This document summarizes a research paper that proposes a system to detect text in images of traffic signs, extract the text, and translate it to English. The system uses convolutional neural networks (CNNs) to detect text areas and recurrent neural networks (RNNs) to translate the extracted text. The goal is to help travelers understand traffic signs written in unfamiliar languages like Spanish or French by automatically translating the text in images to English. The system performs three steps: 1) detect text areas in images of signs, 2) extract the words from the detected text regions, and 3) translate the extracted text to English for the user.
Modeling of Speech Synthesis of Standard Arabic Using an Expert Systemcsandit
This document describes an expert system for speech synthesis of Standard Arabic text. It involves two main stages: 1) creation of a sound database and 2) text-to-speech transformation. The transformation process involves phonetic orthographic transcription of the text and then generating voice signals corresponding to the transcribed phonetic sequence. The expert system uses a knowledge base containing sound data and rewriting rules. It transcribes text using graphemes as basic units and then concatenates sound units from the database to synthesize speech. Tests achieved a 96% success rate in pronouncing sentences correctly. Future work aims to improve prosody and develop fully automatic signal segmentation.
IRJET- On-Screen Translator using NLP and Text DetectionIRJET Journal
This document describes a proposed on-screen text translator system using natural language processing (NLP) and text detection. The system would detect text from images or video frames using tools like OpenCV and Python-tesseract, extract the text as a string, and input it into an NLP model. The NLP model would analyze the string using techniques like LSTM and RNN to tokenize the words and return a translated output. Future work could include improving detection of curved text, integrating detection and recognition, and adding support for more languages. The goal is to provide translations of unfamiliar words directly on screen to aid reading comprehension.
This document summarizes a research paper that proposes and compares fuzzy and Naive Bayes models for detecting obfuscated plagiarism in Marathi language texts. It first provides background on plagiarism detection and describes different types of plagiarism, including obfuscated plagiarism. It then presents the fuzzy semantic similarity model, which uses fuzzy logic rules and semantic relatedness between words to calculate similarity scores between texts. Next, it describes the Naive Bayes model for plagiarism detection using Bayes' theorem. The paper compares the performance of the fuzzy and Naive Bayes models on precision, recall, F-measure and granularity. It finds that the Naive Bayes model provides more accurate detection of obfuscated plagiar
IRJET- Short-Text Semantic Similarity using Glove Word EmbeddingIRJET Journal
The document describes a study that uses GloVe word embeddings to measure semantic similarity between short texts. GloVe is an unsupervised learning algorithm for obtaining vector representations of words. The study trains GloVe word embeddings on a large corpus, then uses the embeddings to encode short texts and calculate their semantic similarity, comparing the accuracy to methods that use Word2Vec embeddings. It aims to show that GloVe embeddings may provide better performance for short text semantic similarity tasks.
A NOVEL APPROACH FOR WORD RETRIEVAL FROM DEVANAGARI DOCUMENT IMAGESijnlc
Large amount of information is lying dormant in historical documents and manuscripts. This information would go futile if not stored in digital form. Searching some relevant information from these scanned images would ideally require converting these document images to text form by doing optical character
recognition (OCR). For indigenous scripts of India, there are very few OCRs that can successfully recognize printed text images of varying quality, size, style and font. An alternate approach using word spotting can be effective to access large collections of document images. We propose a word spotting
technique based on codes for matching the word images of Devanagari script. The shape information is utilised for generating integer codes for words in the document image and these codes are matched for final retrieval of relevant documents. The technique is illustrated using Marathi document images.
Classification improvement of spoken arabic language based on radial basis fu...IJECEIAES
This document summarizes a research paper that aimed to improve classification of spoken Arabic language letters using the Radial Basis Function (RBF) neural network. The paper proposes a three-step approach: 1) preprocessing the speech signals which includes removing noise and segmenting the signals, 2) extracting statistical features from the preprocessed signals like zero-crossing rate and MFCCs, and 3) classifying the letters using an RBF neural network. The researchers tested different parameters and found classification accuracy improved from 90-99.375% compared to prior works. They concluded that combining statistical features with RBF neural networks provided over 1.845% better recognition rates than other methods for Arabic speech classification.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...ijnlc
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is
also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
Sentiment Analysis In Myanmar Language Using Convolutional Lstm Neural Networkkevig
In recent years, there has been an increasing use of social media among people in Myanmar and writing
review on social media pages about the product, movie, and trip are also popular among people. Moreover,
most of the people are going to find the review pages about the product they want to buy before deciding
whether they should buy it or not. Extracting and receiving useful reviews over interesting products is very
important and time consuming for people. Sentiment analysis is one of the important processes for extracting
useful reviews of the products. In this paper, the Convolutional LSTM neural network architecture is
proposed to analyse the sentiment classification of cosmetic reviews written in Myanmar Language. The
paper also intends to build the cosmetic reviews dataset for deep learning and sentiment lexicon in Myanmar
Language.
Modeling of Speech Synthesis of Standard Arabic Using an Expert Systemcsandit
This document describes an expert system for speech synthesis of Standard Arabic text. It involves two main stages: 1) creation of a sound database and 2) text-to-speech transformation. The transformation process involves phonetic orthographic transcription of the text and then generating voice signals corresponding to the transcribed phonetic sequence. The expert system uses a knowledge base containing sound data and rewriting rules. It transcribes text using graphemes as basic units and then concatenates sound units from the database to synthesize speech. Tests achieved a 96% success rate in pronouncing sentences correctly. Future work aims to improve prosody and develop fully automatic signal segmentation.
IRJET- On-Screen Translator using NLP and Text DetectionIRJET Journal
This document describes a proposed on-screen text translator system using natural language processing (NLP) and text detection. The system would detect text from images or video frames using tools like OpenCV and Python-tesseract, extract the text as a string, and input it into an NLP model. The NLP model would analyze the string using techniques like LSTM and RNN to tokenize the words and return a translated output. Future work could include improving detection of curved text, integrating detection and recognition, and adding support for more languages. The goal is to provide translations of unfamiliar words directly on screen to aid reading comprehension.
This document summarizes a research paper that proposes and compares fuzzy and Naive Bayes models for detecting obfuscated plagiarism in Marathi language texts. It first provides background on plagiarism detection and describes different types of plagiarism, including obfuscated plagiarism. It then presents the fuzzy semantic similarity model, which uses fuzzy logic rules and semantic relatedness between words to calculate similarity scores between texts. Next, it describes the Naive Bayes model for plagiarism detection using Bayes' theorem. The paper compares the performance of the fuzzy and Naive Bayes models on precision, recall, F-measure and granularity. It finds that the Naive Bayes model provides more accurate detection of obfuscated plagiar
IRJET- Short-Text Semantic Similarity using Glove Word EmbeddingIRJET Journal
The document describes a study that uses GloVe word embeddings to measure semantic similarity between short texts. GloVe is an unsupervised learning algorithm for obtaining vector representations of words. The study trains GloVe word embeddings on a large corpus, then uses the embeddings to encode short texts and calculate their semantic similarity, comparing the accuracy to methods that use Word2Vec embeddings. It aims to show that GloVe embeddings may provide better performance for short text semantic similarity tasks.
A NOVEL APPROACH FOR WORD RETRIEVAL FROM DEVANAGARI DOCUMENT IMAGESijnlc
Large amount of information is lying dormant in historical documents and manuscripts. This information would go futile if not stored in digital form. Searching some relevant information from these scanned images would ideally require converting these document images to text form by doing optical character
recognition (OCR). For indigenous scripts of India, there are very few OCRs that can successfully recognize printed text images of varying quality, size, style and font. An alternate approach using word spotting can be effective to access large collections of document images. We propose a word spotting
technique based on codes for matching the word images of Devanagari script. The shape information is utilised for generating integer codes for words in the document image and these codes are matched for final retrieval of relevant documents. The technique is illustrated using Marathi document images.
Classification improvement of spoken arabic language based on radial basis fu...IJECEIAES
This document summarizes a research paper that aimed to improve classification of spoken Arabic language letters using the Radial Basis Function (RBF) neural network. The paper proposes a three-step approach: 1) preprocessing the speech signals which includes removing noise and segmenting the signals, 2) extracting statistical features from the preprocessed signals like zero-crossing rate and MFCCs, and 3) classifying the letters using an RBF neural network. The researchers tested different parameters and found classification accuracy improved from 90-99.375% compared to prior works. They concluded that combining statistical features with RBF neural networks provided over 1.845% better recognition rates than other methods for Arabic speech classification.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...ijnlc
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is
also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
Sentiment Analysis In Myanmar Language Using Convolutional Lstm Neural Networkkevig
In recent years, there has been an increasing use of social media among people in Myanmar and writing
review on social media pages about the product, movie, and trip are also popular among people. Moreover,
most of the people are going to find the review pages about the product they want to buy before deciding
whether they should buy it or not. Extracting and receiving useful reviews over interesting products is very
important and time consuming for people. Sentiment analysis is one of the important processes for extracting
useful reviews of the products. In this paper, the Convolutional LSTM neural network architecture is
proposed to analyse the sentiment classification of cosmetic reviews written in Myanmar Language. The
paper also intends to build the cosmetic reviews dataset for deep learning and sentiment lexicon in Myanmar
Language.
A MULTI-LAYER HYBRID TEXT STEGANOGRAPHY FOR SECRET COMMUNICATION USING WORD T...IJNSA Journal
This paper introduces a multi-layer hybrid text steganography approach by utilizing word tagging and recoloring. Existing approaches are planned to be either progressive in getting imperceptibility, or high hiding limit, or robustness. The proposed approach does not use the ordinary sequential inserting process and overcome issues of the current approaches by taking a careful of getting imperceptibility, high hiding limit, and robustness through its hybrid work by using a linguistic technique and a format-based technique. The linguistic technique is used to divide the cover text into embedding layers where each layer consists of a sequence of words that has a single part of speech detected by POS tagger, while the format-based technique is used to recolor the letters of a cover text with a near RGB color coding to embed 12 bits from the secret message in each letter which leads to high hidden capacity and blinds the embedding, moreover, the robustness is accomplished through a multi-layer embedding process, and the generated stego key significantly assists the security of the embedding messages and its size. The experimental results comparison shows that the purpose approach is better than currently developed approaches in providing an ideal balance between imperceptibility, high hiding limit, and robustness criteria.
This document summarizes and compares entropy and dictionary-based techniques for lossless data compression. It discusses how entropy coding like Huffman coding assigns shorter codes to more frequent symbols, making it well-suited for JPEG images. Dictionary techniques like LZW replace strings with codes, performing better on files with repetitive data like TIFFs. While entropy coding has simpler encoding, dictionary methods have better compression ratios and decoding capability. The document also presents the proposed Huffman coding of a bitmap image to assign variable-length codes based on color probabilities.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
Implementation and Performance Evaluation of Neural Network for English Alpha...ijtsrd
One of the most classical applications of the Artificial Neural Network is the character recognition system. This system is the base for many different types of applications in various fields, many of which are used in daily lives. Cost effective and less time consuming, businesses, post offices, banks, security systems, and even the field of robotics employ this system as the base of their operations. For character recognition, there are many prosperous algorithms for training neural networks. Back propagation (BP) is the most popular algorithm for supervised training multilayer neural networks. In this thesis, Back propagation (BP) algorithm is implemented for the training of multilayer neural networks employing in character recognition system. The neural network architecture used in this implementation is a fully connected three layer network. The network can train over 16 characters since the 4-element output vector is used as output units. This thesis also evaluates the performance of Back propagation (BP) algorithm with various learning rates and mean square errors. MATLAB Programming language is used for implementation. Myat Thida Tun"Implementation and Performance Evaluation of Neural Network for English Alphabet Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15863.pdf http://www.ijtsrd.com/engineering/information-technology/15863/implementation-and-performance-evaluation-of-neural-network-for-english-alphabet-recognition-system/myat-thida-tun
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Speech to text conversion for visually impaired person using µ law compandingiosrjce
The paper represents the overall design and implementation of DSP based speech recognition and
text conversion system. Speech is usually taken as a preferred mode of operation for human being, This paper
represent voice oriented command for converting into text. We intended to compute the entire speech processing
in real time. This involves simultaneously accepting the input from the user and using software filters to analyse
the data. The comparison was then to be established by using correlation and µ law companding techniques. In
this paper, voice recognition is carried out using MATLAB. The voice command is a person independent. The
voice command is stored in the data base with the help of the function keys. The real time input speech received
is then processed in the speech recognition system where the required feature of the speech words are extracted,
filtered out and matched with the existing sample stored in the database. Then the required MATLAB processes
are done to convert the received data and into text form.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Combined Approach to Part-of-Speech Tagging Using Features Extraction and H...Editor IJARCET
This document summarizes an approach to part-of-speech tagging that combines features extraction and hidden Markov models. It extracts morphological features from words to categorize closed-class words, while using hidden Markov models to determine tag probabilities for other words based on contextual meaning and previous tags. The approach was tested on a manually tagged dataset of 1409 words from a natural language processing textbook, achieving accurate part-of-speech tagging.
Real Time Speaker Identification System – Design, Implementation and ValidationIDES Editor
This paper presents design, implementation and
validation of a PC based Prototype speaker recognition and
verification system. This system is organized to receive speech
signal, find the features of speech signal, and recognize and
verify a person using voice as the biometric. The system is
implemented to capture the speech signal from microphone
and to compare it with the stored data base using filter-bank
based closed-set speaker verification system. At first, the
identification of the voice signals is done using an algorithm
developed in MATLAB. Next, a PC based prototype system is
developed and is validated in real time. Several tests were made
on different sets of voice signals, and measured the performance
and the speed of the proposed system in real environment. The
result confirmed the use of proposed system for various real
time applications.
Automatic speech recognition system using deep learningAnkan Dutta
This document describes the development of an automatic speech recognition system using deep learning techniques. It discusses extracting MFCC features from audio signals and using a convolutional neural network for feature extraction, followed by a Gaussian mixture model-hidden Markov model for recognition. It also describes implementing a speech recognition system using the Kaldi toolkit on a digits dataset consisting of 10 speakers, as well as an automatic speaker recognition system using MFCC features and K-nearest neighbors classification. The speech recognition system achieved an accuracy of 72% and the speaker recognition system achieved 80% accuracy on the digits dataset.
Challenge of Image Retrieval, Brighton, 2000 1 ANVIL: a System for the Retrie...Petros Tsonis
ANVIL is a system designed for the retrieval of images annotated with short captions. It uses NLP techniques to extract dependency structures from captions and queries, and then applies a robust matching algorithm to recursively explore and compare them. There are currently two main interfaces to ANVIL: a list-based display and a 2D spatial layout that allows users to interact with and navigate between similar images. ANVIL was designed to operate as part of a publicly accessible, WWW-based image retrieval server. Consequently, product-level engineering standards were required. This paper examines both the research aspects of the system and also looks at some of the design and evaluation issues.
ATAR: Attention-based LSTM for Arabizi transliterationIJECEIAES
A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present ATAR, an ATtention-based LSTM model for ARabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49).
Deep Learning in practice : Speech recognition and beyond - MeetupLINAGORA
Retrouvez la présentation de notre Meetup du 27 septembre 2017 présenté par notre collaborateur Abdelwahab HEBA : Deep Learning in practice : Speech recognition and beyond
This document discusses text clustering and sentiment analysis using machine learning. It provides an overview of text clustering for topic modelling using techniques like vector space models and cosine similarity. It also discusses sentiment analysis using machine learning algorithms and provides examples of document clustering using k-means and sentiment analysis of Amazon movie reviews. Finally, it briefly introduces chatbots.
IRJET- Image to Text Conversion using TesseractIRJET Journal
This document discusses using Tesseract OCR engine to convert images containing text into editable text files. It begins with an abstract describing how digital images often contain text data that users need to access and edit digitally. Tesseract is an open-source OCR tool that uses neural networks like LSTM to recognize text in images with high accuracy and convert it into editable text. It then reviews existing OCR methods before describing Tesseract's image processing and recognition steps in more detail. The document also notes that the converted text could then be used to create audio files for visually impaired users to hear the text content.
IRJET- Text Extraction from Text Based Image using AndroidIRJET Journal
1) The document describes a study that developed an Android application to extract text from images captured using a mobile phone camera. It uses the Tesseract OCR engine and Google Vision API to recognize text in images and display it on the screen.
2) The application aims to allow users to extract text from images for translation or reading aloud, helping those who cannot read text like images, such as non-native speakers or visually impaired people.
3) The study implemented text feature filtering, text-based retrieval algorithms and used Google APIs like Translate for translation and text-to-speech conversion to develop the application. The application performance was tested based on text extraction accuracy from images.
IRJET- ASL Language Translation using MLIRJET Journal
This document presents a survey of technologies for hand sign language recognition and translation to text using machine learning. It discusses using CNN models to identify hand gestures in real-time from video input and translate the gestures to words rather than individual letters for better communication between deaf and hearing people. The system architecture involves hand detection, gesture recognition using a CNN model, and a login system for users. Previous approaches discussed include using sequential pattern mining and hidden Markov models on extracted motion features from video frames. The goal is to build an effective communication medium between deaf and hearing individuals.
An Efficient Segmentation Technique for Machine Printed Devanagiri Script: Bo...iosrjce
Segmentation technique plays a major role in scripting the documents for extraction of various
features. Many researchers are doing various research works in this field to make the segmenting process
simple as well as efficient. In this paper a simple segmentation technique for both the line and word
segmentation of a script document has been proposed. The main objective of this technique is to recognize the
spaces that separate two text lines.For the Word segmentation technique also similar procedure is followed. In
this work ,three different scanned document have been taken as input images for both line and word
segmentation techniques. The results found were outstanding with average accuracy for both line and word. It
provides 100% accuracy for line segmentation and 100% for line segmentation as well. Evaluation results show
that our method outperforms several competing methods.
The document discusses a new approach for identifying the script of words in low-resolution images of display boards using texture features. It aims to identify 3 Indian scripts: Hindi, Kannada, and English. The proposed method extracts discrete cosine transform-based texture features from word images and uses a threshold-based function to classify the script. When evaluated on 800 word images, it achieved an overall accuracy of 85.44% and individual accuracies of 100% for Hindi, 70.33% for Kannada, and 86% for English. The method is robust to variations in fonts, character spacing, noise and other degradations.
The Project is based on design & implementation of smart hybrid system for street sign boards recognition, text and speech conversions through character extraction and symbol matching. The default language use to pronounce signs on the street boards is English. Here we are proposing a novel method to convert identified character or symbol into multiple languages like Hindi, Marathi, Urdu, etc. This Project is helpful to all starting from the visually impaired, the tourists, the illiterates and all the people who travel. The system is accomplished with the speech pronunciation in different languages and to display on screen. This Project has a multidisciplinary approach as it belongs to the domains like computer vision, speech processing, & Google cloud platform. Computer vision is used for character and symbol extraction from sign boards. Speech processing is used for text to speech conversion. GCP is used for multiple language conversion of original extracted text. Further programming is done for real time pronunciation and displaying desired output.
A NEURAL MACHINE LANGUAGE TRANSLATION SYSTEM FROM GERMAN TO ENGLISHIRJET Journal
The document describes a neural machine translation system that translates from German to English. It uses a Transformer model integrated with Fuzzy Semantic Representation and Latent Topic Representation to handle rare words and capture sentence context. FSR groups rare words together and LTR uses CNN to represent sentence context as topic vectors. The model achieves improved translation performance on the WMT En-De corpus compared to baselines by handling out-of-vocabulary words and incorporating sentence level context.
Signboard Text Translator: A Guide to TouristIJECEIAES
The travelers face troubles in understanding the signboards which are written in local lan- guage. The travelers can rely on smart phone for traveling purposes. Smart phones become most popular in recent years in terms of market value and the number of useful applications to the users. This work intends to build up a web application that can recognize the English content present on signboard pictures captured using a smart phone, translate the content from English to Telugu, and display the translated Telugu text back onto the screen of the phone. Experiments have been conducted on various signboard pictures and the outcomes demonstrate the viability of the proposed approach.
A MULTI-LAYER HYBRID TEXT STEGANOGRAPHY FOR SECRET COMMUNICATION USING WORD T...IJNSA Journal
This paper introduces a multi-layer hybrid text steganography approach by utilizing word tagging and recoloring. Existing approaches are planned to be either progressive in getting imperceptibility, or high hiding limit, or robustness. The proposed approach does not use the ordinary sequential inserting process and overcome issues of the current approaches by taking a careful of getting imperceptibility, high hiding limit, and robustness through its hybrid work by using a linguistic technique and a format-based technique. The linguistic technique is used to divide the cover text into embedding layers where each layer consists of a sequence of words that has a single part of speech detected by POS tagger, while the format-based technique is used to recolor the letters of a cover text with a near RGB color coding to embed 12 bits from the secret message in each letter which leads to high hidden capacity and blinds the embedding, moreover, the robustness is accomplished through a multi-layer embedding process, and the generated stego key significantly assists the security of the embedding messages and its size. The experimental results comparison shows that the purpose approach is better than currently developed approaches in providing an ideal balance between imperceptibility, high hiding limit, and robustness criteria.
This document summarizes and compares entropy and dictionary-based techniques for lossless data compression. It discusses how entropy coding like Huffman coding assigns shorter codes to more frequent symbols, making it well-suited for JPEG images. Dictionary techniques like LZW replace strings with codes, performing better on files with repetitive data like TIFFs. While entropy coding has simpler encoding, dictionary methods have better compression ratios and decoding capability. The document also presents the proposed Huffman coding of a bitmap image to assign variable-length codes based on color probabilities.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
Implementation and Performance Evaluation of Neural Network for English Alpha...ijtsrd
One of the most classical applications of the Artificial Neural Network is the character recognition system. This system is the base for many different types of applications in various fields, many of which are used in daily lives. Cost effective and less time consuming, businesses, post offices, banks, security systems, and even the field of robotics employ this system as the base of their operations. For character recognition, there are many prosperous algorithms for training neural networks. Back propagation (BP) is the most popular algorithm for supervised training multilayer neural networks. In this thesis, Back propagation (BP) algorithm is implemented for the training of multilayer neural networks employing in character recognition system. The neural network architecture used in this implementation is a fully connected three layer network. The network can train over 16 characters since the 4-element output vector is used as output units. This thesis also evaluates the performance of Back propagation (BP) algorithm with various learning rates and mean square errors. MATLAB Programming language is used for implementation. Myat Thida Tun"Implementation and Performance Evaluation of Neural Network for English Alphabet Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15863.pdf http://www.ijtsrd.com/engineering/information-technology/15863/implementation-and-performance-evaluation-of-neural-network-for-english-alphabet-recognition-system/myat-thida-tun
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Speech to text conversion for visually impaired person using µ law compandingiosrjce
The paper represents the overall design and implementation of DSP based speech recognition and
text conversion system. Speech is usually taken as a preferred mode of operation for human being, This paper
represent voice oriented command for converting into text. We intended to compute the entire speech processing
in real time. This involves simultaneously accepting the input from the user and using software filters to analyse
the data. The comparison was then to be established by using correlation and µ law companding techniques. In
this paper, voice recognition is carried out using MATLAB. The voice command is a person independent. The
voice command is stored in the data base with the help of the function keys. The real time input speech received
is then processed in the speech recognition system where the required feature of the speech words are extracted,
filtered out and matched with the existing sample stored in the database. Then the required MATLAB processes
are done to convert the received data and into text form.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Combined Approach to Part-of-Speech Tagging Using Features Extraction and H...Editor IJARCET
This document summarizes an approach to part-of-speech tagging that combines features extraction and hidden Markov models. It extracts morphological features from words to categorize closed-class words, while using hidden Markov models to determine tag probabilities for other words based on contextual meaning and previous tags. The approach was tested on a manually tagged dataset of 1409 words from a natural language processing textbook, achieving accurate part-of-speech tagging.
Real Time Speaker Identification System – Design, Implementation and ValidationIDES Editor
This paper presents design, implementation and
validation of a PC based Prototype speaker recognition and
verification system. This system is organized to receive speech
signal, find the features of speech signal, and recognize and
verify a person using voice as the biometric. The system is
implemented to capture the speech signal from microphone
and to compare it with the stored data base using filter-bank
based closed-set speaker verification system. At first, the
identification of the voice signals is done using an algorithm
developed in MATLAB. Next, a PC based prototype system is
developed and is validated in real time. Several tests were made
on different sets of voice signals, and measured the performance
and the speed of the proposed system in real environment. The
result confirmed the use of proposed system for various real
time applications.
Automatic speech recognition system using deep learningAnkan Dutta
This document describes the development of an automatic speech recognition system using deep learning techniques. It discusses extracting MFCC features from audio signals and using a convolutional neural network for feature extraction, followed by a Gaussian mixture model-hidden Markov model for recognition. It also describes implementing a speech recognition system using the Kaldi toolkit on a digits dataset consisting of 10 speakers, as well as an automatic speaker recognition system using MFCC features and K-nearest neighbors classification. The speech recognition system achieved an accuracy of 72% and the speaker recognition system achieved 80% accuracy on the digits dataset.
Challenge of Image Retrieval, Brighton, 2000 1 ANVIL: a System for the Retrie...Petros Tsonis
ANVIL is a system designed for the retrieval of images annotated with short captions. It uses NLP techniques to extract dependency structures from captions and queries, and then applies a robust matching algorithm to recursively explore and compare them. There are currently two main interfaces to ANVIL: a list-based display and a 2D spatial layout that allows users to interact with and navigate between similar images. ANVIL was designed to operate as part of a publicly accessible, WWW-based image retrieval server. Consequently, product-level engineering standards were required. This paper examines both the research aspects of the system and also looks at some of the design and evaluation issues.
ATAR: Attention-based LSTM for Arabizi transliterationIJECEIAES
A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present ATAR, an ATtention-based LSTM model for ARabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49).
Deep Learning in practice : Speech recognition and beyond - MeetupLINAGORA
Retrouvez la présentation de notre Meetup du 27 septembre 2017 présenté par notre collaborateur Abdelwahab HEBA : Deep Learning in practice : Speech recognition and beyond
This document discusses text clustering and sentiment analysis using machine learning. It provides an overview of text clustering for topic modelling using techniques like vector space models and cosine similarity. It also discusses sentiment analysis using machine learning algorithms and provides examples of document clustering using k-means and sentiment analysis of Amazon movie reviews. Finally, it briefly introduces chatbots.
IRJET- Image to Text Conversion using TesseractIRJET Journal
This document discusses using Tesseract OCR engine to convert images containing text into editable text files. It begins with an abstract describing how digital images often contain text data that users need to access and edit digitally. Tesseract is an open-source OCR tool that uses neural networks like LSTM to recognize text in images with high accuracy and convert it into editable text. It then reviews existing OCR methods before describing Tesseract's image processing and recognition steps in more detail. The document also notes that the converted text could then be used to create audio files for visually impaired users to hear the text content.
IRJET- Text Extraction from Text Based Image using AndroidIRJET Journal
1) The document describes a study that developed an Android application to extract text from images captured using a mobile phone camera. It uses the Tesseract OCR engine and Google Vision API to recognize text in images and display it on the screen.
2) The application aims to allow users to extract text from images for translation or reading aloud, helping those who cannot read text like images, such as non-native speakers or visually impaired people.
3) The study implemented text feature filtering, text-based retrieval algorithms and used Google APIs like Translate for translation and text-to-speech conversion to develop the application. The application performance was tested based on text extraction accuracy from images.
IRJET- ASL Language Translation using MLIRJET Journal
This document presents a survey of technologies for hand sign language recognition and translation to text using machine learning. It discusses using CNN models to identify hand gestures in real-time from video input and translate the gestures to words rather than individual letters for better communication between deaf and hearing people. The system architecture involves hand detection, gesture recognition using a CNN model, and a login system for users. Previous approaches discussed include using sequential pattern mining and hidden Markov models on extracted motion features from video frames. The goal is to build an effective communication medium between deaf and hearing individuals.
An Efficient Segmentation Technique for Machine Printed Devanagiri Script: Bo...iosrjce
Segmentation technique plays a major role in scripting the documents for extraction of various
features. Many researchers are doing various research works in this field to make the segmenting process
simple as well as efficient. In this paper a simple segmentation technique for both the line and word
segmentation of a script document has been proposed. The main objective of this technique is to recognize the
spaces that separate two text lines.For the Word segmentation technique also similar procedure is followed. In
this work ,three different scanned document have been taken as input images for both line and word
segmentation techniques. The results found were outstanding with average accuracy for both line and word. It
provides 100% accuracy for line segmentation and 100% for line segmentation as well. Evaluation results show
that our method outperforms several competing methods.
The document discusses a new approach for identifying the script of words in low-resolution images of display boards using texture features. It aims to identify 3 Indian scripts: Hindi, Kannada, and English. The proposed method extracts discrete cosine transform-based texture features from word images and uses a threshold-based function to classify the script. When evaluated on 800 word images, it achieved an overall accuracy of 85.44% and individual accuracies of 100% for Hindi, 70.33% for Kannada, and 86% for English. The method is robust to variations in fonts, character spacing, noise and other degradations.
The Project is based on design & implementation of smart hybrid system for street sign boards recognition, text and speech conversions through character extraction and symbol matching. The default language use to pronounce signs on the street boards is English. Here we are proposing a novel method to convert identified character or symbol into multiple languages like Hindi, Marathi, Urdu, etc. This Project is helpful to all starting from the visually impaired, the tourists, the illiterates and all the people who travel. The system is accomplished with the speech pronunciation in different languages and to display on screen. This Project has a multidisciplinary approach as it belongs to the domains like computer vision, speech processing, & Google cloud platform. Computer vision is used for character and symbol extraction from sign boards. Speech processing is used for text to speech conversion. GCP is used for multiple language conversion of original extracted text. Further programming is done for real time pronunciation and displaying desired output.
A NEURAL MACHINE LANGUAGE TRANSLATION SYSTEM FROM GERMAN TO ENGLISHIRJET Journal
The document describes a neural machine translation system that translates from German to English. It uses a Transformer model integrated with Fuzzy Semantic Representation and Latent Topic Representation to handle rare words and capture sentence context. FSR groups rare words together and LTR uses CNN to represent sentence context as topic vectors. The model achieves improved translation performance on the WMT En-De corpus compared to baselines by handling out-of-vocabulary words and incorporating sentence level context.
Signboard Text Translator: A Guide to TouristIJECEIAES
The travelers face troubles in understanding the signboards which are written in local lan- guage. The travelers can rely on smart phone for traveling purposes. Smart phones become most popular in recent years in terms of market value and the number of useful applications to the users. This work intends to build up a web application that can recognize the English content present on signboard pictures captured using a smart phone, translate the content from English to Telugu, and display the translated Telugu text back onto the screen of the phone. Experiments have been conducted on various signboard pictures and the outcomes demonstrate the viability of the proposed approach.
Product Label Reading System for visually challenged peopleIRJET Journal
1) The document proposes a camera-based assistive text reading system to help blind people read text labels on handheld objects. It uses computer vision techniques like stroke width transform to isolate the object of interest and detect the region of interest.
2) In the region of interest, the system performs text localization using gradient features and edge distributions. It then recognizes text using optical character recognition and outputs it verbally for the user.
3) The system aims to achieve robust text extraction and recognition from complex backgrounds while focusing on usability. It analyzes existing assistive technologies and proposes an improved workflow including image capture, processing, and audio output.
This document discusses a proposed speech-to-speech translation system that would allow translation between English and Hindi. It outlines the objectives of integrating speech recognition, text translation, text-to-speech synthesis, and text extraction from images into a single application. The proposed system would use neural networks like RNNs and LSTMs to perform these functions. It describes the overall architecture and flow of information between the various modules, including preprocessing text, translating with rules and word embeddings, and generating speech output. The goal is to develop a user-friendly system to help overcome language barriers.
IRJET - Speech to Speech Translation using Encoder Decoder ArchitectureIRJET Journal
This document summarizes a research paper on speech-to-speech translation using an encoder-decoder architecture. It describes a system that takes speech in one language as input, recognizes the speech to generate text, translates the text to another language, and synthesizes speech in the other language as output. The system consists of three main modules: speech recognition in the source language, text translation between languages, and speech generation in the target language. It aims to enable two-way translation between spoken sentences in different languages.
Text region extraction from low resolution display board imaIAEME Publication
The document presents a new method for extracting text regions from low resolution display board images using wavelet features. The method divides the input image into 50x50 pixel blocks and extracts wavelet energy features from each block at two resolution levels. These features are used to classify blocks as text or non-text using discriminant functions. Detected text blocks are then merged to extract text regions. The method achieved a 97% detection rate on a variety of 100 low resolution display board images each sized 240x320 pixels.
Design of a Communication System using Sign Language aid for Differently Able...IRJET Journal
This document describes a proposed system to design a communication system using sign language to aid differently abled people. The system aims to use image processing and artificial intelligence techniques to recognize characters in sign language from video input and convert them to text and speech output. It discusses technologies like blob detection, skin color recognition and template matching that would be used for sign recognition. The system is intended to help deaf and mute people communicate by translating their sign language to a format understandable by others.
IRJET- Vision Based Sign Language by using MatlabIRJET Journal
This document discusses vision-based sign language translation using MATLAB. It describes a system that uses a camera to capture images of hand gestures representing letters or words in sign language. MATLAB is used to analyze the images, recognize the gestures, and translate them into spoken words that are output through a speaker. The system aims to help deaf, mute, and blind individuals communicate more easily. Several image processing and machine learning techniques for hand segmentation, feature extraction, and classification are reviewed from previous studies. The results suggest this type of system could accurately translate sign language in real-time.
Sign language recognition System is one of the systems that have major use for the peoples who are deaf dumb. With the development of this system, we can provide such kind of peoples, a medium to communicate with peoples and their family member. As we all know deaf dumb peoples are very far from the mainstream, such kind of person don’t have proper job and proper livelihood. They spent their whole life in learning sign languages, that are not understandable for a normal people. Here sign languages detection system plays a major role by providing a platform between deaf dumb peoples and normal people, so that they can communicate with each other. Sign language detection systems can be setup at schools, hospitals, hotels, malls etc. which will make it very simple for such peoples to communicate. Hand gestures is easiest way of nonverbal communication which plays vital role in daily life. The propped paper provides a user friendly way of communication with the help of CNN algorithm. Taokeer Alam | Dr. Murugan R "Sign Language Detector Using Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49698.pdf Paper URL: https://www.ijtsrd.com/computer-science/speech-recognition/49698/sign-language-detector-using-cloud/taokeer-alam
The document presents a novel pre-processing approach to improve optical character recognition (OCR) accuracy on document images. The approach uses a stack of enhancement techniques including illumination adjustment, grayscale conversion, sharpening, and binarization. Specifically, it applies contrast limited adaptive histogram equalization to adjust illumination, uses a luminance algorithm for grayscale optimization, employs unsharp masking to enhance text, and performs Otsu's method for binarization. The proposed method aims to compensate for distortions and improve OCR accuracy in a nonparametric and unsupervised manner. It is evaluated on standard datasets and shown to significantly improve text detection and OCR performance.
A Survey On Thresholding Operators of Text Extraction In VideosCSCJournals
The document describes a technique for text detection in video frames that uses morphological operations. It involves four phases: 1) image enhancement to improve edge detection, 2) edge extraction using morphological operations, 3) labeling connected text regions, and 4) extracting text using Hough transforms. The technique is evaluated using quantitative metrics like boundary precision-recall and volume precision-recall to measure the accuracy of detected text boundaries and regions compared to ground truth. Experimental results show the technique can accurately detect and extract text from video frames.
A Survey On Thresholding Operators of Text Extraction In VideosCSCJournals
Video indexing is an important problem that has interested by the communities of visual information in image processing. The detection and extraction of scene and caption text from unconstrained, general purpose video is an important research problem in the context of content-based retrieval and summarization. In this paper, the technique presented is for detection text from frames video. Finding the textual contents in images is a challenging and promising research area in information technology. Consequently, text detection and recognition in multimedia had become one of the most important fields in computer vision due to its valuable uses in a variety of recent technical applications. The work in this paper consists using morphological operations for extract text appearing in the video frames. The proposed scheme well as preprocessing to differentiate among where it as the high similarity between text and background information. Experimental results show that the resultant image is the image with only text. The evaluated criteria are applied with the image result and one obtained bay different operator.
IRJET - Automatic Lip Reading: Classification of Words and Phrases using Conv...IRJET Journal
This document presents research on developing an automatic lip reading system using convolutional neural networks. The system takes in video frames of a speaker's face without audio and classifies the words or phrases being spoken. The researchers preprocessed the data by detecting faces in video frames and cropping them. They then trained a CNN model on concatenated frames. Their model achieved 80.44% accuracy on the test set in classifying 10 words and 10 phrases from 17 speakers. The researchers concluded the model could be improved by addressing overfitting to unseen speakers with a larger dataset and regularization techniques.
Real Time Sign Language Translation Using Tensor Flow Object DetectionIRJET Journal
This document describes a real-time sign language translation system developed using TensorFlow object detection. The system was able to detect Indian sign language alphabets in real-time with an average accuracy of 87.4% after training an SSD MobileNet v2 model on a dataset of 500 images containing signs for the English alphabet. Future work may focus on improving accuracy, reducing latency for real-time translation, and recognizing facial expressions in addition to hand gestures.
IMAGE TO TEXT TO SPEECH CONVERSION USING MACHINE LEARNINGIRJET Journal
This document describes a study on converting images to text to speech using machine learning. The researchers developed a system that uses optical character recognition to extract text from images, then converts the text to speech. They achieved over 99% accuracy on their test dataset of over 1 million images. Their integrated system was able to accurately extract and convert text from various real-world images like street signs and menus. The system has potential to improve accessibility for people with visual impairments by allowing printed information to be converted to audio. Future work includes handling lower quality images and expanding the system to support additional languages and applications.
Similar to IRJET - Language Linguist using Image Processing on Intelligent Transport Systems (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024