Advances in Computer Recognition, Image Processing and Communications

This Special Issue aimed to gather high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity [...]

Software tool for recognition of Ukrainian-language scientific articles

The problem of computer recognition, both separately printed characters and whole texts, which may contain mathematical formulas, and further saving the resulting document in the "Latex" format, is considered. The developed software implements the ability to recognize printable Latin, Cyrillic, Greek letters and special mathematical symbols. For this, a multilayer convolutional neural network built using the Keras machine learning library and additional validation heuristics are used. To improve the quality of neural network recognition, a sophisticated image processing mechanism has been developed that helps to remove noise from the image, eliminate errors associated with the inclination of characters, and correct character defects associated with the quality of the input image. Also implemented are mechanisms for collecting individual characters into words or mathematical formulas, reproducing the position of signs of indices and degrees, forming ordinary fractions and expressions under the root sign. The results of the recognized text are saved in a file with the simultaneous construction of the "latex" document structure. To demonstrate the capabilities of the developed software, a graphical user interface has been added, with which you can select and inspect the input image even before the start of recognition. During testing of the software, the recognition of images of different types was carried out: completely textual, mathematical formulas without text, mathematical formulas that are between blocks of text.

364. Individualized Prognostics in COVID-19 Facilitated by Computer Recognition of Blood Leukocyte Subsets

Background To determine whether CBC differentials of COVID+ inpatients can predict, at admission, both maximum oxygen requirements (MOR) and 30-day mortality. Methods Based on an approved IRB protocol, CBC differentials from the first 3 days of hospitalization of 12 SARS CoV-2 infected patients were retrospectively extracted from hospital records and analyzed with a privately owned Pattern Recognition Software (PRS, US Patent 10,429,389 B2) previously validated in sepsis, HIV, and hantavirus infections. PRS partitions the data into subsets immunologically dissimilar from one another, although internally similar. Results Regardless of the angle considered, the classic analysis −which measured the percentages of lymphocytes, monocytes, and neutrophils− did not distinguish outcomes (A). In contrast, non-overlapping patterns generated by the PRS differentiated 3 (left, vertical, and right) groups of patients (B). One subset was only composed of survivors (B). The remaining subsets included the highest oxygenation requirements (B). At least two immunologically interpretable, multi-cellular indicators distinguished the 3 data subsets with statistically significant differences (C, p≤ 0.05). Survivors (the left subset) showed lower N/L and/or higher M/L ratios than non-survivors (the vertical subset, C).Therefore, PRS partitioned the data into subsets that displayed both biological and significant differences. Because it offers visually explicit information, clinicians do not require a specialized training to interpret PRS-generated results. CBCs vs. outcomes - Software-analyzed CBCs vs. outcomes Conclusion (1) Analysis of blood leukocyte data predicts MOR and 30-d mortality. (2) Real time PRS analysis facilitates personalized medical decisions. (3) PRS measures two dimensions rarely assessed: multi-cellularity and dynamics. (4) Even with very small datasets, PRS may achieve statistical significance. (5) Larger COVID+ infected cohort is being analyzed for potential commercialization. Disclosures Claudia R. Libertin, MD, Gilead (Grant/Research Support)

Conversation Engine for Deaf and Dumb

Deaf and hard hearing people use linguistic communication to exchange information between their own community and with others. Sign gesture acquisition and text/speech generation are parts of computer recognition of linguistic communication. Static and dynamic are classified as sign gestures. Both recognition systems are important to the human community but static gesture recognition is less complicated than dynamic gesture recognition. Inability to talk is taken into account to be a disability among people. To speak with others people with disability use different modes, there are number of methods available for his or her communication one such common method of communication is linguistic communication. Development of linguistic communication recognition application for deaf people is vital, as they’ll be able to communicate easily with even people who don’t understand language. Our project aims at taking the fundamental step in removing the communication gap between normal people, deaf and dumb people using language.

Audio Visual Reception of American Sign Language for Blind, Deaf and Dumb People

The computer recognition of sign language is an important process for enabling communication with the visually and hearing impaired people. This proposed project introduces an efficient way of computer recognition of sign languageby using a simplified method by the use of an accelerometer sensor which is a three axis sensor and a voice IC. The main objective of our project is to convert the sign language into a voice format and display the corresponding message on the LCD screen. The basic idea of this project is to have accelerometer sensors attached to the gloves worn by the impaired person. When the person flexes his/her hand for the pre-coded commands, the accelerometer sensors senses the change due to the angular movement of fingers and produces a corresponding output voltage. The sensed analog signal is converted to digital signal by ADC and transmits it to the voice IC via a microcontroller. The objective of the microcontroller is to perform the matching of the obtained hex-code with its corresponding pre-coded commands using Keil software. Once the code is matched with its pre-coded commands the output is delivered through a speaker via a voice IC and the command is also displayed in a LCD screen.

Automatic Recognition Systems and Human Computer Interaction in Face Matching

The human face facilitates identification in security and policing scenarios. In these settings, automatic face recognition systems have increased in prevalence and accuracy in recent years. As a result, the identification task, which once fell entirely to humans, is now a process performed by man and machine. Automatic face recognition systems provide image similarity comparisons and can create candidate lists to narrow down potential targets. There is increasing interest in the accuracy of these systems, and the role that algorithms can play in the identification effort. The design, operational usage, and effectiveness of these automatic systems, as well as the interaction of human and computer recognition are the topics of this chapter.

Metode Multimedia Development Life Cycle Pada Media Pembelajaran Pengenalan Perangkat Komputer Bagi Siswa Sekolah Dasar

The condition of the students who sit in elementary school but difficulty in attending computer recognition lessons, the teacher in explaining the material is not accompanied by props so that elementary school students have difficulty in following computer recognition lessons. Based on existing problems the author tries to design a learning media prop in explaining the lessons of computer device introduction. This learning medium contains written materials, videos, Multiple Choice Quiz, and Puzzles. The method used is Multimedia Development Life Cycle (MDLC) where this method is arranged based on 6 (six) stages namely concept, design, obtaining content material, assembly, testing, and distribution. In this study the authors were only new to the assembly stage. With this learning media, it is expected that elementary school children can know the form of computer devices and understand how to use them, as well as learning activities become more interesting, effective and able to foster students' learning interest in knowing computers. Keywords: Learning Media, Elementary School, MDLC, Computer, Introduction Abstrak Kondisi siswa yang duduk di bangku sekolah dasar kelas namun kesulitan dalam mengikuti pelajaran pengenalan komputer, guru dalam menjelaskan materi tidak disertai dengan alat peraga sehingga siswa sekolah dasar kesulitan dalam mengikuti pelajaran pengenalan komputer. Berdasarkan masalah yang ada penulis mencoba merancang sebuah media belajar alat peraga dalam menjelaskan pelajaran pengenalan perangkat komputer. Media pembelajaran ini berisi mengenai materi tertulis, video, Quiz Pilihan Ganda, dan Puzzle. Metode yang digunakan adalah Multimedia Development Life Cycle (MDLC) dimana metode ini tersusun berdasarkan 6 (enam) tahapan yaitu concept, design, obtaining content material, assembly, testing, dan distribution. Dalam penelitian ini penulis hanya baru sampai tahap assembly. Dengan adanya media pembelajaran ini diharapkan anak sekolah dasar dapat mengetahui bentuk perangkat komputer dan mengerti cara penggunaannya, serta kegiatan belajar menjadi lebih menarik, efektif serta mampu menumbuhkan minat belajar siswa dalam mengenal komputer. Kata kunci: Media Pembelajaran, Sekolah Dasar, MDLC, Komputer, Pengenalan

Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas

Objective: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annotated data available for training. Convolutional neural networks (CNNs) have the potential to improve image analysis and have demonstrated good performance in many settings. This study applied machine-learning technologies to laryngoscopy to determine the accuracy of computer recognition of known laryngeal lesions found in patients post-extubation. Methods: This is a proof of concept study that used a convenience sample of transnasal, flexible, distal-chip laryngoscopy images from patients post-extubation in the intensive care unit. After manually annotating images at the pixel-level, we applied a CNN-based method for analysis of granulomas and ulcerations to test potential machine-learning approaches for laryngoscopy analysis. Results: A total of 127 images from 25 patients were manually annotated for presence and shape of these lesions—100 for training, 27 for evaluating the system. There were 193 ulcerations (148 in the training set; 45 in the evaluation set) and 272 granulomas (208 in the training set; 64 in the evaluation set) identified. Time to annotate each image was approximately 3 minutes. Machine-based analysis demonstrated per-pixel sensitivity of 82.0% and 62.8% for granulomas and ulcerations respectively; specificity was 99.0% and 99.6%. Conclusion: This work demonstrates the feasibility of machine learning via CNN-based methods to add objectivity to laryngoscopy analysis, suggesting that CNN may aid in laryngoscopy analysis for other conditions in the future.