autoencoders (VAEs). From creating simple and coherent images from basic textual descriptions using Generative Adversarial Networks (GANs) and models like DALL-E in text to image generation, the AI leaped towards generating coherent frames which aligned for improving video quality, length, and the ability to generate more complex scenes in video with natural transitions in the field of text to video generation. The T2V models made use of models such as diffusion model, transformer model and the latest and the best one which is diffusion transformer model (DiT model). This paper basically gives us the overview of text to image and text to video generation models and their advancement in the field of generative AI.
Authors - Lyndon Avendano Abstract - The Philippine tourism sector significantly contributes to the national GDP, hence continuous improvements is necessary to achieve competitive advantage within the region, evidently the hotel industry plays an integral role in this objective. Several studies posits that effective Supply Chain Management (SCM) is crucial for competitive advantage and customer satisfaction in the hotel industry. Given today's technological advancements, integrating technology into SC operations is imperative. This study investigates the nuances of Internet of Things (IoT) integration within Hotel Supply Chain and its perceived contribution to supply chain performance from the perspective of various SC functions. This research posits that IoT integration can foster competitive advantage in hotel supply chains, enhancing their reliability, responsiveness, realignment, and resilience against disruptions. The analysis revealed a consistent, moderate perceived contribution of IoT integration across all four SC performance dimensions: Responsiveness, Reliability, Realignment, and Resilience. Further analysis indicated: Logistics demonstrated the highest overall perceived IoT contribution particularly strong in resilience, highlighting IoT's role in tracking and disruption management. Demand Planning showed the highest mean for realignment suggesting IoT's strength in data analytics for market adaptation. A significant difference was also noted for resilience across functions. These findings emphasize understanding each supply chain function's unique needs for effective IoT implementation. The results offer valuable insights for hotel operations aiming to strategically leverage IoT to optimize supply chain performance and gain a sustained competitive advantage in the dynamic hotel industry. Future research should delve deeper into factors driving these functional variations and explore tailored IoT applications.
Authors - Bharathidasan, Dhivyalakshumi, Vasanthi Abstract - This paper investigates an innovative, community-driven model of vulture conservation developed by Arulagam, an environmental NGO operating within the Nilgiri Biosphere Reserve in Tamil Nadu, India. Declination of vulture population due to the misuse of non-steroidal anti-inflammatory drugs (NSAIDs) in veterinary care, has been observed in recent decades. This study examines how Arulagam's interdisciplinary approach combines scientific outreach, cultural engagement, and stakeholder collaboration has evolved into a replicable model for sustainable ecological entrepreneurship. Using qualitative methods, including in-depth interviews, field observation, and campaign-based ethnography, this study documents how interventions such as pharmacist sensitisation, ethno-veterinary advocacy, school programs, and mobile awareness drives have contributed to the formation of Vulture Safe Zones (VSZs). It highlights how creative mediums like shadow puppetry, murals, and mythological storytelling have shifted negative public perceptions of vultures and introduced conservation ethics within local communities. This paper also explores entrepreneurial pathways emerging from the conservation framework, including eco-tourism, ethical livestock branding, and native plant nurseries. Emphasizing on the principles of nature-based solutions and aligned with SDG 15 (Life on Land), this study proposes a measurable intervention model that merges biodiversity protection with livelihood generation, offering policy-relevant insights for governing conservation in other biodiversity hotspots.
Authors - Md Shahim Uddin Saba, Mohitul Shafir, Md Zahid Hasan, Md Asfaqur Rahman, Farhan Khan, Mahady Hasan, Farzana Sadia Abstract - Social media has reshaped customer engagement, leading to widespread use of digital marketing. However, the effects of specific social media marketing practices on purchase decisions, participation, and brand loyalty remain underexplored. This study investigates the impact of social media marketing activities on consumer behaviour in Bangladesh’s technology product sector. Based on a quantitative survey and Structural Equation Modeling (SEM) using SmartPLS 4, the results show that both social media marketing and customer experience significantly influence behavioural outcomes. The findings provide strategic guidance for brands that seek to strengthen engagement in competitive digital markets.
Authors - Jasleidy Astrid Prada Segura, Efren Danilo Ariza, Maria del Pilar Corredor Garcia, Eduard Ricardo Espinosa Armero Abstract - The objective of this research is to design a comprehensive model aimed at reducing credit risk in the solidarity sector. This effort seeks a substantial improvement in the efficiency of the recovery of non-performing loans, considering that this process involves a significant level of risk. It is an applied and mixed research in terms of its sources of information, the scope is exploratory and explanatory, and the approach is quantitative. The model contains crucial parameters for effective risk mitigation. It should be noted that, as additional support, information from the sectoral analysis of the cooperatives in the report of the Superintendencia Solidaria and portfolio balances by city and entity with cutoff to 2021 and 2022 generated by the Superintendencia Solidaria will be used to support the current situation of the portfolio in the solidarity sector in Colombia. The methodology developed in this research can be extrapolated and applied in other regional contexts and economic sectors, offering a valuable tool to address credit risk and strengthen the solidarity economy in Colombia.
Authors - Neha A. Samsir, Arpit A. Jain Abstract - With the pandemic taking a serious impact, it is becoming progressively clearer that India needs to boost funding and develop its healthcare system. The integration of Information and Communication Technology (ICT) into the healthcare sector is transforming healthcare systems worldwide by improving patient access to and availability of healthcare services. A basic requirement for utilizing ICT in healthcare is having an electronic health record. It is a digital record that combines patient health information and is utilized for handling data, educational purposes, research, and reference purposes. Cloud-based electronic health records are constantly evolving and have transparency, trust, and confidentiality, they are vulnerable to several security and privacy threats. In the new Digital India, digital health (DH) plays a big role in healthcare. Utilizing technology, the audacious goal of offering universal health coverage—first to 500 million citizens with low incomes— is being accomplished. The widespread adoption of electronic health records (EHRs) has presented several obstacles, even though they have transformed healthcare by facilitating rapid access to patient information. The implementation of EHRs raises ethical and legal concerns, which are examined in this study with a specific emphasis on the Indian healthcare system. The implementation of EHRs should be guided by ethical issues, including medical beneficence, fairness, autonomy for patients, confidentiality, and beneficence. Strong regulations and security measures are essential considering legal difficulties such as medical errors, data breaches, and billing discrepancies. This paper aims to analyze all the legalities and medical issues of storing EHR using Blockchain Technology.
Authors - Sudhir B.Agarmore, Hemant S.Mahalle Abstract - This research paper analyses the key determinants affecting LAN performance and articulates algorithmic solutions for improving the efficiency of such networks. Elaborating on factors like bandwidth, latency, packet loss, and congestion, against the backdrop of cutting-edge algorithmic interventions, inclusive of dynamic routing, congestion control, and traffic engineering, this current research offers a comprehensive insight into the augmentation of LAN performance. A local area network is a major part of any modern computing infrastructure. The user experience and productivity depend to a great degree on their performance; at the same time, the overall network efficiency depends on them. This paper explores the main factors of LAN performance and examines algorithmic solutions to optimize these factors. Such factors that will be discussed are the impact of network topology, traffic patterns, hardware capabilities, software configuration, and their effective combinations on LAN performance. We will also investigate some higher-level algorithms, such as those for congestion control, routing protocols, and QoS mechanisms, which help increase network throughput, reduce latency, and deal with reliability in data transmission. Proper understanding of these determining factors and implementing the relevant algorithmic solutions significantly improve performance and reliability in organizational LANs.
Authors - Shaily Sharma, Disha Shah Abstract - Hormone level fluctuation is connected with several health problems affecting human beings. To properly managing and diagnosing hormone levels, it is essential to understand hormonal imbalance and its detection. Commonly the levels of hormones were detected with the help of laboratory tests like chromatographic methods and immunoassays which gives accurate results but are very slow process and there is no real time detection proficiency. Hormone monitoring is crucial and wearable devices are progressing very effectively with the help of biosensors to monitor hormones to provide better understanding of health control. This review paper is focusing on the theory of hormones, hormonal imbalance indicators, and detection approaches which are available. Several tests for diagnosing hormone level imbalance were investigated including chromatographic techniques (HPLC, GC-MS), biosensors and immunoassays (ELISA, RIA) while also focusing on machine learning including deep learning algorithms, pattern recognition, regression, classification to provide customized treatment. Deep learning approaches like CNN (Convolutional Neural Networks) and Supervised learning approaches like SVM (Support Vector Machine) and Random Forests have demonstrated encouraging uses in real-time monitoring and the prediction of hormone related diseases. The review also highlights wearable devices available for hormonal levels monitoring, comparison of wearable sensors like electrochemical, optical and microfluidic systems. By empowering real-time surveillance of hormone level imbalances and promoting early detection, diagnosing and illness prevention, these advancements have improved conveniency. According to the research, combining Machine learning (ML) algorithms with wearable biosensors can transform monitoring of hormone levels may result in better results and can provide early medication.
Authors - Sneha Sarah Alex, Jains P Chacko Abstract - This study explores the evolution of venture capital (VC) research through a bibliometric analysis of 2,577 journal articles from 1972 to 2022, using Scopus, VOSviewer, and Biblioshiny. It identifies five key research clusters: private equity and IPOs, entrepreneurship, innovation, human capital, and institutional factors. Highly cited works highlight VC’s impact on firm performance, governance, and innovation. Emerging themes include syndication, decision-making dynamics, globalization, and sectoral shifts like biotech. Grounded in agency, human capital, and institutional theories, this study provides insights for researchers, investors, and policymakers, while also suggesting broader future research directions.
Authors - Ayatulloh Michael Musyaffi, Rida Prihartini, Muhammad Ikhwan, Rochma Sudiati, Diah Armeliza, Maulana Amirul Adha, Maudy Shinta Maychella Abstract - Over the past decade, the integration of Artificial Intelligence (AI) into personal financial management has emerged as a strategic trend, transforming how individuals approach budgeting, investing, retirement planning, and debt management. Although academic interest in this area has grown, systematic mapping of publication trends, intellectual structures, and research gaps remains limited. This study conducts a bibliometric analysis of 624 Scopus-indexed publications from 2015 to 2024 using the Biblioshiny platform. The findings indicate a marked increase in publications following the COVID-19 pandemic, with most contributions originating from institutions in Asia, while Western countries produced the most highly cited works. Author keyword analysis and Multiple Correspondence Analysis (MCA) mapping identified three major thematic dimensions: algorithmic decision support, behavioral finance applications, and experimental AI methodologies. Additionally, several underexplored yet promising areas were identified, including demographic personalization, the integration of ethical considerations, and behavioral-based AI design. This study provides a comprehensive knowledge map and offers actionable insights for advancing research in AI-driven personal finance.
Authors - S Anupama Kumar, Vijayalakshmi M N, Divya T L, Narasimha Swamy S Abstract - Recent advancements in the Higher Education have led to the adoption of diverse learning approaches across the globe. The current educational frame-work incorporates multiple techniques, such as collaborative learning, active learning, cognitive learning, and more to improve the performance of the student. Experiential learning is one of the promising approaches, enabling students to experiment with concepts and learn through practical application. This paper explores the effectiveness of Project-Based Learning (PBL), an experiential learning approach, in a higher education course to improve student learning outcome. This research focuses on understanding the relationship between PBL, Collaborative Learning (CL), Iterative Learning (IL), Authentic Learning (AL), Disciplinary Course Learning . The research contributes towards the effectiveness of PBL in improving the student learning outcome. Structural equation model is used to attain the findings. A positive relation is found between the PBL, collaborative learning, disciplinary course learning, iterative learning, and authentic learning. This relation in large has improved the learning outcome of the student to a greater extent. Hence PBL is found to be an effective technique to be adopted in higher education and enable the students to acquire new knowledge using various methods. This in turn will improve the learning outcome of the students.
Authors - Dhvani Shah, Disha Chandaria, Harshil Shah, Janhavi Patel, Abhijit Joshi Abstract - Non-parametric tests are important in statistical analysis where data fails to meet assumptions like normality or homogeneity of variance. Nevertheless, choosing and implementing the right test is frequently statistical in nature, which is a limitation for most users. Therefore, we suggest an intelligent framework that automates the selection and running of non-parametric tests through machine learning. A well-curated dataset of statistical problems is fed into a Decision Tree Classifier, which is labeled as the best model with 93.4 percent accuracy to identify the most appropriate non-parametric test, such as the Runs Test, Wilcoxon Signed-Rank Test, and Mann-Whitney U Test. After prediction, the identified test is run automatically, and outputs are produced in a well-structured, interpretable manner. This method increases accessibility, enhances statistical analysis efficiency, and opens the way to further application of non-parametric testing in data-driven inquiry.
Authors - Sarvani Kocherlakota, Poojitha Panchakarla, Vellore Vandana, Buchi Harika Abstract - Circular economy is gaining momentum in the field of production and consumption to achieve sustainable goals in this digital era. Many strategies have been evolved in functioning the digital technologies to enhance the circular economy. But out of these technologies, we are not aware which technology could be used to emerge circular economy to a great extent. Entrepreneurship represents a key factor in capturing new circular business opportunities. To investigate its current research landscape and emerging trends, we conducted a Bibliometric analysis by using R software 4.5.0, and VOSviewer to identify the research trend, emerging topics, collaborations among countries, authors, documents and the contributing academic journals during the study period. Journal of Cleaner Production is having the highest publications and Resources, Conservation and Recycling is having the most cited document. Kumar A is the most contributing author in publications and Kumar V is the most cited author. Netherlands is the most contributing affiliation for publications and United Kingdom high international references.
Authors - Matipa Ricky Ngandu, Ntima Mabanza, Gardner Mwansa Abstract - Cybersecurity Information Sharing (CIS) among Small and Medium-sized Enterprises (SMEs) is increasingly recognized as essential for collective threat mitigation, yet participation remains low due to complex human, social, and organizational barriers. Underpinned by Social Cognitive Theory (SCT) this study explores how human factors such as trust, leadership engagement, awareness levels, and communication practices influence CIS participation within SME ecosystems. Drawing on academic literature and analysis of primary quantitative and qualitative data, we present a socio-technical model that conceptualizes CIS as a form of digital crowdsourcing, embedded within informal and semi-formal social networks. Our findings indicate that SME participation in CIS is significantly enhanced when trust-building practices, cultural alignment, and role-specific awareness are actively supported through peer in-fluence and community-based engagement mechanisms. The proposed model identifies the conditions under which SMEs are most likely to share security intelligence, including personalization of tools, clear governance structures, and inter-organizational trust scaffolding. This work emphasizes the role of social structures and crowdsourcing dynamics in driving effective cybersecurity engagement, offering practical implications for designing inclusive, scalable, and human-aware CIS platforms tailored for resource-constrained SMEs. This work contributes to broader efforts in boosting cyber resilience through socially grounded, technology-supported approaches.
Authors - Bhupinder Kaur, Shivani Gupta, Kritika Sheoran Abstract - The corporate world is increasingly becoming aware of the dynamic nature of its laws and policies, and has actively started functioning towards it. The observance of ESG goals by corporations is a de-novo approach towards the achievement of SDGs, and is an improvement over the already existing concept of CSR. Even credit rating agencies are considering ESG factors while assessing the creditworthiness of corporates, because under the ESG regime, corporations are responsible for their Environmental, Social and Governance initiatives, towards a better and sustainable tomorrow. This paper studies the environmental sustainability of various industries of the energy sector that have been filing their BRSR reports while following their environmental policy. Studying energy sector is important, because ultimately, one of the prominent parameters of every BRSR report is the amount of energy consumed in the industrial process. Herein, a comparative study shall be made taking regard to amount of energy generated and the various other parameters reported in a BRSR report. The study reveals that BRSR reporting is an effective and reliable method to access the actual compliance of environmental policy regulations by corporations. Further, the paper also enlists various shortcomings in the BRSR reporting system.
Authors - Eka Septariana Puspa, Laode Arahman Nasir, Dwi Handarini, Gentiga Muhammad Zairin, Surya Anugrah, Windy Permata Suyono, Septi Nurmalita, Rochma Sudiati, Irima Rahmadani, Nazwa Febriyani Abstract - This study explores the harmonization of financial reporting and accounting standards in the merger of two Indonesian state-owned enterprises (SOEs), Perum X and Perum Y. Employing a qualitative case study approach, the research investigates the systemic differences in accounting policies, financial information systems, and reporting structures that pose significant challenges in the post-merger integration process. Data were collected through in-depth interviews, document analysis, and observation of the integration process. The findings reveal disparities in Chart of Accounts (COA) structure, PSAK implementation—particularly PSAK 16 and PSAK 71—and the use of financial systems, with X operating an ERP-based system while Y remains reliant on Excel-based processes. Regulatory interventions, such as asset restatement and capital participation adjustments, are also discussed as critical factors influencing harmonization efforts. A conceptual framework is proposed to guide future SOE mergers in aligning accounting systems and policies. The harmonization process enhances financial transparency, managerial decision-making, and auditability, but also highlights the need for national guidelines and institutional coordination. This study contributes to the literature on public sector accounting by providing practical insights and policy recommendations for improving the consistency and integrity of financial reporting in SOE mergers. It also opens avenues for further research on long-term outcomes and cross-sector comparisons of accounting harmonization in public enterprises.
Authors - Mohima Rahman, Mehzabul Hoque Nahid, Khandakar Tahurul Islam, Erfan Ovee Nomaan, Kazi Tareq Ullah Abstract - This study investigates the critical factors influencing the post-ERP performance shift of Huamn Resource Management (HRM) in the Fast-Moving Consumer Goods (FMCG) organizations in Bangladesh, utilizing the Technology–Organization–Environment (TOE) framework. Drawing on the empirical evidence, the research evaluates the impact of five determinants, Competitive Environment, Perceived Compatibility, Perceived Efficiency, Top Management Support, and Training, on post-ERP performance shift in HRM. The findings re-veal that Competitive Environment, Perceived Compatibility, Perceived Efficiency, and Top Management Support significantly enhance post-ERP performance, whereas Training does not show a statistically significant effect. These results underscore the importance of aligning ERP systems with organizational processes, supported by effective leadership, system fit, and external responsiveness. The study also emphasizes the need for fostering an innovation-driven culture, strengthening vendor partnerships, and ensuring system scalability and compliance in the post-ERP phase. By addressing these strategic and operational factors, HR departments can improve efficiency, agility, and data integrity, ultimately enhancing organizational competitiveness. The research contributes to the growing discourse on digital transformation in HRM and offers actionable insights for practitioners, policymakers, and enterprise system stake-holders in emerging market contexts.
Authors - Dwi Handarini, Eka Septariana Puspa, Nuramalia Hasanah, Ratna Anggraini, Surya Anugrah, Windy Permata Suyono, Septi Nurmalita, Rochma Sudiati , Sabo Hermawan, Stefany Indiraguna Joka Abstract - The rapid growth of Indonesia’s digital economy has brought millions of Micro, Small, and Medium Enterprises (MSMEs) into online marketplaces such as Shopee, Tokopedia, and TikTok Shop. However, despite their economic significance, MSME tax compliance remains critically low due to limited tax literacy and weak financial administration. This study examines the impact of digital tax education and financial application usage on tax literacy and tax compliance intention among e-commerce MSMEs. Drawing on the Theory of Planned Behavior (TPB), a quantitative explanatory approach was employed using data from 100 MSME respondents in Indonesia. Structural Equation Modeling (PLS-SEM) was applied to test direct and mediating effects. The results show that both digital tax education and financial app usage significantly improve tax literacy, which in turn strongly predicts tax compliance intention. Tax literacy also mediates the relationship between digital interventions and compliance behavior. These findings offer theoretical contributions by integrating digital behavioral factors into tax compliance models, and practical implications for policymakers to develop integrated strategies that combine tax education with financial technology empowerment. Strengthening digital literacy and promoting fintech adoption can increase voluntary compliance, expand the tax base
Authors - Harsh Sobhashana, Jiya Bera, Margin Ankola, Nishat Shaikh Abstract - Voice-Enabled Chess is an innovative Python-based application that enhances traditional chess gameplay with modern voice recognition technology. Designed for accessibility and engagement, the system allows players to exe-cute moves using voice commands, providing an intuitive and hands-free gaming experience. The application integrates speech-to-text conversion via the Speech Recognition library, while AI- driven decision-making ensures a challenging opponent. A graphical user interface (GUI) enhances usability, and Docker deployment simplifies distribution across platforms. This project targets a diverse audience, including chess enthusiasts, individuals with disabilities, and technology enthusiasts interested in voice-controlled applications. By leveraging Python’s extensive ecosystem, including NumPy, Pandas, and the Chess library, the system efficiently manages game logic and interactions. Through the fusion of traditional chess mechanics with modern artificial intelligence and speech recognition, Voice-Enabled Chess pro- motes inclusivity and innovation in digital game development.
Authors - Kantharaju, Srinidhi G A Abstract - Healthcare institutions worldwide are increasingly adopting digital im-aging solutions to replace traditional film-based radiological systems. This re-search presents the development of an advanced Picture Archiving and Communication System (PACS) designed to address current limitations in medical image management. Our proposed system focuses on seamless integration of legacy im-aging equipment through innovative format conversion capabilities, specifically transforming proprietary image formats into standardized DICOM files. The implementation demonstrates significant improvements in storage efficiency, trans-mission speed, and overall workflow optimization within hospital environments. Through comprehensive testing and validation, we established that our solution successfully bridges the gap between older imaging modalities and modern digital infrastructure requirements. The system architecture incorporates robust security measures, user-friendly interfaces, and scalable database management to support growing institutional needs.
Authors - Ronald L. Pancho, Maria Teresa N. Pancho, Roland M. Simbre Abstract - Using digital technologies to enhance government operations and e-service delivery, e-governance encompasses diverse components crucial for improving digital initiatives and government e-service performance. This study examines how various elements of e-governance influence the effectiveness and efficiency of e-government e-services. The Philippines has actively pursued e-governance initiatives to encourage citizen participation, provide access to information and service delivery, enhance transparency and accuracy, and promote communication and interaction. In this context, the government's e-service performance becomes pivotal in assessing the effectiveness and efficiency of digital initiatives undertaken by the government. There were six hundred eighty-three (683) government e-services users to whom the respondents replied through Google Forms. The study used a self-made survey questionnaire and employed Cronbach's Alpha to test reliability and internal consistency. The experts conducted content validation, and non-probability purposive sampling techniques were used in which respondents were directly involved. The finding reveals that e-governance components positively correlate with government e-services' performance in effectiveness and efficiency. The study relates how the use of e-governance components affects e-services performance. The idea suggests that increasing e-governance practices enhances government e-services performance. Therefore, optimizing the e-governance components and enhancing their performance are essential in improving digital public service initiatives.
Authors - Sabo Hermawan, Ryna Parlyna, Surya Anugrah, Inkreswari Retno Hardini, Ferry Setyadi Atmadja, Fajri Hamdani, Alifah Nur Rahmawati, Cornellius Seno Adriano Abstract - This study examines the effectiveness of combining AI-optimized mobile design with Bootstrap performance improvements and ChatGPT-driven text analysis for Micro, Small, and Medium Enterprise (MSME) websites. A mixed-methods approach was utilized to assess the effects of these interventions. Performance measures, particularly Largest Contentful Paint (LCP), exhibited a significant 40% enhancement following modification. Simultaneously, text analysis performed with ChatGPT-4 attained an accuracy of 86.7% in detecting significant content concerns. This research fills a notable vacuum in the literature by offering a comprehensive analysis of technical and AI-based solutions for enhancing the online presence of MSMEs. The results highlight the capability of AI to improve the technical performance and content quality of mobile-optimized websites, providing practical insights for MSME digital strategies. Subsequent research will investigate wider geographical contexts. These findings are further contextualized within the Technology Acceptance Model (TAM), emphasizing the perceived usefulness and ease of use of AI-enhanced mobile websites.
Authors - Veena N D, Anithadevi M D Abstract - This investigation examines contemporary satellite imaging method-ologies and their implementation in Earth observation applications. We analyze the operational principles of various sensing platforms, data acquisition techniques, and processing algorithms used in modern remote sensing applications. Our research focuses on spectral band optimization and image reconstruction methodologies using actual satellite datasets. A comprehensive case study utilizing Kilauea Volcano imagery from Hawaii demonstrates practical applications of multi-band data processing techniques. Through systematic analysis of seven spectral channels from Landsat-7 Enhanced Thematic Mapper Plus sensor, we evaluate the effectiveness of different wavelength combinations for terrain characterization. Our experimental results indicate optimal spectral configurations for various surface feature identification tasks. The study concludes with an assessment of current technological capabilities and operational constraints affecting satellite-based Earth observation systems.
Authors - Sanjay Yadav, Sanjay Kumar, Sakshi, Aaryan Abstract - Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostics, optimizing treatment plans, and enabling datadriven public health strategies. This paper focuses on the application of AI to combat the opioid overdose crisis in the United States, utilizing machine learning techniques—particularly reinforcement learning—to inform policy interventions. We explore the use of deep Q-networks (DQN) trained on CDC opioid mortality data to simulate optimal intervention strategies across states, demonstrating a potential reduction of 15% in projected deaths. Additionally, we apply clustering and trend analysis on multi-year overdose data to uncover regional dynamics and emerging hotspots. The study also examines key ethical considerations, data privacy solutions, and integration challenges within AI-driven healthcare systems. By combining algorithmic insights with epidemiological realities, this work offers a scalable and explainable AI framework to support equitable healthcare policy design.
Authors - Septi Nurmalita, Surya Anugrah, Gentiga Muhammad Zairin, Rochma Sudiati, Eka Septariana Puspa, Windy Permata Suyono, Hera Khairunnia Abstract - In the era of accelerated digital transformation, blockchain has emerged as a disruptive force in the auditing profession. This study presents a Systematic Literature Review (SLR) of 20 peer-reviewed articles from Scopusindexed journals (2020–2025), critically examining the opportunities and challenges of blockchain adoption in auditing. The review finds that blockchain enhances audit quality through real-time data access, immutable transaction records, and automation via smart contracts—leading to improved transparency, fraud detection, and operational efficiency. However, adoption remains limited due to regulatory uncertainty, high implementation costs, technical interoperability issues, and organizational resistance within traditional audit firms. Emerging concerns such as private key vulnerabilities, ethical ambiguities, and role conflicts in interdisciplinary audit teams further complicate adoption. Despite these barriers, recent literature highlights new opportunities, including collaborative audit ecosystems and the rise of Audit 4.0, where blockchain is integrated with AI and IoT for predictive, real-time assurance. The study calls for global regulatory harmonization, cross-platform audit standards, and empirical validation of blockchain-based audit systems. It also proposes a future research agenda centered on the convergence of blockchain and artificial intelligence to enhance risk detection, audit automation, and digital accountability. The findings offer actionable insights for regulators, auditors, and scholars navigating the evolving nexus of technology and assurance.
Authors - Sabo Hermawan, Surya Anugrah, Ryna Parlyna, Windy Permata Suyono, Eka Septariana Puspa Abstract - This study investigates the impact of integrating web-based Enterprise Resource Planning (ERP) systems, Big Data Analytics (BDA), and AI-powered dashboards to create a Smart Campus environment that enhance student engagement in international programs. Employing a mixed-methods approach over one academic semester, the research incorporates system log analysis, surveys, inter-views, and focus groups. The findings reveal a significant improvement in aca-demic engagement (mean score = 7.8, σ = 1.1), student satisfaction (68% positive feedback), and retention rates among international students. AI dashboards enabled early interventions for at-risk students, while ERP and BDA supported administrative efficiency and personalized services. These outcomes contribute to Sustainable Development Goal (SDG) 4: Quality Education, by promoting inclusivity, efficiency, and digital transformation in higher education.
Authors - Ketaki Patil, Om Nalagune, Shravani Patil, Sangita Bavkar Abstract - This paper presents the design and construction of a low-cost, Arduino Nano-based myoelectric prosthetic arm to increase access to assistive technology to underprivileged populations. The system leverages surface electromyography (sEMG) signals recorded from non- invasive Ag/AgCl electrodes to signal voluntary muscle contraction. The signals are interpreted by a simple threshold-based control algorithm, eschewing the use of advanced machine learning in favor of reliable and responsive performance. Upon exceeding a calibrated threshold, servo motors built into a 3D-printed PLA arm are actuated to mimic simple hand movements like opening and closing. The use of open-source hardware, low-cost materials, and additive manufacturing processes like FDM 3D printing guarantees a low-cost manufacturing process, permitting simple assembly, repair, and modification. The prosthesis is energy efficient, modular, and lightweight, ideal for educational use, rural hospitals, and prototyping. Experimental testing confirms consistent performance and real-time responsiveness to muscle activity. In summary, this paper demonstrates a low-cost, accessible, and scalable approach to functional prosthetic limb design, closing the gap between advanced biomedical solutions and those communities in need of them.
Authors - Unggul Purwohedi, Sabo Hermawan, Ria Rahma Nida, Yeti Lastuti Abstract - This study proposes a sustainable business model for BUMDes (Village-Owned Enterprises) Wukirsari, specifically examining a circular economy approach integrated with information systems to support Sustainable Development Goal 12 (Responsible Consumption and Production). The research ad-dresses the gap of limited circular economy adoption and digital integration in rural enterprises. Utilizing a mixed-methods approach, the study involved 18 respondents and employed tools such as Business Model Canvas, SWOT analysis, and Circular Mapping. Google Sheets, Forms, and Drive were integrated as the core information system for data tracking and management, demonstrating a practical application of information systems for business management in a rural context. Validation was achieved through triangulation of data sources and expert review. The results identify key circular units, including composting, recycled crafts, and eco-tourism, integrated into a comprehensive circular model with digital tracking mechanisms. The proposed model demonstrates improved efficiency, enhanced transparency, and local feasibility, offering a replicable frame-work for other rural enterprises. This research contributes to the novelty of tech-enabled circularity in rural contexts, providing a practical pathway for BUMDes to enhance sustainability and economic resilience through a robust business model. While the study's limitations include a small sample size and single-site focus, it lays a foundational understanding for future broader implementations of sustainable circular business models.
Authors - Rama Satish K V, Hemavathi J, Shrihari Kulkarni, V.V Sathya Sai Likhith, Balasubramani A, Shashaank Raj CS Abstract - The integration of AI-driven voice-controlled systems is transforming human-drone interaction, enabling intuitive and efficient UAV operation. This paper presents a novel approach for voice-commanded drone navigation using a fine-tuned T5 model, ensuring structured execution through predefined flight functions. The system incorporates Whisper for speech-to-text conversion and AirSim for high-fidelity simulation via its Python API. By leveraging NLP and real-time processing, the model accurately maps user commands to drone actions while maintaining execution safety. Experimental results demonstrate 97% accuracy in function mapping, highlighting the system’s reliability and responsive-ness. This work advances AI-powered UAV control, enabling applications in search-and-rescue, autonomous inspections, and hands-free drone operations.
