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.
