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arXiv 2022年

作者： Ignatiadis, Nikolaos Wang, Ruodu Ramdas, Aaditya Department of Statistics University of Chicago United States Department of Statistics and Actuarial Science University of Waterloo Canada Departments of Statistics and Machine Learning Carnegie Mellon University United States

We study how to combine p-values and e-values, and design multiple testing procedures where both p-values and e-values are available for every hypothesis. Our results provide a new perspective on multiple testing with data-driven weights: while standard weighted multiple testing methods require the weights to deterministically add up to the number of hypotheses being tested, we show that this normalization is not required when the weights are e-values that are independent of the p-values. Such e-values can be obtained in the meta-analysis setting wherein a primary dataset is used to compute p-values, and an independent secondary dataset is used to compute e-values. Going beyond meta-analysis, we showcase settings wherein independent e-values and p-values can be constructed on a single dataset itself. Our procedures can result in a substantial increase in power, especially if the non-null hypotheses have e-values much larger than one. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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The Age and Gender Detection using Deep Convolution Neural Network

The Age and Gender Detection using Deep Convolution Neural N...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Shweta Chauhan Rahul Kumar Bibek Kumar Department of artificial intelligence and Machine Learning Madhav Institute of science and technology Gwalior Madhya Pradesh 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)9798350385205

ISBN: (纸本)9798350385212

Automatic age and gender classification has become important researcher since the rise of social platforms and social media. The main advancements in face recognition are the ability to determine a person's age and gender. This paper makes a significant contribution by providing a survey of different face recognition methods for determining age and gender. A deep neural network (DNN) known as a convolutional neural network is frequently used for natural language processing and image recognition and processing. The proposed method significantly enhances three elements: face alignment, face recognition, and face recognition accuracy. All weight vectors of people in the same age group within a given database are averaged together. To predict gender and age, picture classification and regression models have been discussed in this paper respectively. Although training the proposed network to make accurate predictions takes a lot of time, it significantly improves age and gender categorization precision. From the experimental results we compared and analyzed the work to the most recent UTK-Face benchmark for estimating age and gender.

关键词： Training Surveys Accuracy Image recognition Social networking (online) Face recognition Predictive models Vectors Natural language processing Convolutional neural networks

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ANALYTIC DAG CONSTRAINTS FOR DIFFERENTIABLE DAG learning

arXiv

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arXiv 2025年

作者： Zhang, Zhen Ng, Ignavier Gong, Dong Liu, Yuhang Gong, Mingming Huang, Biwei Zhang, Kun van den Hengel, Anton Shi, Javen Qinfeng Australian Institute for Machine Learning The University of Adelaide Australia Department of Philosophy Carnegie Mellon University United States School of Computer Science and Engineering The University of New South Wales Australia School of Mathematics and Statistics The University of Melbourne Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates UC San Diego United States

Recovering the underlying Directed Acyclic Graph (DAG) structures from observational data presents a formidable challenge, partly due to the combinatorial nature of the DAG-constrained optimization problem. Recently, researchers have identified gradient vanishing as one of the primary obstacles in differentiable DAG learning and have proposed several DAG constraints to mitigate this issue. By developing the necessary theory to establish a connection between analytic functions and DAG constraints, we demonstrate that analytic functions from the set {f(x) = c0 + P∞i=1 cixi|∀i > 0, ci > 0;r = limi→∞ ci/ci+1 > 0} can be employed to formulate effective DAG constraints. Furthermore, we establish that this set of functions is closed under several functional operators, including differentiation, summation, and multiplication. Consequently, these operators can be leveraged to create novel DAG constraints based on existing ones. Using these properties, we design a series of DAG constraints and develop an efficient algorithm to evaluate them. Experiments in various settings demonstrate that our DAG constraints outperform previous state-of-the-art comparators. Our implementation is available at https://***/zzhang1987/AnalyticDAGlearning. © 2025, CC BY-NC-SA.

关键词： Constrained optimization

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CROSS-TECHNOLOGY GENERALIZATION IN SYNTHESIZED SPEECH DETECTION: EVALUATING AST MODELS WITH MODERN VOICE GENERATORS

arXiv

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arXiv 2025年

作者： Ustinov, Andrew Yordanov, Matey Kuchma, Andrei Bychkov, Mikhail Department of Machine Learning and Data Analytics ITMO University Russia AI Innovation Hub ANTEI Limited Hong Kong Department of Integrative Systems and Design Hong Kong University of Science and Technology AI Innovation Hub ANTEI Limited Hong Kong

This paper evaluates the Audio Spectrogram Transformer (AST) architecture for synthesized speech detection, with focus on generalization across modern voice generation technologies. Using differentiated augmentation strategies, the model achieves 0.91% EER overall when tested against ElevenLabs, NotebookLM, and Minimax AI voice generators. Notably, after training with only 102 samples from a single technology, the model demonstrates strong cross-technology generalization, achieving 3.3% EER on completely unseen voice generators. This work establishes benchmarks for rapid adaptation to emerging synthesis technologies and provides evidence that transformer-based architectures can identify common artifacts across different neural voice synthesis methods, contributing to more robust speech verification systems. © 2025, CC BY-NC-ND.

关键词： Spectrographs

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Advanced Process Control in Manufacturing Using IoT Devices and Artificial Neural Networks 4

Advanced Process Control in Manufacturing Using IoT Devices ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sumathi, V. Ramesh, S. Basavaraddi, Chethan Chandra S. Visumathi, J. Ishwarya, M.V. Srinivasan, S. Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India R&d Don Bosco Institute of Technology Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural Networks (ANNs) and the Internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. Recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems. © 2024 IEEE.

关键词： Adaptive control systems

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Estimating fish weight growth in aquaponic farming through machine learning techniques

Estimating fish weight growth in aquaponic farming through m...

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Intelligent Technologies (CONIT), International Conference on

作者： Purushottam Kumar Pranav Tiwari U Srinivasulu Reddy CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won’t be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions

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ML Powered Analytics for Sensing Demand in Consumer Industry

ML Powered Analytics for Sensing Demand in Consumer Industry

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IEEE Punecon

作者： Rahul Shanbhogue Yashwanth D N Anwesh Reddy Paduri Panish Ramakrishna Srikanth Prabhu Muralidhara Sarma Dhulipati Narayana Darapaneni Data Science & Machine Learning PES University Bangalore India Department of Commerce and Management Koneru Lakshmaiah Eduaction foundation Hyderabad India Director – AIML Great Learning/Northwestern University Illinois USA

The consumer goods industry is facing significant challenges in meeting the consumer's everevolving demands and preferences. The e-commerce and online delivery brought in the ease of access to purchase products, while the popularity of social media brought the social sentiment angle which are impacting customer purchases patterns. These factors are leading to a rise in influencer marketing campaigns and have enabled businesses or brands to reach consumers faster. The goal of the study is to address these challenges and to explore the use of machine learning and big data analytic techniques to provide insights by understanding consumer purchase patterns, sales data, market and social media trends, news feed and stock levels to predict future demand for products in advance which will better enable inventory planning.

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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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Band selection in Hyperspectral Images using information similarity ranking

Band selection in Hyperspectral Images using information sim...

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machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： T. Hitendra Sarma R. Dharma Reddy K. Mrudula K. Rammohan Rao Sankalp Dhondi Murali Kanthi Department of Information Technology Vasavi College of Engineering Hyderabad India Department of Mathematics GNITS Hyderabad India Machine Learning Engineer Brane Enterprises Pvt. Ltd. Hyderabad India Department of Data Science CMR Technical Campus Hyderabad India

ISBN: (数字)9798350389678

ISBN: (纸本)9798350389685

Band selection in hyperspectral images is crucial for reducing redundant features and improving subsequent tasks like classification and segmentation. This article presents a novel band ranking method based on information similarity measures from the concepts of information theory. We introduce the Normalized Mutual Information for similarity and Variation of Information for dissimilarity to create a ranking scheme based on the affinity of a band to become a cluster center. We explore hybrid schemes combining structural and informational attributes. Supervised approaches using label-wise similarities using ground truth supervision are also investigated. Evaluation against state-of-the-art band selection algorithms on popular datasets, using both traditional machine learning and Deep learning models, demonstrates superior results. This study underscores the significance of informational and structural attributes in unsupervised band selection and the potential for further enhancement through supervised similarity with ground truth information.

关键词： Deep learning Image segmentation machine learning algorithms Clustering algorithms Task analysis Mutual information machine intelligence

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Bayesian Model Averaging for Partial Ordering Continual Reassessment Methods

arXiv

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arXiv 2024年

作者： Kovačević, Luka Jaki, Thomas Barnett, Helen Mozgunov, Pavel MRC Biostatistics Unit University of Cambridge United Kingdom Department of Machine Learning and Data Science University of Regensburg MRC Biostatistics Unit University of Cambridge United Kingdom Lancaster University United Kingdom

Phase I clinical trials are essential to bringing novel therapies from chemical development to widespread use. Traditional approaches to dose-finding in Phase I trials, such as the’3+3’ method and the Continual Reassessment Method (CRM), provide a principled approach for escalating across dose levels. However, these methods lack the ability to incorporate uncertainty regarding the dose-toxicity ordering as found in combination drug trials. Under this setting, dose-levels vary across multiple drugs simultaneously, leading to multiple possible dose-toxicity orderings. The Partial Ordering CRM (POCRM) extends to these settings by allowing for multiple dose-toxicity orderings. In this work, it is shown that the POCRM is vulnerable to’estimation incoherency’ whereby toxicity estimates shift in an illogical way, threatening patient safety and undermining clinician trust in dose-finding models. To this end, the Bayesian model averaged POCRM (BMA-POCRM) is proposed. BMA-POCRM uses Bayesian model averaging to take into account all possible orderings simultaneously, reducing the frequency of estimation incoherencies. The effectiveness of BMA-POCRM in drug combination settings is demonstrated through a specific instance of estimate incoherency of POCRM and simulation studies. The results highlight the improved safety, accuracy and reduced occurrence of estimate incoherency in trials applying the BMA-POCRM relative to the POCRM model. © 2024, CC BY.

关键词： Toxicity

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