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.
