Journal of Advanced Applied Scientific Research (JOAASR)
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Advances in Text Summarization and Generation for Indian Languages: A Survey
Get PDFIn the era of Big Data, Textual data is expanding quickly and is accessible in a wide variety of languages. This text collection is an invaluable source of information and understanding that must be effectively compiled. This is challenging to read all the text information in the world that moves quickly. Thus, the importance of text summary is being highlighted. A method called Automatic Text Summarizing (ATS) shrinks lengthy texts into shorter ones that nevertheless contain the essential information. This research work provides an overview of the available most well-known ATS systems. The purpose of this paper is to discuss the advantages and potential future applications of text summarizing techniques. This work guides about procedures that have been recommended in several research studies. Researchers can better grasp the implications, application, and efficacy of these methods by examining a variety of text summarizing techniques. This research also highlights the requirement for an enhanced text summarization architecture. The research work offers a survey of the numerous types of text summarizing methods, ranging from simple to complex methods
Image-based Pretreatment Study of Rice Blast Disease
Get PDFRice blight has a great impact on rice yield and can lead to yield reduction of up to 70% in severe cases. Traditional detection methods require professional technicians to operate and are costly and inefficient, and cannot detect rice diseases in real-time. In this paper, we applied image detection technology to study rice blast disease based on the Matlab platform. Firstly, a basic rice blast database is built, and then a discussion is made on how to effectively improve the recognition success rate of rice blast images by two aspects: image pre-processing and feature extraction. The main research contents are as follows. (1) After studying the existing plant disease database, a basic rice blast database was constructed by field photography and other means. (2) Preprocessing of the collected rice blight images. Using the algorithm of rgb2gray function in Matlab, the images were grayed out; based on this, median filtering was used for noise reduction; then histogram equalization technique was used for image enhancement to increase the contrast and make the images clear; finally, various segmentation algorithms were used for image segmentation. (3) For the pre-processed rice blight images, feature extraction was performed in terms of the color of the disease to pave the way for feature selection
Text-to-Speech Synthesis for Hindi Language Using MFCC and LPC Feature Extraction Techniques
Get PDFIndia is a large country with over a billion populations who speak numerous languages. 43% of Indians speak Devanagari Hindi script, followed by Bengali, Telugu, Marathi, and other languages. The widespread generation of content and accessibility would therefore greatly benefit from text-to-speech systems for such languages. In this research work we improve the already available Text-to-Speech (TTS) system using advance preprocessing techniques to the Hindi corpus database and applied various feature extraction techniques for better result. Finally we got the accuracy as 98% using MFCC and LPC feature extraction techniques. The developed model is capable for getting the input from audio file and read it loudly using developed TTS system
Novel Adsorbent from Biowaste (Shrimp Shells): Metal-Impregnated Activated Carbon for Efficient Dye Removal
Get PDFActivated carbon (AC) is a valuable material utilized in multiple sectors owing to its versatility and ability to absorb various compounds effectively. Its adsorption characteristics are due to a large surface area and extensive porous network. Metal impregnation into activated carbon is for the improvement of its adsorption capacity and the elimination of specific contaminants like heavy metals, organic pollutants, or gases assessing with long-term performance. The chitosan derived from shrimp shells has the property of dye adsorption. Such a polymer chitosan with adsorption property is reformed into activated carbon by pyrolysis to enhance its adsorption ability in the removal of hazardous dyes. The XRD of produced AC shows its characteristics peak at 26.6˚. Field Emission Scanning Electron Microscopy (FESEM) of the AC showed a porous surface. Aluminium (Al), Iron (Fe), and Silver (Ag) were incorporated into the activated carbon individually by simple chemical method, at low temperatures. The structural investigation results give the Ag-imposed AC forms in a polycrystalline phase with crystallite size in the nanoscale. FTIR data of metal-imposed AC proves that chemical modification occurs in activated carbon by the inclusion of metals. The adsorption of Rhodamine 6G and Amaranth dyes by AC/Al, AC/Fe, and AC/Ag were investigated by UV analysis. This work shows that about 47% concentration of Amaranth dye was adsorbed by AC/Al composite, and to the maximum 21% of Rhodamine was adsorbed by AC/Ag sample in an experiment time of 10 hours at room temperature
Comparison of Sentences using POS Tagging Tool under Subjective Examination
Get PDFThe Question answering system is used to generate the correct result that is asked by humans in natural language. In an online examination system, most of the work has been done but still, problems occur in preprocessing i.e. Part Of Speech (POS). POS tagger is used to properly tag each word in the sentences. In this paper, we used two datasets i.e. TREC DATA and data collected from the student. We apply the POS tagger to both datasets and compare the result. For generating the POS tagger we used NLTK and spaCy libraries for comparison. We observed that using those libraries the same word has a different tag. Using both tools, we computed the difference between the words and assigned the count to the POS tagging on that result, we calculate the accuracy of both libraries. The result shows that the spaCy library is best for POS tagging because it generates more correct results as compared to NLTK
A Named Entity Recognition System for the Marathi Language
Get PDFNamed entity recognition is a complex task in developing many NLP applications. This is one of the essential requirements of language modeling in NLP; without it, it is not possible to proceed further and achieve better results. In this proposed task, we have designed a hybrid technique that is a combination of machine learning and a rule-based approach. This system is to identify such named entities that belong under a specific class, creating a special identification and importance in the meaning generation as well as understanding of the language. This is concerned with the input text. Named entity recognition is important for different group items, such as a person’s name, location or place, animals, organization, time or date, etc. Named entities are informative and good representatives of knowledge. NE also explores the knowledge of artificial intelligence-based systems or expert systems. Using the proposed hybrid model, we have achieved 59.40% performance in identifying named entities and properly labeling for the Marath
Palmprint spoofing detection by using deep learning technique on Multispectral database
Get PDFThe protection of Biometric systems against attacks is crucial as biometric devices proliferate in the field of personal authentication. The presentation assault is the most prevalent type of attack on biometric systems; it entails presenting a fake copy (artefact) of the true biometric to the sensor in order to gain unauthorised access. The vulnerability in palmprint-based biometric systems has not received much attention despite the substantial threat posed by these assaults. In this research, we show how to detect a spoof palmprint image. Spoofing attacks involving faked images pose a significant threat to biometric systems. For the suggested method, we use the CASIA palmprint database, from which we constructed our own spoof database using printed photos. After that, we did some pre-processing to obtain the ROI image and a noise-free image for feature extraction using the SIFT approach. We use the convolution neural network for classification and the SVM for comparison. We obtained a result of 96.2% for our proposed palmprint system identification and 89% for SVM. But our main goal is to train the model for spoof detection, so we take some normal images and some spoof images for our train model and use the confusion matrix to calculate the accuracy of our model. We obtain an overall accuracy of 86% for our spoof detection by computing the confusion matrix
Deep Learning Approach towards Emotion Recognition Based on Speech
Get PDFFeelings are incredibly vital in the internal actuality of humans. It's a means of communicating one's point of view or emotional condition to others [5]. The birth of the speaker's emotional state from his or her speech signal is appertained to as Speech Emotion Recognition (SER) [2]. There are a many universal feelings that any intelligent system with finite processing coffers can be trained to honour or synthesize as demanded, including Neutral, wrathfulness, Happiness, and Sadness. Because both spectral and prosodic traits contain emotional information, they're utilized in this study for speech emotion identification. One of the spectral parcels is Mel- frequency cepstral portions (MFCC). Prosodic variables similar as abecedarian frequency, loudness, pitch, and speech intensity, as well as glottal factors, are utilized to model colorful feelings. For the computational mapping between feelings and speech patterns, possible features are recaptured from each utterance. The named features can be used to identify pitch, which can also be used to classify gender. In this study, the gender is classified using a Support Vector Machine (SVM) on Ravdess dataset. The Radial Base Function and Back Propagation Network are used to honour feelings grounded on specified features, and it has been shown that the radial base function produces more accurate results for emotion recognition than the reverse propagation network
Vision-based primary localization method for SLAM mobile robots
Get PDFThe AMCL (Adaptive Monte Carlo Localization) algorithm with visual provision of initial values is proposed to address the slow localization speed caused by conventional laser SLAM (Simultaneous Localization and Mapping) without initial poses and the global localization failure after a robot abduction event. In the initial map building phase, the ORB (Oriented FAST and Rotated BRIEF) feature values are extracted from the camera and the wall corners are identified, and then the pose information is stored in the database and a feature dictionary is constructed. After restarting, the dictionary is called to perform loopback detection by receiving the images captured by the current camera, and a successful detection results in a rough initial pose. If the detection fails, the initial pose is roughly calculated by identifying the wall corners. Finally, the particle filtering algorithm scatters particles in a small area near the obtained pose and converges to obtain a relatively accurate pose
RETRACTED:Indoors Fitness Training Monitoring based on OpenPose
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Refered by
Joaasr, E. (2024). Retraction notice to "Indoors Fitness Training Monitoring based on OpenPose" Vol. 6 No. 3 (2024):Journal of Advanced Applied Scientific Research-ICKE-2023 . JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 6(6). https://doi.org/10.46947/joaasr6620241175