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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Yadav, Ram Kumar Soni, Ashish Kumar Sakare, Apeksha Manipal University Department of Data Science and Engineering Jaipur India G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331529833

This article describes an interactive physical activity system designed to use React Three Fiber to assist patients with hand rehabilitation. The game simulates a virtual environment where rods controlled by motion-sensing devices represent the patient's hand. Through the "Create"function, a sphere can be created from the previous position and the patient must hold their hand firmly within the sphere's boundaries. Adjustable sliders allow optimal control of hold time and sensitivity to adapt to the patient's growth and treatment needs. The game provides immediate visual feedback, encouraging patients to practice stability and mobility, which are essential elements of good physical therapy. The app simulates real-world movements in a virtual environment, creating a controlled environment that provides immediate visual feedback to encourage patients to take action. This technology uses React Three Fiber to create a 3D environment, including writing information for precise control of the stick. This interactive model is designed to not only develop motor skills through consistent, guided exercises, but also to improve overall patient compliance. Through this application, we explore the intersection of play and physical therapy by examining the potential for digital therapy tools to complement and enhance traditional practices This approach uses the appeal of interactive games to potentially increase treatment compliance, making it an important tool in digital therapy. This article discusses the use of React Three Fiber's techniques, methods, and therapeutic potential for physical therapy. © 2025 IEEE.

关键词： Virtual environments

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Red deer algorithm to detect the secret key of the monoalphabetic cryptosystem

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Soft Computing 2024年第17-18期28卷 10569-10582页

作者： Jain, Ashish Bansal, Sulabh Das, Nripendra Narayan Gupta, Shyam Sunder Department of Information Technology School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior474005 India

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Tabu search

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Interval Estimation Methods for the Variance of a Lognormal Model with Zero Inflation and Their Applications 24

Interval Estimation Methods for the Variance of a Lognormal ...

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13th International Conference on Informatics, Environment, Energy and Applications, IEEA 2024

作者： Maneerat, Patcharee Nakjai, Pisit Yosboonruang, Noppadon Niwitpong, Sa-Aat Department Of Data Science Uttaradit Rajabhat University Uttaradit Thailand Department Of Statistics School Of Science University Of Phayao Phayao Thailand Department Of Applied Statistics King Mongkut's University Of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798400716423

data on natural rainfall and fish density often exhibit positively right-skewed distributions and include zero observations, thus making them fit a lognormal model with zero inflation. Estimation methods for the variance of a lognormal model with zero inflation using the modified Fletcher (MFL) interval, the generalized confidence interval, and the method of variance estimate recovery are derived herein. The coverage rates and average widths of the proposed methods were assessed via Monte Carlo simulation. Numerical results reveal that the MFL satisfied the requirements for handling large variances, even in situations where the proportion of zero data items was high. Natural rainfall and fish density and data are used to illustrate the efficacies of the proposed methods for estimating rainfall and fish dispersion, respectively. © 2024 Owner/Author.

关键词： Fish

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Word-Diffusion: Diffusion-Based Handwritten Text Word Image Generation 27th

Word-Diffusion: Diffusion-Based Handwritten Text Word Image ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Gurav, Aniket Krishnan, Narayanan C. Chanda, Sukalpa Department of Computer Science and Communication Østfold University College Halden1757 Norway Department of Data Science Indian Institute of Technology Palakkad Kerala India

ISBN: (纸本)9783031784941

Generating realistic handwritten word images that closely resemble a target style remains a challenging task in document image analysis. In recent years, deep learning techniques, such as Latent Diffusion Models (LDM), have shown promise in generating styled handwritten text. However, these models face significant challenges when creating images for ‘Out of Vocabulary’ (OOV) words, impacting their overall effectiveness. In this paper, we introduce an extended diffusion-based Handwritten generation method that incorporates a novel conditioning mechanism. It is based on the Pyramidal Histogram of Shapes (PHOS) representation, which takes into account the spatial and structural characteristics of the target handwriting style. By conditioning the diffusion model on input text, PHOS vector, and writer ID, our approach enables the generation of handwritten word images. Notably, our approach outperforms the original diffusion model, which only uses text and writer ID as conditions, in generating both in-sample and out-of-sample. Furthermore, we have developed a faster inference method that significantly reduces the number of steps required for generating the output. Through qualitative and quantitative evaluations, we demonstrate the effectiveness of our proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Predictive Cardiovascular Health Assessment with Flask and Machine Learning Models

Predictive Cardiovascular Health Assessment with Flask and M...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Mahesh, Kurma Aishwarya, Paladi Nithin, Sabavath Hariharan, Shanmugasundaram Vaishnavi, Bandar Vasantha, S.V. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331527495

Cardiovascular Health Forecaster Employing Flask is a cutting-edge online application that combines Python for its backend and Flask for its frontend. It anticipates heart disease risk with a smooth integration of Flask for data input and Python for machine learning, making it perfect for both users and healthcare professionals. As heart attacks take one life every minute, prediction automation becomes essential. Information science handles large amounts of data in healthcare, tackling the difficulty of predicting coronary heart disease. making use of a dataset on cardiac illness from the UCI AI repository. The AI algorithms-Light GBM, AdaBoost, Gradient Boosting, XG Boost, and Random Forests-are used in this study to predict and classify the risk of cardiovascular illness. The study shows Light GBM's remarkable 90% accuracy, making it a relative report on algorithm performance. © 2025 IEEE.

关键词： Cardiology

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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On the Noise Robustness of In-Context Learning for Text Generation 38

On the Noise Robustness of In-Context Learning for Text Gene...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gao, Hongfu Zhang, Feipeng Jiang, Wenyu Shu, Jun Zheng, Feng Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology China School of Economics and Finance Xi'an Jiaotong University China National Key Laboratory for Novel Software Technology Nanjing University China School of Mathematics and Statistics Xi'an Jiaotong University China Department of Computer Science and Engineering Southern University of Science and Technology China

Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning. To circumvent the issue, we propose a simple and effective approach called Local Perplexity Ranking (LPR), which replaces the "noisy" candidates with their nearest neighbors that are more likely to be clean. Our method is motivated by analyzing the perplexity deviation caused by noisy labels and decomposing perplexity into inherent perplexity and matching perplexity. Our key idea behind LPR is thus to decouple the matching perplexity by performing the ranking among the neighbors in semantic space. Our approach can prevent the selected demonstrations from including mismatched input-label pairs while preserving the effectiveness of the original selection methods. Extensive experiments demonstrate the effectiveness of LPR, improving the EM score by up to 18.75 on common benchmarks with noisy annotations. Our code is available at https://***/ml-stat-Sustech/Local-Perplexity-Ranking. © 2024 Neural information processing systems foundation. All rights reserved.

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Advanced prediction and classification of pulmonary emphysema in COPD using deep graph convolutional neural network with improved weighted quantum ant bee optimization for CT-based diagnosis

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Iran Journal of computer science 2025年 1-18页

作者： Jeyakumar, Suganya Selvaraj, Senthil Kumar Muthusamy, Prakash Sritharan, Akilandeswari Mariadass, Paul Arokiadass Jerald Department of Computer Applications SRM Institute of Science and Technology Tiruchchirappalli India Department of Information Technology Sri Krishna Adithya College of Arts and Science Coimbatore India Department of Artificial Intelligence and Data Science E.G.S. Pillay Engineering College Nagapattinam India Department of Computer Science Periyar Arts College Cuddalore India

Pulmonary emphysema, a progressive respiratory disease and a subset of Chronic Obstructive Pulmonary Disease (COPD), is characterized by lung tissue degradation. Accurate prediction and classification of emphysema are essential for effective management and early detection. Although existing diagnostic methods such as spirometry and CT-based evaluations are valuable, they often have limitations due to interpretive variability and the inability to detect specific emphysema patterns. This paper introduces a novel approach to enhancing the accuracy and efficiency of CT-based pulmonary emphysema diagnosis by integrating an improved weighted quantum ant bee optimization (IWQABO) method with a deep convolutional neural network (DCNN). The primary challenges addressed include improving classification reliability across various emphysema subtypes and extracting optimal features from high-dimensional CT scan data. The IWQABO method optimizes the network’s hyperparameters to enhance classification accuracy, while the DCNN excels at extracting features from complex imaging data. Two data sets were employed in the study: the CTED data set and a real-time emphysema data set. The experimental results demonstrate significant improvements in classification accuracy, with a precision of 95.8%, a sensitivity of 94.5%, and a specificity of 96.3%. These results outperform traditional diagnostic methods, such as spirometry and manual CT assessments. The findings highlight the advantages of combining DCNN and IWQABO techniques for diagnosing pulmonary emphysema, showcasing their potential to improve treatment outcomes and enhance clinical decision-making for COPD patients. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Automated diagnosis COPD CT-based diagnosis DCNN Hyperparameter optimization Improved weighted quantum ant bee optimization Pulmonary emphysema

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Unveiling Consumer Segmentation: Harnessing K-means Clustering Using Elbow and Silhouette for Precise Targeting 3rd

Unveiling Consumer Segmentation: Harnessing K-means Clusteri...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Saraswat, Shweta Agrohi, Vaibhav Kumar, Mahesh Lamba, Monica Kaur, Raminder Department of Artificial Intelligence and Data Science Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Graphics Era Deemed to Be University Dehradun India Department of Computer Science and Engineering Department JECRC University Jaipur India

ISBN: (纸本)9789819708918

Consumer segmentation is essential for accurate targeting and successful marketing efforts in today’s competitive business environment. Modern marketing groups individuals by interests and attributes. Segmentation drives targeting, personalization, and ROI. This article segments customers using K-means clustering. The Elbow approach and Silhouette score determine the appropriate number of clusters and increase segmentation accuracy. They also examine the possibility of precision targeting and customized marketing techniques across sectors. Businesses may optimize marketing, improve customer happiness, and increase profits by using K-means clustering. To compete in today’s market, this research helps marketers enhance targeting. Elbow and Silhouette K-means clustering may enhance client segmentation, engagement, loyalty, and economic success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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Small Area Estimation Under A Pseudo-Unit Level Model with First-Order Autoregressive Time Effects and Measurement Error

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 365-372页

作者： Kurnia, Anang Surida, Dian Muchlisoh, Siti Wulandari, Ita Handayani, Dian Statistics and Data Science Department of Statistics IPB University 16680 Indonesia Ministry of Women’s Empowerment and Child Protection Indonesia Politeknik Statistika STIS Indonesia IPB University Politeknik Statistika STIS Indonesia Statistics at State University of Jakarta Indonesia

statistics Indonesia (BPS) provides employment data through the National Labor Force Survey (SAKERNAS). SAKERNAS data collection is carried out quarterly under a rotating panel survey with a sample unit of the rotation group. Our study focuses on the Rao-Yu unit level model and its modifications. The modified Rao-Yu unit level model is required to handle pseudo panel units i.e., the units are replaced by the summary of units in the rotation group. In this context, these units are referred to as pseudo units. A modification was made by adding a measurement error pseudo unit into the Rao-Yu unit level model. The simulation study showed that the greater variance between areas, the better the capability of Rao-Yu pseudo unit level model compared with the Rao-Yu unit level model in estimating the parameters of interest. Our method proposal was applied to data on the quarterly unemployment rate at the district/city level in one of Indonesia’s provinces according to the 2011–2014 SAKERNAS panel rotation data. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Measurement errors

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