Pengenalan Karakter Hieroglif Mesir Kuno Menggunakan Convolutional Neural Network

This research implements a Convolutional Neural Network (CNN) to recognize ancient Egyptian hieroglyphics. CNN is a deep learning architecture that automatically learns the features of data hierarchically. The CNN technique effectively integrates feature extraction and classifiers into one system. This study used hieroglyphic characters from the pyramid of Unas, which consisted of 170 kinds of characters, but this study only used 11 kinds of characters that had a sample size above 100, namely characters D21, E34, G17, G43, I9, M17, N35, O50, S29, V31, and X1. The results showed that the accuracy achieved was 99%. This research is expected to help archaeologists, enthusiasts, tourists, and museum visitors to recognize hieroglyphic characters as historical objects that only a few people know. Keywords: character recognition, ancient Egyptian hieroglyphics, convolutional neural networkPenelitian ini mengimplementasikan Convolutional Neural Network (CNN) untuk mengenali Hieroglif Mesir kuno. CNN adalah salah satu arsitektur deep learning yang secara otomatis mempelajari fitur pada sebuah data secara hierarki. CNN secara efektif mengintegrasikan ekstraksi fitur dan pengklasifikasi ke dalam satu sistem. Penelitian ini menggunakan karakter hieroglif dari piramida Unas yang terdiri dari 170 jenis karakter, namun penelitian ini hanya menggunakan 11 jenis karakter yang memiliki jumlah sampel di atas 100 yaitu karakter D21, E34, G17, G43, I9, M17, N35, O50, S29, V31, dan X1. Hasil penelitian menunjukkan bahwa akurasi yang diperoleh mencapai 99%. Penelitian ini diharapkan dapat membantu arkeolog, peminat, turis, dan pengunjung museum untuk mengenali karakter atau tulisan hieroglif sebagai salah satu benda bersejarah yang hanya diketahui oleh beberapa orang saja. Kata Kunci: pengenalan karakter, hieroglif Mesir kuno, convolutional neural networ

An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text

With the surging inclination towards carrying out tasks on computational devices and digital mediums, any method that converts a task that was previously carried out manually, to a digitized version, is always welcome. Irrespective of the various documentation tasks that can be done online today, there are still many applications and domains where handwritten text is inevitable, which makes the digitization of handwritten documents a very essential task. Over the past decades, there has been extensive research on offline handwritten text recognition. In the recent past, most of these attempts have shifted to Machine learning and Deep learning based approaches. In order to design more complex and deeper networks, and ensure stellar performances, it is essential to have larger quantities of annotated data. Most of the databases present for offline handwritten text recognition today, have either been manually annotated or semi automatically annotated with a lot of manual involvement. These processes are very time consuming and prone to human errors. To tackle this problem, we present an innovative, complete end-to-end pipeline, that annotates offline handwritten manuscripts written in both print and cursive English, using Deep Learning and User Interaction techniques. This novel method, which involves an architectural combination of a detection system built upon a state-of-the-art text detection model, and a custom made Deep Learning model for the recognition system, is combined with an easy-to-use interactive interface, aiming to improve the accuracy of the detection, segmentation, serialization and recognition phases, in order to ensure high quality annotated data with minimal human interaction.Comment: 17 pages, 8 figures, 2 table

Penerapan Convolutional Neural Network untuk Handwriting Recognition pada Aplikasi Belajar Aritmatika Dasar Berbasis Web

Aritmatika merupakan cabang ilmu matematika yang berhubungan dengan angka, pengukuran, dan komputasi numerik seperti penjumlahan, pengurangan, perkalian, dan pembagian. Mengajar aritmatika memiliki tantangan tersendiri bagi pengajar. Pada umumnya pengajaran aritmatika bersifat satu arah, sehingga bersifat monoton dan kemampuan dalam mengingat materi menjadi rendah. Salah satu cara untuk meningkatkan daya ingat dan penyerapan materi yang disampaikan adalah dengan menulis. Pada penelitian ini dirancang suatu aplikasi berbasis web belajar aritmatika dengan menulis. Untuk mengenali tulisan digital berupa angka dan operator aritmatika dibutuhkan handwriting recognition system. Convolutional Neural Network (CNN) dapat melakukan pengenalan tulisan tangan dengan tepat, baik yang bersifat off-line maupun online. Dataset diperlukan dalam training model CNN untuk mampu mengenal tulisan. Bobot yang diperoleh dari hasil training model CNN akan diintegrasikan dengan aplikasi. Melalui penelitian ini, dapat diketahui bahwa CNN memiliki tingkat akurasi yang baik dalam mengenali dan mengklasifikasikan tulisan tangan. Tingkat akurasi dari CNN dalam mengenal tulisan tangan yang diperoleh dari hasil pengujian adalah 95.36%

Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network

.In this work, a sentiment analysis method that is capable of accepting audio of any length, without being fixed a priori, is proposed. Mel spectrogram and Mel Frequency Cepstral Coefficients are used as audio description methods and a Fully Convolutional Neural Network architecture is proposed as a classifier. The results have been validated using three well known datasets: EMODB, RAVDESS and TESS. The results obtained were promising, outperforming the state-of–the-art methods. Also, thanks to the fact that the proposed method admits audios of any size, it allows a sentiment analysis to be made in near real time, which is very interesting for a wide range of fields such as call centers, medical consultations or financial brokers.S

Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network

In this work, a sentiment analysis method that is capable of accepting audio of any length, without being fixed a priori, is proposed. Mel spectrogram and Mel Frequency Cepstral Coefficients are used as audio description methods and a Fully Convolutional Neural Network architecture is proposed as a classifier. The results have been validated using three well known datasets: EMODB, RAVDESS, and TESS. The results obtained were promising, outperforming the state-of-the-art methods. Also, thanks to the fact that the proposed method admits audios of any size, it allows a sentiment analysis to be made in near real time, which is very interesting for a wide range of fields such as call centers, medical consultations, or financial brokers

Optimisation of convolutional neural network architecture using genetic algorithm for the prediction of adhesively bonded joint strength

The classical method of optimising structures for strength is computationally expensive due to the requirement of performing complex non-linear finite element analysis (FEA). This study aims to optimise an artificial neural network (ANN) architecture to perform the task of predicting the strength of adhesively bonded joints in place of non-linear FEA. A manual multi-objective optimisation was performed to find a suitable ANN architecture design space. Then a genetic algorithm optimisation of the reduced design space was conducted to find an optimum ANN architecture. The generated optimum ANN architecture predicts efficiently the strength of adhesively bonded joints to a high degree of accuracy in comparison with the legacy method using FEA with a 93% savings in computational cost

A sequential handwriting recognition model based on a dynamically configurable CRNN

Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods

Detection and Localization of Root Damages in Underground Sewer Systems using Deep Neural Networks and Computer Vision Techniques

Indiana University-Purdue University Indianapolis (IUPUI)The maintenance of a healthy sewer infrastructure is a major challenge due to the root damages from nearby plants that grow through pipe cracks or loose joints, which may lead to serious pipe blockages and collapse. Traditional inspections based on video surveillance to identify and localize root damages within such complex sewer networks are inefficient, laborious, and error-prone. Therefore, this study aims to develop a robust and efficient approach to automatically detect root damages and localize their circumferential and longitudinal positions in CCTV inspection videos by applying deep neural networks and computer vision techniques. With twenty inspection videos collected from various resources, keyframes were extracted from each video according to the difference in a LUV color space with certain selections of local maxima. To recognize distance information from video subtitles, OCR models such as Tesseract and CRNN-CTC were implemented and led to a 90% of recognition accuracy. In addition, a pre-trained segmentation model was applied to detect root damages, but it also found many false positive predictions. By applying a well-tuned YoloV3 model on the detection of pipe joints leveraging the Convex Hull Overlap (CHO) feature, we were able to achieve a 20% improvement on the reliability and accuracy of damage identifications. Moreover, an end-to-end deep learning pipeline that involved Triangle Similarity Theorem (TST) was successfully designed to predict the longitudinal position of each identified root damage. The prediction error was less than 1.0 feet

AI Watch: Assessing Technology Readiness Levels for Artificial Intelligence

Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. However, the main unanswered questions about this foreseen transformation are when and how this is going to happen. Not only do we lack the tools to determine what achievements will be attained in the near future, but we even underestimate what various technologies in AI are capable of today. Many so-called breakthroughs in AI are simply associated with highly-cited research papers or good performance on some particular benchmarks. Certainly, the translation from papers and benchmark performance to products is faster in AI than in other non-digital sectors. However, it is still the case that research breakthroughs do not directly translate to a technology that is ready to use in real-world environments. This document describes an exemplar-based methodology to categorise and assess several AI research and development technologies, by mapping them into Technology Readiness Levels (TRL) (e.g., maturity and availability levels). We first interpret the nine TRLs in the context of AI and identify different categories in AI to which they can be assigned. We then introduce new bidimensional plots, called readiness-vs-generality charts, where we see that higher TRLs are achievable for low-generality technologies focusing on narrow or specific abilities, while low TRLs are still out of reach for more general capabilities. We include numerous examples of AI technologies in a variety of fields, and show their readiness-vs-generality charts, serving as exemplars. Finally, we use the dynamics of several AI technology exemplars at different generality layers and moments of time to forecast some short-term and mid-term trends for AI.JRC.B.6-Digital Econom