Authors - Viet Anh DUONG, Hai Phong BUI, Van Son NGUYEN Abstract - This article presents a neuro-symbolic modelling approach grounded in qualitative data collected from 25 sports clubs located in R´eunion. The study develops a methodological chain linking structured semantic extraction, ontological formalisation in OWL, and agent-based simulation implemented in NetLogo. Rather than modifying structural scenarios across experiments, the design introduces two contrasting organisational sensitivity profiles derived from field observations: a damped profile and a high-gain profile. The structural configurations remain identical between profiles; only the coefficients of the commitment update function vary, ensuring strict experimental comparability. Results indicate that identical structural conditions produce differentiated collective trajectories depending on internal sensitivity parameters. In highgain configurations, dominance-weighted interactions increase variance and generate polarised engagement distributions, whereas damped configurations maintain relative stability across scenarios. These findings suggest that modelling organisational sensitivity parameters is critical for understanding the robustness of digitally mediated collaboration in volunteer-based organisations.
Authors - Allezandra A. Adriano, Joshua Basile Mhar L. Austria, Benjamin L. Carnate, Xamantha Angelique E. Ruiz, Wilben Christie R. Pagtaconan Abstract - Plant diseases due to various pathogens can cause significant loss in yield and productivity. The classification of these diseases is necessary to prevent damage to crops. For classification, a large number of Machine learning and deep learning algorithms have been developed. In this research, five classes of plant leaves and a further fifteen different diseases of these plants (three subcategories for each class) are used for classification. In the proposed methodology, we have used three pre-trained models, namely, ResNet 152v2, InceptionResNetV2, and mGoogleNet, and a custom-built model. This research has used three basic steps to classify the disease categories, namely image preprocessing, image segmentation, and feature extraction. Fifteen thousand plant leaf images have been collect-ed from the online available Kaggle PlantVillage dataset. This data is present in a JPG file format. After the class label distribution of the dataset, the dataset is first trained and then tested on these deep learning models. The label distribution is done in such a way that each of these fifteen categories has 80% training images and 20% validation images. We have used different performance measures, namely, precision, recall, F1-score, and support, to calculate the accuracy. The obtained validation accuracy of ResNet152V2 is 97%, GoogleNet is 96%, Incep-tionResNetV2 is 93%, and a custom-built model is 99%. These results show that the custom-built model has attained the highest accuracy. These models can also be used to build a recommender system framework for the recommendation of fertilizers in the future.
Authors - E. Praveen Kumar, Shankar Lingam. M Abstract - Quantum computers are a major threat to the existing encryption mechanisms. In terms of security, the traditional encryption algorithm depends on complex problems like discrete logarithm as well as factorization of integer. Shor’s algorithm is believed to break the current Public Key Encryption algorithms such as Advanced Encryption Standard (AES). Therefore, several research are carried out in the area of PQC (Post Quantum Cryptography). PQC are based on very complex mathematical problems like Learning with error (LWE) which are robust against quantum computers. The National Institute of Standard and Technology (NIST) has initiated several rounds of standardization process for PQC algorithms, among which NTRU, SABER, CRYSTAL-KYBER are the leading candidates. CRYSTALS-KYBER (Kyber) is the first chosen PQC for standardization. This works explores the recent development in Crystals Kyber implementation and its optimization. Researchers can approach for new research challenges and target for improvement thereby increasing efficiency.
Authors - An Doan Van, Dong Nguyen Doan, Quynh Tran Duc, Thuan Nguyen Quang, Bao Phan Gia, Hieu Doan Minh, Van Khanh Doan Abstract - Performance bottlenecks in Python programs arise from a wide variety of sources, and no single technique reliably catches them all. This paper proposes CodeForge, a sequential three-stage optimization system that unites deterministic Abstract Syntax Tree (AST) inspection, CodeBERT embedding-based retrieval, and Gemini LLM-driven rewriting into one end-to-end pipeline. A rule engine in the first stage pinpoints well-known structural problems; a neural similarity search in the second stage captures harder-to-spot variants; and a Gemini LLM in the third stage performs the actual rewrite, guided by a structured hint block assembled from both preceding stages. Before any result is returned, a configurable validator rejects changes that fail minimum speedup, memory, or complexity criteria. Alongside each accepted optimization, a composite confidence score and a plain-language rationale are produced. Tests on six representative Python patterns show that hint-guided LLM prompting raises successful detection from four to six out of six cases compared with unguided prompting, while the validation layer blocks every harmful transformation in the test suite. The system is available as a FastAPI REST service accepting both raw source text and uploaded .py files.
Authors - Jyotika R. Yadav, Arpit A. Jain Abstract - Internet of Things (IoT) with AI techniques help healthcare industry for patient monitoring and diagnosis. Wearable devices integrated with the Internet of Medical Things (IoMT) have transformed modern healthcare by enabling continuous, real-time monitoring of physiological parameters. The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), edge computing, and federated learning has further enhanced the reliability, privacy, and intelligence of such systems. Wearable devices like smart watch or smart sensors help doctors to monitor patient’s daily activities. However, these devices generate huge amount of data on day-to-day basis which makes analysis, monitoring, and diagnosis challenging. Machine Learning or Deep Learning models used for handling such large healthcare data. This survey consolidates and critically reviews recent research works to provide a holistic understanding of the current state-of-the-art in wearable AI-enabled healthcare. A detailed comparative analysis is provided to highlight similarities, differences, strengths, and limitations of existing approaches. Finally, key challenges and future research directions are discussed to guide the development of secure, scalable, and intelligent wearable healthcare solutions.
Authors - Shweta H. Jambukia, Pooja R. Makawana, Prapti G. Trivedi Abstract - This paper presents a case study on a High Voltage Jet (HVJ) electric boiler, focusing on current unbalance (CU) risk identification and mitigation us ing a combined data-analytics and Failure Mode and Effects Analysis (FMEA) framework. Power-quality assessment follows IEC 61000-4-30 for voltage un balance (VU), while CU interpretation refers to NEMA MG-1 and IEEE recom mendations. The proposed workflow integrates (i) instrument classification (Class A for voltage), (ii) time synchronization across logger/PLC/power-quality analyzer to avoid timestamp drift, and (iii) historian-based data pre-processing (outlier cleaning, scaling, and missing-data handling) prior to statistical analysis. Results show an average CU of 6.85% with a standard deviation of 0.48% and a maximum of 15.92%, indicating operational periods exceeding common industry limits. FMEA highlights electrode aging/damage, loose/corroded cable connec tions, and supply power-quality issues as the dominant contributors. Recom mended actions include online phase-current monitoring, improved water-chem istry and blowdown management, and control optimization of the VFD-driven boiler circulation pump (BCP).
Authors - Priyanka K, Vinay R K, Vansh Jain, Vinit Kulkarni Abstract - This study examines the influence of both demographic and natural factors on climate change risk perception in New Zealand. Using data from a nationally representative survey, the analysis applies exploratory factor analysis to construct a composite measure of risk perception, followed by correlation and regression modeling to evaluate the relative contribution of environmental exposure and human characteristics. The findings indicate that while natural factors such as temperature anomalies and extreme weather exposure significantly shape perceived risk, demographic variables including prior disaster experience, trust in scientific institutions, and media exposure exert a stronger overall influence. These results underscore the importance of incorporating social and behavioral dimensions into climate risk assessments and policy development to enhance public engagement and adaptive capacity.
Authors - Piyush Tewari, Rohit, Rujal Agarwal, Yanshi Sharma Abstract - Current Network Intrusion Detection Systems (NIDS) typically analyze traffic as independent tabular records, largely ignoring the relational and temporal dependencies inherent in real-world communications. This limitation is particularly critical for detecting botnets, which rely on coordinated, evolving interactions rather than isolated malicious packets. To address this, we propose a topology-aware framework that models network traffic as a sequence of dynamic communication graphs. Using the CICIDS2017 dataset, we construct sliding-window snapshots where IP addresses form nodes and flows form edges. A spatiotemporal graph neural network is employed to learn evolving structural representations, integrated with a novel learnable gated fusion mechanism that adaptively balances graph-based context with conventional flowlevel statistics. The model is optimized using a hybrid objective combining class-weighted cross-entropy and center loss to mitigate data imbalance. Experimental results demonstrate that the framework achieves improved performance on structural attacks, with botnet detection reaching an AUC of 0.999. Furthermore, the learned gating values reveal a strong model preference for topological features over static statistics, empirically validating that structural context is superior for identifying coordinated threats. These findings underscore the effectiveness of spatiotemporal modeling in enhancing the robustness and interpretability of next-generation NIDS.
Authors - Bikkam Hemanth Reddy, Allu Eswar Kaushik, Tiyyagura Mohit Reddy, Kuruboor Venkatesha Deepak, Bharathi D Abstract - Cloud cover generally limits the applicability of optical remote sensing images for tasks such as agriculture monitoring and disaster relief. Cloud removal is an inherently difficult problem because of the lack of spatial structures and spectral information. To effectively remove cloud contamination from SAR and optical images, we propose a speckle-aware global cross-attention network. The proposed SAR-optical cloud removal network architecture consists of a dual encoder with a global cross-attention mechanism that allows for effective cross-modal interactions. Additionally, a refining module and symmetric decoders improve the accuracy of the reconstructed image. Furthermore, we propose a speckle-aware gating mechanism to perform speckle filter adaptation. The experimental results affirm that our proposed network outperformed the baseline by increasing Peak Signal-to-Noise Ratio(PSNR) by +0.86 dB, Structural Similarity Index Measure(SSIM) by +0.142, and reducing the spectral distortion of the image. Additionally, we noticed a decrease in the Root Mean Square Error(RMSE) and Spectral Angle Mapper(SAM) values. This infers that selective SAR-Optical fusion with an adaptive noise-aware gating mechanism improves the accuracy of cloud-free optical images and optical remote sensing images.
Authors - Azamat Kasimov, Kholida Bekpolatovna Saidrasulova, Zebo Abduxalilovna Shomirova, Shoh-Jakhon Khamdаmov, Safiya Karimova, Dilshoda Akramova, Doniyor Niyozmetov Abstract - Inconsistent medication intake is a major issue, especially for elderly individuals and patients with memory problems [1]. The MediMitra: Voice Enabled Medicine Alert System seeks to tackle this problem by offering an automated, low-cost and user-friendly medication reminder solution. The system combines Raspberry Pi with Optical Character Recognition (OCR) technology to pull medicine names, dosage details and intake times directly from scanned prescriptions. This reduces manual input and user reliance. The information is stored in a central database and connected to a scheduler that sends timely voice alerts through smart speakers or Bluetooth devices. This ensures users receive reliable and easy-to-access reminders. The OCR module is designed for high accuracy in processing printed prescription images by using image preprocessing techniques like noise reduction and thresholding, which helps in effectively extracting key medication details [2]. The system focuses on accessibility, affordability and ease of use in home or clinical settings. Overall, MediMitra provides a useful technological solution to improve medication adherence and supports independent living. It also has potential for future integration with health-monitoring systems.
