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.
Authors - Sunkyo Jeong, Yongbeom Park Abstract - The brisk developments of advanced deep learning techniques have led to diverse applications of it in different sectors, including healthcare sec tor. Breast cancer is one of the most common and deadly cancer amongst women and the success percentage of the treatment depends heavily on the stage at which the detection happens. This field opens gateway of deep learn ing application in detecting of breast cancer tumour type at an early stage. In this research paper, model and the application of a CNN based early breast cancer detection algorithm is proposed. In this approach, the Wisconsin Hos pital Breast Cancer Database is considered to train the model and test the accu racy of the model. This study shows promising results by concluding Convolu tional neural network-based model is 98.24 % accurate which this better than previous models. Moreover, this paper proves that such application of deep learning techniques holds huge promise for bettering healthcare sector.
Authors - Francklin Rivas, Thanh Tran, Jorge J Roman, Aysha Al Ketbi Abstract - The rapid proliferation of GenAI has transformed the phishing threat landscape into one characterized by realistic, tailored, and scalable attacks on text-based, web-based, and multimodal platforms. The success rate of social engineering attacks has increased significantly due to advances in large language models, deep-fake technology, and automated phishing-as-a-service offerings. Despite notable advances in current phishing detection technologies, many oper ate as black-box systems and struggle to detect AI-generated, context-specific, zero-day phishing attempts. The resulting lack of transparency, combined with poor realistic dataset quality and inadequate resilience against adaptive threats, has further amplified trust concerns. This survey presents a comprehensive over view of the detection strategies based on semantic, structural, and multi-quality feature representations, with a concise review of the models of GenAI-enabled phishing attacks. Various detection methodologies, including machine learning, deep learning, and fusion-based techniques, are reviewed, with an emphasis on explainable AI methods like SHAP, LIME, attention visualization, and Grad CAM, which provide more understandable interpretations of AI-driven deci sions. To facilitate transparent, reliable, and trustworthy phishing defenses that make use of GenAI, the survey concludes with discussions of response mecha nisms, privacy-preserving learning strategies, and governance issues, with open questions and potential directions for future research.
Authors - Malika Acharya, Ankit Jain Abstract - Since Lin and Zadeh proposed granular computing in 1996, an increasing number of researchers have begun to study information granularity, which simulates human cognition to handle complex problems. Granular computing advocates observing and analyzing the same problem at different levels of granularity. Coarser granularity leads to more efficient learning processes and stronger robustness to noise, whereas finer granularity is able to capture more detailed characteristics of objects. Selecting appropriate granularity according to different application scenarios can therefore solve practical problems more effectively. This paper proposes a novel support vector regression algorithm via granular computing approach, which constructs regression models using granular balls generated from the dataset as inputs rather than individual data points. First, we analyze the geometric relationship between classification tasks and regression tasks. Then, based on this geometric relationship, we employ twin support vector classification algorithm via granular computing approach to address regression problems.
Authors - Dang Trong Hop, Than Ngoc Thien Abstract - Medical image classification is of immense importance in the context of early-stage diagnosis of various neurological diseases, including Alzheimer’s disease and brain tumours. However, it remains infeasible for conventional deep learning architectures to efficiently encode frequency domain information and long-range spatial dependencies found in medical images. In this paper, a novel Hybrid Wavelet CNN Vision Trans-former, coupled with Explainable Artificial Intelligence, has been proposed for efficient and accurate medical image classification. In the proposed architecture, the application of discrete wavelet transform, convolutional neural networks, and Vision transformers for medical image classification has been presented. Additionally, explainability aspects have been addressed using the Grad-CAM technique. The proposed model was experimented with using two datasets: one for Alzheimer’s disease MRI and another for brain tumours. The experimental results reveal that the proposed deep learning architecture achieves an accuracy of 96.8%, precision of 0.96, and recall of 0.97, F1score of 0.97 for the brain tumours dataset, which beats conventional CNN, vision Transformer, and Wavelet CNN architectures. The integration of explainable AI further enhances model transparency and clinical reliability, making the proposed framework suitable for real-world medical diagnostic applications.
Authors - Neha Aggarwal, Rajiv Singh, Swati Nigam Abstract - One advantage of using Large Language Models (LLMs) is the automation of tasks and the analysis of information. Engineering drawings, on the other hand, are standardized representations of products; they document their dimensions and geometries. Users can utilize them for manufacturing parts, assembly guides, and engineering analysis, among other uses. This article aims to 1) evaluate whether an LLM is capable of interpreting engineering drawings, 2) identify how it interprets them, as it may use a standard on which the generation of these drawings or the interpretation of images is based, and 3) determine if users as students can employ LLMs as a guide to interpret drawings. The results showed that the user requesting an interpretation of an engineering drawing must be familiar with the field, as the LLM sometimes fails to extract the correct in-formation from a drawing; furthermore, any detail in the drawing can confuse the LLM. Once the LLM extracts the correct information from the drawing, it can use it to generate CNC code to machine a part, predict its behavior using a neural network, or perform engineering analysis, to name just a few examples.
Authors - Muhammad Elfata Rasyid Hammuda, Irmawan Rahyadi Abstract - Inventory management in warehouse environments frequently faces recurring limitations related to material searching, manual record updating, and control inconsistencies, which increase delays and disrupt operational continuity. This study develops an intelligent stock-tracking system based on weight sensing using load cells, signal conditioning through the HX711 module, and processing via an ESP32 microcontroller, with real-time data transmission using MQTT and visualization through a Unity-based mobile application with augmented reality (AR) support. The study included the diagnosis of the current process through process mapping and ABC analysis to prioritize critical consumables, the design of the system architecture, the implementation of the IoT prototype and its integration with the AR interface, and performance evaluation through time comparisons, before-and-after record analysis, and administration of the System Usability Scale (SUS) questionnaire. Findings indicate operational improvements in efficiency and record consistency, along with a favorable perceived usability among the evaluators.
Authors - Ikram Ahamed Mohamed, Hafiz Abdulla, Mohaideen Mohamed Mohabilasha, Fiyaz Ahmed, Pankaj Chandre, Rohini Bhosale Abstract - The Electric vehicles (EVs) are one way to help the environment by reducing carbon emissions and aiming for the net zero supply chain in logistics. This paper is a complete readiness assessment frame work for the green logistics practices on using electrics vehicles. The method categorizes preparation factors in five key categories, i.e., strategic and governance commitment, technological and infrastructure capability, financial and Investment Capacity, operational and human resource readiness, and environmental and policy alignment. It is pro-posed to use a multi-criteria decision-making framework to analyze the relation-ship between these variables and quantify the level of organizational readiness by using language evaluation scales converted to fuzzy numbers. The study con-tributes to the theoretical knowledge of creating a unified property of the various readiness criteria in a unitary evaluation framework and synthesises empirical methods with a measurable metric of the uptake of the electric vehicle in the logistics networks. Practically, the framework assists logistics managers, legislators, and sustainability planners in identifying issues, establishing priorities on investments, and accelerating the transition to the low-carbon transportation systems. The findings support the concept of fact-based decision-making that can lead to a green logistics revolution which can expand and remain sustainable.
Authors - Sabid Rahman, Sadah Anjum Shanto, Segufta Nasrin Tamanna, Zurin Alam Aongon, Md. Soadul Islam, Nasirul Islam Abstract - This research suggests a system for the real-time detection of road hazards, specifically potholes, cracks, and open manholes, using deep learning and image processing, and pinpointing the exact geographical location of the defects. These defects can cause road accidents, vehicle damage, traffic congestion, and other inconveniences. To solve these, a YOLOv8m model integrated with the CBAM module was developed for enhanced feature attention and trained on a custom dataset of 2,400 road images containing the three hazard classes. The model achieved a mAP@50 of 82.2%, and the individual class performance scores are 72.2% for potholes, 81.0% for cracks, and 93.3% for open manholes, and a recall of 76.4%, demonstrating reliable performance under varied conditions. An OCR module was integrated with the CBAM-YOLOv8 model to extract GPS coordinates from user-captured photos and videos, and an interactive mapping interface was designed to show and report the exact locations of detected hazards for timely action by authorities.
Authors - Mandar K Mokashi, Sonali P Bhoite, Vishal Nayakwadi, Atul P Kulkarni, Parikshit Mahalle, Pankaj Chandre Abstract - The purpose of this study is to examine the impact of DAT, AIR and ICM to-ward DAM in SMEs and at the same time determine the moderating effect of in-ternal control maturity. Drawing on the technology–organization–environment (TOE) framework and Resource-Based View (RBV), this study utilises a quantitative approach by employing Partial Least Squares Structural Equation Model-ling (PLS-SEM). Data was collected through structured questionnaires sent out to SMEs that have begun using digital audit tools. The relationships with DAM of DAT, AIR and ICM presented evidence on the individual impact on DAM indicating that technological readiness, organizational willingness to accept AI solutions successfully and mature internal controls are vital. Nevertheless, internal control maturity is not conducive to stronger.
Authors - Nguyen Thi Hoi, Dao Thi Huong Abstract - This аrticle exаmines the impаct of аccelerаted digitаlizаtion of the Uzbek econo-my on improving the effectiveness of pаrticipаtory budgeting. Reforms аimed аt creаting а "New Uzbekistаn" hаve elevаted pаrticipаtory budgeting to а key tool for citizen engаgement аnd increаsing the trаnspаrency of budget аllocаtion. However, the complexity аnd multifаceted nаture of this work аnd the further de-velopment of pаrticipаtory budgeting require the constаnt аdаptаtion of proce-dures, tools, аnd mаnаgement аpproаches to the emerging digitаl reаlities. The purpose of this study is to substаntiаte the need to trаnsform the pаrticipаtory budgeting mechаnism using аrtificiаl intelligence technologies аnd propose prаcticаl solutions to improve the efficiency, fаirness, аnd sustаinаbility of this process. Bаsed on аn аnаlysis of the regulаtory frаmework аnd current prаctices in implementing pаrticipаtory budgeting projects in the Republic of Uzbekistаn, key chаllenges limiting the potentiаl of pаrticipаtory budgeting hаve been identi-fied, including: low digitаl literаcy аmong some of the populаtion, limited func-tionаlity of digitаl plаtforms, insufficient аutomаtion of project evаluаtion аnd se-lection processes, weаk integrаtion with government informаtion systems, аnd а lаck of аnаlyticаl tools for forecаsting sociаl performаnce. The study proposes аreаs for improving the mechаnism, including expаnding the functionаlity of the Open Budget plаtform, implementing аrtificiаl intelligence, big dаtа, аnd digitаl plаtforms to increаse the openness аnd effectiveness of аnаlyticаl dаtа, аs well аs using elements of finаnciаl modeling to forecаst future stаte budget expenditures аnd develop multifаctor criteriа for аssessing the effec-tiveness of pаrticipаtory budgeting projects. The prаcticаl significаnce of the аrti-cle lies in the development of а comprehensive аpproаch to modernizing pаr-ticipаtory budgeting, which contributes to increаsing citizen trust in government institutions, optimizing the use of budgetаry resources, аnd аchieving the goаls of the Digitаl Uzbekistаn 2030 strаtegy. The results obtаined cаn be used by gov-ernment аgencies, locаl governments, аnd developers of digitаl solutions in public finаnce.
Authors - Gargi P. Lad, Abhijeet R. Raipurkar Abstract - Remote sensing imagery plays an important role in applications such as environmental monitoring, disaster management, urban planning and agricultural analysis. However, the spatial resolution of such imagery is often limited by sensor constraints, revisit frequency and acquisition cost. To address this challenge, this paper presents RCAN-RS, an enhanced Residual Channel Attention Network for remote sensing image super-resolution. The proposed model extends the RCAN framework through three targeted modifications: a dual-pooling channel attention mechanism, a spectral attention module and an edge enhancement module. These components are designed to improve detail reconstruction while preserving inter-channel consistency and sharp structural boundaries in remote sensing imagery. The model was trained and evaluated on the DOTA dataset un-der a 2× super-resolution setting from 256 × 256 to 512 × 512 pixels. Quantitative evaluation using both conventional image-quality metrics and remote-sensing-oriented measures shows that RCAN-RS achieves a mean PSNR of 34.42 dB, SSIM of 0.9398, Edge Preservation Index of 0.9524, ERGAS of 6.68 and UQI of 0.9846 on the test set. These results demonstrate the effectiveness of integrating attention-guided and edge-aware mechanisms for remote sensing image super-resolution.
Authors - A. Harshavardhan, Krishna Anirudh Gunturi, Rikhil Rao Janagama, Navaneeth Reddy Nalla, N V Abhijeet Mukund, Avire Kaushik Abstract - The Age classification is a critical task in computer vision with widespread applications in fields such as healthcare, security, and autonomous systems. This project presents a deep learning approach for multi-class image classification using feature extraction with the EfficientNetB3 architecture. The model was trained on a dataset that has images labeled according to different age groups, where the images were preprocessed, normalized, and sized to a steady resolution appropriate for EfficientNetB3 input. Data handling was simplified using pandas and ImageDataGenerator, ensuring proper splitting into training, validation, and test sets, with suitable shuffling and augmentation strategies applied to improve generalization. This model influences EfficientNetB3 as a feature extractor, combined with a custom classification head containing Batch Normalization, L1/L2 regularization with Dense layers, Dropout, and a SoftMax output layer. This model was trained using the Adamax optimizer and categorical cross-entropy loss, with performance monitored through accuracy and loss metrics over multiple epochs. Training history was seen to identify the epochs corresponding to the best validation performance. Assessment of the model on the test data-set includes loss, accuracy, confusion matrix, and a comprehensive classification report with precision, recall, and F1-score for each set. The results demonstrate that transfer learning, combined with careful preprocessing and regularization, can achieve robust performance in image classification tasks. This pipeline provides a producible and scalable framework for multi-class image classification and can be extended to other datasets and real-world applications requiring automatic image recognition.
Authors - M Purushotham, Ch Sandeep Kumar, G Jayendra Kumar, Tummalapalli Venkata Jayanth, Akula Manoj Kumar, Purna Saradhi Chinthapalli. Abstract - Wireless Body Area Network (WBAN) is an innovative network system, which consists of numerous wearable or implantable devices that monitors and transmits the physio-logical data. Designing a wearable patch antenna for WBAN is a challenging, because human body is a lossy medium which can absorb and scatter electromagnetic waves, thus leads to degrade of antenna performance. In this paper, the proposed antenna is a wearable 6G microstrip patch antenna, which is very flexible and light with a flat surface, unlike traditional counterparts and these can be placed directly on a human body and are comfortable to wear for long periods. The antenna is designed, simulated, and analyzed using Computer Simulated Technology (CST) studio suite and the design consists of microstrip patch, substrate, feedline, and ground plane. The simulation parameters such as S-Parameter, Voltage Standing Wave Ration (VSWR) and far field radiation are calculated. The results of proposed wearable 6G patch antennas with varying THz frequencies shows, it is very appropriate for WBAN at 2.56THz and 4THz.
Authors - Arathi B K, Rishikeshwar Kumaresan, S Kanagalakshmi, Sathish Kumar S Abstract - Single magnetic resonance imaging (MRI) super‑resolution remains challenging due to the substantial heterogeneity between low‑ and high‑resolution (LR-SR) inputs. This paper presents an ablation analysis of three convolutional neural‑network architectures, namely Conv2D, fully convolutional network (FCN), and U‑Net, combined with four activation functions (Linear, Tanh, ReLU, Leaky ReLU). LR inputs are generated through mean- and max‑pooling with a 6×6 scale factor, enabling evaluation under both smooth and heterogeneous degradation conditions. The results show that U‑Net achieves the highest reconstruction accuracy, reducing MAE by 8% relative to FCN and 10% relative to Conv2D. ReLU-based activations provide stable convergence for shallow models, while the U-Net remains robust across all activation functions. These findings emphasise the importance of selecting appropriate architectures and activation functions to achieve robust and high‑quality MRI super‑resolution in real‑world applications.
Authors - Susmita Adhikary, Aswin Babu VP, Dinesh U, Harish M, Karthik M, Gokul A Abstract - Urban metro rail systems are the key to urban sustainable mobility; however, in spite of the developed technologies, projects regularly experience delays and contractual disputes. These perceived challenges are highly attributed by prior scholarship to matters of the execution phase and restricted illumination is given on the institutional circumstances that form system performance in ICT intensive infrastructure. This paper examines procurement strategy as a govern ance tool that affects the results of digital system integration and sustainability in Indian metro rail projects. Based on statutory performance audit reports and com parative case studies, the analysis indicates that fragmented procurement arrange ments fragment the integration functions to several contracts, leading to coordi nation failure, delayed commissioning, and high claims. Instead, the more coor dinated procurement models with consolidated interdependent systems and de fined integration roles have a better coordination structure and predictable deliv ery. The results indicate that the problem of metro project integration is more of an institutional than a technological problem. This research study adds to the body of knowledge on infrastructure governance by noting the design of procure ment to be one of the determinatives in the realization of effective and sustainable urban transit outcomes.
Authors - Mousami Turuk, Anirudha Page, Tina Chugera, Gauri Desale, Mrunmayee Kulkarni Abstract - Unstructured vehicle traffic (i.e. those containing multiple users such as automobile drivers, pedestrians, cyclists, and even animals) creates a significant challenge for road safety. This work presents the development of a real-time road risk assessment (RRA) system for analyzing dashcam video that combines several computer vision techniques: object detection, semantic segmentation, multi-object tracking, and alert classification, into a unified, integrated processing pipeline. Object detection and multi-object tracking are accomplished using the YOLOv8m and ByteTrack with Kalman Filter algorithms. Additionally, semantic segmentation of the road scene is achieved using a SegFormer-B2. Finally, a segmentation-assisted fusion filter and perspective-aware danger zone are applied (to define each point in the field of view as belonging to a zone with certain levels of risk). The Road Intrusion Risk Score (RIRS) is a composite score that quantifies the severity of intrusion accumulated over time, and provides graduated alert levels. Testing of the system on COCO val2017 and four dashcam videos produced reliable object detections with significantly fewer false positives and very close to real-time performance, demonstrating the potential of the system to improve driver assistance systems in unstructured road environments.
Authors - Mousami Turuk, Harshwardhan Sawant, Jatin Bhate, Yash Gosavi, Sakshi Hosamani Abstract - The global tourism industry has strongly recovered in the post-pandemic era, with border tourism becoming an important platform for regional economic cooperation and cultural exchange. Nong Khai, Thailand, with its geographic advantages and its role as a cross-border hub, has the potential to transform from a transit point into a cultural hub. However, its tourism destination image has been constrained by its perception as a transit point. This study, based on tourism destination image theory and the cognitive-affective frame-work, integrates online review text analysis and semi-structured interviews to analyze the cognitive, emotional, and overall dimensions of Nong Khai's tour-ism image. The results show that Nong Khai’s tourism image reflects a triad of culture, ecology, and cross-border relations. Buddhist culture and the Mekong River are key attractions, but visitors generally have short stays and low spending. 52% of cross-border tourists view it as a transit point to Vientiane. Positive feedback accounts for 65.17%, largely driven by cultural experiences and local service friendliness; negative feedback accounts for 8.86%, focusing on inefficient transportation, poor facility maintenance, and weak cultural symbolism. Based on these findings, this paper suggests four optimization strategies: enhancing the Buddhist cultural experience, improving service systems, strengthening digital marketing, and promoting cross-border collaboration. This study provides empirical evidence for Nong Khai’s efforts to overcome the transit point challenge and offers a model for ASEAN border cities to build differentiated tourism images and sustainable development paths.
Authors - CH VENKATA NARAYANA, G VAMSI KRISHNA, K SIDDARTHA, G MADHU Abstract - Software-Defined Networking (SDN) offers central control and management of traffic flow, which is currently facing increasing security threats from ever-changing and voluminous attacks. The traditional signature-based intrusion detection system is not capable of identifying unknown attacks in real time. The proposed paper suggests a hybrid model for intrusion detection based on CNN and Transformer architectures for Software-Defined Networking. The proposed model will be tested and validated on a real-time testbed based on the Mininet network simulator, Open vSwitch, and Ryu Controller. The proposed model will be trained on the InSDN dataset and will utilize the SHAP technique for model interpretation and will be capable of automatic mitigation of attacks by blocking malicious traffic.
Authors - Abhishek Sawant, Manas Bhansali, Naman Shah, Mandar Kakade Abstract - The integration of Traditional Medicine (TM) into global healthcare standards faces challenges due to the gap between clinician-entered free text and standardized terminologies like ICD-11. In India, AYUSH providers must document diagnoses using local terms while also supporting dual coding across NAMASTE, ICD-11 Traditional Medicine Module 2 (TM2), and ICD-11 Biomedicine. However, most EMRs do not provide unified support for these coding systems. This paper proposes a human-centric, AI-Assisted Terminology Microservice that standardizes diagnosis entry and automates the mapping between these terminologies. The system has a hybrid architecture. A Spring Boot orchestration layer manages the terminology graph and the EMR-facing APIs. Meanwhile, a Python-based machine learning service handles semantic matching from free-text descriptions to concept codes. It uses TF-IDF features and a Linear Support Vector Machine(SVM) classifier that is trained on a Silver Standard Dataset of approximately 3,250 synthetic clinical descriptions covering 75 common health issues,morbidities, with conservative lexical augmentation applied during training to improve robustness. A safety-critical fallback mechanism was designed, which detects predictions with confidence below θ = 0.45 and directs out-ofdistribution inputs to manual search workflows. This ensures a human-in-the-loop model and makes it safe to use in clinical environments. The microservice provides APIs that are EMR-friendly and produce dual-coded FHIR format diagnosis resources. This setup ensures safety along with scalability and interoperability so that it can be deployed in diverse healthcare environment.
Authors - Atharva Sachan, Aryan Gupta, Aditya Varshney, Abhishek Sharma, Surendra Kumar Keshari, Veepin Kumar Abstract - Mobile Health (mHealth) has been regarded as a potentially transform-ative element for enhancing health service delivery in low-income nations. The effective integration of technology relies on ongoing usage rather than just initial acceptance. While the body of literature on factors influencing continued mHealth use is expanding, post-adoption expectations are proposed as indicators of the success or failure of mHealth implementation. There is limited research on how community health workers' post-adoption expectations influence their inten-tions to persist in using mHealth in developing regions. Consequently, this study explores the effect of post-adoption expectations on satisfaction and ongoing us-age behaviour regarding mHealth among community health workers in Malawi, which represents a developing country context. The research introduces a frame-work that builds upon the expectation confirmation model and incorporates ele-ments from the updated information success model. A mixed-methods conver-gent design was utilised for the study. Data were collected through surveys and semi-structured interviews with community health workers who utilise Cstock. Cstock is an mHealth application that facilitates the ordering of medical supplies via text message. The findings generally support the notion that post-usage use-fulness, along with information quality, system quality, and service quality, pos-itively influences community health workers’ satisfaction and their intention to continue using the Cstock application. The results indicate that the ongoing usage behaviour of mHealth among community health workers is shaped not solely by behavioural expectation beliefs (i.e., post-usage usefulness) but also by objective expectation beliefs, including system quality, service quality, and information quality. Therefore, these findings provide valuable insights to policymakers, practitioners, mHealth developers, and other relevant parties regarding the post-user expectations essential for maintaining future mHealth solutions in develop-ing countries, particularly in Malawi.
