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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Ruchika Vaidya Rahul Dattangire Divya Biradar Prateek Verma Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Data Engineering Publicis Sapient Houston Texas USA Department of Computer Science University of Texas Arlignton Texas USA

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The current study focuses on how various machine learning (ML) algorithms can be used in waste management and emphasizes the significance of intelligent technologies in addressing contemporary issues. By leading a broad writing survey, the paper features the meaning of incorporating these advances into waste management practices. The system segment subtleties the information assortment, preprocessing, model determination, and assessment measurements utilized in the review. The accuracy and performance metrics of various classifiers, including K Nearest Neighbors, Linear Support Vector Classifier, Logistic Regression, Decision Tree, Multi-Layer Perceptron, and Random Forest, are demonstrated by the results of experiments with them. Especially noteworthy is the MLP classifier's 91.81% accuracy. The discoveries highlight the viability of ML approaches in ssmart waste management. In addition, a comparison table that summarizes the classifiers' accuracy scores provides insight into their suitability for applications in waste management.

关键词： Waste management Surveys Productivity Measurement Accuracy Reviews Vectors

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Harmonizing Precision Agriculture: Augmented Insights into Plant Disease Detection Using Deep learning

Harmonizing Precision Agriculture: Augmented Insights into P...

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Integrated Circuits, Communication, and Computing Systems (ICIC3S), International Conference on

作者： Shweta Chauhan Rahul Kumar Bibek Kumar Department of Artificial Intelligence and Machine Learning MITS College of Engineering Gwalior India ECE Department GLA University Mathura Uttar Pradesh India School of Data Science Symbiosis University of Applied Sciences Indore Madhya Pradesh India

ISBN: (数字)9798350364088

ISBN: (纸本)9798350364095

This research addresses the critical issue of accurate plant disease identification, recognizing its paramount significance in preserving agricultural yields and safeguarding natural ecosystems. Leveraging the power of deep learning, specifically Convolutional Neural Networks (CNNs), our study focuses on developing a robust image classification model capable of precisely detecting various plant diseases. However, the challenge of limited training data necessitated the implementation of diverse data augmentation techniques to enrich the dataset. The research involves a meticulous exploration of different augmentation strategies, including rotation, flipping, zooming, translation, shearing, and cropping. The acquired dataset comprises 25,000 images categorized into 38 classes, with 20,000 designated for training and 5,000 for validation. The CNN model, with convolutional, pooling, dense, dropout, and flatten layers, exhibits a remarkable accuracy of 93.5% and 95% on the valid and training dataset, reflecting its adeptness in learning intricate patterns. Furthermore, the study reviews recent advancements in plant disease identification, including innovative models like PPLC-Net, Deep CNN, SG2-ADA, and HQIA. These breakthroughs underscore the continuous evolution of techniques to address challenges such as imbalanced datasets, computational demands, and overfitting. The research concludes with recommendations for future exploration, emphasizing the potential refinement of data augmentation techniques, hyperparameter tuning, and broader disease dataset acquisition. The outcomes presented here contribute to the growing body of knowledge in precision agriculture and pave the way for further advancements in plant disease detection methodologies.

关键词： Training Precision agriculture Deep learning Plant diseases Accuracy Training data data augmentation

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Computing the Bounds of the Number of Reticulations in a Tree-Child Network That Displays a Set of Trees

arXiv

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arXiv 2023年

作者： Wu, Yufeng Zhang, Louxin Department of Computer Science and Engineering University of Connecticut StorrsCT06268 United States Department of Mathematics Center for Data Science and Machine Learning National University of Singapore Singapore119076 Singapore

Phylogenetic network is an evolutionary model that uses a rooted directed acyclic graph (instead of a tree) to model an evolutionary history of species in which reticulate events (e.g., hybrid speciation or horizontal gene transfer) occurred. Tree-child network is a kind of phylogenetic network with structural constraints. Existing approaches for tree-child network reconstruction can be slow for large data. In this paper, we present several computational approaches for bounding from below the number of reticulations in a tree-child network that displays a given set of rooted binary phylogenetic trees. In addition, we also present some theoretical results on bounding from above the number of reticulations. Through simulation, we demonstrate that the new lower bounds on the reticulation number for tree-child networks can practically be computed for large tree data. The bounds can provide estimates of reticulation for relatively large *** Codes 05C30 © 2023, CC BY.

关键词： Phylogenetic

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Deep learning models for enhanced in-field maize leaf disease diagnosis

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machine learning with Applications 2025年 20卷

作者： Nakatumba-Nabende, Joyce Murindanyi, Sudi Department of Computer Science College of Computing and Information Sciences Makerere University Uganda Marconi Machine Learning Lab College of Engineering Design Art and Technology Makerere University Uganda Makerere Center for Artificial Intelligence and Data Science Makerere University Uganda

Maize leaf diseases significantly threaten crop yields, and there is need for accurate, and accessible diagnostic tools. This research addresses this need by developing and evaluating deep learning (DL) and machine learning (ML) models for in-field classification and detection of four critical maize diseases: Maize Leaf Blight, Maize Lethal Necrosis, Maize Streak Virus, and Fall Armyworm damage. Utilizing field imagery captured via digital cameras and smartphones across Uganda, Tanzania, Ghana, and Namibia, we developed and compared custom Convolutional Neural Networks (CNNs), transfer learning (MobileNetV2, InceptionResNetV2), Vision Transformers (ViT), and classical ML models. For detection, a transformer-enhanced YOLOv10 architecture was implemented. Explainable AI (XAI) techniques (Grad-CAM, LIME) were incorporated to ensure model transparency. MobileNetV2 achieved the highest classification accuracy (97%), while the enhanced YOLOv10 reached a mean Average Precision (mAP) of 0.995 for object detection. The best models were integrated into a mobile application deployed on edge devices for real-time diagnosis by smallholder farmers in Uganda, with performance validated through field tests. This study demonstrates a powerful, interpretable, and field-deployable solution combining advanced DL, transformers, and XAI for effective maize health management. © 2025

关键词： Convolutional neural networks

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From Galton to AI: A data-Driven Approach to Height Prediction Using Growth Curve Comparison and Parental data

From Galton to AI: A Data-Driven Approach to Height Predicti...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Praisy Petra C Sujitha Juliet Anitha J Division of Data Science and Cybersecurity Karunya Institute of Technology and Sciences Coimbatore India Division of Arificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Accurately predicting a child's height based on genetic and environmental factors is a longstanding challenge in medical and data science research. Traditional models, such as linear regression, SVM, and Random Forest, have been widely used. However, their accuracy varies due to the complexity of the environment, genetics which affects the height. This paper investigates the possibility of machine learning techniques, including deep learning techniques, accompanied by traditional models, to improve prediction accuracy for height. By combining more features and systematically partitioning datasets, the study compares various algorithms perform in predicting child height. These findings provide a comparative evaluation of these models, demonstrating the impact of feature selection and model architecture on prediction performance. The study aims to offer insights into selecting optimal models for practical applications in genetics, health analytics, and predictive modelling.

关键词： Deep learning Support vector machines Pediatrics Analytical models Accuracy Predictive models Epigenetics Complexity theory Cultural differences Random forests

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Evolving Interpretable Classification Models via Readability-Enhanced Genetic Programming

Evolving Interpretable Classification Models via Readability...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： João Victor T. De Souza Abreu Denis Mayr Lima Martins Fernando Buarque De Lima Neto Polytechnic School - PPGEC University of Pernambuco Recife Brazil Machine Learning and Data Engineering University of Münster ERCIS Muesnter Germany

ISBN: (纸本)9781665487696

As the impact of machine learning (ML) on busi-ness and society grows, there is a need for making opaque ML models transparent and interpretable, especially in the light of fairness, bias, and discrimination. Nevertheless, interpreting complex opaque models is not trivial. Current interpretability approaches rely on local explanations or produce long explanations that tend to overload the user's cognitive abilities. In this paper, we address this problem by extracting interpretable, transparent models from opaque ones via a new readability-enhanced multi-objective Genetic Programming approach called REMO-GP. To achieve that, we adapt text readability metrics into model complexity proxies that support evaluating ML interpretability. We demonstrate that our approach can generate global interpretable models that mimic the decisions of complex opaque models over several datasets, while keeping model complexity low.

关键词： Adaptation models Computational modeling Genetic programming machine learning Readability metrics Complexity theory Computational intelligence

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Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI data 5

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses. © 2024 IEEE.

关键词： Convolutional neural networks

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Less is More: Facial Landmarks can Recognize a Spontaneous Smile 33

Less is More: Facial Landmarks can Recognize a Spontaneous S...

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33rd British machine Vision Conference Proceedings, BMVC 2022

作者： Tushar, Md Tahrim Faroque Yang, Yan Hossain, Md Zakir Naim, Sheikh Motahar Mohammed, Nabeel Rahman, Shafin Department of Electrical and Computer Engineering North South University Bangladesh Biological Data Science Institute The Australian National University Canberra Australia CSIRO Agriculture and Food Canberra Australia CSIRO Machine Learning and Artificial Intelligence Future Science Platform Canberra Australia Amazon Web Services

Smile veracity classification is a task of interpreting social interactions. Broadly, it distinguishes between spontaneous and posed smiles. Previous approaches used hand-engineered features from facial landmarks or considered raw smile videos in an end-to-end manner to perform smile classification tasks. Feature-based methods require intervention from human experts on feature engineering and heavy pre-processing steps. On the contrary, raw smile video inputs fed into end-to-end models bring more automation to the process with the cost of considering many redundant facial features (beyond landmark locations) that are mainly irrelevant to smile veracity classification. It remains unclear to establish discriminative features from landmarks in an end-to-end manner. We present a MeshSmileNet framework, a transformer architecture, to address the above limitations. To eliminate redundant facial features, our landmarks input is extracted from Attention Mesh, a pre-trained landmark detector. Again, to discover discriminative features, we consider the relativity and trajectory of the landmarks. For the relativity, we aggregate facial landmark that conceptually formats a curve at each frame to establish local spatial features. For the trajectory, we estimate the movements of landmark composed features across time by self-attention mechanism, which captures pairwise dependency on the trajectory of the same landmark. This idea allows us to achieve state-of-the-art performances on UVA-NEMO, BBC, MMI Facial Expression, and SPOS datasets. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Relativity

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Sample complexity of posted pricing for a single item 24

Sample complexity of posted pricing for a single item

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Billy Jin Thomas Kesselheim Will Ma Sahil Singla Cornell University School of Operations Research and Information Engineering University of Bonn Institute of Computer Science and Lamarr Institute for Machine Learning and Artificial Intelligence Columbia University Graduate School of Business and Data Science Institute Georgia Tech School of Computer Science

ISBN: (纸本)9798331314385

Selling a single item to n self-interested buyers is a fundamental problem in economics, where the two objectives typically considered are welfare maximization and revenue maximization. Since the optimal mechanisms are often impractical and do not work for sequential buyers, posted pricing mechanisms, where fixed prices are set for the item for different buyers, have emerged as a practical and effective alternative. This paper investigates how many samples are needed from buyers' value distributions to find near-optimal posted prices, considering both independent and correlated buyer distributions, and welfare versus revenue maximization. We obtain matching upper and lower bounds (up to logarithmic factors) on the sample complexity for all these settings.

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Translation System for the Visually Impaired from English to Braille

Translation System for the Visually Impaired from English to...

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Communication & Computing (WCONF), World Conference on

作者： J. Aswini Lokesh Krishnaa M C. Lakshmipriya G. Lavanya Siva Subramanian R Dept of Artificial Intelligence & Machine Learning Saveetha Engineering College(Autonomous) Department of Artificial Intelligence and a Science Saveetha Engineering College (Autonomous) Department CSE S.A. Engineering College Department of Artificial Intelligence and Data Science Saveetha Engineering College(Autonomous) Department CSE R.M.K College of Engineering and Technology

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The amount of information in the modern world is huge and is growing at a rapid speed because of new technological advancements and new researches being conducted every day. Internet has paved a pivotal role in the distribution of information easily which has enriched human knowledge. This interconnected digital landscape has significantly enhanced accessibility and the flow of information, marking a transformative shift in how knowledge is shared and updated. Various software and websites enable the translation of information into any language, providing flexibility and accessibility across diverse linguistic preferences which helps people to grow their knowledge. But the Visually Impaired have no standard means to translate information to Braille which has caused a significant information gap for them. They are unable to get updated to new information and the information is not available for them in their language. This paper proposes to use deep learning which facilitates the translation of English text to Braille that aids the visually impaired in staying updated and also get access to information in Braille language.

关键词： Training Knowledge engineering Braille Accuracy Visual impairment Linguistics Software Internet Reliability Standards

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