Authors - Mainford Mutandavari, D. Hemavathi Abstract - Maize (Zea mays L.) is an essential staple produce for smallholder farmers in developing nations, yet Northern Corn Leaf Blight (NLB), Grey Leaf Spot (GLS), and Common Rust foliar diseases cause yield losses of 30–70%. Infection detection is done at advanced stages due to labor intensity resulting from the conventional disease monitoring methods. A Low-Cost Drone-Mounted Multispectral Imaging (LCDMI) framework for resource-constrained smallholder systems is presented in this paper, pairing a consumer-grade UAV with a five-band multispectral sensor. The vegetation-index features are fused with multispectral band data using a Spectral-Spatial Attention Vision Transformer (SSAViT) classifier and a Spectral-Constrained Synthetic Data Generation (SC-SDG) module addresses training-data scarcity. A hardware cost of USD1,940 is projected for field evaluations across twelve plots in Zimbabwe over two growing seasons yielding 95.8% detection accuracy, identifying diseases 7–12 days before visible symptom onset. A multi-label extension enables simultaneous classification of co-occurring infections. Georeferenced disease maps are delivered within 6.3 min/ha. With perhectare costs as low as USD2.10 on a scale, the economic analysis projects ROI within two seasons for cooperatives managing 50+ hectares.
Authors - IGN Oka Ariwangsa, Komang Widhya Sedana Putra P, Wayan Sri Maitri Abstract - The rapid adoption of artificial intelligence (AI) in higher education has transformed how students access information and engage in academic activities. While AI-powered technologies enhance efficiency and provide personalised support, their uncritical use may weaken independent reasoning and reduce meaningful social-academic participation. This raises concerns in digitally mediated environments where individuals must interpret complex information, evaluate uncertainty, and make informed judgments. Despite growing attention, most studies emphasise functional outcomes such as academic performance, overlooking the mechanisms through which AI-integrated teaching can foster deeper, more sustainable learning. This study examines how AI-aware pedagogy—defined as the intentional and reflective integration of AI in instructional design—enhances critical thinking through social-academic engagement. A quantitative approach was employed, involving 200 undergraduate students in Indonesia. Data were collected via structured questionnaires and analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that AI-aware pedagogy has no significant direct effect on critical thinking. However, it significantly influences critical thinking indirectly through social-academic engagement. This indicates that higher-order thinking develops not merely through technological integration, but through socially embedded learning processes that encourage interaction, reflection, and evaluation. Theoretically, this study links digital pedagogy with cognitive and social learning processes. Practically, it highlights the need for AIsupported environments that foster critical evaluation and responsible decisionmaking under conditions of uncertainty. Future research should explore its applicability across contexts and its long-term cognitive implications.
Authors - Mary Diana C. Yamzon, Janelli M. Mendez Abstract - This study provides a data-driven analysis of the academic performance of Bachelor of Science in Office Administration (BSOAD) students at Tagbilaran City College from Academic Year 2021 to 2024, employing data mining clustering techniques to ascertain the five most challenging subjects. The study specifically aimed to: (1) construct and preprocess a dataset of pertinent academic attributes; (2) employ K-Means, K-Medoids, and Agglomerative Hierarchical Clustering algorithms to discern groupings of subject difficulty; (3) validate clustering results utilizing the Davies-Bouldin Index (DBI); and (4) develop evidence-based recommendations for curriculum enhancement and academic assistance. The analysis involved a dataset of 26,965 valid student grade records across 68 subjects, all of which were processed using RapidMiner Studio. The research utilized the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework within the context of Educational Data Mining (EDM). The DBI for K-Means (DBI = 0.461; Excellent) and K-Medoids (DBI = 0.9145) were used to check the clusters, and the visual dendrogram was used to check the Agglomerative Hierarchical Clustering. All three algorithms consistently recognized OA113 Advanced Shorthand and OA111 Foundations of Shorthand as the two most challenging subjects in the program. The results offer statistically substantiated, evidence-based insights to facilitate curriculum evaluation, instructional enhancement, and the formulation of specialized academic intervention programs for BSOAD students.
Authors - Hussein P. El Sayed Ahmed, Ardee Joy T. Ocampo Abstract - Youth participation in local governance remains a persistent challenge despite institutional mechanisms designed to promote engagement. In the Philippines, the Sangguniang Kabataan (SK) serves as a formal platform for youth involvement in local decisionmaking; however, many SK programs continue to experience low participation, limited feedback integration, and repetitive activity design. This study presents EmpowerSK, a data-driven framework that leverages data mining techniques to enhance youth engagement in SK programs. Using structured survey data from 1,055 youth respondents aged 18–25 across the nine barangays of Alilem, Ilocos Sur, the study applies the Knowledge Discovery in Databases (KDD) framework, K-Means clustering, and sentiment analysis to transform raw feedback into governance intelligence. K-Means clustering (k=3) identified three statistically validated engagement profiles: Highly Active (61.6%), Moderately Involved (17.0%), and Disengaged (21.3%). Sentiment analysis of open-ended responses revealed appreciation (77.8% positive), diagnosis (73.2% negative), and aspiration (85.5% neutral-aspirational) as a coherent three-phase youth governance narrative. An overall weighted mean of 3.75 ("Very Good") across eleven Likert-scale items confirmed a critical institutional gap: Digital Engagement (4.14) significantly outpaced SK Support Initiatives (3.52), with SK Training recording the lowest item score (3.44). A five-pillar data-driven action plan—Awareness and Inclusion, Program Diversification, Digital Transformation, Capacity Building, and Monitoring and Evaluation—was developed, validated by SK officials, and aligned with SDG 4, 11, and 16. The findings demonstrate that freely available data mining tools can transform rural youth governance into an annually replicable, evidence-based participatory system.
Authors - Reynaldo F. Agunod, Janelli M. Mendez Abstract - Higher education institutions collect large volumes of student data but these are underutilized for institutional planning. This study applies the CRISP-DM framework to enrolment records of a freshman cohort of 1,916 students across four academic years (2021-2025) across 28 academic programs from a private higher education institution in Central Visayas, Philippines, to forecast institutional progression metrics using predictive analytics. Descriptive analytics and three predictive models were applied based on their suitability for the dataset with 3-4 data points, namely: Linear Regression, Holt-Winters Exponential Smoothing, and ARIMA. Six institutional performance metrics were analyzed: enrolment, retention, persistence, attrition, program shifts, and graduation. Key findings reveal a continuous 29.6% enrolment decline within the cohort, an im-proving retention and persistence profile, a program-shift surge largely due to migrations from Accountancy to Finance, and a rapidly increasing graduation rate. Linear Regression (OLS) was identified as the most effective forecasting model for the study’s single-cohort dataset.
Authors - Edimar J. Rato, Janelli M. Mendez Abstract - Student dropout has remained a major problem in all higher education institutions globally, including in the Philippines, where the total college dropout rate in the country was recorded at about 35.15% in the Academic Year 2023–2024. This study aimed to develop a predictive analytics model that identifies dropout and retention patterns among students of Tagbilaran City College to support evidence-based intervention strategies. offered by the school from Academic Year 2021-2024. The algorithms implemented for the supervised learning process include Random Forest and Gradient Boosting, while the algorithm for the unsupervised learning process is K-Means Clustering implemented using the RapidMiner Studio tool. Results revealed that both supervised models had a poor performance due to class imbalance issues as well as a small feature set; the Random Forest model had an accuracy of 59.59%, while it had an AUC of 0.575. The Gradient Boosting model had an accuracy of 60.51%, while it had an AUC of 0.508. The K-Means Clustering model had a good performance since it resulted in three interpretable student risk clusters: a moderate-risk group with a dropout rate of 27.3%, a highest-risk group with a dropout rate of 44.7%, and a lower-risk but larger group with a dropout rate of 41.9%. The Davies-Bouldin Index of 0.967 confirmed adequate cluster separation. The K-Means model demonstrated the most practical utility as an early-warning risk stratification tool applicable at the start of each academic year, forming the foundation of an evidence-based intervention plan to improve student retention at Tagbilaran City College.
Authors - Ni Made Prasiwi Bestari, Jonathan Jacob Paul Latupeirissa, Suryanto Nugroho, Iwan Adinugroho, Melati Budi Srikandi, Ayu Made Bianca Juarez Abstract - The Fourth Industrial Revolution has been transforming the global tourism industry, shifting toward a dynamic Tourism 4.0 ecosystem. Given that the adoption of AI is expected to increase the revenue of the tourism industry, it is necessary to conduct a Systematic Literature Review to fill the gap in empirical research on the relationship between technological innovation and long-term sustainability. Most studies on smart tourism from different perspectives, including tourist behavior, tourist service quality, innovation, and sustainability, focus on the "hardware construction" at the macro level and its implementation based on related policies, ignoring the psychological mechanisms affecting tourists' experiences at the micro level. This study aims to identify the key technological drivers, including AI, IoT, and computer vision, and their influence on operational innovation and Sustainable Development Goals. A total of 23 core manuscripts from 2020 to 2025 gathered from Scopus database were synthesized and analyzed based on PRISMA guidelines. The results showed that smart tourism technologies can greatly improve efficiency and enhance hyper-personalization. However, most current applications of smart tourism technologies do not take adequate account of social and environmental metrics. Also, many digital tourism strategies prioritize revenue over social inclusion. For the future of smart tourism destinations, frameworks such as Society 5.0 that integrate high-tech with the human touch of hospitality and tourism are needed. Destinations should also seek governance models that ensure long-term resilience by moving the focus away from infrastructure and toward "Smart People" initiatives and the development of standardized real-time sustainability metrics.
Professor, National Institute of Technology Raipur, India.
Narendra D. Londhe is presently working as Associate Professor in the Department of Electrical Engineering of National Institute of Technology Raipur, Chhattisgarh INDIA. He completed his B.E. from Amravati University in 2000 followed by M.Tech and Ph.D. from Indian Institute of Technology... Read More →
Authors - Max Angelo D. Perin, Lenie B. Maligmat, Darrel A. Cardana, Renante S. Digamon, Joan Mae G. Lagumbay, Cecilia T. Gumanoy Abstract - The Quality Assurance Office of a Philippine state university campus conducts 7S evaluations across all offices each semester, producing numeric scores and written evaluator comments. Consolidating the narrative comments has depended on manual review, which is time-consuming across more than a hundred offices per cycle. This paper describes a two-phase AI-assisted analytics pipeline. Phase 1 retrieves audit records from a MySQL database via a stored procedure, formats them with a Python ETL script, and submits them to Grok (xAI) to draft scorecards and action items; evaluators then review the drafts be-fore consolidation into the official PDF report. Phase 2 parses the validated PDF with Python to extract structured fields and compute descriptive statistics, office rankings, a priority index, and TF-IDF text clustering. Applied to the November 2025–January 2026 cycle (112 offices; 107 scored, 5 with no submission), most units cluster in the moderate-to-great compliance range while a meaningful minority fall below threshold. Among the top 25 priority offices, Standardize (20/25) and Safety (19/25) are the most frequently flagged dimensions. The pipe-line shows that AI assistance structured around human review can accelerate QA consolidation while preserving evaluator accountability.
Authors - Nur Fajrina, Felina C. Young, Rosita Widya Putri Abstract - This study investigates the relationships among Stakeholder Integration (STI), Digital Transformation Capability (DTC), Absorptive Capacity (AEC), and Sustainable Supply Performance (SSP) within a knowledge-intensive supply chain context. Employing a quantitative methodology alongside Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from 262 respondents involved in strategic and operational functions. The results reveal that stakeholder integration significantly enhances digital transformation capability, thereby strengthening absorptive capacity. Both digital transformation capability and absorptive capacity have direct positive effects on sustainable sup-ply performance. However, stakeholder integration does not directly influence sustainable supply performance. Instead, its effect becomes significant only when mediated by absorptive capacity, indicating that internal knowledge assimilation and utilization mechanisms are essential for translating collaborative efforts into sustainability outcomes. The results highlight the critical role of dynamic capabilities in accomplishing sustainable supply performance, particularly in environments characterized by digital transformation and stakeholder complexity. The study contributes theoretically by integrating stakeholder theory and dynamic capability perspectives, emphasizing absorptive capacity as a key mediating mechanism. The results suggest that firms should complement external stakeholder collaboration with investments in digital infrastructure and organizational learning systems to enhance long-term sustainability performance.
Authors - Ayush Ghumare, Reena S. Satpute Abstract - Mobile application development has evolved rapidly with the emergence of advanced technologies such as 5G connectivity, Artificial Intelligence (AI), Machine Learning (ML), and Mobile Edge Computing (MEC). These technologies are transforming the mobile ecosystem by enabling the development of intelligent, data-driven applications and accelerating development cycles. Mod-ern mobile applications are expected to provide real-time services, personalized user experiences, and seamless connectivity, which has significantly increased the complexity of mobile application design and implementation. It is resulting into many challenges. One of the major challenges in mobile application development is the inherent limitation of mobile devices, including restricted pro-cessing power, limited memory capacity, and battery constraints. Developers must optimize application performance while ensuring energy efficiency to pre-vent excessive battery consumption and degraded user experience. Additionally, the increasing reliance on third-party libraries and analytics tools may introduce security vulnerabilities, creating potential security gaps within applications. These risks are often intensified by the lack of specialized security expertise within development teams, raising concerns related to data privacy, application security, and software supply chain vulnerabilities. Another challenge is platform fragmentation, particularly within the Android ecosystem, where diverse devices, operating system versions, and hardware configurations complicate compatibility and performance optimization. This diversity increases testing complexity and development costs. Furthermore, integrating AI and ML models into mobile ap-plications requires careful decisions regarding cloud-based versus on-device pro-cessing. Therefore, developers must balance scalability, performance, security, and energy efficiency when designing modern mobile applications. This study presents systematic literature evaluation methodology, comparative analysis of native and cross-platform paradigms, software supply chain security frameworks, measurable energy optimization strategies, and practical industry case studies from healthcare, fintech, and mobile commerce sectors.
Authors - Murali Mohan Reddy Seelam, VyshnaviThanneeru, Ajay Kumar Reddy Vemireddy, Srilatha Kudumula Abstract - This paper shows a new approach to implement the Agentforce-NS framework to provide zero touch salesforce deployment pipelines by integrating it with the Neuro Symbolic AI Agents. Even though the complete salesforce deployment pipelines have been automated end to end, it has been very difficult to achieve zero touch deployments due to its nature of the handling of metadata due to the interdependency of the components within the salesforce. The regular pipeline processes still heavily depend on the manual intervention to resolve the merge conflicts, resolve the dependency errors, working on the roll back deployments and following the compliances. The architecture we are proposing will solve all these problems by integrating the adaptive and predictive capabilities of the neural networks with rule based, transparent precision of the symbolic reasoning. The proposed Agentforce architecture will have five agents that will collaborate and will execute the deployments without any human intervention. These five agents are used to learn the deployment strategies, roll back planning, analyzing the metadata, autonomous execution and verification of the governance. After many tests in the enterprise level environments, we see that it is resolving so many blockers, issues and increasing the deployment success rate, improving the governance, and reducing the meantime to recover. By covering the technical gap between logical interface and the deep learning, the Agentforce-NS represents a break through advancement to have the fully automated, autonomous and auditable salesforce devops pipelines.
Authors - Shreya S. Partake, Reena S. Satpute Abstract - Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, achieving human-level performance on many semantic and syntactic benchmarks. However, their competence in pragmatics—the study of how context shapes meaning—remains a critical and underexamined frontier. This paper presents a unified analysis of the “pragmatic gap” in LLMs, arguing that it stems from a fundamental distinction between the co-textual statistical patterns LLMs are trained on and the contextual world knowledge humans use for inference. We first establish a theoretical baseline by reviewing foundational linguistic concepts, including Grice’s maxims, implicature, presupposition, speech acts, and deixis. We then systematically evaluate LLM performance, contrasting successes in pattern-rich tasks like coreference resolution with systemic failures in tasks requiring novel inference, such as non-conventionalized indirect speech acts and irony. We analyze the development of new evaluation tools, particularly the Pragmatics Understanding Benchmark (PUB), which quantifies the persistent gap between model and human performance. Subsequently, we synthesize emerging technical solutions, including “thought-based” fine-tuning and the injection of Gricean principles into Retrieval-Augmented Generation (RAG) frameworks. Finally, we dissect the profound cognitive and philosophical implications of this gap, critically examining the debates on the Symbol Grounding Problem and Theory of Mind (ToM). We conclude that while LLMs can pass “literal” ToM tests, they fail “functional” ToM, revealing them to be sophisticated co-text manipulators rather than context-aware agents. We propose that future progress lies in developing a “machine pragmatics” based on probabilistic models rather than flawed anthropomorphic imitation.
Authors - Ayushi Chapate, Reena S. Satpute Abstract - Natural language helps us to interact with the computer through human language. This article investigates how Natural Language Processing (NLP) can enhance our understanding of social media changes. To its audience, social media provides a large - arguably unlimited - and otherwise untapped linguistic re-source, revealing information about government behavior, civic participation, in-dividual mental well-being, and consumption behavior, among many other things. Using machine learning analytical methods such as sentiment analysis, topic modeling, stance detection, and misinformation tracking, researchers can begin to study the social, psychological, and economic implications of web-based inter-action. In terms of civic and political implications, to analyze user-generated con-tent, discourse networks, and hashtags using NLP applications can produced new insights into online mobilization and collective action. For example, researchers studying the political movement’s #MeToo and #BlackLivesMatter, based on analysis of Twitter data, have employed topic modeling techniques to reveal their influence and significance in innovative ways. From a psychological perspective, NLP methods make it possible to examine prevalent mental health indicators across separated populations, through the analysis of emotional tone, pronoun use, and distress markers. In studies conducted between 2020–2025, the application of BERT based embedding models were found to detect online indicators of depression, anxiety, and social comparison leverage's based on word meaning. Further, understanding the depth of these psychological consequences remains nebulous and limited to a range of social categories in the digital landscape, similar to previous notions of 'self-checking' across the digital commons exploring citizen engagement.
Authors - Jes Maries M. Mendez, Max Angelo D. Perin, Joan Mae G. Lagumbay, Mae S. Dagupan, Elizabeth A. Orapa, Marcelina S. Butlig Abstract - Educational tours are widely used in higher education to connect class-room learning with real settings, yet evaluations often stop at overall ratings that do not explain why students endorse a tour or which delivery issues weaken the experience. This study applies a student experience intelligence workflow that integrates survey analytics with offline text mining to produce planning-relevant evidence. A survey of 156 students captured demographics, three 10-item Likert constructs—motivation, perceived effectiveness, and problems encountered (4-point scale)—a recommendation rating, and open-ended comments. Responses were cleaned through category standardization and rule-based numeric conversion. Internal consistency was good for motivation (α = 0.877) and excellent for effectiveness (α = 0.960) and problems (α = 0.958). Learning beyond classroom instruction (M = 3.71) and interest in tour inclusions (M = 3.68) led motivation; creative learning (M = 3.67), resourcefulness (M = 3.66), and social skills (M = 3.65) led effectiveness; tour expense (M = 3.21) and short time per attraction (M = 2.60) led problems. 73.1% gave the top recommendation. Recommendation correlated positively with motivation (ρ = 0.317, p < 0.001) and effectiveness (ρ = 0.328, p < 0.001); a binary logistic model showed perceived effectiveness as the strongest predictor of the top recommendation category. Open-ended comments (171 entries) were summarized through TF–IDF with K-Means clustering (k = 6) and complemented with a VADER polarity pass on 155 meaningful entries (68.4% positive, 21.9% neutral, 9.7% negative; mean compound = +0.365). The combined evidence points to improvements that preserve educational value while addressing cost and pacing, and shows that the workflow is portable to other programs and experiential learning activities.
Authors - Kent Cyryl A. Campit, Christian Kelvin Gonzales Abstract - This study explored a data-driven approach to evaluating citizen feedback within the Provincial Government of La Union (PGLU) by integrating quantitative and qualitative analytical techniques. Traditional feedback systems in government offices often rely on averages and summary reports, limiting the ability to capture deeper citizen experiences and concerns. To address this gap, the research transformed paper-based feedback forms into a structured digital dataset covering responses from 34 frontline offices and service units from July 2025 to January 2026. The study applied Customer Satisfaction Score (CSAT), Weighted Mean, and Range of Interval to measure and classify service performance levels. For qualitative analysis, Latent Dirichlet Allocation (LDA) was used to identify recurring themes in open-ended responses, while a dual-model sentiment analysis approach combining VADER and RoBERTa classified citizen feedback into positive, neutral, and negative sentiments. The analytical pro-cesses were implemented using Microsoft Excel, Google Sheets, and Python through Google Colaboratory. Findings revealed consistently high satisfaction ratings across offices, while qualitative analysis uncovered recurring themes related to service efficiency, staff assistance, facility conditions, and operational concerns. RoBERTa demonstrated better contextual understanding and achieved higher performance metrics compared to VADER. The study further developed an Observed Satisfaction Classification Framework to support evidence-based decision-making and service improvement. Ultimately, the re-search demonstrated how citizen feedback can be transformed into actionable governance insights that promote transparency, accountability, and continuous improvement in public service delivery, aligned with Sustainable Development Goal 16.
Professor, National Institute of Technology Raipur, India.
Narendra D. Londhe is presently working as Associate Professor in the Department of Electrical Engineering of National Institute of Technology Raipur, Chhattisgarh INDIA. He completed his B.E. from Amravati University in 2000 followed by M.Tech and Ph.D. from Indian Institute of Technology... Read More →
Authors - Kalva Yamini, Kapilesh C, Hari Kishore R, Giri Karthick S Abstract - Phishing attacks remain among the most prevalent cybersecurity threats, exploiting deceptive URLs that imitate legitimate domains. Traditional blacklist and heuristic-based methods fail to detect zero-day phishing URLs, leaving users exposed to novel attack vectors. This paper presents 3P-VAD (Three-Phase Verification and Detection), an AI-powered system for real-time URL classification integrating three complementary layers: (i) threat intelligence dataset lookup against live feeds, (ii) multi-engine verification via the VirusTotal API aggregating results from 70+ security vendors, and (iii) a Convolutional Neural Network (CNN)-based zero-day detection model operating exclusively on URL character sequences. A selection-based scanning mechanism enables on-demand URL verification, enhancing user privacy by preventing inadvertent submission of sensitive internal URLs to third-party services. Evaluated on 2 million URLs, the framework achieved 95.0% accuracy, 94.5% precision, 86.0% recall, and 90.0% F1-score on the CNN zero-day component, with 100% combined detection rate across all three phases. Ablation experiments confirm non-redundant, complementary coverage.
Authors - Farai C. Jonha, Arthur Ndlovu, Mainford Mutandavari Abstract - This study presents a systematic review on the use of deep learning and density-based techniques for explainable segmentation of banking customers. We analyze 71 peer-reviewed papers published between 2015 and 2025 to investigate their methodological trends, validation approaches, and the degree of incorporation of interpretability into proposed models. Our findings suggest that autoencoders and variational autoencoders provide better separation of clusters than models using raw data. In terms of clustering methods, density-based clustering algorithms perform better than clustering algorithms based on centroids since banking data exhibit highly skewed and non-Gaussian patterns. We also observe a common deficiency in explainability, with less than 26% of the re-viewed papers considering approaches such as SHAP or LIME. Furthermore, considerations of external validity, operational governance, regulation, and scalability of implementation are rare. We therefore propose an explainable customer segmentation (XCS) framework based on deep representation learning, density-based clustering, post-hoc explainability, and an operationally ready pipeline that is suitable for use in regulated banking environments
Authors - Mohammed Sulaiman I, Shreevatsa DS, Kavitha Sooda, Revanth L, Dhanush M Abstract - The unintentional release of API keys, tokens, and any other credentials in the source code is an obvious security threat to contemporary software development. Old rule-based scanners produce too many false positives and cannot scan through obfuscated secrets or secrets that are unknown. This paper introduces AV-SHIELD (Automated Vulnerability Scanning Hybrid, Implementing integrated Leakage Detection) which is a hybrid framework that brings together pattern matching and machine learning to identify credential leaks in real time. The system serves to monitor development spaces in event-driven fashion and scan repositories in GitHub up to size limitations. One uses a Random Forest type of classifier, which is trained on entropy based features to combatSecret vs Benign strings and a risk scoring engine which gives priority to create alerts. Records of the identified exposures are archived in a fingerprint-tracked vault, batch-processed into mail notifications, and include professionally-formatted PDF records. A trade analysis using an interactive Streamlit dashboard allows viewing trends of exposure, provider profiles, and risk allocations. The synthetic data generated has demonstrated a high precision and recall rate that is much lower than the explanation of the uses of regex alone, tested through experimental evaluation. The framework was implemented as a systemd service, which shows its applicability to the enterprise DevSecOps pipelines.
Authors - Alfred ADINSI, Pelagie HOUNGUE Abstract - This systematic review evaluates AI-based techniques for rice dis-ease detection with a focus that existing surveys have not adopted: their deploy-ability in West African smallholder conditions, using Benin as the reference case. Based on 220 studies selected from 390 Scopus publications (2019–2025) via PRISMA, it goes beyond performance benchmarking to assess what actually works under resource constraints. Rice blast (70.9% of studies), brown spot (60.9%), and bacterial blight (44.5%) dominate the literature. Deep Learning accounts for 64.5% of approaches, hybrid methods for 21.8%, and classical Machine Learning for 13.6%. Mean accuracy reaches 94.2% for pure Deep Learning and 95.8% for hybrid architectures. Res-Net+ViT (96.4% ± 2.1%) and CNN+SVM (94.1% ± 4.1%) are the strongest per-formers, but performance alone is not the right metric for Benin. While 85% of studies apply to tropical climates, only 30.5% propose solutions running on limited hardware. Three approaches clear both bars: MobileNet+SVM (89.4%), optimized YOLOv8 (89.2%), and ResNet-based Transfer Learning (91–94% after fine-tuning). That AI can detect rice diseases accurately is no longer in question. The harder problem is which systems beninese farmers and extension agents can actually use. This review provides an answer.
Authors - I Gusti Ayu Agung Dewi Sucitawathi Pinatih, Jonathan Jacob Paul Latupeirissa Abstract - The objective of this study is to examine and analyze the integration of technology, governance, and sustainability in the context of e-government and public services, with a particular focus on the implementation of these three dimensions at the global and local levels, specifically in the Province of Bali. This study employs a Systematic Literature Review (SLR), beginning with the identification of relevant keywords such as “e-government,” “public service,” and “sustainabil-ity,” which were validated using WordCloud. Next, strict inclusion and exclusion criteria were used to select articles. These criteria included relevance to the topic, year of publication (2016-2026), and the journal’s peer-review status. Initial identification, screening of titles and abstracts, and in-depth reading of articles were part of the article selection process. The research findings indicate that in the digital transformation of the public sector, technology, governance, and sustainability are interrelated, and Bali serves as an example of how the integration of these three dimensions is reinforced by local values such as Tri Hita Karana and the subak system. These findings underscore that the digitization of public services in Bali will succeed if the principle of sustainability is applied in tandem with technology, governance, and local culture.
Authors - Aymane Chekira, Aziz Hmioui Abstract - The rapid expansion of digital technology in recent years has significantly changed the way international supply chains (SCs) are structured, operated, and how well they perform. Among these transformations, blockchain has grown to be a major enabler for addressing continuous concerns with transparency, traceability, collaboration, and trust throughout supply chain networks. As companies seek more and more to raise supply chain performance and sustainability, scholarly investigations of blockchain-based supply chain management have grown dramatically. Descriptive and content analysis of co-occurrence key-words using Biblioshiny and VOSviewer software revealed the main research subjects and their linkages across 145 peer-reviewed Scopus-indexed publications spanning 2019–2026. Scientometrically speaking, this study examines this expanding body of research. The results point to two primary research directions: (i) how blockchain uptake influences organizational performance and supply chains, and (ii) how transparency, traceability, decision-making, and sustainable development enabled by blockchain are present in supply chains. The data analysis reveals that blockchain technology is a key and unifying feature that connects performance improvement with the goals of governance and sustainability. It emphasizes new ways for more investigation in blockchain-enabled supply chain performance and offers a systematic overview of the intellectual environment of blockchain research in supply chain management, as well as comprehensible in-sights on its thematic evolution.
Authors - Putu Putri Prawitasari, Shefali Saluja, Jonathan Jacob Paul Latupeirissa Abstract - Financial statements are vital for conveying a company's performance and financial health, yet fraudulent financial reporting remains a significant concern, especially involving fraud hexagon schemes. This study investigates the integration of advanced technologies to combat fraud hexagon schemes and improve auditing effectiveness in the digital era. Through a comprehensive literature review of academic sources from the Scopus database, this research identifies the limitations of traditional auditing in detecting complex fraud patterns. Findings reveal that the adoption of technology-based tools such as data analytics, artificial intelligence, machine learning, and blockchain enhances auditors’ ability to detect anomalies and suspicious activities more efficiently and accurately. Furthermore, combining these technologies with robust corporate governance and auditor expertise strengthens fraud prevention mechanisms. The study concludes that leveraging digital innovations within a holistic fraud detection framework significantly advances audit quality and fraud mitigation strategies in contemporary financial environments.
