Authors - Reepu Abstract - This paper presents a hybrid diagnostic approach for an engine air-path benchmark characterised by environmental variability, limited labelled faults, and the need for reliable online decisions. The proposed method combines physics-guided residual features with datadriven temporal representation learning. Residuals derived from grey-box relations capture physically meaningful deviations, while a lightweight encoder extracts temporal patterns across operating regimes. To enhance robustness under changing ambient conditions, the model is explicitly conditioned on measured environmental variables and trained to favour stable representations across sessions. An open-set decision policy with calibrated rejection is incorporated to reduce misclassification when encountering unseen fault magnitudes or insufficient evidence. The method is evaluated under the official benchmark protocol using online processing constraints and standard metrics, including false alarm rate, detection rate, isolation rate, detection delay, and computational cost. Results show improved reliability compared to competitive baselines, with lower false alarms, higher detection and isolation performance, and stable behaviour across sessions. The approach remains computationally efficient and suitable for real-time deployment in practical diagnostic pipelines.
Authors - Zala Bhargavi Harshadbhai, Priyank D. Doshi Abstract - Brain tumor classification using MRI is very important for early diagnosis. While convolutional neural networks (CNNs) showed strong performance in medical image analysis, but transformer-based architectures have recently gained popularity because of their ability to model long-range spatial dependencies through self-attention mechanisms. Our work lines up two such models - Vision Transformer and Swin Transformer to see how each handles tumor spot-ting in brain MRIs from the BRISC2025 collection. Same training setup applied to both keep things balanced and evaluated on the official test split for ensuring fairness. The official test set showed that both ViT (99.17 ± 0.26%) and Swin (99.27 ± 0.13%) have nearly identical predictive performance. Despite similar outcomes, their inner workings differ sharply behind the scenes. Swin Trans-former have approximately 40% and inference cost by nearly 50% compared to ViT while maintaining similar accuracy. The study provides insights into the performance and efficiency of trade-offs between global and hierarchical trans-former architectures in medical imaging applications.
Authors - Eduardo J. Lopez, Angelin Y. Alarcon, Marco Riofrio-Morales, Jose E. Naranjo Abstract - Higher education institutions often face challenges with fragmented student services and the reliance on manual workflows. Although Large Language Models (LLMs) present opportunities for service integration, their application in administrative contexts introduces specific risks, notably “transactional hallucinations” and the potential for unauthorized system actions. To explore potential mitigations for these challenges, this paper presents SUEMas as a proposed alternative: a configuration-driven, multi-agent ecosystem designed to help regulate LLM interactions within university domains. The proposed framework implements a Dynamic Tool Registry aimed at enforcing phase-aware tool exposure, alongside a Closed-World Action Gating mechanism intended to restrict sensitive operations to verified session candidates. Initial evaluations of this proposal indicate that SUEMas can support consistent policy enforcement, achieving high recall in RAG-based tasks under test conditions. Furthermore, the system maintained strong multi-turn coherence while keeping latency low, suggesting that structured security governance might practically coexist with conversational flexibility.
Authors - Surya Anugrah, Dwi Handarini, Eka Septariana Puspa, Windy Permata Suyono, Sabo Hermawan, Irima Rahmadani, Nazwa Febriyani Abstract - This paper presents the design, modelling, fabrication flow and analysis of multi-functional photonic crystal (PhC) nano-cavity sensors integrated with cantilever beams and diaphragms on a Silicon-On- Insulator (SOI) platform. The device architecture leverages defect-based two-dimensional PhC nano-cavities to obtain high quality (Q) factors and small mode volumes, while mechanically compliant structures transduce force and pressure into measurable optical resonance shifts. Biochemical and chemical detection is achieved via refractive-index based transduction and temperature sensing via thermo-optic effects. A machinelearning (ML)-assisted calibration and sensitivity enhancement framework is proposed to improve resolution and compensate for fabrication tolerances. Fi-nite-difference time-domain (FDTD) optical simulations and finite-element method (FEM) mechanical simulations validate device performance. Noise analysis, limit-of-detection (LOD) calculations, and comparison against state-of-the-art devices are provided. The architecture is CMOS-compatible and suitable for lab-on-chip photonic sensing applications.
Authors - Raina Thakkar Abstract - This work investigates the Evolutionary Matrix Factorization (EMF) model proposed in Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming. The EMF model employs genetic programming to optimize the matrix product function used in traditional Matrix Factorization recommender systems. The primary objective of this project is to develop a GP-based matrix factorization model that outperforms EMF in prediction accuracy. To facilitate comparison, we first reproduce the EMF model’s results using standardized metrics. Subsequently, we design and implement a custom data structure for GP, along with the full pipeline for reproducible model execution. Finally, we analyze the performance of our proposed model and compare it against EMF, demonstrating its improvements in prediction precision.
Authors - Srikumar Nayak Abstract - Anti–money laundering (AML) monitoring is difficult because suspicious behavior is rarely a single abnormal transaction; it is usually a short sequence of linked transfers across many entities. Standard tabular models miss these links and often produce alerts that are hard to justify during review. To address this, we propose GraphAML-X, a practical pipeline that turns raw transaction logs into a knowledge graph and produces case-level evidence for analysts. The main issue we target is fragmented identity (the same actor appearing under noisy identifiers) and weak case explanations (high scores without clear paths or rule triggers). GraphAML-X first performs entity resolution to merge duplicate accounts and identifiers using rules plus a learned match score, so the graph represents real actors. It then learns temporal graph embeddings from the timeordered transaction network to capture multi-hop laundering patterns such as rapid circulation and hub–spoke behavior. Finally, it combines graph risk with rule-hybrid case reasoning: regulatory red-flag rules propose candidate alerts, and the graph model ranks them while emitting audit-ready evidence (top subgraph paths, key neighbors, and triggered rules) and alert-volume control via a calibrated threshold. Using the Micro-AmlSim dataset, GraphAML-X achieves an AUC-ROC of 0.982 and an AUC-PR of 0.741, improving the strongest baseline GNN by +0.034 AUC-PR. At a fixed alert rate of 1% of transactions, it attains 0.686 recall while reducing false alerts by 18.9% compared to rule-only screening. These results show that GraphAML-X can improve detection while producing reviewable and policy-aligned AML cases.
Authors - Nguyen Ngoc Dung, Doan Van Thang Abstract - Memory encryption is a key security requirement for modern computing systems, addressing vulnerabilities between CPUs and main memory. Traditional storage encryption is insufficient for protecting volatile data in RAM, which remains exposed to bus sniffing, cold boot attacks, and side-channel exploits. This paper therefore systematically reviews memory encryption techniques focused on hardware-based solutions like Intel Total Memory Encryption (TME), Multi-Key TME, and AMD Secure Memory Encryption, which provide robust protection while minimising performance overhead. The paper also explores integrity protection via Merkle trees and side-channel countermeasures against Differential Power Analysis and Simple Power Analysis attacks. Additionally, granular memory encryption methods for multi-tenant environments are discussed, highlighting their role in isolating sensitive data across security domains. By examining security guarantees and performance trade-offs, we emphasise the necessity of efficient memory encryption to safeguard against evolving threats targeting the CPU-memory interface, providing hardware engineers a foundation for ensuring data confidentiality and integrity.
Authors - Chaitrasree S, Srinidhi G A Abstract - The Research will shows how app-based omnichannel ICT-enabled marketing shapes customer engagement and service loyalty in the culinary hospitality industry within an urban emerging-market context. Drawing on an ICT-centered and service-systems perspective, the research conceptualizes mobile applications as integration hubs that coordinate multiple service modes—delivery, dine-in, takeaway, and drive-thru—into a unified customer experience. The study approach was using a quantitative design with a cross-sectional survey of 150 chain-restaurant mobile app users in Jakarta. Structural Equation Modeling (PLS-SEM) were used to analyze the data. The results shows that app-based omnichannel ICT-enabled marketing has a positive and significant effect on customer engagement and service loyalty. Customer engagement also demonstrates a positive effect on service loyalty and mediates the relationship between omnichannel ICT-enabled marketing and loyalty, partially. These findings suggest that perceived ICT integration quality, reflected through consistency, seamlessness, and coordination across service modes, plays a pivotal role in translating technology-enabled service design into relational outcomes. This study contributes to the ICT literature specially in hospitality by extending omnichannel research beyond a marketing-centric perspective and highlighting the strategic role of integrated mobile app infrastructures in high-frequency culinary service environments. Based on a managerial standpoint, the results emphasize the importance of treating mobile applications as core service platforms that support engagement-driven loyalty in chain-restaurant operations.
Authors - Pei-Yi Hao Abstract - Digital transformation is reshaping education systems worldwide, with significant implications for rural and underserved regions. In India, initiatives aligned with the National Education Policy (2020) have promoted online learning platforms, digital classrooms, and technology-enabled teacher training to enhance access, equity, and quality in education. However, rural schools continue to face structural challenges such as limited infrastructure, digital divides, and inadequate teacher preparedness, which influence the effectiveness of digital integration.This conceptual paper examines the transformation of rural education in India from traditional teacher-centred classrooms to digitally enabled learning ecosystems. Grounded in Constructivist Learning Theory, the Technology Acceptance Model (TAM), Diffusion of Innovation Theory, and the TPACK framework, the study proposes an integrated conceptual model linking digital infrastructure, pedagogical innovation, and teacher competence to improved access, engagement, and learning outcomes. The paper argues that digital transformation represents a systemic pedagogical and institutional reform rather than a mere technological shift. Its success depends on inclusive infrastructure development, sustained teacher capacity building, and context-sensitive implementation in rural settings.
Authors - Aryan Dholi and Malathi P Abstract - Smart contract vulnerabilities have continuously been a major source of threat to blockchain security, with billions of dollars being accounted for losses every year. This review paper delves into over 15 different detection methods utilizing static analysis, dynamic monitoring, machine learning, and hybrid approaches. Sustainability metrics such as the Green Detection Score and the Energy Efficiency Index are first proposed by us to gauge the environmental cost in relation to the accuracy. From our review of 28 papers, we conducted research studies to points out a significant discovery: transformer models reach 0.91 F1-score but use 1,475× more energy than static analyzers. Hybrid approaches present a viable compromise with 0.89 F1-score and 62% energy savings. We thus offer deployment advice, sustainable architecture templates, and a 2030 roadmap for green blockchain security.
