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Classification of Millet Cultivars Using Image Processing and Machine Learning Techniques

Classification of Millet Cultivars Using Image Processing an...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Bharambe, Dewendra Aher, Pushpalata Sandip University Department of Computer Science and Engineering Nashik India

ISBN: (纸本)9798331512965

India is one of the leading producers of various millet cultivars. Indian millets are a class of highly nutritious, drought-resistant crops that are mostly cultivated in the country's semi-arid and drought-prone regions. The most popularly cultivated millet varieties in India are Sorghum (Jowar), Pearl millet (Bajra), Barnyard Millet (Bhagar), Foxtail millet (Kangi), Amaranth (Rajgira), Red sorghum (Chikani), Finger millet (Nachani), and White sorghum (Dadar). In addition to being a wholesome dietary source, millets offer several health advantages. Since they don't contain gluten, they're an excellent alternative for anyone with gluten sensitivity or coeliac disease. Additionally low in glycemic index;millets are a great meal choice for diabetics. Each cultivar has a different nutritional composition and uses. Also, the majority of the time the farmers adapted the millet seeds obtained from their crop hence to preserve the purity of seeds and quality of produce it is very important to classify different millet cultivars. However, the classification of millet cultivars is a complex and time-consuming process. automation in millet cultivar classification becomes greatly beneficial with improved accuracy and efficiency. This study presents the effective classification of eight different millet cultivars using different machine-learning classifiers along with image processing techniques, based on the derived color features. © 2025 ieee.

关键词： Seed

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Automated X-Ray Image Classification for COVID-19 Detection Using DenseNet-121 and SVM 3

Automated X-Ray Image Classification for COVID-19 Detection ...

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3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Ameta, Gaurav Kumar Khekare, Ganesh Phulre, Ajay Kumar Jayachitra, S. Singh, Vijendra Pratap Vijayan, Vinodh P. Parul Institute of Technology Parul University Department of Computer Science and Engineering Gujarat Vadodara India School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Sehore India Psna College of Engineering and Technology Department of Ece Dindigul India Mahatma Gandhi Kashi Vidyapith Department of Computer Science & Applications Uttar Pradesh Varanasi India Mangalam College of Engineering Kerala Kottayam India

ISBN: (纸本)9798331542375

This paper provides an algorithm to detect COVID-19 from the chest X-Ray images using Two-class classification with DenseNet-121 and Support Vector Machine. For effective identification of patterns suggestive of COVID-19 in the provided X-ray images, we leverage the DenseNet-121 model due to its feature extracting prowess, complemented by the SVM classifier to accurately classify the images between the COVID-19 to non-COVID-19 positive cases. The method delivers a high accuracy, which is more than 93% for accuracy, precision, recall, and F1-score, which makes it efficient in the COVID-19 quick screening. The Improved DenseNet-121 + SVM was compared with other deep learning models like ResNet50, VGG16, and confirmed that the proposed model has better efficiency in classifying COVID-19 cases. The performance of the model is checked with a five fold cross validation to ensure consistency and reliability on different sets of data not only in terms of performance with the IMDB data but also with the test data. The working paper is relevant to the current research practices in an effort to construct machine diagnostic systems that can be used by practicing healthcare professionals for identifying COVID-19 in the early stages, thus enhancing outcomes in patients' treatment. © 2025 ieee.

关键词： COVID-19

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 ieee.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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Animated Virtual Therapist in Virtual Reality Environment for People with Parkinson's Disease 7

Animated Virtual Therapist in Virtual Reality Environment fo...

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7th ieee international conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Li, Wenting Agada, Ruth Yan, Jie Washington University Biomedical Engineering Dept. St. Louis United States Bowie State University Department of Computer Science Bowie United States

ISBN: (纸本)9798331521578

This study investigates the effectiveness of virtual rehabilitation using a 3D animated virtual therapist delivering the LSVT-BIG program for people with Parkinson's disease (PwPD). Integrating an animated virtual therapist in a VR environment aims to overcome accessibility barriers, especially for individuals in remote areas. This VR-enhanced system provides immersive experiences, real-time feedback, and posture correction through computer vision, along with progress-tracking via personalized reports. This approach offers potential improvements in accessibility, engagement, and motivation, which may contribute to enhanced quality of life for individuals with Parkinson's disease. © 2025 ieee.

关键词： Virtual environments

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Predicting Pest Infestation Patterns with Graph Convolutional Networks (GCN) in Precision Farming 3

Predicting Pest Infestation Patterns with Graph Convolutiona...

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3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Sireesha, Marri Hussein, Laith Manju, M. Johri, Prashant Kuppuraj, T. Aanandha Saravanan, K. Department of Computer Science and Engineering CMR Technical Campus Hyderabad India College of technical engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Kongu Arts and Science College Department of Computer Science TamilNadu Erode India School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331542375

Due to the increases pest impacts on agricultural productivity, effective pest prediction models are required for efficient pest control in precision farming. The framework outlined in this research uses a Graph Convolutional Network (GCN) to estimate pest infestation by exploiting spatial and temporal relationships present in agri-ecosystems. The methodology combines weather, soil, pest, and crop health data into a graph-based representation of spatial units, and the directed connections between them that reflect their proximity and climatic similarity. The GCN converts these graphs to learn such complex relationship as the infestation prediction at various severity levels. The proposed model has produced a better accuracy of 94.8%, better precision of 95.2%, better recall of 94.5% and better F1-score of 94.8% as compared from Random forest, SVM, LSTM, CNN as per the comparison of table 3. Comparing them focuses on graph construction approaches and temporal aspects, mixed P/S and temporal emb has improved the model performance. This framework is practical for further pest management and employing the result without harming the environment. The results show the effectiveness of GCNs for use in pest prediction in precision farming and the opportunity to develop sustainable and scalable data-based agriculture. © 2025 ieee.

关键词： Weed control

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Human Drawing-Driven Automated Evaluation for Parkinson's Disease Detection

Human Drawing-Driven Automated Evaluation for Parkinson's Di...

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Pareek, Keshav Jain, Garima Jain, Sanat Bhargav, Vaibhav Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Sehore India Vit Bhopal University School of Computing Science and Engineering Sehore India

ISBN: (纸本)9798331529833

Globally, Parkinson's Disease is the second most prevalent neurodegenerative disorder, which is identified by distinct symptoms like tremors, bradykinesia, rigidity, and postural instability. Early signs may include reduced facial expression, impaired hand-eye coordination, and decreased muscle-brain coordination. As PD progresses, symptoms worsen, potentially leading to severe disability. Although there is no cure, early detection and intervention can improve patient outcomes. Traditional PD diagnosis methods, involving clinical assessments and neurological tests, are often time-consuming and subjective. Machine Learning (ML) provides a promising alternative for automating PD detection using data-driven algorithms. This paper explores various ML methods, including Logistic Regression, K-Nearest Neighbors (KNN), Linear Discriminant Analysis, Random Forest, Gaussian Naive Bayes, Decision Tree, and Support Vector Classifier (SVC), to predict early-stage PD symptoms. After experiments, the Gaussian Naive Bayes model scored the highest accuracy of 78%, showcasing the potential of ML to improve early detection. © 2025 ieee.

关键词： Neurodegenerative diseases

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Federated Learning in Healthcare: A Privacy-Preserving Approach to Predictive Analytics

Federated Learning in Healthcare: A Privacy-Preserving Appro...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Reddy, M. Jithender Kotiyal, Arnav Lakhanpal, Sorabh Badhoutiya, Arti Mounika, T. Jayanthi, Muthuswamy Vasavi College of Engineering Dept. of Computer Science and Engineering Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Punjab Phagwara India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331512965

Technology such as machine learning in healthcare has pushed privacy concerns to the point that the handling of sensitive patient data has become paramount. In the context of FL, predictive analytics without data sharing between institutions can be performed through the process. Next, we explore the application of FL in healthcare in order to maintain data privacy while obtaining robust predictive models. FL utilizes aggregated decentralized data from multiple healthcare providers to reduce the chance of data breaches and continue to be compliant with rigorous regulatory frameworks, like HIPAA and GDPR, while doing so. In this work, we evaluate the model performance of FL models in predicting different types of health outcomes in comparison with traditional centralized models. We show that FL can achieve similar predictive accuracy while maintaining data privacy, which makes the choice of FL as a viable alternative to privacy sensitive healthcare environments. Second, applying FL is very challenging: there are communication overhead, data heterogeneity, and security risks. This study also considers ways to minimize these challenges. The results demonstrate the promise of Federated Learning as an innovative paradigm for applying predictive analytics in healthcare, indicative of a robust shift toward more secure, efficient and privacy preserving healthcare systems. © 2025 ieee.

关键词： Federated learning

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Delving into Transformer-based Network Architecture for Guided Depth Super-Resolution

Delving into Transformer-based Network Architecture for Guid...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ye, Xinchen Zhang, Aokai Xu, Rui Li, Haojie DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350368741

Guided Depth Super-Resolution (GDSR) enhances low-resolution (LR) depth maps by leveraging high-resolution (HR) color images. The primary challenges involve achieving effective cross-modal data alignment and fusion, as well as incorporating multi-scale information within the Transformer architecture. To address these challenges, we propose a novel network architecture named DRMPNet which integrates two key components: Offset-based Detail Refinement (ODR) and Structure-guided Multi-scale Perception (SMP). ODR leverages offset calibration and window cross-attention to align and fuse LR depth maps with color images, effectively recovering local depth details. Meanwhile, SMP employs a structure generator and multi-scale cross-attention to capture scene details and structures at multiple scales, thereby enhancing the network's contextual understanding. Extensive experiments on various benchmark datasets demonstrate the effectiveness of our method. © 2025 ieee.

关键词： Depth Map Guided Super-Resolution Transformer

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A Fuel Cell Powered Soft Switched based Buck Boost Converter

A Fuel Cell Powered Soft Switched based Buck Boost Converter

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Baig, Mirza Jawad Singh, Rishi Kumar Maulana Azad National Institute of Technology Department of Electrical Engineering Bhopal India

ISBN: (纸本)9798331529833

In recent decades, fuel cells (FCs) have attracted significant interest in the automotive industry owing to their ease of use, silent operation, outstanding performance, and modular design. Traditional converter switching induces power losses during the switching process, hence diminishing system efficiency. This research examines solid oxide fuel cell-powered buck-boost converters using zero voltage switching (ZVS), requiring few auxiliary components, namely one capacitor and one diode. This paper presents an innovative method for the design of the ZVSABuck-Boost converter, which operates as a resonant buck-boost regulator. The@buck-boostcconverter has been modeled, and the necessary output simulation results and waveforms are shown. MATLAB Simulink was used to develop and simulate an 850 W prototype model. © 2025 ieee.

关键词： Buck converter

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Comparative Study of Deep and Machine Learning Models in Knee Osteoarthritis Detection 3

Comparative Study of Deep and Machine Learning Models in Kne...

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3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Kapoor, Vaidhi Mahajan, Makul Batra, Salil School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798331542375

The knee osteoarthritis (KOA) is a common type of arthritis characterised by recent cartilaginous joint destruction and alteration of bone, ligaments and synovial fluid. This paper presents a brief knowledge about the knee OA, including its cause and effect, assessment, signs, prevalence, and the methods of managing it together with Different algorithms like Decision Tree, Random Forest, Artificial Neural Network (ANN) Naive Bayes and CNN. The dataset was collected from GitHub which was from OAI dataset which consists of csv datasets. The accuracy of the algorithms were 85%, 89%, 88%, 95% and 85%. Out of five algorithms, the best algorithm were Naïve Bayes and Random Forest algorithm. © 2025 ieee.

关键词： Deep neural networks

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