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.
