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Object Detection and Recognition System Using Deep Learning Method

Object Detection and Recognition System Using Deep Learning ...

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international conference for Convergence of Technology (I2CT)

作者： Yashal Railkar Aditi Nasikkar Sakshi Pawar Pranjal Patil Rohini Pise Pimpri Chinchwad College of Engineering

Object detection has been studied by many researchers for important applications in the industry like detecting a road object for self-driving cars, medical research for detecting particular diseases, gesture control, etc. Object detection and recognition is incredibly very important wrt security purposes. As computers and models can work 24/7 it can watch for video surveillance in secure areas. Humans can quickly detect or make out what items are there in photos and photographs, where these images and pictures are located, and how they interact with systems when they see them. [1]. Object identification and tracking is a key challenge in CV systems and interactions, such as visual surveillance and human computer vision systems. Human visual systems are quick and precise, allowing them to handle complicated activities such as driving. computers will be able to drive automobiles using improvised and speedy errorfree object identification algorithms, yet they will require specialized sensors and auxiliary gadgets to relay real-time scenarios. [1]Using exact object recognition and picture classification approaches, strategies, and methodologies, it is critical and essential for deciding autonomous driving in metropolitan situations. Many big companies are currently working on this and achieving their goals day by day. In this report a object detection system has been proposed which can detect various objects, in fact it can detect almost any object wrt. training given to the model. Proposed methodology for object detection in the report is You Look Only Once (YOLO).

关键词： computers Training Visualization Computational modeling Object detection Visual systems Video surveillance

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Information Technology for Contact Tracking in the COVID-19 Pandemic 8

Information Technology for Contact Tracking in the COVID-19 ...

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8th ieee international conference on Problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Kuznetsov, Alexandr Gorbenko, Yuriy Kiian, Anastasiia Kuznetsova, Tetiana Ulianovska, Yuliia Rudyanova, Tatyana V. N. Karazin Kharkiv National University Department of Information Systems And Technologies Security Kharkiv Ukraine Jsc 'Institute of Information Technologies' Kharkiv Ukraine University of Customs And Finance Department of Computer Science And Software Engineering Dnipro Ukraine

ISBN: (纸本)9781665406826

the development and implementation of the latest information technologies for contact tracking in the context of the COVID-19 pandemic is an extremely important and urgent task, which is directly related to the possible adjustment and mitigation of quarantine restrictions without increasing the risks and threats of infection. In this article, we analyze, research, and substantiate various information technologies (such as Bluetooth, Wi-Fi, GPS, etc.) in contact tracking tools to prevent the spread of infectious diseases. We explore various aspects such as centralization;the need to use a central server (data warehouse);used cryptographic protection algorithms;acceptable actions when establishing contact, etc. the research results make it possible to formulate practical recommendations on the choice of information technologies for building global contact tracing systems. © 2021 ieee.

关键词： Data warehouses

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Different Categories of Feature Extraction and Machine Learning Classification Models Used for Hand Gesture Recognition systems: A Review

Different Categories of Feature Extraction and Machine Learn...

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international conference for Convergence of Technology (I2CT)

作者： Richa Dhiman Pooja Luthra Nongmeikapam thoiba Singh Department of Computer Science Engineering Chandigarh University Punjab India

Hand gestures are among the most common aspects of body expression that can be used for interactive communications. Hand movements can convey language information. Since they can facilitate interaction and provide a practical paradigm of interaction that can be applied in a number of situations, hand gestures are a fascinating issue for research. the HGR (Hand Gesture Recognition) system is used to recognise hand gestures. Feature extraction and classification are two stages that are crucial to the fundamental architecture of hand gesture recognition. In a hand gesture recognition system, feature extraction refers to techniques for establishing connections between two variables and accurately representing the information. the classifiers are used to classify the features of the images. Several feature extraction techniques and classifiers are mentioned in this paper. Hand gesture recognition is extensively used in SL (sign language), TV controlling, HCI (human computer interaction) systems, VR (virtual reality), games, etc. due to its creativity and rapidity of involvement. the various existing techniques for HGR are surveyed and analyzed. the analysis of existing techniques showed that there are some major issues with existing techniques, such as data augmentation and generalisation issues, reliability issues, limited datasets, Complex backgrounds degrade the performance of the existing techniques. the diversity and complexity of actions have a massive effect mostly on classification performance and accuracy throughout the purpose of selecting hand movements. In order to increase the accuracy of hand gesture recognition, machine learning methods can be applied. In this paper, various existing techniques are surveyed and compared with different parameters such as accuracy, precision, sensitivity, specificity, etc.

关键词： Human computer interaction Solid modeling TV Sensitivity Gesture recognition Machine learning Virtual reality

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A Probabilistic Model for Long-Term Trajectory Prediction in Multi-Agent systems

A Probabilistic Model for Long-Term Trajectory Prediction in...

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ieee international conference on Intelligent Transportation engineering (ICITE)

作者： Haonan Cui Jianyu Zhou College of Software Nankai University Tianjin China

ISBN: (数字)9798350314212

ISBN: (纸本)9798350314229

the accurate prediction of vehicle trajectories in complex traffic environments is essential for ensuring the safety and effectiveness of autonomous driving systems. In this paper, we propose a novel probabilistic model for long-term trajectory prediction that consists of two components: a single-agent probabilistic model and a multi-agent risk-averse sampling algorithm. the single-agent probabilistic model is based on a dynamic Bayesian network, which considers the driver's maneuvering decisions and integrates surrounding lane information. In the multi-agent risk-averse sampling algorithm, feasible future positions are sampled simultaneously for all agents based on the probabilistic model, and a risk potential field model is then applied to reject the high-collision-risk samples. Eventually, a probability distribution of the combinations of long-term trajectories is predicted. After conducting experiments on the nuScenes dataset, our method achieved competitive performance in trajectory prediction compared with other state-of-the-art methods.

关键词： Accuracy Heuristic algorithms Transportation Predictive models Probabilistic logic Prediction algorithms Trajectory Bayes methods Safety Vehicle dynamics

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Deep Feature Rotation for Multimodal Image Style Transfer 8

Deep Feature Rotation for Multimodal Image Style Transfer

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8th NAFOSTED conference on Information and computer Science, NICS 2021

作者： Nguyen, Son Truong Tuyen, Nguyen Quang Phuc, Nguyen Hong Hanoi University of Science and Technology School of Mechanical Engineering Viet Nam University of Aizu School of Computer Science and Engineering Japan Eastern International University Department of Software Engineering Viet Nam

ISBN: (纸本)9781665410014

Recently, style transfer is a research area that attracts a lot of attention, which transfers the style of an image onto a content target. Extensive research on style transfer has aimed at speeding up processing or generating high-quality stylized images. Most approaches only produce an output from a content and style image pair, while a few others use complex architectures and can only produce a certain number of outputs. In this paper, we propose a simple method for representing style features in many ways called Deep Feature Rotation (DFR), while not only producing diverse outputs but also still achieving effective stylization compared to more complex methods. Our approach is representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. We also analyze our method by visualizing output in different rotation weights. Our code is available at https://***/sonnguyen129/deep-feature-rotation. © 2021 ieee.

关键词： Rotation

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Wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine 8th

Wind Power Forecasting Model Based on Feature Processing and...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wei, Xiongfei Wang, Jing Ruan, Yi Fang, Yuanjie College of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China School of Electronic Engineering Chaohu University Anhui Chaohu238024 China

ISBN: (纸本)9789819602315

To improve the forecasting accuracy of wind power, a wind power forecasting (WPF) model based on feature cleaning combined with deep hybrid kernel extreme learning machine (DHKELM) is proposed. the Pearson correlation coefficient (PCC) method is used to optimize the features, eliminate irrelevant features, and the isolated forest (IF) algorithm is used to clean the anomalous data in the retained features, which enhances the correlation between the selected features and wind power, and then combines the DHKELM to predict the wind power. Finally, the proposed model is applied to predict the wind power of a 1MW wind turbine, and the results of the progressive forecasting of the proposed method and the forecasting results of different models are compared. the results show that the PCC-IF-DHKELM forecasting model has a root-mean-square error of 0.01928 MW, an average absolute error of 0.015254 MW, and a correlation coefficient of 0.99435, which is significantly better than the other forecasting models, proving the effectiveness of the PCC-IF-DHKELM forecasting method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind forecasting

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XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU)

XNLI 2.0: Improving XNLI dataset and performance on Cross Li...

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international conference for Convergence of Technology (I2CT)

作者： Ankit Kumar Upadhyay Harsit Kumar Upadhya Dept. of Computer Science and Engineering JSS Academy of Technical Education Bengaluru India Dept. of Information Science and Engineering JSS Academy of Technical Education Bengaluru India

Natural Language Processing systems are heavily dependent on the availability of annotated data to train practical models. Primarily, models are trained on English datasets. In recent times, significant advances have been made in multilingual understanding due to the steeply increasing necessity of working in different languages. One of the points that stands out is that since there are now so many pre-trained multilingual models, we can utilize them for cross-lingual understanding tasks. Using cross-lingual understanding and Natural Language Inference, it is possible to train models whose applications extend beyond the training language. We can leverage the power of machine translation to skip the tiresome part of translating datasets from one language to another. In this work, we focus on improving the original XNLI dataset by re-translating the MNLI dataset in all of the 14 different languages present in XNLI, including the test and dev sets of XNLI using Google Translate. We also perform experiments by training models in all 15 languages and analyzing their performance on the task of natural language inference. We then expand our boundary to investigate if we could improve performance in low-resource languages such as Swahili and Urdu by training models in languages other than English.

关键词： Training Analytical models Transfer learning Data models Internet Machine translation Task analysis

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A Sizing and Techno-Economic Analysis for Local Hybrid Microgrid

A Sizing and Techno-Economic Analysis for Local Hybrid Micro...

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international Multidisciplinary conference on computer and Energy Science (SpliTech)

作者： Marija Mandić Motalleb Miri Mario Barišić Iva Popović KONČAR - Electrical Engineering Institute Ltd. Zagreb Croatia

Concerns about climate change and global warming are increasing, and it seems that hydrogen will be one of the keys as a potential energy carrier in energy systems. In this paper, a techno-economic analysis has been carried out for a small-scale hybrid microgrid powered exclusively by renewable energy sources (RES) and operating continuously without CO 2 emissions. the goal is to produce green hydrogen using electrolysis technology powered by solar and wind energy. When surplus electricity is available, it is used to charge the batteries and/or power electrolyzers to produce hydrogen, which will be stored in a tank. the hydrogen will then be converted to electrical energy using fuel cell (FC) technology when solar energy is insufficient to meet electricity needs, ensuring continuous supply to meet long-term autonomy requirements. the sizing of the photovoltaic (PV) cells, batteries, electrolyzers, FC and hydrogen tank have been studied with the objective of obtaining a fully autonomous hybrid microgrid. Using HOMER software, five different scenarios have been tested considering various storage components. the optimal solution is a system with batteries and a high-pressure hydrogen tank in the form of a bundle containing 12 gas bottles that includes electrically driven compressor (300 bar).

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Noise Cancellation by Fast Fourier Transform for Wav2Vec2.0 based Speech-to-Text System

Noise Cancellation by Fast Fourier Transform for Wav2Vec2.0 ...

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international conference for Convergence of Technology (I2CT)

作者： Surya Prakash Gupta Spoorthy. V Shashidhar G Koolagudi Department of Computer Science & Engineering National Institute of Technology Karnataka Mangalore India

Speech-To-Text (STT) systems are a part of the Speech Recognition domain in which speech is given as input, and it generates the transcript. the input speech sometimes disrupts the STT system and generates incorrect transcripts because of background noise. In this work, we have discussed a Fast Fourier Transform (FFT) based noise cancellation method for Hindi words with background noise and performed speech to text conversion using a fine-tuned and pre-trained Wav2Vec2.0 model. the background noise added to the audio samples is Gaussian white noise with three different intensity levels, 0.01, 0.03, and 0.05 units, indicated by the Gaussian distribution’s standard deviation (STD). the model has been trained on the OpenSLR Hindi dataset. the proposed system is evaluated by the metric Character Error Rate (CER). the testing of the model is done using 20 Hindi words in both clean and noisy conditions. the results obtained proved that the noise cancellation was found effective in terms of CER, and on first level noise with an STD of 0.01, the CER is better after noise cancellation than its noisy counterpart.

关键词： Fast Fourier transforms Error analysis Speech recognition White noise Noise cancellation Background noise Noise measurement

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Framework for Improving the Accuracy of the Machine Learning Model in Predicting Future Values

Framework for Improving the Accuracy of the Machine Learning...

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international conference for Convergence of Technology (I2CT)

作者： Vinodha K Geetha Dayalan Dept of Computer Science and Engineering PES University-ECC Bangalore India

Machine Learning models are generated by providing training data to the learning algorithms. the model designed is rated by the accuracy in predicting future values. Unfortunately, the accuracy of the models is not satisfactory. the presence of different domains requires various data optimization techniques to improve the accuracy of machine learning models. For any predictive analyst, there exists a high correlation between the learning model and the input data. Moreover, the optimization level of the machine learning algorithm is characterized by its speed, accuracy, training time, the volume of data, and implementation level. Hence, for a data scientist, it is a difficult task to decide the granularity of the algorithm on training data considering the issues related to data quality viz, missing values or errors in input data set, presence of textual or Boolean values in data set, an improper number of domains than required, non-uniformity in the features. In general, machine learning systems are considered black boxes even to the research community that builds them. this makes it hard for them to access the results. In this paper, the main focus is laid on testing the quality of training data through a proposed framework. the framework will be designed in line with the aspects of a machine learning model to be tested based on the quality of data, quality of features, and quality of algorithms.

关键词： Training Machine learning algorithms Data integrity Training data Machine learning Predictive models Prediction algorithms

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