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On-Device Deep learning for Retrieving System and User Timestamps from Noisy Chat Images

On-Device Deep Learning for Retrieving System and User Times...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Kayarvizhy, N. Choudhury, Antara Roy Rekha, G.S. Bhuvan, G. Sanchi, Chandramouli BMS College of Engineerning Bangalore560019 India

Extracting information from scanned or digital images are helpful in different real time applications. Optical Character recognition (OCR) is popular in extraction of information from digital images taken in the ideal lighting condition. But OCR method is not efficient when the images are scanned and clicked with the noisy and blurry environment. Deep learning-based OCR method performs good in text detection and localization in the digital images. Existing methods do not focus on the timestamp detection in screenshots. In this paper, YoloV5 (You Only Look Once) model and Faster RCNN (Faster Region-Convolutional Neural Network) are implemented to locate the region of time stamps from the screenshot images from various messaging applications. Our models take mobile screenshot of conversation applications as input and detect the system timestamps and User timestamps. 5k screenshot images have been collected from 16 messaging applications with various backgrounds. The images are annotated with 3 classes. YoloV5 and faster RCNN models are trained with the annotated dataset. Accuracy of YoloV5 model increased to 97.6 with training it with more dataset than RCNN and also the size is limited to 3.85MB. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Convolutional neural networks

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ProductGuard: Empowering Informed Choices for Safe Living

ProductGuard: Empowering Informed Choices for Safe Living

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Prof. Sandyarani Vadlamudi K. S. Shashikala Suyash Ganesh Patil Rajath U Shetty Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bangalore Karnataka

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In a landscape where consumer decisions significantly influence personal health and well being, the necessity for informed choices regarding product safety is paramount. This project introduces ProductGuard, a mobile application designed to empower users in making conscientious and informed decisions about skincare and personal care products. The project integrates four core components aimed at assisting users in evaluating product safety, comparing ingredients, providing personalized recommendations based on skin type, and offering expert guidance through a chatbot interface. The first component employs advanced image analysis technology utilizing a dataset extracted from the Environmental Working Group (EWG) website, enabling users to capture product ingredient images and receive detailed safety analyses. The second component allows comparative analysis by uploading images of two product ingredient lists, swiftly determining the safer option. Leveraging deep learning models and MobileNetV2, the third component performs skin type classification based on facial images, ensuring accurate categorization into dry, oily, normal, or sensitive skin, further providing tailored product recommendations. Lastly, a chatbot powered by ChatGPT serves as the fourth component, furnishing users with real-time guidance on skincare, ingredient queries, and safe living practices. Developed using React Native, Flask integration, and Python backend, this application amalgamates cutting-edge technology with user-centric design for seamless navigation and accessibility across various devices. ProductGuard surpasses existing systems by offering a holistic approach to skincare analysis, integrating advanced image recognition, personalized recommendations, and real-time expert assistance, thereby empowering users to make informed and safer skincare choices.

关键词： Deep learning Image analysis Accuracy Chatbots Skin Real-time systems User experience Reliability Product safety Usability

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international conference on Mathematics, Statistics and Computing Technology 2021, ICMSCT 2021

International Conference on Mathematics, Statistics and Comp...

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3rd international conference on Mathematics, Statistics and Computing Technology 2021, ICMSCT 2021

The proceedings contain 30 papers. The topics discussed include: the modelling of heteroscedastics IDR-USD exchange rate with intervention and outlier factors;supervised and unsupervised data mining techniques on employability of public higher learning institute graduates in Malaysia;Box-Jenkins and state space model in forecasting Malaysia road accident cases;accuracy and bias of the Rasch rating scale person estimates using maximum likelihood approach: a comparative study of various sample sizes;the effect of chemical parameters on water quality index in machine learning studies: a meta-analysis;identification of inhomogeneous temperature on stainless steel using statistical analysis;and predicting particulate matter (PM2.5) in Malaysia using multiple linear regression and artificial neural network.

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Efficient Approximate Multipliers for Neural Network Applications 6th

Efficient Approximate Multipliers for Neural Network Applic...

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6th international conference on Computational Intelligence in data mining, ICCIDM 2021

作者： Aizaz, Zainab Khare, Kavita Tirmizi, Aizaz Department of Electronics and Communication Engineering Maulana Azad National Institute of Technology MANIT Mata Mandir Link Road 3 M.P. Bhopal462003 India Department of Electronics and Communication Engineering Rabindranath Tagore University Bhojpur Chiklod Rd M.P. Bhopal464993 India

ISBN: (纸本)9789811694462

In this era of machine learning, computationally efficient multiplier circuits are essential as most of the calculations on a neural network-based processor involve the multiplication process. Multiplication operation is a major bottleneck on a neural network accelerator. Approximate computing is a recent paradigm in which exact circuits are deliberately made inaccurate to reduce the circuit complexity. The loss in accuracy is tolerated by neural networks because of their inherent error tolerance, to achieve reduced area, delay and energy consumption benefits. In this paper, neural network is designed on an FPGA using an approximate Booth multiplier by replacing the exact multiplication. The proposed multipliers are analyzed on handwritten digit recognition using neural networks. A novel 4-2 compressor is designed using the concept of approximate computing to be used in Booth multiplier circuit. The k-map of exact compressor is modified to design approximate compressor, and a comparatively simpler circuit is obtained. The synthesis result of the proposed approximate multipliers shows 17.69 % energy reduction with a 0.9 % loss in accuracy as compared to exact multipliers. Upon comparison with the state-of-the-art neural networks, the proposed neural network provides high classification accuracy of 96.23 %. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Compressors

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Probabilistic Neuro-Fuzzy System in Medical Diagnostic Task and its Lazy learning-Selflearning 3

Probabilistic Neuro-Fuzzy System in Medical Diagnostic Task ...

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3rd international conference on Informatics and data-Driven Medicine (IDDM)

作者： Bodyanskiy, Yevgeniy Deineko, Anastasiia Pliss, Iryna Chala, Olha Kharkiv Natl Univ Radio Elect Control Syst Res Lab Kharkiv Ukraine

The computational intelligence system that is a hybrid of probabilistic neural network and the neuro-fuzzy system is proposed for solving the medical diagnostic tasks. The distinctive feature of the proposed system is the ability to process data that are given in different scales: numerical, ordinal, nominal, and binary. Also, the tuning process of the system is a hybrid of lazy learning and selflearning, according to T. Kohonen. Moreover, it is characterized by high processing speed, comparing to deep neural networks which are learning with error backpropagation procedures. The diagnostic system that is under consideration is characterized by uncomplicated computational implementation. It is intended to work with both short and long datasets in conditions of overlapping classes of diagnosis, which is typical for medical applications.

关键词： Medical data mining Probabilistic Neural Network Neuro-Fuzzy System Membership Function Lazy learning pattern recognition

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Research on Unbalanced data Processing Algorithm Base Tomeklinks-Smote 20

Research on Unbalanced Data Processing Algorithm Base Tomekl...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Li Ai-jun Zhang Peng Shanxi Univ Finance & Econ Taiyuan 030006 Peoples R China

ISBN: (纸本)9781450375511

Traditional machine learning algorithms tend to bias "majority" for classification of unbalanced data, which makes the classification accuracy of less-class samples lower. In order to improve the classification accuracy of less-class samples in the data set, this paper proposes a method based on imbalance TLS algorithm for data processing. This method first deletes duplicate samples in the original data set, and then deletes the boundary samples and noise samples that are Tomek links pairs' in the majority class and the minority class in the data set through the Tomek links undersampling algorithm and then oversampling the minority class samples with the smoth algorithm. After the obtained large-class sample and the obtained small-class sample are approximately balanced. On several commonly used data sets in the UCI database, compare with the original data set, the tradition al SMOTE oversampling only for small samples, and the traditional Tomek link under-sampling method for large samples only, Use SVM, logistic regression, Multilayer Perceptron (Neural Network) and random forest for classification. The experiment proves that the sampling method adopted in this paper can indeed improve the recognition of small class samples.

关键词： unbalanced data Tomek Links Smote classification

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Heterogeneous Feature Fusion Based machine learning on Shallow-Wide and Heterogeneous-Sparse Industrial datasets 25th

Heterogeneous Feature Fusion Based Machine Learning on Shall...

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Yang, Zijiang Watari, Tetsushi Ichigozaki, Daisuke Mitsutoshi, Akita Takahashi, Hiroaki Suga, Yoshinori Liao, Wei-keng Choudhary, Alok Agrawal, Ankit Department of Electrical and Computer Engineering Northwestern University Evanston United States Toyota Motor Corporation Toyota Japan

ISBN: (纸本)9783030687984

Although machine learning has gained great success in industry, there are still many challenges in mining industrial data, especially in manufacturing domains. Because industrial data can be 1) shallow and wide, 2) highly heterogeneous and sparse. Particularly, mining on sparse data (i.e. data with missing features) is extremely challenging, because it is not easy to fill in some features (e.g. images), and removing data points would reduce the data size further. Thus, in this work, we propose a machine learning framework including transfer learning, heterogeneous feature fusion, principal component analysis and gradient boosting to solve these challenges and effectively develop predictive models on industrial datasets. Compared to a non-fusion method and a traditional fusion method on two real world datasets from Toyota Motor Corporation, the results show that the proposed method can not only maximize the utility of available features and data to achieve more stable and better performance, but also give more flexibility when predicting new unseen data points with only partial set of features available.(Code and data are available at: https://***/zyz293/FusionML.) © 2021, Springer Nature Switzerland AG.

关键词： machine learning

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Chess Piece Image recognition Using Transfer learning, Simple Neural Network, and Convolutional Neural Network

Chess Piece Image Recognition Using Transfer Learning, Simpl...

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international conference on Artificial Intelligence and data Sciences (AiDAS)

作者： Gabriel Yohanes Mario Nursalim Nicholas Felix Indra Kurniadi Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Online chess has opened up a way for players to sharpen their skills through move analytics. Based on this feature, a support system called chess advisor can be utilized to assist players in a real-time match. However such system doesn't exist within the website itself, rather an involvement of 3rd party software is required. These kinds of software usually need assistance for input. This paper aims to create a system that is able to recognize chessboard state in real-time. We have proposed a chess piece image recognition system using transfer learning using ResNet50, a simple neural network, and a convolutional neural network (CNN). We collected our own 693 2D chess piece images dataset and performed data preprocessing to standardize the image dimensions. The proposed system detects and converts individual positions of each piece into a computer-readable output. This output can be used to facilitate the chess advisor in determining the best move. The transfer learning approach utilizing the ResNet50 model exhibited superior performance with an accuracy of 95.65%. This outperformed alternative methods such as the simple neural network and the CNN. The ResNet50 model's advantage stemmed from its pre-training on the ImageNet dataset, enabling effective pattern generalization. In contrast, the simple neural network and CNN achieved lower accuracies of 73.4% and 85%. These models encountered difficulties due to limited dataset quantity and quality, as well as the absence of regularization techniques. Nevertheless, the CNN displayed better consistency in generalizing patterns compared to the simple neural network.

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Classifying Severity Level of Psychiatric Symptoms on Twitter data 3

Classifying Severity Level of Psychiatric Symptoms on Twitte...

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3rd international conference on Information and Communication Technology for Development for Africa (ICT4DA)

作者： Negash, Meron Aragaw Woldeyohannis, Michael Melese Univ Gondar Dept Informat Syst Gondar Ethiopia Addis Ababa Univ Sch Informat Sci Addis Ababa Ethiopia

ISBN: (纸本)9781665436663

Internet-based social media sites such as Twitter represent a growing level of modern experience. These sites claim a large number of users and their influence is increasingly being experienced in clinical practice. Psychiatric disorders currently are affecting many people from different cultures, ages, and geographic locations. Although, the majority of individuals who experience symptom of psychiatric disorder practice the desire to be isolated, which drives them to use online channels to share their feelings. Hence, this sites provide a way to detect undiagnosed psychiatric disorders. In order to address this issue, we propose a model to classify the severity level of psychiatric symptoms (i.e. depression, anxiety, and bipolar) based on a data extracted from Twitter. The model is employed by fusing the linguistic features of Term Frequency Inverse Document Frequency (TFIDF) weighed by N-gram (unigram, bigram, and trigram), and word2vce, with pattern of Life Feature (PLF) that take polarity, subjectivity, and gender. The experiment, is conducted by incorporating with machine learning classifiers of Support Vector machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB). Experimental results show that SVM with features of TFIDF weighed by unigram combined with PLF outperforms with an accuracy score of 97.3%. In future, the proposed model could be employed to include lexicon-based features for classifying various psychiatric symptoms with a combined approach of machine learning and lexicon-based classification.

关键词： Psychiatric symptom Social media Natural Language Processing machine learning

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UPLIFT: Usage of Poverty Level Indicators to Facilitate Transformation 3

UPLIFT: Usage of Poverty Level Indicators to Facilitate Tran...

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3rd IEEE Bombay Section Signature conference, IBSSC 2021

作者： Ramesh, Namrata Chandra, Diya Jawahar, Chiranth Srinivasa, Gowri Pes University Pes Center for Pattern Recognition Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665417587

UPLIFT: Usage of Poverty Level Indicators to Facilitate Transformation, as the name suggests, is a study that aims at analysing data consisting of various socio-economic factors that affect households in order to alleviate poverty by understanding and identifying various aspects that may have an impact on poverty levels. As a use case, we have studied data pertaining to the Latin American nation of Costa Rica. Initially, we perform exploratory data analysis to discover patterns in the data and gain an insight to the relationship between attributes. We then perform feature selection and normalization to render the data suitable for modeling with machine learning techniques. We use these models to predict the economic health of each household, mapping them into four categories corresponding to the level of poverty. Finally, the data is reinterpreted in the light of this classification to elicit the factors that strongly affect each category, towards recommending methods for improvement and an appropriate allocation of social welfare benefits. © 2021 IEEE.

关键词： Feature extraction

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