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Detection of Emergency Vehicles in Traffic and Assign Traffic Free Path Using Deep learning

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Detection of Emergency Vehicles in Traffic and Assign Traffi...

Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Sriharsha Vikruthi Tejesh Reddy Singasani V T Ram Pavan Kumar M P V V S D Nagendrudu Ch Raghavendra R. Sahith Dept of CSE B V Raju Institute of Technology Narsapur Medak Telangana India University of Cumberlands Lousiville KY Dept of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhrapradesh India Department of Artificial Intelligence and Machine Learning Aditya Univeristy Suramplaem India Dept of CSE(Data Science) KKR & KSR Institute of Technology and Sciences Guntur Andhra Pradesh Dept of Computer Science and Engineering CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Too much of Traffic jams are in a highly populated country. Sometimes emergency vehicles such as fire-fighter and ambulance get stuck in traffic, putting lives at threat in many cases. Priority should be given to special vehicles and their way should be cleared. However, traffic police find it difficult or sometimes impossible to handle with it. Emergency vehicles have visual and audio warning devices help them to overtake traffic by demanding as little space as possible from other vehicles. Many death happen due to delay in doctors, ambulance and fire engine vehicles while reaching their destinations. The traditional systems didn't provide path free route accurately. Therefore, by using detection of emergency vehicles in traffic and assign traffic free path using deep learning system, emergency vehicles can get free path or fast route by observing the traffic cameras. So, those emergency vehicles can reach the location on time by using RNN system. Therefore, the main objective is to improve this system for better results interms of accuracy, detection rate and precision.

关键词： Deep learning Space vehicles Visualization Sentiment analysis Recurrent neural networks Accuracy Law enforcement Roads Medical services Automobiles

Translation System for the Visually Impaired from English to Braille

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

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

A Privacy-Preserving Framework for Collaborative machine learning with Kernel Methods

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A Privacy-Preserving Framework for Collaborative Machine Lea...

IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Anika Hannemann Ali Burak Ünal Arjhun Swaminathan Erik Buchmann Mete Akgün Dept. of Computer Science Leipzig University Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany Medical Data Privacy and Privacy-preserving Machine Learning (MDPPML) University of Tübingen Institute for Bioinformatics and Medical Informatics (IBMI) University of Tübingen Germany

It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, if the use case rules out privacy-preserving approaches that introduce noise or entail significant computational overhead. An example for such a use case is machine learning on clinical data. To realize exact and efficient privacy preserving computation of kernel methods, we propose FLAKE, a Framework for learning with Anonymized KErnels on horizontally distributed data. With our method, the data sources mask their data so that a Gram matrix can be computed without compromising privacy or utility. The Gram matrix allows to calculate many kernel matrices, which can be used to train kernel-based machine learning algorithms such as Support Vector machines. We prove that our framework prevents an adversary from learning the input data or the number of input features under a semi-honest threat model. The conducted experiments on clinical, genomic, and image data provide confirmation that our approach is applicable across a wide range of settings. Additionally, our method outperforms comparable approaches in both computational efficiency and accuracy. Thus, FLAKE is a lightweight, applicable approach suitable for various use cases.

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A Fast and Scalable Method for Inferring Phylogenetic Networks from Trees by Aligning Lineage Taxon Strings

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arXiv 2023年

作者： Zhang, Louxin Abhari, Niloufar Colijn, Caroline Wu, Yufeng Dept. of Mathematics Centre for Data Science Machine Learning National University of Singapore 119076 Singapore Dept. of Mathematics Simon Fraser University BurnabyBCV5A 1S6 Canada Dept. of Computer Science and Engineering University of Connecticut StorrsCT06269 United States

The reconstruction of phylogenetic networks is an important but challenging problem in phylogenetics and genome evolution, as the space of phylogenetic networks is vast and cannot be sampled well. One approach to the problem is to solve the minimum phylogenetic network problem, in which phylogenetic trees are first inferred, then the smallest phylogenetic network that displays all the trees is computed. The approach takes advantage of the fact that the theory of phylogenetic trees is mature and there are excellent tools available for inferring phylogenetic trees from a large number of biomolecular sequences. A tree-child network is a phylogenetic network satisfying the condition that every non-leaf node has at least one child that is of indegree one. Here, we develop a new method that infers the minimum tree-child network by aligning lineage taxon strings in the phylogenetic trees. This algorithmic innovation enables us to get around the limitations of the existing programs for phylogenetic network inference. Our new program, named ALTS, is fast enough to infer a tree-child network with a large number of reticulations for a set of up to 50 phylogenetic trees with 50 taxa that have only trivial common clusters in about a quarter of an hour on *** Codes 05C30 © 2023, CC BY.

关键词： Phylogenetic

Facial Expression Recognition Using Multi-Block Deep CNN

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Facial Expression Recognition Using Multi-Block Deep CNN

International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

SEMIPARAMETRIC EFFICIENT INFERENCE IN ADAPTIVE EXPERIMENTS

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arXiv 2023年

作者： Cook, Thomas Mishler, Alan Ramdas, Aaditya Dept. of Mathematics and Statistics University of Massachusetts United States J.P. Morgan AI Research J.P. Morgan Chase & Co. United States Dept. of Statistics & Data Science and Machine Learning Carnegie Mellon University United States

We consider the problem of efficient inference of the Average Treatment Effect in a sequential experiment where the policy governing the assignment of subjects to treatment or control can change over time. We first provide a central limit theorem for the Adaptive Augmented Inverse-Probability Weighted estimator, which is semiparametric efficient, under weaker assumptions than those previously made in the literature. This central limit theorem enables efficient inference at fixed sample sizes. We then consider a sequential inference setting, deriving both asymptotic and nonasymptotic confidence sequences that are considerably tighter than previous methods. These anytime-valid methods enable inference under data-dependent stopping times (sample sizes). Additionally, we use propensity score truncation techniques from the recent off-policy estimation literature to reduce the finite sample variance of our estimator without affecting the asymptotic variance. Empirical results demonstrate that our methods yield narrower confidence sequences than those previously developed in the literature while maintaining time-uniform error control. © 2023, CC BY.

关键词： Inverse problems

ChatGPT-guided Semantics for Zero-shot learning

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ChatGPT-guided Semantics for Zero-shot Learning

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Fahimul Hoque Shubho Townim Faisal Chowdhury Ali Cheraghian Morteza Saberi Nabeel Mohammed Shafin Rahman Dept. of Electrical and Computer Engineering North South University Bangladesh Australian Institute for Machine Learning University of Adelaide Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia School of Computer Science and DSI University of Technology Sydney Australia

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics include manual attributes or automatic word vectors from language models (like word2vec). We know attribute annotation is costly, whereas automatic word vectors are relatively noisy. To address this problem, we explore how ChatGPT, a large language model, can enhance class semantics for ZSL tasks. ChatGPT can be a helpful source to obtain text descriptions for each class containing related attributes and semantics. We use the word2vec model to get a word vector using the texts from ChatGPT. Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT. More specifically, we leverage ChatGPT to provide extra supervision for the class description, eventually benefiting ZSL models. We evaluate our approach on various 2D image (CUB and AwA) and 3D point cloud (ModelNet10, ModelNet40, and ScanObjectNN) datasets and show that it improves ZSL performance. Our work contributes to the ZSL literature by applying ChatGPT for class semantics enhancement and proposing a novel word vector fusion method.

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ChatGPT-guided Semantics for Zero-shot learning

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arXiv 2023年

作者： Shubho, Fahimul Hoque Chowdhury, Townim Faisal Cheraghian, Ali Saberi, Morteza Mohammed, Nabeel Rahman, Shafin Dept. of Electrical and Computer Engineering North South University Bangladesh Australian Institute for Machine Learning University of Adelaide Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia School of Computer Science and DSI University of Technology Sydney Australia

关键词： Zero-shot learning

All labels are not created equal: Enhancing semi-supervision via label grouping and co-training

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arXiv 2021年

作者： Nassar, Islam Herath, Samitha Abbasnejad, Ehsan Buntine, Wray Haffari, Gholamreza Dept of Data Science and AI Faculty of IT Monash University Australia Australian Institute for Machine Learning University of Adelaide Australia

Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on iteratively using the model to generate artificial labels for the unlabeled data to train against. A common property among its various methods is that they only rely on the model's prediction to make labeling decisions without considering any prior knowledge about the visual similarity among the classes. In this paper, we demonstrate that this degrades the quality of pseudo-labeling as it poorly represents visually similar classes in the pool of pseudo-labeled data. We propose SemCo, a method which leverages label semantics and co-training to address this problem. We train two classifiers with two different views of the class labels: one classifier uses the onehot view of the labels and disregards any potential similarity among the classes, while the other uses a distributed view of the labels and groups potentially similar classes together. We then co-train the two classifiers to learn based on their disagreements. We show that our method achieves state-of-the-art performance across various SSL tasks including 5.6% accuracy improvement on Mini-ImageNet dataset with 1000 labeled examples. We also show that our method requires smaller batch size and fewer training iterations to reach its best performance. We make our code available at https://***/islam-nassar/semco. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics