Computed tomography scans image processing for nasal symptoms severity prediction

This paper aims to use a new technique of computed tomography (CT) scan image processing to correlate the image analysis with sinonasal symptoms. A retrospective cross-sectional study is conducted by analyzing the digital records of 50 patients who attended the ear, nose and throat (ENT) clinics at King Abdullah University Hospital, Jordan. The coronal plane CT scans are analyzed using our developed software. The purposes of this software are to calculate the surface area of the nasal passage at three different levels visible on coronal plane CT scans: i) the head of the inferior turbinate, ii) the head of the middle turbinate, and iii) the tail of the inferior turbinate. We employ image processing techniques to correlate the narrowing of nasal surface area with sinonasal symptoms. As a consequence, obstruction in the first level is correlated significantly with the symptoms of nasal obstruction while the narrowing in the second level is related to frontal headache. No other significant correlations are found with nasal symptoms at the third level. In our study, we find that image processing techniques can be very useful to predict the severity of common nasal symptoms and they can be used to suggest treatment and to follow up on the case progression.

Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

Optical character recognition (OCR) is a technology to digitize a paper-based document to digital form. This research studies the extraction of the characters from a Thai vehicle registration certificate via a Google Cloud Vision API and a Tesseract OCR. The recognition performance of both OCR APIs is also examined. The 84 color image files comprised three image sizes/resolutions and five image characteristics. For suitable image type comparison, the greyscale and binary image are converted from color images. Furthermore, the three pre-processing techniques, sharpening, contrast adjustment, and brightness adjustment, are also applied to enhance the quality of image before applying the two OCR APIs. The recognition performance was evaluated in terms of accuracy and readability. The results showed that the Google Cloud Vision API works well for the Thai vehicle registration certificate with an accuracy of 84.43%, whereas the Tesseract OCR showed an accuracy of 47.02%. The highest accuracy came from the color image with 1024×768 px, 300dpi, and using sharpening and brightness adjustment as pre-processing techniques. In terms of readability, the Google Cloud Vision API has more readability than the Tesseract. The proposed conditions facilitate the possibility of the implementation for Thai vehicle registration certificate recognition system.

Text pre-processing of multilingual for sentiment analysis based on social network data

<span>Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text pre-processing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition.</span>

Devanagari Handwritten Character Recognition Using Deep Learning

In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

EM-X-DL: Efficient Cross-device Deep Learning Side-channel Attack With Noisy EM Signatures

This work presents a Cross-device Deep-Learning based Electromagnetic (EM-X-DL) side-channel analysis (SCA) on AES-128, in the presence of a significantly lower signal-to-noise ratio (SNR) compared to previous works. Using a novel algorithm to intelligently select multiple training devices and proper choice of hyperparameters, the proposed 256-class deep neural network (DNN) can be trained efficiently utilizing pre-processing techniques like PCA, LDA, and FFT on measurements from the target encryption engine running on an 8-bit Atmel microcontroller. In this way, EM-X-DL achieves >90% single-trace attack accuracy. Finally, an efficient end-to-end SCA leakage detection and attack framework using EM-X-DL demonstrates high confidence of an attacker with <20 averaged EM traces.

Acne Scan - Application In Assessment Of Acne And Supports Treatment Road

As society develops, many aspects of life are concerned by people, including facial skincare, avoiding acne-related diseases. In this work, we will propose a complete solution for treating acne at home, including 4 processors. First, the anomaly detector uses image processing techniques by Multi-Threshold and Color Segmentation, depending on each color channel corresponding to each type of acne. The sensitivity of the detector is 89.4%. Second, the set of anomalies classifiers into 6 main categories, including 4 major acne types and 2 non-acne types. By applying the convolutional neural model, the accuracy, sensitivity, and F1 are 84.17%, 81.5%, and 82%, respectively. Third, the acne status assessment kit is based on the mGAGS method to classify the condition of a face as mild, moderate, severe, or very severe with an accuracy of 81.25%. Finally, the product recommender, which generalizes from the results of the previous processors with an accuracy of 70-90%. This is the premise that helps doctors as well as general users to evaluate the level of acne on a face effectively and save time.

Changes in some nutritional components of Vigna racemosa as influenced by hydrothermal processing techniques

Vigna racemosa is an underutilised hard-to-cook legume in Southwest Nigeria. The seed of V. racemosa was subjected to varying degrees of hydration levels (0%, 10%, 25%, 50% 75% and 100%) before treatment with four hydrothermal techniques, atmospheric boiling, atmospheric steaming, pressure boiling and pressure steaming. The highest percentage reduction of 65.56% in cooking time was achieved at a 100% hydration level when the seeds were processed by boiling at elevated pressure. All the hydrothermal techniques had a significant effect (p<0.05) on the protein content of the seeds. Hydration of the seeds to 100% followed by boiling at elevated pressure caused the least reduction in the protein and mineral element contents. The seed is a good source of important nutrients that are not degraded beyond the levels necessary to meet physiological needs after processing. Widening the scope of utilisation of this legume seed will make critically important nutrients such as protein more available for the teeming populations in developing countries at affordable prices, and thus, alleviating the problem of protein-energy malnutrition (PEM).

Progress in Starch-Based Materials for Food Packaging Applications

The food packaging sector generates large volumes of plastic waste due to the high demand for packaged products with a short shelf-life. Biopolymers such as starch-based materials are a promising alternative to non-renewable resins, offering a sustainable and environmentally friendly food packaging alternative for single-use products. This article provides a chronology of the development of starch-based materials for food packaging. Particular emphasis is placed on the challenges faced in processing these materials using conventional processing techniques for thermoplastics and other emerging techniques such as electrospinning and 3D printing. The improvement of the performance of starch-based materials by blending with other biopolymers, use of micro- and nano-sized reinforcements, and chemical modification of starch is discussed. Finally, an overview of recent developments of these materials in smart food packaging is given.

System for Identifying Pests and Diseases in Soybean Crop through Natural Language Processing

The presence of technologies in the agronomic field has the purpose of proposing the best solutions to the challenges found in agriculture, especially to the problems that affect cultivars. One of the obstacles found is to apply the use of your own language in applications that interact with the user in Brazilian Agribusiness. Therefore, this work uses Natural Language Processing techniques for the development of an automatic and effective computer system to interact with the user and assist in the identification of pests and diseases in soybean crop, stored in a non-relational database repository to provide accurate diagnostics to simplify the work of the farmer and the agricultural stakeholders who deal with a lot of information. In order to build dialogues and provide rich consultations, from agriculture manuals, a data structure with 108 pests and diseases with their information on the soybean cultivar and through the spaCy tool, it was possible to pre-process the texts, recognize the entities and support the requirements for the development of the conversacional system.