Authors - Rashmi Vipat, Priyank Doshi Abstract - Agriculture plays a vital role in ensuring food security, yet traditional crop selection and yield estimation practices often fail to account for complex interactions among soil, climatic, and environmental factors. Recent advances in machine learning (ML) have shown significant potential in addressing these challenges by enabling data-driven decision support for farmers. This paper presents a comprehensive review of machine learning–based crop recommendation and yield prediction techniques, focusing on their effectiveness in improving agricultural productivity and sustainability. The study analyzes various supervised and ensemble learning models applied to soil quality parameters such as nitrogen, phosphorus, potassium, pH, moisture, and climatic variables. Emphasis is placed on multimodal data integration, highlighting how the fusion of soil, weather, and remote sensing data enhances prediction accuracy. The review also discusses current limitations, including data scarcity, model generalization, and real-time deployment challenges, particularly in resource-con-strained farming environments. Finally, the paper identifies key research gaps and future directions toward developing robust, scalable, and intelligent agricultural decision-support systems.
Authors - Amit Kalita, Himashree Kalita, Manjit Kalita, Abhijit Chakraborty, Kalpita Dey, Prajukta Deb Abstract - The significance of M- Health platforms to promote health equity has reached critical levels as digitalization in the healthcare sector continues to grow post pandemic. M-Health platform utilization in developing countries like Bangladesh has unique challenges: inconsistent adoption of the digital healthcare system, thus leading to a suboptimal delivery of healthcare services to customers. Using blended models i.e., Expectation-Confirmation Model (ECM), UTAUT2, and the DeLone & McLean IS Success Model, with Training on Virtual Consultation Skills as the moderating variable, the study intends to examine the adoption intention of healthcare providers to continuously use M-Health Platforms for a myriad of services like virtual consultation, remote patient monitoring, electronic prescriptions, and e-health record keeping. This study used Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate 898 responses. Social influence, relative advantage, regulatory clarity, digital literacy, trust in technology, and system quality, which collectively improve doctors’ satisfaction with virtual consultation platforms, were identified as important to the purpose of the study. The results offer concrete steps that healthcare providers, platform creators, and policymakers can take to build and improve a solid and dependable M-Health platform that encourages sustained partnership with physicians by alleviating resistance that physicians may have about M-Health platforms in comparable developing countries.
Authors - Yaram Srinivasa Reddy, Bairoju Sreelatha, Shankar Lingam. M Abstract - Knowledge from a resource-rich source domain is leveraged in traditional transfer learning to enhance classification in a relatively data-scarce target domain. However, the resulting target models often suffer from overfitting and limited generalization, which restricts their utility in noisy and resource-constrained environments such as remote sensing. To mitigate these limitations, this work introduces a nuclear norm–regularized teacher–student framework for hyperspectral scene classification. In particular, the student model is regularized with the nuclear norm to encourage low-rank parameter representations, improving robustness to ambient noise. Further, we introduce a relative reconstruction loss (RRL) metric to measure the robustness of the student model to environment noise. Trained on several benchmark datasets, the proposed student model attains up to 87.0% classification accuracy on the independent test sets of UC Merced and EuroSAT, while remaining substantially lighter than the teacher network. Further, relative reconstruction values are computed for different amounts of noise added in the input space; RRL saturate to values less than 1.0 for all the datasets, substantiating that the regularized student model is indeed robust. The competitive performance of the regularized student model compared to the teacher network, its lightweight design, together with RRL values less than one, suggest that the proposed student model can effectively be deployed in noisy and resource-constrained environments such as edge and fog devices.
Authors - Nagesh Sharma, Priyanka Yadav, Kavita Singh Abstract - An accurate determination of childhood malnutrition is necessary for preventive measures. This paper proposes a modified scoring scheme comprising two new elements: the Integrated Anthropometric Score (IAS) and the Hybrid Integrated Score (HIS). IAS uses six primary anthropometric measurements, such as BMI, MUAC, WHZ, WAZ, HAZ, and skinfold thickness, along with selected interaction terms that capture the non-linear connections between growth parameters. The weights are determined by regularized logistic regression, allowing the score to be transparent while still adapting to the statistical structure of the data set. To further stabilize the predictions, the HIS combines BAI, IAS, and a machine learning probability component to make the predictions robust in both synthetic and real-world samples. The models were developed using a synthetic dataset of 9,456 children and tested with five-fold cross-validation and a separate real-world dataset of 38 children. Interaction selection and regularization were performed to control noise sensitivity and avoid overfitting. The findings indicate that the IAS model outperforms BAI with its higher cross-validated accuracy (0.93) and strong performance on real data (0.95). The HIS stays consistent in accuracy across areas and indicates better generalization. The results suggest that by combining multidimensional anthropometric characteristics, interaction-aware modeling, and hybrid learning, a new, more adaptable, and clinically interpretable tool for predicting nutritional risk has been developed, surpassing traditional composite indices.
Authors - Mehak Mukesh Agrawal, Saumya Kumari, Gaganam H V S M Soma Sai, Ankit A. Bhurane Abstract - Most existing artificial intelligence (AI) based assistants are cloud-dependent and require constant internet connectivity. User data is sent to external servers for processing. While this data is often encrypted, it is prone to risks such as cloud security threats. Additionally, users need to be cautious not to share sensitive information. To overcome the aforementioned privacy and internet availability concerns, this paper proposes a completely offline, on-device, cross-device, and open-source system to ensure complete data privacy. The proposed system was tested with several datasets, including AI2 Reasoning Challenge, SQuAD 1.1, CoNLL 2003, GSM8K and StrategyQA to evaluate the closed-form question answering (QA), contextual understanding, named entity recognition, mathematical reasoning and truthfulness, respectively, and with five on-device large language models (LLMs), including Gemma3 1B, SmolLM 1.7B, Qwen2 1.5B, TinyLlama-1.1B, and Phi-2. The system achieved the highest score for closed-form accuracy of 1.0. Its performance on reasoning ranged from 0.01 to 0.23. Truthfulness scores ranged from 0.24 to 0.59. High F1 scores for named entity recognition ranged from 0.74 to 0.79, and contextual understanding scores ranged from 0.02 to 0.17 across the different LLMs. The average response time of the system on mobile and desktop devices was evaluated and observed to vary according to system capability and model size. The system allows users to choose between multiple wake words specific to the Indian context. The proposed system functions on limited RAM and in constrained resource environments.
Authors - Mahzuzah Afrin, Rajasree Das Chaiti, Gazi Tahsina Sharmin Jahin, M. M. Musharaf Hussain, Mohammad Shamsul Arefin Abstract - Reliable identification of pneumonia from chest radiographs plays a central role in supporting clinical decision-making and patient management. Although deep learning models have shown favourable results for automated diagnosis, most existing studies rely on fully supervised training and mainly evaluate performance using accuracy or ROC-AUC metrics. Such evaluations may fail to capture clinical decision reliability, particularly in imbalanced medical datasets. In this work, we examine the effectiveness of self-supervised learning (SSL) for chest X-ray pneumonia classification through a controlled empirical study. A contrastive pretraining strategy is used to learn image representations from unlabeled chest X-rays, followed by supervised linear evaluation. The SSL-pretrained model is compared with a fully supervised model trained from scratch under identical experimental conditions. Our experiments reveal that the supervised baseline attains a slightly higher ROC-AUC; however, this improvement comes at the cost of increased false positive predictions, leading to lower overall accuracy. In contrast, the SSL-pretrained model exhibits a distinct prediction pattern. It achieves higher accuracy and notably improved precision and F1-score, indicating more balanced and reliable predictions. Precision– recall analysis further demonstrates the advantage of SSL in reducing false positive decisions. In addition, Grad-CAM visualizations suggest that the SSL-pretrained model focuses on clinically relevant lung regions. From a clinical decision-making perspective, these results suggest that self-supervised learning offers tangible advantages for chest X-ray analysis when prediction reliability is prioritized. This distinction is especially relevant in clinical settings.
Authors - Prerna Agarwal, Pranav Shrivastava, Samya Ali, Sachit Dadwal, Shubh Om Yadav, Saquib Hussain, Kareena Tuli Abstract - Most existing artificial intelligence (AI) based assistants are cloud-dependent and require constant internet connectivity. User data is sent to external servers for processing. While this data is often encrypted, it is prone to risks such as cloud security threats. Additionally, users need to be cautious not to share sensitive information. To overcome the aforementioned privacy and internet availability concerns, this paper proposes a completely offline, on-device, cross-device, and opensource system to ensure complete data privacy. The proposed system was tested with several datasets, including AI2 Reasoning Challenge, SQuAD 1.1, CoNLL 2003, GSM8K and StrategyQA to evaluate the closed-form question answering (QA), contextual understanding, named entity recognition, mathematical reasoning and truthfulness, respectively, and with five on-device large language models (LLMs), including Gemma3 1B, SmolLM 1.7B, Qwen2 1.5B, TinyLlama-1.1B, and Phi-2. The system achieved the highest score for closed-form accuracy of 1.0. Its performance on reasoning ranged from 0.01 to 0.23. Truthfulness scores ranged from 0.24 to 0.59. High F1 scores for named entity recognition ranged from 0.74 to 0.79, and contextual understanding scores ranged from 0.02 to 0.17 across the different LLMs. The average response time of the system on mobile and desktop devices was evaluated and observed to vary according to system capability and model size. The system allows users to choose between multiple wake words specific to the Indian context. The proposed system functions on limited RAM and in constrained resource environments.
Authors - Roshani Tawale, Jayshri Todase, Manisha Bharati Abstract - Enforcement of helmet regulations and accurate vehicle identification remain essential components of intelligent traffic management systems. Conventional supervision approaches depend heavily on manual inspection, which is labor-intensive and unsuitable for continuous large-scale monitoring. This study presents an automated framework for helmet violation detection and number plate lo-calization using the YOLOv8 deep learning architecture [3]. The proposed system supports static image analysis, recorded video processing, and live-stream detection within a unified pipeline. Performance is assessed using precision, re-call, and mean Average Precision (mAP@50). Experimental findings demonstrate consistent detection reliability and validate the framework’s applicability for real-time traffic surveillance systems.
Authors - Gunjan Pareek, Rajiv Singh, Swati Nigam Abstract - This research examines the transfer learning deep learning models in multimodal human activity recognition based on wearable sensor data. Raw IMU signals are converted to Gramian Angular Field (GAF) images to improve the feature representation and tested on WISDM and PAMAP2 datasets of 18 activity classes. Five CNN models, namely VGG16, MobileNetV2, ResNet50, DenseNet121, and EfficientNetB0, are trained and evaluated in the same conditions and measured by classification accuracy, statistical significance, and computation efficiency. GAF representations are always better than raw signals. DenseNet121 and ResNet50 have 99% accuracy, VGG16 and MobileNetV2 perform competitively and EfficientNetB0 performs worse. Most of the differences in performance are statistically significant (p < 0.05).
Authors - Devang Rupesh Dalvi, Gaurav Suresh Malik, Abhishek Jairaj Kunder Abstract - Prompt engineering has emerged as an essential paradigm in leveraging desired behaviors from large language models (LLMs) without altering their parameters. Although the majority of the current literature has revolved around the introduction of novel prompt engineering strategies, there has been comparatively less emphasis on the contribution of the evaluation and optimization of prompts in concrete systems. In this paper, we offer a specialized review of prompt engineering from an evaluation/optimization centric viewpoint with a larger nod to conceptual developments and illumination rather than detailing the comparisons of approaches. Furthermore, we attempt to establish the concrete importance of prompt engineering via a real-life application, which resulted in improved performances in tasks through the process of prompt refinement and informal evaluations without the need to change the architecture and weights of the models. The paper will also introduce the deficiencies in prompt engineering in the realms of re-producibility, robustness, and the unavailability of standardized approaches in the aspect of concrete evaluations.
Assistant Professor, Assistant Head Research- Department of Information Technology, Vishwakarma Institute of Technology Pune (Affiliated to Savitribai Phule Pune University, Maharashtra, India
Authors - Shruti Thakur, Shilpa Nikhil Bhosale, Priti Prakash Jorvekar, Sandeep Muktinath Chitalkar, Harshala Shingne, Rupali Vairagade Abstract - This study examines the effectiveness of ensemble learning models for detecting fraud in e-wallet transactions under extreme class imbalance and temporal dependence. Using the PaySim bench-mark dataset, a time-aware experimental framework is developed that incorporates forward-chaining evaluation, imbalance-aware resampling, hyperparameter optimisation, probability calibration, and cost-sensitive threshold tuning to reflect real-world deployment conditions. RF and XGBoost are systematically compared across multiple dataset scales and train–test splitting strategies. Empirical findings show that XGBoost consistently outperforms RF, achieving the highest F1-score, maintaining PR-AUC above 0.88, and demonstrating near-perfect ROC-AUC, indicating strong discriminative capability. Following isotonic calibration, XGBoost also produces the lowest Brier score, highlighting superior probability reliability for risk-based decisions. Performance gains plateau beyond a 75% training share, while XGBoost preserves stable performance as the test window expands, unlike RF. Overall, the results support prioritising gradient boosting models, adopting time-aware validation, and integrating calibrated risk scoring in operational e-wallet fraud detection systems.
Authors - Shoh-Jakhon Khamdаmov, Muazzam Akramova, Rano Abdullaevna Sadikova, Azamat Kasimov, Jasurbek Pozilovich Kurbonov, Alisher Bakberganovich Sherov, Dilshoda Akramova Abstract - Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in children, characterized by inattention, hyperactivity, and impulsivity that impair academic and social functioning. Due to its heterogeneous presentation and symptom overlap with other cognitive disorders, early and accurate diagnosis remains challenging. This study proposes a multimodal machine learning framework integrating behavioral, neuroimaging, and physiological data to predict ADHD in children. Convolutional Neural Networks (CNNs) are used to extract features from brain MRI scans, Long Short-Term Memory (LSTM) networks model temporal patterns in physiological signals such as EEG and heart rate variability, and ensemble learning methods incorporate behavioral and clinical attributes. Both feature-level and decision-level fusion strategies are evaluated. Results on benchmark datasets show that the multimodal model consistently outperforms unimodal approaches in accuracy, sensitivity, and F1- score, demonstrating the potential of AI-driven multimodal systems for early, objective, and interpretable ADHD diagnosis.
Authors - Matjere Matsebe, Nobubele Angel Shozi Abstract - It is possible to increase the acceptability of small wind turbines for wind regions with low wind velocities for rural as well as urban sectors by placing them inside diffusers. The research on development of various diffusers is a major re-search area nowadays. Curved flanged diffusers can deliver better performance by adding a cylindrical throat section between converging and diverging sections. This research paper presents a systematic study on short curved flanged diffusers with converging-diverging sections and extended uniform throat between them. Twenty-five diffuser models are studied using Computational Fluid Dynamics using ANSYS Fluent. These models are finalized using the design of experiments for six variables at five levels. The throat diameter for all diffuser models is fixed. The investigation is performed by considering radial average velocity and percentage velocity variation along the radial planes. The global velocities are observed as 1.18 to 1.47 times that of the radial average velocities. The diffuser dimensions are optimized to maximize radial average velocity and to minimize the velocity variation along the radial planes. The diffuser with optimized dimensions is manufactured and tested experimentally in a wind tunnel. Good matching is seen between the predicted results and experimental results. The optimized diffuser has the ability to produce more than two times the power that of the turbine without a diffuser.
Authors - Murodov Gayrat Nekovich, Kholmuhamedov Bakhtiyor Farkhodovich, Avezov Sukhrob Sobirovich, Khudayberganov Nizomaddin Uktambay ogli, Yunusova Maftuna Shokirovna, Mansurova Shahinabonu Najmiddin qizi Abstract - The classification of ECG signals continues to be a major focus in intelligent healthcare systems, especially for the early identification of cardiac arrhythmias. In this work, we propose a hybrid probabilistic neural strategy that integrates Bayesian Networks with Artificial Neural Networks (ANNs) to enhance the reliability of ECG classification. The approach begins by extracting informative ECG features, such as crosscorrelation and phase-based characteristics. A Bayesian Network is then applied to model the probabilistic dependencies among these features and identify those most relevant to classification. At the same time, an ANN is trained on the refined feature set to learn complex non-linear patterns present in the signals. The two models are subsequently combined through a weighted voting mechanism to form an ensemble classifier. Experimental evaluation using an ECG dataset indicates that the proposed ensemble achieves higher accuracy and stability compared to its individual components. Notably, the method demonstrates strong capability in distinguishing multiple arrhythmia categories, which are typically difficult to classify. Overall, the results highlight the promise of hybrid probabilistic–neural models for improving automated ECG interpretation and supporting more accurate diagnosis of cardiac abnormalities.
Authors - K S Shubham, Uma Mudengudi, Ujwala Patil Abstract - Secure, compliant, and interoperable data sharing remains a core bottleneck for cross-organizational analytics and AI, particularly under evolving privacy regulations, contractual obligations, and adversarial threats. This paper introduces HARMONIA, a pluggable, risk-aware data sharing framework that integrates policy-as-code enforcement, continuous compliance monitoring, provenance-grade evidence, and revocation with machine unlearning. HARMONIA is inspired by the iterative Analyzer–Mechanic and Conductor–Observer operational pattern described in the HARMONIA strategic perspective, generalizing its quality-gate-and-repair loop to a policyand- risk-gated release lifecycle. We formalize an architecture that separates governance, control, and data planes; define a release-mode lattice that enables explainable fallbacks among raw export, masking, kanonymity, differential privacy, synthetic data, query-only access, and federated compute; and propose an evidence model aligned with W3C PROV. We provide a proof-of-concept (POC) blueprint implemented with commodity components (OPA, OAuth2/OIDC, PostgreSQL, and object storage) and specify interfaces that support end-to-end request-to-release-to-revocation workflows, including batch-scoped unlearning for model derivatives. The paper concludes with an evaluation methodology and a standards-aligned roadmap for deployment in sovereign data spaces.
Authors - Mehzabul Hoque Nahid, Fatema Tuz Zahra, Mubashshir Bin Mahbub, Saleh Ahmed Jalal Siam Abstract - Personalizing learning in higher education presents a significant challenge due to the difficulty of providing individual feedback to large student cohorts. This study proposes an intelligent tutoring system based on a multi-agent architecture utilizing Large Language Models (LLMs) to address scalability and adaptability issues. The proposed architecture integrates two complementary subsystems: a reactive module that answers student queries using Retrieval-Augmented Generation (RAG) to ensure accuracy based on course materials, and a proactive module that autonomously analyzes student profiles to generate personalized study plans without direct instructor intervention. The system was implemented using Lang- Graph for agent orchestration and MongoDB for state persistence. Experimental validation was conducted using a curated golden dataset from a university course. Results demonstrate a retrieval precision of 94.2% and a faithfulness score of 87.8%, significantly mitigating hallucinations common in monolithic models. Furthermore, the operational cost analysis indicates high financial viability for mass implementation. This dual approach offers a robust solution for automated, highquality educational support, effectively bridging the gap between standardized teaching and personalized learning needs.
Authors - Vemuri Bharath Kumar, Anjan Babu G Abstract - Healthcare data scarcity poses significant challenges for machine learning applications in clinical settings, particularly for conditions with limited patient populations. This paper presents a novel quantumenhanced data augmentation framework that addresses this challenge through a three-pillar architecture: Quantum Random Number Generation (QRNG) for true randomness, Statistical AI for intelligent parameter optimization, and Generative AI for clinical interpretability. Our implementation utilizes Bell state quantum circuits to generate genuinely random perturbations, ensuring higher entropy than classical pseudorandom methods. The framework incorporates medical domain knowledge through constraint-aware augmentation, maintaining clinical validity while generating synthetic patient records. Experimental evaluation on the Pima Indians Diabetes dataset (768 samples, 8 features) demonstrates that our quantum-enhanced approach achieves 100% medical constraint compliance while generating high-quality synthetic data. The system provides both command-line and web interfaces, with automatic fallback to classical methods when quantum resources are unavailable. Our contributions include: the first practical application of quantum computing to healthcare data augmentation, an AI-driven optimization system that automatically determines augmentation parameters, integration with large language models for non-technical summarization of validation reports, and a production-ready implementation with comprehensive validation mechanisms. The framework represents a significant advancement in synthetic medical data generation, offering a scalable solution for addressing data scarcity in healthcare AI applications.
Authors - Sandhya Awate, Vipin Kumar Gupta Abstract - Rural communities face significant challenges in accessing essential healthcare services due to language barriers, limited health literacy, and insufficient medical support. Difficulties in understanding medical information, communicating symptoms, and interpreting diagnostic reports further hinder effective healthcare delivery. Additionally, unreliable internet connectivity restricts the reach of conventional digital health platforms. To address these challenges, this paper presents a Multilingual AI Health Assistant designed to operate on low-cost edge devices, enabling offline functionality to ensure continuous access and data privacy in low-connectivity areas. The proposed system integrates Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Optical Character Recognition (OCR), and speech recognition, allowing users to interact in their native languages via text or voice. It analyzes user-reported symptoms to predict probable health conditions, translates complex medical reports and prescriptions into simplified, localized explanations, and provides recommendations for nearby healthcare facilities. Unlike internetdependent telemedicine systems, this edge-based solution processes data directly on the device, safeguarding sensitive health information while maintaining reliability. By bridging linguistic and literacy gaps, the proposed assistant empowers rural populations with accessible and actionable healthcare insights, ultimately improving health outcomes in underserved regions.
Authors - P. Sivaperumal, R. Naresh, S. Prawin, B. E. Viruthatchanan Abstract - The food portion estimation is a critical component of automated dietary assessment systems, enabling better monitoring of nutritional intake and supporting healthcare, weight management, and public health applications. Traditional self-reporting methods are often inaccurate and time-consuming, motivating the need for computer vision–based approaches that can reliably estimate food portions from images captured in real-world conditions. This paper presents deep learning pipeline for food portion estimation that integrates image preprocessing, deep learning–based segmentation, and geometric volume computation. The data preprocessing with Mask R-CNN used for precise food seg-mentation, providing pixel-level masks and bounding boxes that isolate individual food items from complex backgrounds. The segmented mask is used to estimate the pixel area of the food region. Experimental evaluation demonstrates that the proposed method achieves high segmentation accuracy, with a segmentation IoU of 87.6%, precision of 90.3%, recall of 88.9%, and an F1-score of 89.6%. The pixel area estimation error is limited to 6.8%, resulting in an overall portion estimation accuracy of 89.1%, indicating reliable and consistent performance across different food images. The proposed framework highlights the effectiveness of combining deep instance segmentation with geometric volume estimation for accurate food portion assessment. Future work will focus on multi-view image integration and real-time deployment in mobile dietary monitoring systems to enhance robustness and scalability.
Authors - Chalani Dinitha, Saadh Jawwadh Abstract - Automated Image Enhancement from CCTV surveillance relies heavily on accurate image segmentation; however, real-world footage is often degraded by low illumination, motion blur, occlusion, and background clutter, causing conventional segmentation models to lose boundary precision and small object details. This paper proposes EdgeLite-CrimSegNet, a novel lightweight boundary-aware segmentation network designed specifically for crime scene analysis. Unlike existing fast segmentation models that prioritize global context, the proposed architecture adopts a boundary-first learning strategy, where crime-relevant contours are explicitly extracted and refined before region-level segmentation. A compact edge-aware encoder, boundary-guided feature refinement module, and progressive region filling strategy are introduced to improve segmentation accuracy while maintaining real-time performance. Experiments on CCTV frames derived from the UCF-Crime dataset demonstrate improved boundary preservation, higher IOU, and better segmentation of overlapping and small objects compared to conventional lightweight segmentation networks, confirming the suitability of EdgeLite-CrimSegNet for real-time surveillance applications.
Assistant Professor, Assistant Head Research- Department of Information Technology, Vishwakarma Institute of Technology Pune (Affiliated to Savitribai Phule Pune University, Maharashtra, India
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.
